Water abandoning risk quantitative evaluation optimization scheduling method

Abstract

The invention discloses a water discarding risk quantitative evaluation optimization scheduling method, which comprises the following steps: step 1, when a hydropower station generates power and the hydropower station generates water storage in a flood season, taking the discharge flow as the water discarding flow for treatment, and taking the water discarding flow at the maximum current utilization flow as the minimum water discarding flow; 2, when the water level of the reservoir reaches the upper limit, water and electricity reduce the output power generation amount to cause abandoned water, the abandoned water further reduces the actual power grid electric quantity to cause abandoned electricity, the minimum abandoned water flow exceeds the maximum current flow, different abandoned electricity is generated when the minimum abandoned water flow is lower than the maximum current flow, and the minimum abandoned water flow does not exceed the maximum current flow. And the abandoned water amount accumulated along with the time is converted into the energy of acting output as the abandoned electric quantity. According to the water abandoning risk quantitative evaluation optimization scheduling method, the water abandoning strategy with the fractal characteristic is provided, the water abandoning frequency is reduced, the utilization rate of water energy and the maximization of the generating capacity are facilitated, and fluctuation of the water abandoning flow in the daily scale in reservoir scheduling has randomness.

Description

Technical Field

[0001] The invention belongs to the technical field of hydropower station power generation, and in particular relates to a method for quantitatively evaluating and optimizing the scheduling of water abandonment risks. Background Art

[0002] The amount of abandoned water generated by large reservoirs is easily affected by the inflow flow at the monthly and daily scales, which can easily cause the phenomenon of water inflow fluctuation uncertainty, thus causing the risk of abandoned water. It is necessary to make more effective short-term abandoned water scheduling decisions. In recent years, there have been a large number of studies on solving the problem of abandoned water in reservoir scheduling. Considering the impact of daily runoff fluctuations, a method for quantifying the risk of abandoned water during the water storage period is proposed, and a long-term power generation scheduling model of reservoirs coupled with abandoned water risks is established. However, the impact of the uncertainty of short-term abandoned water on reservoir scheduling is not considered, and there may be large errors in calculating daily abandoned water. A complementary long-term optimal scheduling method for reservoirs with nested short-term abandoned power risks is proposed, and a medium- and long-term abandoned power loss function is formulated. Finally, the loss function is embedded in the medium- and long-term optimal scheduling model, but the uncertainty of abandoned power risks is not considered. The randomness of abandoned power flow will affect the actual scheduling. Taking the smoothing of the output fluctuation and power abandonment risk of hydro-photovoltaic system as the starting point for improving the quality of grid connection, the fluctuation parameters and fluctuation smoothing methods are proposed, and the medium-long-term-short-term double-layer nested scheduling model and solution method are established; the scheduling risk situation prediction model is established based on the long short-term memory neural network, and the risk pre-control model of insufficient power generation and the risk pre-control model of water abandonment are proposed. No strategy for reducing the amount of abandoned power is proposed, and the power generation efficiency is not improved. A conditional risk value is proposed to quantify the flood loss under non-stationary conditions, and then the factors related to flood risk within a specific time range are derived.

[0003] Multi-objective optimization decision-making has been widely used in the field of renewable energy. Domestic and foreign researchers have optimized multiple objectives such as reservoir economy, stability, and efficiency. A new multi-objective particle swarm algorithm LMPSO is proposed to effectively output high-dimensional target vectors and large-scale decision variables. The hydropower station unit scale optimization scheduling model considering the coordination and economy of water and light reveals the conversion relationship between system coordination and economy.

[0004] Based on the actual reservoir water storage conditions, this paper proposes a fractal model to describe the fluctuation of water abandonment, finds the connection between runoff uncertainty and the randomness of water abandonment, evaluates the risk of water abandonment by obtaining the amount of water abandoned in different periods, establishes a multi-objective function with maximum power generation and minimum water abandonment, considers and does not consider the fractal of water abandonment to obtain the Pareto optimization solution set, and concludes that the fractal reflects the randomness of water abandonment fluctuations and can better guide power generation strategies.

[0005] In the traditional optimization scheduling of hydropower stations, the runoff inflow is considered as the average flow when calculating the power generation. Although the calculation efficiency is improved in this way, the uncertainty of the runoff inflow is ignored. When faced with the flood peaks in the flood season and shortages in the dry season of the reservoir, the actual runoff characteristics cannot be accurately predicted. Summary of the invention

[0006] The present invention aims to provide a method for quantitatively assessing and optimizing the scheduling of water abandonment risks, so as to solve the technical problems raised in the background technology.

[0007] To achieve the above object, the specific technical scheme of the present invention is as follows: A method for quantitatively evaluating and optimizing water abandonment risk, comprising the following steps:

[0008] Step 1: When generating electricity at a hydropower station and storing water during the flood season, the downstream flow is treated as the abandoned water flow, and the abandoned water flow at the time of maximum power consumption is treated as the minimum abandoned water flow;

[0009] Step 2: When the water level of the reservoir reaches its upper limit, the hydropower reduces the amount of power generated by the reservoir, resulting in abandoned water. This abandoned water will further reduce the actual power of the power grid and cause abandoned power. The minimum abandoned water flow is the flow that exceeds the maximum power consumption. If it is lower than the maximum power consumption, there will be no abandoned power. The abandoned water flow accumulated over time is converted into the energy of work output as the abandoned power. The minimum abandoned water flow and abandoned power without water storage are calculated on a daily basis.

[0010] Step 3: During the flood season, the reservoir runoff increases and the discharge exceeds the planned power generation, resulting in water abandonment. The reservoir water level is adjusted to reduce water abandonment, store water during the flood season, and replenish water during the dry season;

[0011] Step 4: Use the Pearson correlation coefficient to analyze the correlation between the parameters related to water abandonment, so as to find out the factors that have the greatest impact on water abandonment and link the randomness of runoff fluctuation with the randomness of water abandonment;

[0012] Step 5: The hydropower station generates electricity by converting water energy into mechanical energy through the rotation of the turbine of the unit, and then further drives the generator to convert electrical energy. The output of the hydropower station is the power output at the output end of the generator set, which is obtained by the output coefficient of the hydropower station, the generator power, and the generating head;

[0013] Step six, use fractal theory to study the uncertainty of water abandonment, take the minimum water abandonment flow as the water abandonment risk indicator, introduce the water abandonment flow and the number of water abandonments in the water abandonment process into the fractal relationship of the theory. The fractal relationship is the connection between random uncertain variables. The fractal dimension is used to satisfy the dimensional changes between variables, and the fractal relationship between the number of water abandonments and the water abandonment flow corresponding to the abandonment is determined.

[0014] Preferably, the calculation formula for calculating the minimum abandoned water flow and abandoned electricity amount without water storage on a daily basis in step 2 is as follows:

[0015]

[0016] q t is the amount of abandoned current in period t, m3 / s, is the maximum current consumption during period t, m3 / s, E q is the amount of power wasted due to water abandonment, kW·h, P t h is the hydropower output in period t, MW, P t b For load demand.

[0017] Preferably, the specific algorithm of the Pearson correlation coefficient in step 4 is as follows:

[0018]

[0019] X and Y are two variables. is the average of the two variables, ρ x,y is the correlation coefficient between the two variables. The larger the absolute value of the correlation coefficient, the stronger the correlation, the closer the correlation coefficient is to 1 or -1, the stronger the correlation, and the closer the correlation coefficient is to 0, the weaker the correlation.

[0020] Preferably, the specific algorithm for the output of the hydropower station in step 5 is as follows:

[0021] N t =9.81Q t H t

[0022] N t is the hydropower output in period t, MW; the output coefficient of the hydropower station is 9.81; Q t is the power generation flow of the hydropower station during period t, m3 / s; H t is the water head generated by the hydropower station during period t.

[0023] Preferably, the specific algorithm of fractal dimension in step 6 is as follows:

[0024] N(Q≥q)=(q max / q) Df / 2

[0025] q is the abandoned water flow rate, m3 / s; N is the number of abandoned water times when the abandoned water flow rate is greater than q; qmax is the maximum abandoned water flow rate, m3 / s; Df is the fractal dimension.

[0026] Substituting the fractal dimension of the abandoned water into the hydropower station output calculation formula (4), the fractal relationship of hydropower output is obtained. The specific relationship is as follows:

[0027]

[0028] Preferably, in step 6, the fractal dimension is used to satisfy the dimensional change between the variables, and the specific method of determining the fractal relationship between the number of water abandonment and the water abandonment flow corresponding to the water abandonment is as follows:

[0029] S1, according to the change of power generation flow and water head, the corresponding hydropower output at each hour of the day is solved, and then the abandoned water flow that exceeds the current consumption is fractalized to find out the fractal relationship between the number of abandoned water times and the abandoned water flow, and determine the fractal dimension, and finally solve the abandoned power output caused by the abandoned water flow;

[0030] S2, through the fractal water abandonment relationship, optimize the minimum water abandonment flow when the number of water abandonment is 9 in different periods, and subtract the minimum water abandonment flow from the actual outflow flow to obtain the maximum power generation flow, and then calculate the corresponding power generation output as the planned power generation output;

[0031] S3, substitute the planned power generation output into the objective function, take the water level as the independent variable, establish the objective function constraints according to the relationship between water level-reservoir capacity and water level-discharge flow, and compare the influence of the fractal water abandonment strategy on the optimization effect of the multi-objective optimization model, as well as the Pareto solution set that satisfies the maximization of power generation and the minimization of water abandonment;

[0032] S4, main program of multi-objective genetic optimization algorithm:

[0033] Define the problem: Define the optimization algorithm parameters. The population size of the multi-objective genetic algorithm is 80, the crossover ratio is 0.7, the mutation probability is 0.02, the maximum number of iterations is 70, the total time period is 24, and the decision variable is the water level.

[0034] Initialization: Generate an initial feasible solution, check constraints at the same time, and regenerate a solution if the constraints are not satisfied. Generate an initial non-dominated sort and initial crowding distance, and sort the population;

[0035] Crossover: Perform crossover on the population;

[0036] Mutation: Perform mutation processing on the population;

[0037] Non-dominated solution set: Merge the parent and child generations, generate a non-dominated solution set sort, calculate the crowding distance, and obtain the population sort.

[0038] The method for quantitative assessment and optimization scheduling of water abandonment risk of the present invention has the following advantages:

[0039] A method for quantitative assessment of water abandonment risk and optimal scheduling is proposed. A water abandonment strategy with fractal characteristics is proposed. Reducing the number of water abandonments is beneficial to the utilization rate of hydropower and the maximization of power generation. The fluctuation of the abandoned water flow in reservoir scheduling is random on a daily scale, which can be represented by fractal characteristics. After considering the fractal water abandonment strategy, the abandoned water volume of multi-objective optimization is reduced by 30.26%, 80.5% and 55.78% in the flood season, normal water season and dry season respectively, and the actual output is closer to the original planned output. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 The multi-objective optimization scheduling process of the present invention;

[0042] Figure 2 The variation of power generation flow with fractal dimension in different periods;

[0043] Figure 3 The relationship between the abandoned water flow and the power generation flow during the flood season;

[0044] Figure 4 The relationship between the abandoned water flow and the power generation flow during the normal water period;

[0045] Figure 5 The relationship between the abandoned water flow and the power generation flow during the dry season;

[0046] Figure 6 To consider the changes in the original planned output and actual output for fractal water abandonment in the same period;

[0047] Figure 7 Multi-objective Pareto frontier for fractal water abandonment optimization at different periods. DETAILED DESCRIPTION

[0048] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the embodiments of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0049] In the description of the embodiments of the present invention, it should be understood that the terms "length", "vertical", "horizontal", "top", "bottom", etc. indicating the orientation or positional relationship are based on the orientation or positional relationship shown in the accompanying drawings, and are only for the convenience of describing the embodiments of the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the embodiments of the present invention.

[0050] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0051] In the embodiments of the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection, an electrical connection, or a communication; it can be a direct connection, or an indirect connection through an intermediate medium, and it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the embodiments of the present invention can be understood according to specific circumstances.

[0052] The disclosure below provides many different embodiments or examples to implement different structures of the embodiments of the present invention. In order to simplify the disclosure of the embodiments of the present invention, the parts and settings of specific examples are described below. Of course, they are only examples, and the purpose is not to limit the embodiments of the present invention. In addition, the embodiments of the present invention can repeat reference numbers and / or reference letters in different examples, and this repetition is for the purpose of simplification and clarity, which itself does not indicate the relationship between the various embodiments and / or settings discussed.

[0053] In order to better understand the purpose, structure and function of the present invention, a method for quantitatively assessing and optimizing the scheduling of water abandonment risk of the present invention is further described in detail below in conjunction with the accompanying drawings.

[0054] like Figure 1-5 As shown, a method for quantitatively assessing and optimizing water abandonment risk of the present invention comprises the following steps:

[0055] Step 1: When generating electricity at a hydropower station and storing water during the flood season, the downstream flow is treated as the abandoned water flow, and the abandoned water flow at the time of maximum power consumption is treated as the minimum abandoned water flow;

[0056] Step 2: When the water level of the reservoir reaches its upper limit, the hydropower reduces the amount of power generated by the reservoir, resulting in abandoned water. This abandoned water will further reduce the actual power of the power grid and cause abandoned power. The minimum abandoned water flow is the flow that exceeds the maximum power consumption. If it is lower than the maximum power consumption, there will be no abandoned power. The abandoned water flow accumulated over time is converted into the energy of work output as the abandoned power. The minimum abandoned water flow and abandoned power without water storage are calculated on a daily basis.

[0057] The calculation formula for the minimum abandoned water flow and abandoned electricity without water storage on a daily basis is as follows:

[0058]

[0059] q t is the amount of abandoned current in period t, m3 / s, is the maximum current consumption during period t, m3 / s, E q is the amount of power wasted due to water abandonment, kW·h, P t h is the hydropower output in period t, MW, P t b For load demand.

[0060] Step 3: During the flood season, the reservoir runoff increases and the discharge exceeds the planned power generation, resulting in water abandonment. The reservoir water level is adjusted to reduce water abandonment, store water during the flood season, and replenish water during the dry season;

[0061] Step 4: Use the Pearson correlation coefficient to analyze the correlation between the parameters related to water abandonment, so as to find out the factors that have the greatest impact on water abandonment and link the randomness of runoff fluctuation with the randomness of water abandonment;

[0062] The specific algorithm of the Pearson correlation coefficient in step 4 is as follows:

[0063]

[0064] X and Y are two variables. is the average of the two variables, ρ x,y is the correlation coefficient between the two variables. The larger the absolute value of the correlation coefficient, the stronger the correlation, the closer the correlation coefficient is to 1 or -1, the stronger the correlation, and the closer the correlation coefficient is to 0, the weaker the correlation.

[0065] Step 5: The hydropower station generates electricity by converting water energy into mechanical energy through the rotation of the turbine of the unit, and then further drives the generator to convert electrical energy. The output of the hydropower station is the power output at the output end of the generator set, which is obtained by the output coefficient of the hydropower station, the generator power, and the generating head;

[0066] The specific algorithm for the hydropower station output in step 5 is as follows:

[0067] Nt =9.81Q t H t

[0068] N t is the hydropower output in period t, MW; the output coefficient of the hydropower station is 9.81; Q t is the power generation flow of the hydropower station during period t, m3 / s; H t is the water head generated by the hydropower station during period t.

[0069] Step six, use fractal theory to study the uncertainty of water abandonment, take the minimum water abandonment flow as the water abandonment risk indicator, introduce the water abandonment flow and the number of water abandonments in the water abandonment process into the fractal relationship of the theory. The fractal relationship is the connection between random uncertain variables. The fractal dimension is used to satisfy the dimensional changes between variables, and the fractal relationship between the number of water abandonments and the water abandonment flow corresponding to the abandonment is determined.

[0070] The specific algorithm of fractal dimension in step 6 is as follows:

[0071] N(Q≥q)=(q max / q) Df / 2

[0072] q is the abandoned water flow rate, m3 / s; N is the number of abandoned water times when the abandoned water flow rate is greater than q; qmax is the maximum abandoned water flow rate, m3 / s; Df is the fractal dimension.

[0073] Substituting the fractal dimension of the abandoned water into the hydropower station output calculation formula (4), the fractal relationship of hydropower output is obtained. The specific relationship is as follows:

[0074]

[0075] In step 6, the fractal dimension is used to satisfy the dimensional changes between variables, and the specific method of determining the fractal relationship between the number of water abandonment and the corresponding water abandonment flow is as follows:

[0076] S1, according to the change of power generation flow and water head, the corresponding hydropower output at each hour of the day is solved, and then the abandoned water flow that exceeds the current consumption is fractalized to find out the fractal relationship between the number of abandoned water times and the abandoned water flow, and determine the fractal dimension, and finally solve the abandoned power output caused by the abandoned water flow;

[0077] S2, through the fractal water abandonment relationship, optimize the minimum water abandonment flow when the number of water abandonment is 9 in different periods, and subtract the minimum water abandonment flow from the actual outflow flow to obtain the maximum power generation flow, and then calculate the corresponding power generation output as the planned power generation output;

[0078] S3, substitute the planned power generation output into the objective function, take the water level as the independent variable, establish the objective function constraints according to the relationship between water level-reservoir capacity and water level-discharge flow, and compare the influence of the fractal water abandonment strategy on the optimization effect of the multi-objective optimization model, as well as the Pareto solution set that satisfies the maximization of power generation and the minimization of water abandonment;

[0079] S4, main program of multi-objective genetic optimization algorithm:

[0080] Define the problem: Define the optimization algorithm parameters. The population size of the multi-objective genetic algorithm is 80, the crossover ratio is 0.7, the mutation probability is 0.02, the maximum number of iterations is 70, the total time period is 24, and the decision variable is the water level.

[0081] Initialization: Generate an initial feasible solution, check constraints at the same time, and regenerate a solution if the constraints are not satisfied. Generate an initial non-dominated sort and initial crowding distance, and sort the population;

[0082] Crossover: Perform crossover on the population;

[0083] Mutation: Perform mutation processing on the population;

[0084] Non-dominated solution set: Merge the parent and child generations, generate a non-dominated solution set sort, calculate the crowding distance, and obtain the population sort.

[0085] The specific embodiments are as follows:

[0086] This paper takes a hydropower station in southwest my country as the engineering background. July 11, 21, and 30, 2024 are used as representative flood season typical days, January 4, 14, and 29 of the same year are typical days of normal water period, and March 6, 16, and 26 of the same year are typical days of dry season. The daily scale inflow and outflow are used as input data for scheduling optimization. The total reservoir capacity is about 502 million m3, and the regulating reservoir capacity is nearly 123 million m3, which has seasonal regulation capacity. The installed capacity of the hydropower station is 1670MW, and the annual average power generation is 6.2 billion kWh. The inflow runoff of the hydropower station with good regulation performance and a long time period is usually used as the basis for selecting the typical period. The water inflow during the flood season (June-October) is large, and the maximum power generation flow during the scheduling period is 1500m3 / s. The regulation capacity during the normal water period and dry season is weak, which is 800m3 / s and 1000m3 / s respectively.

[0087] Through simulation calculation, this paper analyzes the adaptation of water abandonment parameters in hydropower regulation level and improving power generation grid connection, and reduces the number of water abandonment in water abandonment scheduling to ensure maximum power efficiency and maximum power generation. The random fluctuation characteristics of water abandonment flow are studied, and the water abandonment law is obtained using fractal relationship, so as to formulate water abandonment strategy to ensure the optimal multi-objective optimization effect.

[0088] like Figure 2, it can be seen that the power generation flow changes with the fractal dimension. In general, the power generation flow increases with the increase of the fractal dimension, indicating that the volatility of the power generation flow satisfies the fractal relationship, and the fractal dimension reflects the degree of fluctuation of the power generation flow. In the daily scale of power generation flow changes, the rate at which the power generation flow increases with the increase of the fractal dimension gradually decreases, indicating that the power generation flow is less affected by the fractal dimension, and the randomness of the fluctuation of the power generation flow is gradually stabilized. In the flood season, the power generation flow is generally high, because the inflow flow and water level generally increase. In the normal water and dry seasons, the power generation flow in the dry season is slightly higher than that in the normal water season, because there is no water abandonment in the dry season, the power generation flow is fully used, and the power generation flow in the normal water season is used for other purposes.

[0089] In order to calculate the amount of abandoned water, coordinate the abandoned water power generation and grid connection, and finally achieve the optimization target requirements, the abandonment times and abandoned water flow calculated by fractal relationship are used to constrain the power generation flow, and the abandoned water flow corresponding to the power generation flow in different periods is planned to meet the power load while ensuring the minimum amount of abandoned water. Figure 3 The relationship between the abandoned water flow and the power generation flow during the flood season is shown in Figure 4 As shown in the figure, the relationship between the abandoned water flow and the power generation flow during the normal water period is as follows: Figure 5 As shown in the figure, the relationship between the abandoned water flow and the power generation flow in the dry season can be seen. It can be seen that the power generation flow changes with the fractal dimension, and the relationship between the abandoned water flow and the power generation flow in different periods can be known. The increase in power generation flow is often accompanied by an increase in the abandoned power, and the corresponding abandoned power flow also increases. The power generation flow in the flood season is large, and the power generation flow is evenly distributed, between 1600-1940m3 / s, and the abandoned power flow is distributed between 100-450m3 / s. The power generation flow in the normal water season is mainly concentrated between 800-1100m3 / s and 1200-1300m3 / s, because the inflow flow in the normal water season is not sufficient, and it is more concentrated in low and high flow rates. The power generation flow distribution in the dry season is relatively discrete, and it is distributed in all flow rate areas, indicating that the water supply in the dry season is insufficient, and the power generation flow distribution is difficult to be continuous. There is still water abandonment at 1100m3 / s, 1300m3 / s, and 1450m3 / s.

[0090] The power abandonment in different periods is shown in Table 1. Considering the minimization of the loss of water abandonment, it is necessary to formulate a separate water abandonment plan according to different periods, control the cumulative number of water abandonment to 9 times, and at the same time, the maximum power generation flow can meet the power demand. In daily scheduling, water abandonment always exists during the flood season, and there are no water abandonment times during the normal water period and the dry season. Adjusting the outflow flow and water level balance to keep the number of water abandonment and flow stable is conducive to the reservoir's flood control, water supply and other functions, and ensures the efficiency of water resource utilization.

[0091]

[0092] Table 1

[0093] Pareto solutions in different periods

[0094] This paper establishes a daily-scale optimal scheduling model for hydropower stations considering the coordination of water abandonment and power generation. The reservoir characteristics of three typical seasons, namely flood season, normal water season and dry season, are solved, and corresponding different optimization results are obtained, namely the Pareto frontier of minimizing water abandonment and maximizing power generation.

[0095] like Figure 6 It can be seen that the Pareto frontier of multi-objective optimization in different periods, F1 represents power generation output, and F2 represents abandoned power output, indicating the competitive relationship between utilization and efficiency in the reservoir dispatching system, that is, the output efficiency of power generation will decrease with the improvement of energy utilization. In the flood season, the power generation output ranges from 900-1400kW, the abandoned power output is 240-400kW, the power generation output increases by 44.4%, and the abandoned power output decreases by 40%; in the normal water period, the power generation output ranges from 760-1020kW, the abandoned power output is 160-240kW, the power generation output increases by 34.21%, and the abandoned power output decreases by 50%; in the dry season, the power generation output ranges from 680-880kW, the abandoned power output is 200-320kW, the power generation output increases by 29.41%, and the abandoned power output decreases by 60%. From the above, it can be seen that the corresponding power generation is large during the flood season when the water volume is abundant, and the amount of water abandonment also increases, while the opposite is true during the dry season; the power generation output fluctuation range is the largest during the flood season, and the fluctuation range is the smallest during the dry season; the abandonment output range is the smallest during the flood season, and the largest during the dry season. The reason is that the water volume is large during the flood season, and the inflow flow is relatively continuous, while the water volume is small during the dry season, and the inflow flow is relatively discrete. The initial value of the abandoned water output during the flood season is larger than that during the dry season, and the proportion of the abandoned water output change is the largest during the dry season.

[0096] The changes in the original planned output and actual output considering fractal water abandonment at different times. In the flood season, the original planned output ranged from 380-1600kW, and the actual output was 100-1100kW. After considering the fractal water abandonment characteristics, the fluctuation of electricity demand in the flood season became smaller, and the transmission and grid connection became more stable. At 4 o'clock, the maximum difference power was only 500kW, which shows that the power efficiency and overall power have been improved. In the normal water period, the original planned output ranged from 200-800kW, and the actual output was 220-650kW. At 4 o'clock, the maximum difference power was 200kW, indicating that the actual power was basically close to the original planned power consumption, and the fluctuation range was not large. Considering fractal water abandonment effectively improves the efficiency and power of the actual output. In the dry season, the original planned output range was 0-1000kW, and the actual output was 400-600kW. The difference power did not exceed 400kW. It can be seen that the local power generation was uneven due to insufficient water, and the actual output generally met the power demand. In summary, in the multi-objective optimized seasonal scheduling considering the characteristics of water abandonment, the power generation efficiency is increased, the hydropower utilization efficiency is improved, and the transmission and grid connection are stable.

[0097] It is to be understood that the present invention is described by some embodiments, and it is known to those skilled in the art that various changes or equivalent substitutions may be made to these features and embodiments without departing from the spirit and scope of the present invention. In addition, under the teachings of the present invention, these features and embodiments may be modified to adapt to specific circumstances and materials without departing from the spirit and scope of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the scope of protection of the present invention.

Claims

1. A method for quantitative assessment and optimization of water abandonment risk, characterized in that: The steps include: Step 1: When generating electricity at a hydropower station and storing water during the flood season, the downstream flow is treated as the abandoned water flow, and the abandoned water flow at the time of maximum power consumption is treated as the minimum abandoned water flow; Step 2: When the water level of the reservoir reaches its upper limit, the hydropower reduces the amount of power generated by the reservoir, resulting in abandoned water. This abandoned water will further reduce the actual power of the power grid and cause abandoned power. The minimum abandoned water flow is the flow that exceeds the maximum power consumption. If it is lower than the maximum power consumption, there will be no abandoned power. The abandoned water flow accumulated over time is converted into the energy of work output as the abandoned power. The minimum abandoned water flow and abandoned power without water storage are calculated on a daily basis. Step 3: During the flood season, the reservoir runoff increases and the discharge exceeds the planned power generation, resulting in water abandonment. The reservoir water level is adjusted to reduce water abandonment, store water during the flood season, and replenish water during the dry season; Step 4: Use the Pearson correlation coefficient to analyze the correlation between the parameters related to water abandonment, so as to find out the factors that have the greatest impact on water abandonment and link the randomness of runoff fluctuation with the randomness of water abandonment; Step 5: The hydropower station generates electricity by converting water energy into mechanical energy through the rotation of the turbine of the unit, and then further drives the generator to convert electrical energy. The output of the hydropower station is the power output at the output end of the generator set, which is obtained by the output coefficient of the hydropower station, the generator power, and the generating head; Step six, use fractal theory to study the uncertainty of water abandonment, take the minimum water abandonment flow as the water abandonment risk indicator, introduce the water abandonment flow and the number of water abandonments in the water abandonment process into the fractal relationship of the theory. The fractal relationship is the connection between random uncertain variables. The fractal dimension is used to satisfy the dimensional changes between variables, and the fractal relationship between the number of water abandonments and the water abandonment flow corresponding to the abandonment is determined.

2. A method for quantitative assessment and optimization of water abandonment risk according to claim x, characterized in that: The calculation formula for the minimum abandoned water flow and abandoned electricity without water storage on a daily basis in step 2 is as follows: q t is the amount of abandoned current in period t, m3 / s, is the maximum current consumption during period t, m3 / s, E q is the amount of power wasted due to water abandonment, kW·h, P t h is the hydropower output in period t, MW, P t b For load demand.

3. A method for quantitative assessment and optimization of water abandonment risk according to claim x, characterized in that: The specific algorithm of the Pearson correlation coefficient in step 4 is as follows: X and Y are two variables. is the average of the two variables, ρ x,y is the correlation coefficient between the two variables. The larger the absolute value of the correlation coefficient, the stronger the correlation, the closer the correlation coefficient is to 1 or -1, the stronger the correlation, and the closer the correlation coefficient is to 0, the weaker the correlation.

4. A method for quantitative assessment and optimization of water abandonment risk according to claim x, characterized in that: The specific algorithm for the hydropower station output in step 5 is as follows: N t =9.81Q t H t N t is the hydropower output in period t, MW; the output coefficient of the hydropower station is 9.81; Q t is the power generation flow of the hydropower station during period t, m3 / s; H t is the water head generated by the hydropower station during period t.

5. A method for quantitative assessment and optimization of water abandonment risk according to claim x, characterized in that: The specific algorithm of fractal dimension in step six is ​​as follows: N(Q≥q)=(q max / q) Df / 2 q is the abandoned water flow rate, m3 / s; N is the number of abandoned water times when the abandoned water flow rate is greater than q; qmax is the maximum abandoned water flow rate, m3 / s; Df is the fractal dimension. Substituting the fractal dimension of the abandoned water into the hydropower station output calculation formula (4), the fractal relationship of hydropower output is obtained. The specific relationship is as follows:

6. A method for quantitative assessment and optimization of water abandonment risk according to claim x, characterized in that: In step 6, the fractal dimension is used to satisfy the dimensional changes between variables, and the specific method of determining the fractal relationship between the number of water abandonment and the corresponding water abandonment flow is as follows: S1, according to the change of power generation flow and water head, the corresponding hydropower output at each hour of the day is solved, and then the abandoned water flow that exceeds the current consumption is fractalized to find out the fractal relationship between the number of abandoned water times and the abandoned water flow, and determine the fractal dimension, and finally solve the abandoned power output caused by the abandoned water flow; S2, through the fractal water abandonment relationship, optimize the minimum water abandonment flow when the number of water abandonment is 9 in different periods, and subtract the minimum water abandonment flow from the actual outflow flow to obtain the maximum power generation flow, and then calculate the corresponding power generation output as the planned power generation output; S3, substitute the planned power generation output into the objective function, take the water level as the independent variable, establish the objective function constraints according to the relationship between water level-reservoir capacity and water level-discharge flow, and compare the influence of the fractal water abandonment strategy on the optimization effect of the multi-objective optimization model, as well as the Pareto solution set that satisfies the maximization of power generation and the minimization of water abandonment; S4, main program of multi-objective genetic optimization algorithm: Define the problem: Define the optimization algorithm parameters. The population size of the multi-objective genetic algorithm is 80, the crossover ratio is 0.7, the mutation probability is 0.02, the maximum number of iterations is 70, the total time period is 24, and the decision variable is the water level. Initialization: Generate an initial feasible solution, check constraints at the same time, and regenerate a solution if the constraints are not satisfied. Generate an initial non-dominated sort and initial crowding distance, and sort the population; Crossover: Perform crossover on the population; Mutation: Perform mutation processing on the population; Non-dominated solution set: Merge the parent and child generations, generate a non-dominated solution set sort, calculate the crowding distance, and obtain the population sort.

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