Balance tensity evaluation method, system and equipment based on effective standby of power grid

Abstract

The invention discloses a balance tensity evaluation method, system and equipment based on effective reserve of a power grid, and relates to the technical field of electric power engineering, and the method comprises the steps: determining a node stretching factor based on an initial state of the power grid; according to the node stretching factor, obtaining an optimization target and a stretching constraint of the power grid critical balance; obtaining power grid operation constraints according to the current power grid unit operation characteristics; constructing a critical equilibrium state model of the power grid according to the optimization target, the stretching constraint and the power grid operation constraint; obtaining unit output by solving the critical equilibrium state model; according to the unit output, calculating the effective reserve, the key section and the grid structure blocking of the power grid; obtaining a balance tension degree evaluation result of the power system according to the effective reserve, the key section and the network frame blocking; the uncertainty of the load and the new energy is comprehensively considered, the accuracy of effective standby of the power grid is remarkably improved, and the reliability and the efficiency of power grid balance tension degree evaluation are further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power engineering, and specifically to a method, system and device for evaluating the balance tension based on the effective reserve of the power grid. Background Art

[0002] Due to the mismatch between power source construction and load growth, and the insufficient support capacity of new energy for power supply guarantee, the power grid often experiences balance tension, and the size of the power grid reserve is an important indicator for measuring the degree of balance tension. Currently, the nominal reserve used by power dispatching departments at all levels is the maximum generating capacity of the unit minus the current output. However, in real-time operation, due to uneven power flow distribution caused by local weather changes, some units have idle power, resulting in the effective reserve of the power grid being less than the nominal reserve. In extreme cases, power supply gaps may occur in local areas. Therefore, it is necessary to study a more accurate online calculation technology for the effective reserve of the power grid so that power grid operators can more accurately judge the current degree of balance tension.

[0003] The patent "Power Grid Reserve Optimization Method, System and Device Considering Emergency Dispatch Flexibility", publication number: CN118399507A, publication date: July 26, 2024, discloses: analyzing the demand for emergency reserve; obtaining the maximum and minimum limit constraints of emergency reserve based on the demand for emergency reserve, and obtaining the associated constraints between conventional reserve and emergency reserve; introducing the associated constraints between conventional reserve and emergency reserve, and solving the pre-established unit planned output and reserve allocation model to obtain the unit planned output and reserve optimization results of the power grid during the dispatching period. This invention aims to solve the problem that due to the sharp increase or decrease of new energy output during the dispatching period, the power grid may not be able to meet the power balance of the power grid during the dispatching period due to insufficient flexibility of the power grid; however, evaluating the degree of balance tension of the power system is a complex process involving multiple factors such as the accuracy of load forecasting, the availability of generating units, and the access situation of new energy; this solution only optimizes the reserve according to the dispatching control of unit output, and relying on this reserve optimization method may not be able to comprehensively and accurately evaluate the balance state of the power system. Summary of the Invention

[0004] The present invention addresses the problems in existing grid reserve calculation methods, where the uncertainty of node loads and new energy sources is not considered, resulting in inaccurate effective grid reserves and unreliable assessment of the tightness of power system balance. A method, system, and device for assessing the tightness of balance based on effective grid reserves are proposed. By determining the node stretching factor of the grid, the optimization objective and stretching constraints for the critical balance of the grid are obtained. Through the optimization objective and the current operating characteristics of grid units, the grid operating constraints are obtained. Based on the optimization objective, the stretching constraints, and the grid operating constraints, a critical balance state model of the grid is constructed. By solving the model, the effective grid reserves, key sections, and grid frame blockages are obtained, and then the tightness of the power system balance is comprehensively evaluated to obtain the assessment result. The uncertainty of loads and new energy sources is fully considered, significantly improving the accuracy of effective grid reserves and further enhancing the reliability and efficiency of the assessment of the tightness of power system balance.

[0005] To solve the above technical problems, according to the first aspect of the embodiments of the present invention, a method for assessing the tightness of balance based on effective grid reserves is provided, including the following steps: Determine the node stretching factor based on the initial state of the grid; According to the node stretching factor, obtain the optimization objective and stretching constraints for the critical balance of the grid; According to the current operating characteristics of grid units, obtain the grid operating constraints; According to the optimization objective, the stretching constraints, and the grid operating constraints, construct a critical balance state model of the grid; By solving the critical balance state model, obtain the unit output; According to the unit output, calculate the effective grid reserves, key sections, and grid frame blockages of the grid; According to the effective grid reserves, the key sections, and the grid frame blockages, obtain the assessment result of the tightness of the power system balance.

[0006] In this solution, by accurately determining the node stretching factor, the potential change tendency of nodes during the power grid balance adjustment can be quantified, which helps to build a model that better fits the actual operation conditions of the power grid. By constructing the optimization objective of the power grid critical balance, it is helpful to comprehensively consider the regulation ability of the power grid when calculating the effective reserve of the power grid, ensuring the reliability and accuracy of the evaluation results of the power grid balance tension. By establishing the stretching constraint and the power grid operation constraint, the actual limitations of the units in different operation stages are comprehensively considered. According to these constraints, the range of reserve power that the units can provide can be accurately defined, ensuring the rationality and feasibility of the power grid power adjustment, and avoiding the evaluation error of the power grid balance tension caused by ignoring the actual operation characteristics of the units. By integrating the optimization objectives and various constraint conditions determined in the previous steps, a power grid critical balance state model is constructed, which comprehensively and systematically describes the operation characteristics and constraint relationships of the power grid in the critical balance state, helps to comprehensively consider the mutual influence of various factors, improves the accuracy of calculating the effective reserve, the critical section and the grid blockage, further ensures that the evaluation results of the power grid tension are more reliable and practical, and provides strong data support for the safe and stable operation of the power grid.

[0007] Preferably, the determination of the node stretching factor based on the initial state of the power grid includes: Obtain the ultra-short-term load forecast value and new energy forecast value of the power grid within the target time limit; Determine the unit combination change within the target time limit according to the power grid's day-ahead power generation plan; According to the current power grid topology, unit combination, combined with the load forecast value and the new energy forecast value, determine the initial state of the power grid; according to the initial state of the power grid, configure the first power stretching factor with a positive initial state for the load nodes, and configure the second power stretching factor with a negative initial state for the new energy nodes.

[0008] Preferably, the obtaining of the ultra-short-term load forecast value and new energy forecast value of the power grid within the target time limit includes: collecting the historical operation data of the power grid, including at least historical load data, historical new energy operation data, and environmental parameters, and preprocessing the historical load data, the historical new energy operation data, and the environmental parameters, and extracting the target characteristic parameters of the load and new energy power generation; Based on the time series algorithm, using the target characteristic parameters as the input of the algorithm, and through the cross-validation method for model training, obtain the target prediction model; Based on the load forecast error and new energy forecast error, adjust the parameters and structure of the target prediction model to obtain the optimized prediction model; Based on the current moment load data and new energy power generation data, through the optimized prediction model, perform data prediction for the target time limit to obtain the load forecast value and the new energy forecast value.

[0009] Preferably, determining the node stretching factor based on the initial state of the power grid further includes: Obtaining the predicted relative error of each node of the power grid according to the historical operation parameters of the power grid; Obtaining the absolute value interval of the power stretching factor corresponding to each node according to the predicted relative error.

[0010] Preferably, obtaining the optimization objective and stretching constraints for the critical balance of the power grid according to the node stretching factor includes: Establishing an optimization objective for the critical balance of the power grid according to the node stretching factor and the power stretching amount of the corresponding node; Establishing the stretching constraint according to the proportional relationship between the node stretching factor and the bus load or / and the ultra-short-term prediction of power generation of the corresponding node.

[0011] Preferably, obtaining the operation constraints of the power grid according to the operation characteristics of the current power grid units includes: Establishing a first unit constraint according to the power generation capacity of the unit and the start-stop state of the unit; Establishing a second unit constraint according to the output of the unit and the operation rate of the unit; establishing a first balance constraint for the power stretching amount according to the first unit constraint and the stretching constraint; Establishing a second balance constraint representing the power transmission of the branch according to the operation requirements of the power grid.

[0012] Preferably, calculating the effective reserve, critical section and grid frame obstruction of the power grid according to the output of the unit includes: Calculating the effective reserve representing the reserve power added by all units relative to the initial state of the power grid according to the output of the units in the critical balance state of the power grid and the output of the units in the initial state of the power grid; Calculating the critical section representing the critical over-limit section of the power grid in the critical balance state according to the branch power flow in the critical balance state of the power grid; Calculating the grid frame obstruction representing the reserve power not added by all units relative to the initial state of the power grid according to the output of the units in the critical balance state of the power grid and the maximum output of the units.

[0013] In this solution, the effective reserve is calculated by comparing the unit output at the critical balance state of the power grid with the unit output at the initial state of the power grid, which can accurately quantify the actual increased reserve power of all units when dealing with the uncertainty of nodal load and new energy, and improve the accuracy of the effective reserve of the power grid. The determination of the key sections helps to identify the weak links in the power grid transmission network. When evaluating the tightness of the power grid, the existence of these weak links will increase the uncertainty and risk of power grid balance. The grid obstruction reflects the reserve power of units that cannot be fully utilized due to the limitation of the power grid framework structure. Therefore, by accurately calculating the key sections and grid obstruction, the power grid balance status can be evaluated from different perspectives, including a comprehensive evaluation of the situation where the reserve power of units cannot be fully utilized due to the limitation of the power grid framework structure, providing reliable data support for the evaluation of the balance tightness of the power system.

[0014] Preferably, obtaining the evaluation result of the balance tightness of the power system according to the effective reserve, the key sections, and the grid obstruction includes: Dividing the balance tightness of the power grid into levels according to the historical operating state of the power grid, the load forecast value, and the new energy forecast value; Allocating weight coefficients to the effective reserve, the key sections, and the grid obstruction according to the actual operating state of the power grid and the load demand; Calculating the comprehensive evaluation index of the power grid according to the weight coefficients, and matching the comprehensive evaluation index with the balance tightness level of the power grid to obtain the evaluation result of the balance tightness of the power grid.

[0015] In this solution, by comprehensively considering multiple factors, dynamically adapting to the changes in the power grid, and flexibly allocating weights, the accuracy of the evaluation of the balance tightness of the power system is significantly improved; by dividing the balance tightness level according to the historical operating state of the power grid, it can be continuously adjusted according to the development and operating experience of the power grid to achieve dynamic evaluation and ensure that the evaluation result can more effectively adapt to the actual operating conditions of the power grid. By allocating weights to the effective reserve, key sections, and grid obstruction, the importance differences of various factors in different operating scenarios are fully considered, improving the flexibility and adaptability of the evaluation, more accurately reflecting the balance tightness of the power grid at a specific moment, and improving the accuracy of the evaluation result. Furthermore, through the accurate evaluation result, strong decision-making support is provided for the optimal operation and planning of the power grid. For example, based on the accurate evaluation of the balance tightness, power grid operators can formulate more reasonable dispatching strategies, such as optimizing unit combinations, adjusting load distributions, etc., to reduce the balance tightness of the power grid, thereby improving the overall reliability and stability of the power grid.

[0016] According to the second aspect of the embodiments of the present invention, a balance tightness evaluation system based on the effective reserve of the power grid is provided, including: A data analysis module, configured to analyze the initial state of the power grid and the operating characteristics of power grid units, and obtain the objective function and constraints; a data processing module, configured to construct a critical equilibrium state model of the power grid according to the optimization objective and the constraints; A model calculation module, configured to solve the critical equilibrium state model and obtain the unit output; A data scheduling module, configured to obtain the effective reserve, critical sections, and grid frame congestion of the power grid according to the unit output; a power grid evaluation module, configured to obtain the evaluation result of the balance tension degree of the power system according to the effective reserve, the critical sections, and the grid frame congestion.

[0017] On the other hand, a device is provided, which includes a memory and a processor. A computer program is stored in the memory, and the processor executes the computer program to implement the above-mentioned balance tension degree evaluation method based on the effective reserve of the power grid.

[0018] Advantages of the present invention: 1. The three major calculation problems of the effective reserve, grid frame congestion, and critical sections of the power grid are unified and modeled as a linear programming problem of the critical equilibrium state of the power grid. By continuously stretching and amplifying the node power and adding the reserve power of the units until there is exactly a power deficit in the power grid, the total reserve power added by the units at this time is the effective reserve of the power grid, the reserve power that has not been added is the grid frame congestion of the power grid, and the critical over-limit section is the critical section. This model is simple and intuitive, has strong operability, reduces complex data calculation amounts, and improves the calculation efficiency and accuracy of the effective reserve of the power grid; 2. By considering the uncertainties of new energy and load, deviation analysis and confidence interval analysis are performed on their historical predictions and actual powers, and different stretching coefficients are assigned to different nodes, so as to maximize the confidence level of the critical equilibrium state model; 3. By comprehensively considering multiple factors, dynamically adapting to the changes of the power grid, and flexibly allocating weights, the accuracy of the evaluation of the balance tension degree of the power system is significantly improved, the one-sidedness caused by relying only on a single index for evaluation is avoided, and the balance state of the power system can be reflected more comprehensively and truly. Description of the Drawings

[0019] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, purposes, and advantages of the present invention will become more obvious. The drawings are only for the purpose of showing the preferred embodiments and are not considered as limiting the present invention. Moreover, the same reference signs are used to represent the same components throughout the drawings.

[0020] Figure 1 It is a flowchart of a balance tension degree evaluation method based on the effective reserve of the power grid in this embodiment.

[0021] Figure 2 Schematic diagram of a balance tension evaluation system module based on effective grid reserve for this embodiment.

[0022] Figure 3 Schematic diagram of the equipment structure for evaluating the balance tension based on effective grid reserve for this embodiment.

[0023] Figure 4 Schematic diagram of grid boundary condition parameters for this embodiment.

[0024] Figure 5 Schematic diagram of the calculation result of the effective grid reserve for the power grid in the target period for this embodiment.

[0025] Figure 6 Schematic diagram of the calculation result of the section blockage of the power grid in the target period for this embodiment. Specific implementation manner

[0026] To make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific implementation manner described herein is only the best embodiment of the present invention, which is only used to explain the present invention and does not limit the protection scope of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0027] Some nouns or terms that appear in the process of describing the embodiments of the present application are applicable to the following explanations: Critical balance state: Starting from the initial state of the system, by stretching the node power to approach the critical value, so that under the satisfaction of hard constraints such as section constraints, a power deficit exactly appears in the system balance.

[0028] Critical output: Represents the output of the unit under the critical balance state.

[0029] Embodiment 1: As Figure 1 shown, a method for evaluating the balance tension based on effective grid reserve includes the following steps: S1. Determine the node stretching factor based on the initial state of the power grid, where: Obtain the ultra-short-term load prediction value and the new energy prediction value of the power grid within the target time limit; Determine the unit combination change within the target time limit according to the power grid's day-ahead generation plan; Determine the initial state of the power grid according to the current power grid topology, unit combination, combined with the load prediction value and the new energy prediction value; According to the initial state of the power grid, configure the first power stretching factor with a positive initial state for the load node, and configure the second power stretching factor with a negative initial state for the new energy node.

[0030] As an implementation manner, the initial state can be defined as the current wiring mode of the power grid and the unit combination, as well as the ultra-short-term nodal load forecast and new energy forecast for the next 4 hours. For the unit combination changes occurring during the next 4 hours, the day-ahead generation plan shall prevail.

[0031] In this embodiment, through the load forecast value and the new energy forecast value, the change trend of power supply and demand of the power grid in the future period can be predicted in advance, making the setting of the nodal stretching factor more in line with the actual situation and avoiding the miscalculation of the effective reserve caused by the forecast error. By clarifying the unit combination changes, the initial state of the power grid fully considers the adjustability of the power generation side, so as to accurately judge the range of reserve power that the unit can provide when calculating the effective reserve subsequently, and then more accurately analyze the balance characteristics of the power grid under different unit configurations. By integrating multi-faceted information such as the ultra-short-term load forecast value, new energy forecast value, day-ahead generation plan, power grid topology, and unit combination, the initial state of the power grid is defined to comprehensively reflect the current actual operation situation of the power grid, including the power distribution of each node, the operation state of the unit, etc., laying a foundation for model construction and analysis in the subsequent steps. Compared with the method relying on a single data source, it can grasp the initial operation condition of the power grid more accurately and reduce the problems of calculation deviation of the effective reserve and unreliable evaluation caused by incomplete or inaccurate basic data.

[0032] Among them, a first power stretching factor with a positive initial state is configured for the load node, and a second power stretching factor with a negative initial state is configured for the new energy node. This configuration method is based on the physical characteristics of the load node consuming power and the new energy node generating power, intuitively reflecting the different action directions of the load node and the new energy node on the power grid power balance. Through this configuration, it is convenient for the subsequent quantitative analysis of the power grid balance adjustment, helps to more accurately simulate the impact of the node power change on the overall balance of the power grid in the subsequent model construction and calculation, and improves the accuracy of the evaluation of the effective reserve and tightness of the power grid.

[0033] Specifically, obtaining the ultra-short-term load forecast value and new energy forecast value of the power grid within the target time limit includes: Collecting the historical operation data of the power grid, including at least historical load data, historical new energy operation data, and environmental parameters, and preprocessing the historical load data, historical new energy operation data, and environmental parameters to extract the target characteristic parameters of the load and new energy power generation; Based on the time series algorithm, using the target characteristic parameters as the algorithm input and performing model training through the cross-validation method to obtain the target prediction model; Based on the load forecast error and new energy forecast error, adjusting the parameters and structure of the target prediction model to obtain the optimized prediction model; Based on the current moment load data and new energy power generation data, data prediction for the target time limit is carried out by optimizing the prediction model to obtain the load prediction value and the new energy prediction value.

[0034] Specifically, determining the node stretching factor based on the initial state of the power grid further includes: Obtaining the predicted relative error of each node of the power grid according to the historical operation parameters of the power grid; Obtaining the absolute value interval of the power stretching factor corresponding to each node according to the predicted relative error.

[0035] Optionally, for each node, by analyzing the predicted relative error in the recent 60 days, the absolute value of the power stretching factor can be set as the upper limit of the 95% confidence interval of the predicted relative error of similar days.

[0036] Specifically, analyzing the predicted relative error of each node in the recent 60 days can effectively capture the uncertainty of node power prediction. At the same time, the time window is long enough to reflect the fluctuation law in the time series. The data of these 60 days cover various factors such as different meteorological conditions and load change patterns. Through the statistical analysis of these data, it can be understood the deviation degree between the prediction and the actual situation of the node under various actual operation scenarios, and the influence of these periodic changes on the predicted relative error can be found, which helps to more accurately evaluate the uncertainty of the node at different time stages and provides a more practical basis for the subsequent setting of the stretching factor. Setting the absolute value of the power stretching factor as the upper limit of the 95% confidence interval of the predicted relative error of similar days provides a reasonable range for the power stretching of the node, ensuring that the power grid has sufficient adjustment ability when dealing with the uncertainty of node power; Furthermore, since the upper limit of the 95% confidence interval of the predicted relative error of similar days is dynamically determined according to historical data, it can automatically adapt to the change of node uncertainty. While ensuring that the power grid has sufficient adjustment ability, it also helps to balance the economy of power grid operation. If the stretching factor is set too large, it may lead to over - allocation of adjustment resources in the power grid and increase the operation cost; while if the stretching factor is set too small, it may not be able to effectively cope with the uncertainty of node power and increase the risk of power grid instability; by associating the stretching factor with the upper limit of the confidence interval, on the basis of ensuring the safe and stable operation of the power grid, the adjustment resources can be utilized as reasonably as possible to realize the economy of power grid operation.

[0037] In this embodiment, by combining deviation analysis and confidence interval analysis to assign stretching coefficients to different nodes, the confidence level of the power grid balance model can be maximized. The reasonable determination of the stretching coefficients enables the model to better adapt to the uncertainties of new energy and load during the simulation of the power grid operation. When the model can more accurately reflect the changes in various powers in the actual power grid, the output of the unit output is more credible, which helps the power grid operators use the model with more confidence for decision-making and improves the safety and economy of the power grid operation.

[0038] S2. Obtain the optimization objective and stretching constraints for the critical balance of the power grid according to the node stretching factors, where: Establish the optimization objective for the critical balance of the power grid based on the node stretching factors and the power stretching amounts of the corresponding nodes; Establish stretching constraints based on the node stretching factors and the proportional relationship between the bus loads or / and the ultra-short-term power generation forecasts of the corresponding nodes.

[0039] Furthermore, the optimization objective represents the maximum value of the sum of the power stretching amounts of each node during the target operation period of the power grid. For example, maximize the sum of the power stretching amounts at each moment from the current time to the next 4 hours. The optimization objective is expressed as follows: Where, represents the power stretching amount of node i at time t, t 0 represents the current time, N b represents the total number of nodes.

[0040] The stretching constraint indicates that the power stretching factor of the node is proportional to the bus load or / and the ultra-short-term power generation forecast of the node during the power grid operation period; the stretching constraint is expressed as follows: Where, δ t is the power stretching coefficient of all nodes at time t, γ i represents the power stretching factor of node i, d i,t is the bus load / ultra-short-term power generation forecast value of node i at time t.

[0041] In this embodiment, through the optimization objective, that is, the maximum of the sum of the power stretching amounts, the maximum potential of the power grid in terms of balance adjustment can be obtained, and the operating point that enables the power grid to withstand the maximum load change or new energy fluctuation under the critical state can be found, providing a key indicator for evaluating the tightness of the power grid balance; through the stretching constraint, a quantitative relationship between the node stretching factors and the actual load and power generation forecast is established, ensuring that when adjusting the power stretching amount, a reasonable distribution can be made according to the actual load conditions of the nodes and the new energy power generation expectations, making the model more in line with the physical laws of the power grid operation, and thus improving the accuracy and reliability of the calculated unit output value.

[0042] S3. Obtain the grid operation constraints according to the current grid unit operation characteristics, where: Establish the first unit constraint according to the unit power generation capacity and the unit start-stop state; Establish the second unit constraint according to the unit output and the unit operation rate; establish the first balance constraint of the power stretching amount according to the first unit constraint and the stretching constraint; Establish the second balance constraint representing the branch power transmission according to the grid operation requirements.

[0043] Furthermore, the first unit constraint is the upper and lower limit constraint of the unit output, representing that during the unit start-stop period, the unit output is not lower than the minimum power generation declared by the unit and the unit output is not higher than the maximum power generation declared by the unit; it is expressed as follows: Among them, represents the minimum power generation capacity declared by unit g, represents the maximum power generation capacity declared by unit g, u g,t represents the operation state of unit g at time t, where 0 represents shutdown and 1 represents operation, P g,t represents the output of unit g at time t.

[0044] The second unit constraint is the unit ramp-up / ramp-down constraint, representing that the increase in the output of the unit at the current operation time is not higher than the sum of the output of the previous time and the ramp-up rate, and the output of the unit at the current operation time is not less than the difference between the output of the previous time and the ramp-down rate; it is expressed as follows: Among them, RU g represents the ramp-up rate of unit g, RD g represents the ramp-down rate of unit g, and ΔT represents the time interval between two adjacent times.

[0045] The first balance constraint is the active power balance constraint, representing that the power stretching amount of the node needs to meet the relationship threshold between the power of the units connected to the node and the active power flow of the branch; it is expressed as follows: Among them, G i represents the set of units connected to node i, PLF i represents the set of branches flowing out of node i, PLE i represents the set of branches flowing into node i, pf ij,t represents the active power flow of branch ij at time t.

[0046] The second balance constraint is the power transmission constraint, representing that the active power flow of the branch is not lower than the opposite of the maximum transmission power of the branch and not higher than the maximum transmission power of the branch; it is expressed as follows: where, θ i,t represents the phase angle of node i at time t, and θ min represents the lower limit of the phase angles of all nodes, and θ max represents the lower and upper limits of the phase angles of all nodes, and x ij represents the impedance of branch ij, represents the maximum transmission power of branch ij.

[0047] In this embodiment, the unit constraints established based on the power generation capacity, start-stop state, and operating rate of the units comprehensively consider the actual limitations of the units in different operating stages. Through the first unit constraint, the power output range of the units during the start-stop process is restricted to ensure the normal and stable operation of each unit; through the second unit constraint, the dynamic operating characteristics of the units are considered, that is, the units cannot instantaneously change their output significantly, but are restricted by the ramping rate and the sliding rate, ensuring the smoothness and safety of the unit operation. At the same time, the model can more realistically simulate the dynamic response process of the power grid in actual operation, further obtain more accurate unit outputs that conform to the actual operation of the power grid, and ensure the accuracy of the effective reserve of the power grid; through the first balance constraint and the second balance constraint, the dynamic adjustment of the node power stretching amount is ensured within the safe range of the branch active power flow, maintaining the transmission stability and safety of the power grid, so that the entire power grid balance model not only meets the operating restrictions of the units, but also takes into account the operating requirements of the power grid transmission network.

[0048] S4. Construct a critical equilibrium state model of the power grid according to the optimization objective, stretching constraint, and power grid operation constraint.

[0049] S5. Obtain the unit output by solving the critical equilibrium state model.

[0050] S6. Calculate the effective reserve, critical section, and network congestion of the power grid according to the unit output, where: Calculate the effective reserve representing the reserve power added by all units relative to the initial state of the power grid according to the unit output under the critical equilibrium state of the power grid and the unit output under the initial state of the power grid; Calculate the critical section representing the critical over-limit section of the power grid under the critical equilibrium state according to the branch power flow under the critical equilibrium state of the power grid; Calculate the network congestion representing the reserve power not added by all units relative to the initial state of the power grid according to the unit output under the critical equilibrium state of the power grid and the maximum output of the units.

[0051] Furthermore, the calculation formula of the effective reserve is as follows: In the formula, R tDenote the effective reserve of the power grid at time t. Denote the output of unit g at time t under the critical equilibrium state. Denote the output of unit g at time t in the initial state, and G represents the set of all units.

[0052] The calculation formula for the key section is as follows: In the formula, KL t Denote the set of key sections of the power grid at time t. Denote the power flow of branch ij at time t under the critical equilibrium state.

[0053] The calculation formula for the grid obstruction is as follows: In the formula, W t Denote the grid obstruction at time t.

[0054] In this embodiment, by comparing the unit outputs in the critical equilibrium state of the power grid and the unit outputs in the initial state of the power grid to calculate the effective reserve, it is possible to accurately quantify the actual additional reserve power of all units when coping with the uncertainties of nodal load and new energy, improving the accuracy of the effective reserve of the power grid; the determination of the key section helps to identify the weak links in the power grid transmission network, and the existence of these weak links will increase the uncertainty and risk of power grid balance when evaluating the tightness of the power grid; the grid obstruction reflects the reserve power of the unit that cannot be fully utilized due to the limitation of the power grid network structure; therefore, by accurately calculating the key section and the grid obstruction, it is possible to evaluate the power grid balance status from different perspectives, including comprehensively evaluating the situation where the reserve power of the unit cannot be fully utilized due to the limitation of the power grid network structure, providing reliable data support for the evaluation of the balance tightness of the power system.

[0055] It is understandable that accurate effective reserve calculation results provide key decision-making basis for power grid dispatching. Dispatchers can reasonably arrange the power generation plans of units according to the size of effective reserve, and while ensuring the safe and stable operation of the power grid, achieve the optimal allocation of resources. For example, when the effective reserve is low, dispatchers can arrange in advance for some units with fast adjustment capabilities to increase their output, or reduce the load through demand-side management means to ensure that the power grid is always in a safe operating state; for example, in the case of a sudden increase in load or a sudden drop in new energy output, the accurate calculation of effective reserve can help grid operators clearly know how much additional power the existing units can provide to maintain grid balance and avoid power outages. By obtaining accurate effective reserve of the power grid, it helps in the planning and upgrading of the power grid. The changing trend of effective reserve under different working conditions can be analyzed to determine whether new power generation capacity needs to be added, the performance of units improved, or the grid tie lines strengthened in the future to meet the growing load demand and the requirements of new energy access.

[0056] Furthermore, by calculating the key sections, the security risks of the power grid can be evaluated in real time during the operation of the power grid, and the operation mode of the power grid can be adjusted in a timely manner. For example, changing the output of units, adjusting the load distribution, etc., to control the power flow of the key sections within a safe range and ensure the reliability of the power grid. In addition, from the perspective of power grid planning and upgrading, the calculation results of key sections provide an important reference for optimizing the power grid topology. For example, if key section problems often occur in certain areas, technical means such as adding transmission lines, building substations or adopting flexible AC transmission systems can be considered to improve the power grid topology and enhance the power transmission capacity and anti-interference ability of the power grid.

[0057] S7. Obtain the evaluation result of the balance tension degree of the power system according to the effective reserve, key sections and network frame obstruction, where: Divide the balance tension degree of the power grid into levels according to the historical operation state of the power grid, load forecast value and new energy forecast value; assign weight coefficients to the effective reserve, key sections and network frame obstruction according to the actual operation state of the power grid and load demand; calculate the comprehensive evaluation index of the power grid according to the weight coefficients, and match the comprehensive evaluation index with the balance tension degree level of the power grid to obtain the evaluation result of the balance tension degree of the power grid.

[0058] Optionally, the grading of the grid balance tension level can formulate grading criteria based on the matching degree of load demand and power generation capacity, the adequacy of the energy storage system, the power flow situation of the key sections, and the grid stable operation and security, and divide the grid balance tension level into four levels from high to low: balanced state, slightly tense, moderately tense, and severely tense. For example, evaluate whether the current load demand matches the power generation capacity of each power plant in the grid and whether there is an obvious supply-demand gap; analyze whether the reserve capacity of the grid is sufficient to provide timely power support during load fluctuations or sudden failures; analyze whether the power flow of the key sections in the grid exceeds the safety limit and whether there is an overload risk; comprehensively evaluate the overall stability and security of the grid, including aspects such as voltage stability, frequency stability, and system disturbance resistance, and comprehensively divide the degree level.

[0059] Furthermore, the balanced state means that the load demand and power generation capacity are perfectly matched, the reserve capacity is sufficient, the power flow of the key sections is within the safety limit, and the grid is overall stable and secure; slightly tense means that the grid operation remains stable, but more refined scheduling and control of the load and power generation are required; moderately tense means that the grid load rate is high, the reserve capacity is seriously insufficient, and serious over-limit situations occur in the key sections, and emergency measures need to be taken to ensure the stable operation of the grid; severely tense means that the grid operation is in an extremely unstable state and may face the risk of large-scale power outages.

[0060] It should be noted that the grid balance tension is a dynamically changing process. Dividing the grid balance tension into different levels helps to better understand the grid operation status and risk level, and provides a scientific basis for the grid scheduling and operation. When grading, by comprehensively considering multiple factors such as the grid load situation, power generation capacity, reserve capacity, key section status, and grid frame obstruction, fully considering the actual situation and future development trend of the grid, the accuracy and rationality of the grading are ensured. In addition, the grid balance tension level can be adjusted and optimized in a timely manner according to the changes in the grid operation status to ensure the accuracy and reliability of the balance tension assessment results.

[0061] In this embodiment, by comprehensively considering multiple factors, dynamically adapting to grid changes, and flexibly allocating weights, the accuracy of the evaluation of the power system balance tension level is significantly improved. By dividing the balance tension level according to the historical operating state of the grid, it can be continuously adjusted according to the development and operating experience of the grid to achieve dynamic evaluation and ensure that the evaluation results can more effectively adapt to the actual operating conditions of the grid. By allocating weights to effective reserves, key sections, and grid frame blockages, the importance differences of various factors in different operating scenarios are fully considered, improving the flexibility and adaptability of the evaluation, more accurately reflecting the balance tension level of the grid at a specific moment, and improving the accuracy of the evaluation results. Furthermore, the accurate evaluation results provide strong decision-making support for the optimal operation and planning of the grid. For example, based on the accurate evaluation of the balance tension level, grid operators can formulate more reasonable dispatching strategies, such as optimizing unit combinations, adjusting load distributions, etc., to reduce the grid balance tension level, thereby improving the overall reliability and stability of the grid.

[0062] In addition, by considering factors such as key sections and grid frame blockages, potential weak links and risk points can be identified before serious problems occur in the grid. For example, through the real-time monitoring and evaluation of the key section power flow, when the key section is approaching the overload limit, an early warning can be issued in advance, and the operators can timely adjust the unit output or take other control measures, such as adjusting the grid topology structure, performing reactive power compensation, etc., to control the grid balance tension level within the safe range and effectively avoid the occurrence of grid accidents.

[0063] As Figure 2 shown, the balance tension level evaluation system based on the effective reserve of the grid includes: A data analysis module, which is used to analyze the initial state of the grid and the operating characteristics of the grid units to obtain the objective function and constraint conditions; a data processing module, which is used to construct a critical balance state model of the grid according to the optimization objective and constraint conditions; A model calculation module, which is used to solve the critical balance state model to obtain the unit output; A data scheduling module, which is used to obtain the effective reserve, key sections, and grid frame blockages of the grid according to the unit output; A grid evaluation module, which obtains the evaluation result of the balance tension level of the power system according to the effective reserve, key sections, and grid frame blockages.

[0064] As Figure 3 shown, a device includes a memory and a processor. A computer program is stored in the memory, and the processor executes the computer program to implement the above-mentioned balance tension level evaluation method based on the effective reserve of the grid.

[0065] Specifically, the device further includes a communication interface and a communication bus. The memory and the processor in the device communicate through the communication bus and the communication interface. The communication bus can be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus can be divided into an address bus, a data bus, a control bus, and the like.

[0066] The memory can include a Random Access Memory (RAM), and can also include a non-volatile memory, such as at least one disk memory. Optionally, the memory can also be at least one storage device located far from the aforementioned processor.

[0067] The aforementioned processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0068] As a specific example analysis, as Figure 4 shown, this implementation case uses a certain provincial power grid as the specific implementation object. The operation time is 2:45 in the early morning, and the solution period is from 3:00 to 6:45. The boundary conditions include the external power plan, ultra-short-term system load forecast, wind power forecast, and photovoltaic forecast during this period; to simplify the solution, the power stretching factor of the photovoltaic node is set to -0.5, the power stretching factor of the wind power node is set to -0.3, and the power stretching factor of the load node is set to 0.1. The power grid topology is the full connection mode of the provincial power grid. The maximum power generation capacity of the coal-fired units in the grid is 39560 MW, the minimum power generation capacity is 15800 MW, and the section limit is set to the long-term limit of the transmission line. Based on the above power grid parameters and boundary conditions, a model is established and the solver is called to solve, and the effective reserve, grid blockage, and key sections of the power grid during the period from 3:00 to 6:45 are obtained, as shown respectively in Figure 5 、 Figure 6As shown in Table 1, the grid reserve was relatively abundant during this period, all above 5000 MW. However, there were also many grid blockages, with the maximum reaching 2483 MW. Table 1 Blocked Power of Sections and Associated Power Plants from 3:00 to 6:45 in the Early Morning As can be seen from Table 1, the grid blockages during this period were distributed in the transmission sections in the coastal areas of the province, including the double lines from Sidu Substation to Tangling Substation, the double lines from Changtan Substation to Tangling Substation, the supply area of Sidu Substation, and the double lines from Huipu Substation to Changtan Substation. The above sections have all reached a 100% load rate, resulting in curtailment of power at plants such as YQ Plant, TZ Plant, DS Plant, WZ Plant, and YH Plant. Through the above analysis, it can be concluded that when the boundary conditions change, such as the increase in the province's load due to rising temperatures or the wind power falling short of expectations, the above sections may become bottlenecks in power supply capacity. When the total increase or decrease in load / new energy exceeds the effective reserve, a power gap will occur in the system.

[0069] The above specific implementation manners are the preferred implementation manners of the present invention, and do not limit the specific implementation scope of the present invention. The scope of the present invention includes but is not limited to this specific implementation manner. Any equivalent changes made in accordance with the shape, structure, and method of the present invention are within the protection scope of the present invention.

Claims

1. A method for evaluating the balance tension of a power grid based on effective reserve, characterized in that: The steps include: Determine the node stretch factor based on the initial state of the power grid; According to the node stretching factor, an optimization target and a stretching constraint of a critical balance of a power grid are obtained; Obtain grid operation constraints based on current grid unit operation characteristics; Constructing a critical equilibrium state model of a power grid according to the optimization objective, the stretch constraint and the power grid operation constraint; By solving the critical equilibrium model, the unit output is obtained; According to the output of the units, the effective reserve, key sections and grid obstruction of the power grid are calculated; According to the effective reserve, the key section and the grid obstruction, the balance tension evaluation result of the power system is obtained.

2. The method for evaluating the balance tension based on the effective reserve of the power grid according to claim 1 is characterized by: The determining of the node stretching factor based on the initial state of the power grid includes: Obtain the ultra-short-term load forecast value and new energy forecast value of the power grid within the target time limit; Determine the unit mix changes within the target time limit based on the power grid's day-ahead power generation plan; The initial state of the power grid is determined according to the current power grid topology, unit combination, load forecast value and new energy forecast value; according to the initial state of the power grid, a first power stretching factor with an initial state being positive is configured for the load node, and a second power stretching factor with an initial state being negative is configured for the new energy node.

3. The method for evaluating the balance tension based on the effective reserve of the power grid according to claim 2 is characterized by: The obtaining of the ultra-short-term load forecast value and the new energy forecast value of the power grid within the target time limit includes: Collecting historical operation data of the power grid, including at least historical load data, historical new energy operation data and environmental parameters, and preprocessing the historical load data, the historical new energy operation data and the environmental parameters to extract target characteristic parameters of load and new energy power generation; Based on the time series algorithm, the target feature parameters are used as algorithm input, and the model is trained by cross-validation method to obtain a target prediction model; Adjusting the parameters and structure of the target prediction model based on the load prediction error and the new energy prediction error to obtain an optimized prediction model; Based on the current load data and new energy power generation data, the optimization prediction model is used to perform data prediction for the target time limit to obtain load prediction values ​​and new energy prediction values.

4. The method for evaluating the balance tension based on effective reserve of power grid according to claim 1 is characterized in that: The determining of the node stretching factor based on the initial state of the power grid also includes: According to the historical operation parameters of the power grid, the predicted relative error of each node in the power grid is obtained; According to the predicted relative error, the absolute value interval of the power stretch factor corresponding to each node is obtained.

5. The method for evaluating the balance tension based on the effective reserve of the power grid according to claim 3 is characterized by: According to the node stretching factor, the optimization target and stretching constraint of the critical balance of the power grid are obtained, including: Establishing an optimization target of critical balance of the power grid according to the node stretching factor and the power stretching amount of the corresponding node; The stretch constraint is established according to the node stretch factor and the proportional relationship between the bus load and / or the ultra-short-term power generation forecast of the corresponding node.

6. The method for evaluating the balance tension based on the effective reserve of the power grid according to claim 5 is characterized by: The obtaining of grid operation constraints according to the current grid unit operation characteristics includes: Establishing the first unit constraint according to the unit power generation capacity and the start / stop status of the unit; Establishing a second unit constraint according to the unit output and the unit operating rate; establishing a first balance constraint of the power stretching amount according to the first unit constraint and the stretching constraint; A second balancing constraint characterizing branch power transmission is established according to grid operation requirements.

7. The method for evaluating the balance tension based on the effective reserve of the power grid according to claim 1 is characterized by: The calculation of the effective reserve, key sections and grid obstruction of the power grid according to the output of the unit includes: According to the unit output under the critical equilibrium state of the power grid and the unit output under the initial state of the power grid, the effective reserve representing the reserve power added by all units relative to the initial state of the power grid is calculated; According to the branch power flow in the critical equilibrium state of the power grid, the key section representing the critical over-limit section of the power grid in the critical equilibrium state is calculated; According to the unit output under the critical equilibrium state of the power grid and the maximum output of the units, the grid obstruction representing the reserve power not added by all the units relative to the initial state of the power grid is calculated.

8. The method for evaluating the balance tension based on the effective reserve of the power grid according to claim 7 is characterized by: The obtaining of the balance tension evaluation result of the power system according to the effective reserve, the key section and the grid obstruction includes: The balance tension of the power grid is divided into different levels according to the historical operation status of the power grid, load forecast value and new energy forecast value; According to the actual operation status of the power grid and the load demand, weight coefficients are allocated to the effective reserve, the key section and the grid obstruction; The comprehensive evaluation index of the power grid is calculated according to the weight coefficient, and the comprehensive evaluation index is matched with the balance tension level of the power grid to obtain a balance tension level evaluation result of the power grid.

9. A system for evaluating the balance tension based on effective reserve of a power grid, applicable to the method for evaluating the balance tension based on effective reserve of a power grid as claimed in any one of claims 1 to 8, characterized in that: include: Data analysis module, used to analyze the initial state of the power grid and the operating characteristics of the power grid units, and obtain the objective function and constraint conditions; A data processing module, used for constructing a critical equilibrium state model of a power grid according to the optimization target and the constraint conditions; A model calculation module, used to solve the critical equilibrium state model and obtain the unit output; A data scheduling module, used to obtain effective backup, key sections and grid obstruction of the power grid according to the output of the unit; The power grid assessment module obtains the balance tension assessment result of the power system according to the effective reserve, the key section and the grid obstruction.

10. A device, characterized in that: The device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the balance tension assessment method based on effective reserve of the power grid as described in any one of claims 1 to 8.

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