Hydropower station economic operation post-evaluation assessment method based on refined simulation scheduling

Abstract

The invention provides a hydropower station economic operation post-evaluation assessment method based on refined simulation scheduling, and belongs to the field of hydropower station economic operation assessment. The method comprises the following steps: firstly, calculating an increase rate of actual power generation compared with designed power generation by taking actually measured reservoir flow and initial water level of a power station in an assessment period as input; secondly, constructing a power generation optimization scheduling model, and calculating the theoretical maximum generating capacity; calculating the proportion of the actual dispatching power generation capacity in the theoretically optimal dispatching power generation, and obtaining the optimal power generation completion rate; thirdly, determining a scheduling difficulty degree based on expert experience, establishing a difficulty coefficient evaluation model, and calculating a difficulty coefficient; and finally, comprehensively evaluating the economic benefits of the hydropower station by adopting the water energy utilization improvement rate and the optimal power generation completion rate based on the difficulty coefficient as evaluation indexes. A two-dimensional index system is provided, the operation level of the hydropower station can be comprehensively and deeply reflected, and powerful decision support is provided for efficient management of the hydropower station.

Description

Technical Field

[0001] The invention belongs to the field of economic operation assessment of hydropower stations, and relates to a post-evaluation and assessment method for economic operation of hydropower stations based on refined simulation scheduling. Background Art

[0002] The economic operation assessment and evaluation system of hydropower stations, as a mechanism for comprehensive assessment and feedback of the performance of key links in the power generation process of power companies, is a key measure to promote the sustainable development strategy of enterprises and optimize the management structure and operation model. As a scientific management method, economic operation evaluation is an effective way to improve the utilization rate of water resources, enhance the management level of hydropower stations and increase the economic benefits of enterprises [Zhou Jia, Ma Guangwen, Huang Weibin, et al. Research on the economic operation benefit evaluation system of cascade hydropower stations in the basin [J]. Hydropower Energy Science, 2011, 29(05): 145-147+194.]. In order to fully control the operation and management status of hydropower stations and tap the potential for increasing power generation and achieve economic and efficient operation, the study of the post-evaluation and assessment system of economic operation of hydropower stations has great practical significance for hydropower groups. Constructing a reasonable and efficient assessment and evaluation system for hydropower stations, objectively, accurately and scientifically quantifying and evaluating the dispatching and operation capabilities, can stimulate management vitality and work enthusiasm, release power generation potential, and improve economic benefits and comprehensive management level. Power generation efficiency, as an intuitive reflection of the dispatching effect, is a key performance indicator for measuring the effectiveness of economic operation. Therefore, conducting assessment and evaluation of the power generation efficiency of hydropower stations, accurately grasping the dispatching level, analyzing the room for efficiency improvement, and evaluating the actual dispatching results are crucial to the scientific and economical operation and management of hydropower stations, and are an indispensable part of the operation and management system.

[0003] The economic operation assessment index of hydropower stations has evolved from the early absolute assessment index to the relative assessment index, which can be used to conduct parallel comparison of economic dispatching effects between different hydropower stations. In 1981, Zhou Zhiya et al. proposed to use relative water utilization rate and relative power generation water consumption rate as the economic operation assessment index of hydropower stations. This index can be used to compare with the past operation status and other hydropower stations [Zhou Zhiya, Huang Yifen. Discussion on the calculation method of economic dispatching assessment of hydropower station reservoirs [J]. Hydropower, 1981, (11): 43-47.]. In 2001, Wu Dongping compared the actual dispatching with the conventional dispatching method and proposed the water energy utilization improvement rate [Wu Dongping. Suggestions for improving the operating parameters and assessment indicators of hydropower stations [J]. Hydropower, 2001, (02): 46-47+53.]. In 2009, a set of evaluation index system for the dispatching and power generation benefits of hydropower station groups was constructed [Zhang Junliang, Ma Guangwen, Lu Tao. Research on the evaluation index system for energy-saving dispatching benefits of cascade hydropower stations in the basin [J]. East China Electric Power, 2009, 37(05): 812-815.]. In 2011, Cai Zhiguo et al. proposed to optimize the potential and calculated the theoretical optimal power generation. They proposed the optimal power generation completion rate as the evaluation index of the power station, which evaluated the operation level of the power station and clarified the future development potential [Cai Zhiguo, Cao Guangjing, Zheng Ying. Research and application of new methods for economic operation evaluation of cascade hydropower stations [J]. Journal of Hydroelectric Engineering, 2011, 30(02): 15-19.]. In 2012, Xu Tingbing et al. added the impact of the output coefficient value on the assessed electricity, and used the water energy utilization improvement rate indicator to assess the water-saving and power generation of hydropower stations [Xu Tingbing, Ma Guangwen, Li Zehong, et al. Assessment of water-saving and power generation of hydropower stations based on the K-value time-division reverse calibration method [J]. Hydropower Energy Science, 2012, 30(05): 112-114+166.].

[0004] In summary, the economic operation assessment index system of hydropower stations has been increasingly improved. The current evaluation indicators of hydropower stations mainly include the water energy utilization improvement rate and the optimal power generation completion rate, which can reflect the economic operation and dispatching level of hydropower stations. However, the difficulty of dispatching is different every year, and the dispatching level to achieve the same power generation completion rate is different. Therefore, on the basis of the existing assessment indicators, this patent introduces the dispatching difficulty coefficient, formulates a scientific and reasonable assessment index evaluation system, realizes a scientific and comprehensive evaluation of the economic benefits of hydropower stations and the actual dispatching level of managers, and achieves the effect of evaluating the present and guiding the future. Summary of the invention

[0005] In view of the limitations of the existing assessment and evaluation methods for the economic operation of hydropower stations, the present invention provides a post-evaluation and assessment method for the economic operation of hydropower stations, which includes two evaluation indicators: the water energy utilization improvement rate and the optimal power generation completion rate based on the difficulty coefficient. The water energy utilization improvement rate reflects the improvement rate of actual power generation compared to the designed power generation, and the optimal power generation completion rate reflects the proportion of actual power generation to the theoretical maximum power generation. The difficulty coefficient measures the difficulty of scheduling during the assessment period and corrects the optimal power generation completion rate. This method takes into account both the improvement value compared to the designed power generation and the proportion of the optimal theoretical power generation. It can not only mobilize the enthusiasm of management personnel, but also clarify the development space, and realize the scientific evaluation of the economic benefits of hydropower stations and the actual scheduling level of management personnel. The Huanren Hydropower Station in Liaoning Province was used as an actual case to verify the rationality and effectiveness of the present invention.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] A method for post-evaluation and assessment of the economic operation of a hydropower station based on refined simulation scheduling. The method first uses the measured inflow and initial water level of the power station during the assessment period as input, performs simulation scheduling according to the established conventional scheduling diagram and relevant scheduling principles, calculates the designed power generation, and calculates the increase rate of the actual power generation compared with the designed power generation, which is the water energy utilization improvement rate. Secondly, taking the maximum power generation of the hydropower station as the objective function, considering the water storage level, output, discharge (including ecological flow), head, grid restrictions, unit maintenance, market demand and other constraints, constructs a power generation optimization scheduling model, and calculates the theoretical maximum power generation. Calculate the proportion of the actual scheduled power generation to the theoretical optimal scheduled power generation to obtain the optimal power generation completion rate. Afterwards, determine the difficulty of scheduling based on expert experience, establish the relationship between it and key discriminant factors such as water inflow conditions, establish a difficulty coefficient evaluation model, and calculate the difficulty coefficient. Finally, the water energy utilization improvement rate and the optimal power generation completion rate based on the difficulty coefficient are used as evaluation indicators to achieve a comprehensive evaluation of the economic benefits of the hydropower station. The specific steps are as follows:

[0008] Step (1) calculating the designed power generation of the hydropower station;

[0009] According to the established dispatching diagram of the hydropower station, the power generation flow, output, water balance, and water level and storage capacity constraints are taken into account, and the power generation during the assessment period is simulated and calculated with the actual initial water level and the actual water inflow. The specific process is: according to the current water level and time of the hydropower station, the dispatching area is automatically identified, and power is generated according to the output of the dispatching area. The rolling simulation finally calculates the total power generation during the assessment period.

[0010] Step (2) calculating the theoretical optimal power generation;

[0011] The optimal dispatching model of the hydropower station is established with the maximum total power generation during the assessment period as the objective function. The objective function is shown in formula (1):

[0012]

[0013] Where: E is the total power generation benefit of the hydropower station during the assessment period; T is the number of time periods; N t , Q t and μ t are the output, power generation flow and water consumption rate of the hydropower station in period t respectively; Δt is the length of the period.

[0014] The constraint expressions that the hydropower station optimization dispatching model needs to satisfy are as follows:

[0015] 1) Water level constraints

[0016] Z t min ≤Z t ≤Z t max (2)

[0017] In the formula, Z t , Z t max and Z t min They are the water level of the reservoir in the tth period and its upper and lower limits, where the lower limit is the dead water level, the upper limit of the flood season water level is the flood season limit water level, and the non-flood season is the normal high water level.

[0018] 2) Output constraints

[0019] N t min ≤N t ≤N t max (3)

[0020] Where N t 、N t max and N t min They are the output, guaranteed output and installed capacity of the hydropower station in the tth period respectively.

[0021] 3) Power generation flow constraints

[0022]

[0023] In the formula, Q e , Q e max are the power generation flow in the tth period and the maximum current capacity of the unit respectively.

[0024] 4) Minimum downstream flow constraint

[0025]

[0026] In the formula, Q out,t , They are the discharge and minimum downstream flow requirements for the tth period, including flow requirements for ecology, urban residents, agricultural irrigation, shipping, etc.

[0027] 5) Water balance constraints

[0028] V t =V t-1 +(I t -Q out,t )·Δt(6)

[0029] Q out,t =Q e +Q z (7)

[0030] Where V t and V t-1 It is divided into the water storage capacity in the tth period and the water storage capacity in the t-1th period; I t and Q out,t are the inflow and outflow in period t respectively; Q z is the flow rate passing through the gate in period t.

[0031] The state equation of reservoir operation includes the hydraulic characteristics of the reservoir, which include the water level storage capacity curve, the relationship curve between the discharge flow and the tail water level, which are used for the calculation of the operation process. The state equation expression that the hydropower station optimization scheduling model needs to satisfy is as follows:

[0032] V t+1 =V t +I t ×Δt-Q out,t ×Δt(8)

[0033] Where: Q out,t Represents the reservoir discharge; I t represents the inflow flow; Δt represents the time period length; V t represents the storage capacity of the reservoir at the tth period; V t+1 It represents the storage capacity of the reservoir in the t+1 period.

[0034] Through the above-mentioned hydropower station optimization scheduling model, the theoretical optimal power generation is calculated based on the water inflow process during the assessment period.

[0035] Step (3) constructing a difficulty identification model to evaluate the difficulty of scheduling;

[0036] The difficulty of scheduling is evaluated by constructing a data statistical model based on the water inflow characteristics of the year, and ultimately the difficulty of scheduling is quantified as a difficulty coefficient. In addition, the difficulty of scheduling in different years may vary greatly, so it is necessary to select a variety of mathematical methods for statistical analysis.

[0037] 3.1) Collect and compile the hydrological data of the hydropower station under assessment (measured data for no less than 40 years), and calculate the characteristic indicators of the water flow process of the hydropower station under assessment over the years based on the compiled hydrological data, including three characteristic indicators: water inflow, unevenness of water inflow and initial water storage capacity of the reservoir.

[0038] 3.2) Invite a group of dispatching experts from different units and levels, with the number of people controlled at 10 to 15 people, divide the dispatching difficulty into different difficulty levels (such as difficult, medium, and easy) according to the decision makers' wishes, design and distribute anonymous expert questionnaires on the dispatching difficulty of hydropower stations, and each expert gives their own evaluation results and their basis based on the characteristic indicators calculated in 3.1). Finally, summarize the evaluation results of all experts and their main discriminant factors to determine the evaluation results of the dispatching difficulty of hydropower stations over the years.

[0039] 3.3) Combining the scheduling difficulty evaluation results of 3.2) with the relationship between the characteristic indicators calculated in 3.1), and integrating the expert experience survey results, a preliminary scheduling difficulty discrimination factor indicator set is screened out.

[0040] 3.4) Based on the discriminant factor index set screened in step 3.3) and the scheduling difficulty evaluation results in step 3.2), three types of methods, namely economic analysis method, multivariate nonlinear regression method and machine learning, can be used to construct a quantitative correlation model between the discriminant factor index set and the scheduling difficulty.

[0041] 3.5) The discriminant factor index set data of the hydropower station over the years after screening and the scheduling difficulty of the hydropower station over the years are divided into a training set and a verification set. The model parameters are trained by inputting the training set data (the discriminant factor index set and scheduling difficulty of the hydropower station over the years). The generalization ability of the trained model is verified by inputting the verification set data. Finally, the model structure and parameters are determined with the highest model fitting accuracy.

[0042] Step (4) calculating the evaluation index based on the difficulty coefficient;

[0043] a. Calculate the improvement rate of water energy utilization;

[0044] According to step (1), the designed power generation of the hydropower station during the assessment period is calculated using the calculation formula (9):

[0045]

[0046] Where: ΔE is the energy storage difference at the end of the assessment period; E 设计 To design the power generation, the power generation is calculated based on the actual dispatch diagram, taking into account factors such as grid restrictions, unit maintenance, head changes, unit efficiency, etc.; E 实际 is the actual power generation.

[0047] b. Calculate the scheduling difficulty coefficient;

[0048] The evaluation content of the optimal power generation completion rate assessment index based on the difficulty coefficient is: based on the difficulty of dispatching the hydropower station, the actual power generation is evaluated compared with the upper limit of the theoretical optimal power generation, where the difficulty coefficient is the verification indicator, and the probability values ​​of each level of difficulty are mapped to the specific values ​​of the corresponding difficulty coefficient.

[0049] First, the initial value range (a, b) of the difficulty coefficient is determined based on the actual situation of the hydropower station according to the expert experience, where the difficulty coefficient of the dispatch difficulty level is clearly 1 (1>a, 1<b); secondly, the probability value of each difficulty is calculated based on the dispatch difficulty results of the hydropower station over the years; finally, the probability value is calculated using the linear mapping relationship to obtain the dispatch difficulty coefficients of each level, and the calculation formulas are shown in (10) and (11):

[0050] n 易 =a+(1-a)×P(easy)(10)

[0051] n 难 =b-(b-1)×P(difficult)(11)

[0052] Where: n 易 is the difficulty coefficient of easy scheduling; a is the lower limit of the initial value of the difficulty coefficient; n 难 is the scheduling difficulty coefficient; b is the initial upper limit of the difficulty coefficient; P(easy) represents the proportion of years with easy scheduling difficulty in the entire data set; P(difficult) represents the proportion of years with difficult scheduling difficulty in the entire data set.

[0053] c. Calculate the optimal power generation completion rate considering the scheduling difficulty coefficient

[0054] The optimal power generation completion rate based on the scheduling difficulty coefficient is calculated according to the theoretical optimal power generation calculation result of the hydropower station in step (2), the actual power generation in previous years and the difficulty coefficient obtained by mapping. The specific calculation formula is shown in (12):

[0055]

[0056] Where: n i is the difficulty coefficient of different levels; E 最优 The maximum possible power generation obtained by solving the optimal dispatching model of the hydropower station; E实际 is the actual power generation.

[0057] Step (5) The two indicators of water energy utilization improvement rate and optimal power generation completion rate considering the difficulty coefficient are comprehensively considered to make a final evaluation of the dispatching level during the assessment period. Decision makers can set different preferences for the two indicators, perform weighted summation, and determine the final evaluation result of the hydropower station.

[0058] The beneficial effects of the present invention are:

[0059] Traditional means are often limited to a single dimension - the rate of improvement in water energy utilization, lacking a comprehensive consideration of the two key performance indicators of conventional power increase rate and theoretical maximum power generation completion rate, while ignoring the profound impact of water inflow heterogeneity on the complexity and efficiency of the dispatching strategy, resulting in a relatively one-sided evaluation result, which is difficult to fully reflect the actual operation status of the hydropower station. The present invention proposes a two-dimensional indicator system, namely, the rate of improvement in water energy utilization and the optimal power generation completion rate of the integrated dispatching difficulty coefficient. By constructing a refined optimization dispatching model with the maximum power generation during the assessment period as the goal, the maximum power generation potential of the hydropower station under theoretical conditions is accurately measured; and the dispatching difficulty coefficient is introduced, and a difficulty coefficient determination model based on the randomness and heterogeneity of water inflow as the discriminant index is established to quantify the complexity and challenge brought by changes in natural conditions to the dispatching work of the hydropower station. The indicator system proposed by the present invention is scientific and reasonable, comprehensively and deeply reflects the operation level of the hydropower station, and provides strong decision-making support for the efficient management of the hydropower station. It can not only significantly improve the operational efficiency and economic benefits of the hydropower station, but also effectively stimulate the enthusiasm and creativity of the staff, promote the continuous improvement of the management level of the hydropower station, and achieve a double leap in technical evaluation and management efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 It is a flow chart of the post-evaluation and assessment method of the economic operation of a hydropower station based on refined simulation scheduling of the present invention;

[0061] Figure 2 This is a diagram showing the evaluation results of scheduling difficulty in different level years in the example application of the present invention; wherein, Figure 2 (a) is the evaluation result of the dispatching difficulty of the hydropower station in a normal water year; Figure 2 (b) is the evaluation result of the dispatching difficulty of the hydropower station in the dry year; Figure 2 (c) in the figure is the evaluation result of the difficulty of dispatching the hydropower station in the flood year.

[0062] Figure 3 This is a diagram showing the prediction results of the scheduling difficulty recognition model in the example application of the present invention, where Figure 3 (a) in the figure is the identification prediction diagram of the Cobb-Douglas production function model. Figure 3(b) is the recognition prediction diagram of the polynomial nonlinear recognition classification model. Figure 3 (c) in the figure is the recognition prediction graph of the multi-layer perceptron (MLPClassifier) ​​recognition classification model. DETAILED DESCRIPTION

[0063] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0064] The present invention takes the Huanren Hydropower Station in Liaoning Province as an example, selects historical data from 1980 to 2022 as a basis, and describes the specific implementation method in detail in combination with the technical scheme and the accompanying drawings, which specifically includes the following steps:

[0065] Step (1) calculating the designed power generation of the hydropower station;

[0066] The present invention uses the water level at the beginning of the year as the initial water level, and the dispatching diagram of Huanren Hydropower Station as the dispatching rule, simulates and adjusts the storage process during the assessment period, and counts the power generation of the hydropower station as the designed power generation. In the dispatching diagram, 4 lines are used to divide it into 5 dispatching areas: reduced output area, guaranteed output area, compensation output area, increased output area and full output area. In the reduced output area, Huanren Hydropower Station generates electricity at 0.6 times the guaranteed output; in the guaranteed output area, Huanren Hydropower Station generates electricity at the guaranteed output, and the guaranteed output is 24.5MW; in the compensation output area, Huanren Hydropower Station generates electricity at 1.2 times the guaranteed output; in the increased output area, Huanren Hydropower Station generates electricity at 1.5 times the guaranteed output; in the full output area, Huanren Hydropower Station generates electricity at full output. According to the current water level of Huanren Hydropower Station and the time period in which it is located, a power generation decision can be made for Huanren Hydropower Station. The dispatching diagram uses the initial water level, inflow, power generation flow, and output constraints as boundary conditions. It automatically identifies the current dispatching area based on the current reservoir water level and time, and calculates the power generation according to the corresponding output of the dispatching area.

[0067] Step (2) calculating the theoretical optimal power generation;

[0068] The constraint expressions that need to be satisfied to solve the optimal dispatching model of Huanren Hydropower Station are as follows:

[0069] 1) Water level constraints

[0070] The lowest water level in the non-flood season is the flood limit water level of 290m, and the highest water level is the flood control high water level of 300m. The lowest water level in the flood season is the flood limit water level of 290m, and the highest water level is the flood control high water level of 300m. The operating water level of the reservoir must be between the two values ​​to ensure flood control safety and rational use of water resources.

[0071] 2) Power generation flow constraints

[0072] The upper limit of the power generation flow of Huanren Hydropower Station is 500m 3 / s.

[0073] 3) Minimum leakage flow constraint

[0074] In order to protect the river ecosystem, the discharge flow of the hydropower station must exceed the minimum ecological flow of 15.83m 3 / s to maintain the natural ecological functions of the river.

[0075] 4) Maximum discharge constraints during flood season

[0076] In order to ensure the safety of reservoirs and downstream flood control during the flood season, the maximum discharge capacity of the reservoir is limited. Considering the flood discharge capacity of the reservoir, the flood discharge capacity of the downstream river channel and the possible flood volume, the maximum discharge flow cannot exceed the discharge limit in Table 1.

[0077] Table 1 Requirements for controlling flood peak flow outflow from the Huanren Hydropower Station

[0078]

[0079] Based on the measured inflow data, the optimization scheduling model is solved according to the above constraints, and finally the theoretical optimal power generation of Huanren Hydropower Station is obtained.

[0080] Step (3) constructing a difficulty identification model to evaluate the difficulty of scheduling;

[0081] 3.1) Collect and compile the hydrological data of Huanren Hydropower Station (measured data are forty-three years), and calculate and evaluate the water inflow process characteristic indicators of the hydropower station over the years based on the compiled hydrological data, including annual water inflow ratio over many years, GI, 3-day flood volume, flood season ratio over many years, Cv, water level at the beginning of the year, monthly water inflow ratio over many years from April to September, flood events, maximum monthly average ratio over flood season, maximum flood peak flow, July proportion of flood season, August proportion of flood season, April-June water inflow ratio over many years, and July-September water inflow ratio over many years, a total of 19 hydrological characteristic indicators.

[0082] 3.2) This method invited 12 senior experts from different levels and units, including hydropower dispatching experts of this unit, dispatching experts of hydropower stations in the northern basin, engineers of design units, dispatching experts of power grid companies, etc., to conduct a comprehensive evaluation of the dispatching difficulty of the Huanren Basin for a total of 43 years from 1980 to 2022 and provide a basis. The final evaluation results are that the easy difficulty evaluation covers 14 years, the medium difficulty evaluation covers 24 years, and the difficult dispatch evaluation covers 5 years. The specific evaluation results are as follows: Figure 2 shown.

[0083] 3.3) The annual water volume, especially the water volume during the flood season, determines the annual power generation. For this reason, we selected indicators such as the flood season ratio to the multi-year average and the monthly ratio from April to September to the multi-year average to reflect the annual water level and the proportion of water during the flood season. In terms of the unevenness of water flow, we selected indicators such as the 3-day flood volume and the GI from April to September to quantitatively analyze the uneven distribution of water flow in short-duration rainstorms and long-term series. In particular, the GI coefficient provides an intuitive and quantitative evaluation tool by comparing the difference between actual runoff distribution and ideal uniform distribution, which can more accurately reflect the inherent law of scheduling difficulty. The initial water storage of the reservoir represents the initial head and available water volume of the hydropower station, and is the key to determining the power generation in the non-flood season. Based on the results of expert surveys and physical cause analysis, 15 scheduling difficulty discrimination factor indicator sets were preliminarily determined from the three aspects of water flow, unevenness of water flow and initial water storage of the hydropower station. The detailed indicator results are shown in Table 2.

[0084] Table 2. Scheduling difficulty determination factor set

[0085]

[0086] 3.4) Based on the scheduling difficulty results of step 3.2) and the discriminant factor index set after preliminary screening in step 3.3), the Cobb-Douglas production function, polynomial nonlinear identification classification method, and MLP classification method were used to carry out factor index evaluation, and a total of 6 final factors were determined, namely, the water level at the beginning of the year, the annual average ratio of water inflow from April to June, the annual average ratio of water inflow from July to September, the April-September GI, and the 3-day flood volume to annual average ratio.

[0087] 3.5) The final determined factors are input into the recognition and classification model for training and verification. The 43-year characteristic indicator set is divided into three categories according to the wet years, normal years, and dry years. The wet years account for 12% of the total years, the normal years account for 56%, and the dry years account for 32%. According to the same proportion, 36 years of training sets and 7 years of verification sets are randomly selected to ensure that each data set contains indicator data of various years. The final results of the training period and verification period of each method are as follows Figure 3 shown.

[0088] a) The difficulty fitting accuracy of the Cobb-Douglas production function difficulty identification model during the training period was 81%, and the fitting accuracy of the validation set was 86%. It showed strong predictive ability in the scheduling difficulty assessment, especially in the identification of "medium" and "easy" level scheduling. However, the identification result for "difficult" level scheduling was poor. The results of the training and validation periods are shown in Figure 2. Figure 3 (a) shown.

[0089] b) The polynomial nonlinear identification and classification model has an accuracy of 89% in the training set and an accuracy of 86% in the validation set. Compared with the Cobb-Douglas production function model, it has a higher overall accuracy and stability in the scheduling difficulty assessment. It has a certain recognition ability for "difficult" level scheduling, but there is still much room for improvement. The results of the training and validation periods are as follows: Figure 3 (b) as shown.

[0090] c) The multi-layer perceptron model achieved 100% accuracy in the training set, but 71.4% accuracy in the validation set. Although the model performed well during training, it still had inaccurate recognition in actual applications. The results of the training and validation periods are shown in Figure 2. Figure 3 (c) as shown.

[0091] Step (4) Calculate the evaluation index based on the difficulty coefficient

[0092] Based on the actual power generation data of Huanren Hydropower Station over the years, the design power generation calculated according to step (1) is used to calculate the water energy utilization improvement rate of Huanren Hydropower Station using formula (9), and the water energy utilization improvement rate is used as the assessment indicator P1. According to the expert experience and the decision-maker's willingness, the initial value range of the difficulty coefficient is (0.85, 1.1), the probability of a year with a difficult scheduling difficulty is 0.12, and the probability of a year with an easy scheduling difficulty is 0.32. Substituting the above results into formulas (10) and (11), we can get n 易 is 0.9, n 难 The calculated difficulty coefficient, actual power generation data and the theoretical optimal power generation calculated in step (2) are substituted into formula (12) to finally obtain the optimal power generation completion rate of Huanren Hydropower Station based on the difficulty coefficient, which is used as the assessment indicator P2.

[0093] Step (5) comprehensively evaluates the dispatching level during the assessment period by taking into account the two indicators of the improvement rate of water energy utilization and the optimal power generation completion rate considering the difficulty coefficient.

[0094] The economic operation evaluation classification standards of Huanren Hydropower Station are shown in Table 3. Finally, the economic operation level of Huanren Hydropower Station is evaluated based on two evaluation indicators.

[0095] Table 3 Economic operation level classification standards

[0096]

[0097] The above-described embodiments merely express the implementation methods of the present invention, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention.

Claims

1. A method for post-evaluation and assessment of economic operation of a hydropower station based on refined simulation scheduling, characterized in that: The method for evaluating the post-economic operation of a hydropower station firstly takes the measured inflow and initial water level of the power station during the evaluation period as input, performs simulation scheduling according to the established conventional scheduling diagram and relevant scheduling principles, calculates the designed power generation, and calculates the improvement rate of the actual power generation compared with the designed power generation, which is the improvement rate of water energy utilization; secondly, takes the maximum power generation of the hydropower station as the objective function, considers various constraints, constructs a power generation optimization scheduling model, and calculates the theoretical maximum power generation; Calculate the proportion of actual dispatched power generation to the theoretically optimal dispatched power generation to obtain the optimal power generation completion rate; secondly, determine the dispatch difficulty based on expert experience, establish the relationship between it and key discriminant factors such as water supply conditions, establish a difficulty coefficient evaluation model, and calculate the difficulty coefficient; finally, use the water energy utilization improvement rate and the optimal power generation completion rate based on the difficulty coefficient as evaluation indicators to achieve a comprehensive evaluation of the economic benefits of the hydropower station. The specific steps are: Step (1) calculating the designed power generation of the hydropower station; Step (2) calculating the theoretical optimal power generation; Step (3) constructing a difficulty identification model to evaluate the difficulty of scheduling; Step (4) calculating the evaluation index based on the difficulty coefficient; Step (5) comprehensively evaluates the dispatching level during the assessment period by taking into account the two indicators of the improvement rate of water energy utilization and the optimal power generation completion rate considering the difficulty coefficient.

2. According to claim 1, a method for post-evaluation and assessment of economic operation of a hydropower station based on refined simulation scheduling is characterized in that: The step (1) is specifically as follows: According to the established dispatching diagram of the hydropower station, taking into account the power generation flow, output, water balance, water level and storage capacity constraints, the power generation during the assessment period is simulated and calculated with the actual initial water level and the actual water inflow; the dispatching area where the hydropower station is located is automatically identified according to the current water level and time, and power is generated according to the output of the dispatching area. The total power generation during the assessment period is finally calculated through rolling simulation.

3. The method for post-evaluation and assessment of economic operation of a hydropower station based on refined simulation scheduling according to claim 2 is characterized in that: The step (2) is specifically as follows: The optimal dispatching model of the hydropower station is established with the maximum total power generation during the assessment period as the objective function. The objective function is shown in formula (1): Where: E is the total power generation benefit of the hydropower station during the assessment period; T is the number of time periods; N t , Q t and μ t They are the output, power generation flow and water consumption rate of the hydropower station in period t respectively; Δt is the length of the period; Through the above-mentioned hydropower station optimization scheduling model, the theoretical optimal power generation is calculated based on the water inflow process during the assessment period.

4. The method for post-evaluation and assessment of economic operation of a hydropower station based on refined simulation scheduling according to claim 3 is characterized in that: The constraint expressions that the hydropower station optimization dispatching model needs to satisfy are as follows: 1) Water level constraints In the formula, Z t , Z t max and Z t min are the water level of the reservoir in the tth period and its upper and lower limits, respectively. The lower limit is the dead water level, the upper limit of the flood season water level is the flood season limit water level, and the non-flood season is the normal high water level; 2) Output constraints Where N t 、N t max and N t min They are the output, guaranteed output and installed capacity of the hydropower station in the tth period respectively; 3) Power generation flow constraints In the formula, Q e , Q e max are the power generation flow rate and the maximum current capacity of the unit in the tth period respectively; 4) Minimum downstream flow constraint In the formula, Q out,t , They are the discharge and minimum downstream flow requirements for the tth period, including flow requirements for ecology, urban residents, agricultural irrigation, and shipping; 5) Water balance constraints V t =V t-1 +(I t -Q out,t )·Δt(6) Q out,t =Q e +Q z (7) Where V t and V t-1 It is divided into the water storage capacity in the tth period and the water storage capacity in the t-1th period; I t and Q out,t are the inflow and outflow in period t respectively; Q z is the flow rate passing through the gate in period t.

5. The method for post-evaluation and assessment of economic operation of a hydropower station based on refined simulation scheduling according to claim 3 is characterized in that: The state equation of the reservoir operation of the hydropower station optimization scheduling model includes the hydraulic characteristics of the reservoir, and the hydraulic characteristics include the water level storage capacity curve, the relationship curve between the discharge flow and the tail water level, which are used for the calculation of the operation process; the state equation expression that the hydropower station optimization scheduling model needs to satisfy is as follows: V t+1 =V t +I t ×Δt-Q out,t ×Δt(8) Where: Q out,t Represents the reservoir discharge; I t represents the inflow flow; Δt represents the time period length; V t represents the storage capacity of the reservoir at the tth period; V t+1 It represents the storage capacity of the reservoir in the t+1 period.

6. The method for post-evaluation and assessment of economic operation of a hydropower station based on refined simulation scheduling according to claim 3 is characterized in that: The step (3) is specifically as follows: utilizing the water inflow characteristics of the current year to evaluate the dispatching difficulty by constructing a data statistical model, and finally achieving the purpose of quantifying the dispatching difficulty as a difficulty coefficient; in addition, the dispatching difficulty and water inflow conditions of different years may vary greatly, so it is necessary to select a variety of mathematical methods for statistical analysis.

7. A method for post-evaluation and assessment of economic operation of a hydropower station based on refined simulation scheduling according to claim 6, characterized in that: The step (3) is specifically as follows: 3.1) Collect and compile hydrological data of the hydropower station under assessment, of which the measured data shall be no less than 40 years, and calculate the characteristic indicators of the water flow process of the hydropower station under assessment over the years based on the compiled hydrological data, including three characteristic indicators: water inflow, uneven degree of water inflow and initial water storage capacity of the reservoir; 3.2) Invite a group of dispatching experts from different units and levels, with the number of experts controlled at 10 to 15 people, and divide the dispatching difficulty into different difficulty levels according to the decision-makers' wishes. Each expert gives their own evaluation results and their basis based on the characteristic indicators calculated in 3.1); finally, summarize the evaluation results of all experts and their main discriminant factors to determine the evaluation results of the dispatching difficulty of the hydropower station over the years; 3.3) Combining the dispatch difficulty evaluation results of 3.2) with the relationship between the characteristic indicators calculated in 3.1), and combining the expert experience survey results, a preliminary dispatch difficulty discrimination factor indicator set is screened out; 3.4) Based on the discriminant factor index set screened in step 3.3) and the dispatch difficulty evaluation result in step 3.2), three methods, namely economic analysis method, multivariate nonlinear regression method and machine learning, can be used to construct a quantitative correlation model between the discriminant factor index set and the dispatch difficulty; 3.5) The discriminant factor index set data of the hydropower stations over the years after screening and the scheduling difficulty of the hydropower stations over the years are divided into a training set and a validation set. The model parameters are trained by inputting the training set data, and the generalization ability of the trained model is verified by inputting the validation set data. Finally, the model structure and parameters are determined based on the highest model fitting accuracy.

8. The method for post-evaluation and assessment of economic operation of a hydropower station based on refined simulation scheduling according to claim 7 is characterized in that: The step (4) is specifically as follows: a. Calculate the improvement rate of water energy utilization; According to step (1), the designed power generation of the hydropower station during the assessment period is calculated using the calculation formula (9): Where: ΔE is the energy storage difference at the end of the assessment period; E 设计 To design the power generation, the power generation is calculated based on the actual dispatch diagram, taking into account factors such as grid restrictions, unit maintenance, head changes, unit efficiency, etc.; E 实际 is the actual power generation; P1 is the designed power generation of the hydropower station during the assessment period; b. Calculate the scheduling difficulty coefficient; The evaluation content of the optimal power generation completion rate assessment index based on the difficulty coefficient is: based on the dispatch difficulty of the hydropower station, the completion level of the actual power generation compared with the theoretical optimal power generation upper limit is evaluated, where the difficulty coefficient is the verification index, and the probability values ​​of each level of difficulty are mapped to the specific values ​​of the corresponding difficulty coefficient; c. Calculate the optimal power generation completion rate considering the scheduling difficulty coefficient The optimal power generation completion rate based on the scheduling difficulty coefficient is calculated according to the theoretical optimal power generation calculation result of the hydropower station in step (2), the actual power generation in previous years and the difficulty coefficient obtained by mapping. The specific calculation formula is shown in (12): Where: n i is the difficulty coefficient of different levels; E 最优 The maximum possible power generation obtained by solving the optimal dispatching model of the hydropower station; E 实际 is the actual power generation, and P2 is the optimal power generation completion rate based on the scheduling difficulty coefficient.

9. A method for post-evaluation and assessment of economic operation of a hydropower station based on refined simulation scheduling according to claim 8, characterized in that: In the process of calculating the scheduling difficulty coefficient: First, the initial value range (a, b) of the difficulty coefficient is determined based on the actual situation of the hydropower station according to the expert experience, where the difficulty coefficient of the dispatch difficulty level is clearly 1 (1>a, 1<b); secondly, the probability value of each difficulty is calculated based on the dispatch difficulty results of the hydropower station over the years; finally, the probability value is calculated using the linear mapping relationship to obtain the dispatch difficulty coefficients of each level, and the calculation formulas are shown in (10) and (11): n 易 = a + (1 - a) × P (easy) (10) n 难 =b-(b-1)×P(difficult) (11) Where: n 易 is the difficulty coefficient of easy scheduling; a is the lower limit of the initial value of the difficulty coefficient; n 难 is the difficulty coefficient of difficult scheduling; b is the upper limit of the initial value of the difficulty coefficient; P(easy) represents the proportion of years with easy scheduling difficulty in the entire data set; P(difficult) represents the proportion of years with difficult scheduling difficulty in the entire data set.

10. A method for post-evaluation and assessment of economic operation of a hydropower station based on refined simulation scheduling according to claim 9, characterized in that: In the step (5), the decision maker can set different preferences for the two indicators, perform weighted summation, and determine the final evaluation result of the hydropower station.

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