A reservoir scheduling method considering grid load changes

Abstract

This invention discloses a reservoir scheduling method that considers grid load changes, compensating for the lack of consideration for the overall grid trend and the lack of consideration for power plant benefits when power plants perform peak shaving. It proposes the concept of grid load demand satisfaction rate to characterize the strength of the consistency between reservoir output and grid load changes. In the traditional reservoir power generation scheduling model, it introduces a grid demand satisfaction reward / penalty factor, designs ten sets of transformation functions, calculates the reward / penalty factor using the reservoir power generation process and the grid load change process, and constructs a cascade reservoir scheduling model that considers the grid load demand satisfaction rate. This invention takes into account the demands of both the grid side and the power generation side, enhancing the consistency between the hydropower scheduling scheme and load changes during off-peak periods.

Description

Technical Field

[0001] This invention relates to the field of reservoir scheduling methods, and in particular to a reservoir scheduling method that takes into account changes in power grid load. Background Technology

[0002] Optimal scheduling of cascade hydropower stations is crucial for the safe operation of the power grid. It is a typical high-dimensional, multi-stage, nonlinear, and discrete problem, aiming to respond to changes in grid load demand in a balanced and accurate manner during the scheduling period and improve power generation utilization. Research on optimal scheduling of cascade reservoirs considering grid load demand includes: Liu Benxi's construction of a large-scale hydropower coordinated scheduling model based on decision trees, effectively improving the utilization rate of the external power grid channel; Wang Jiayang's use of load deviation smoothing technology to reduce the deviation between load and power generation plan, ensuring the synchronous and rapid distribution of load deviations between cascade reservoirs across multiple time periods, effectively adjusting the compilation of power generation scheduling plans; Cheng Xiong's proposal of a short-term scheduling method for small and medium-sized cascade hydropower stations in river basins to respond to peak-shaving demand, effectively increasing cascade power generation and achieving efficient utilization of water resources for power generation while responding to grid peak-shaving demand; and Zhou Binbin's establishment of a short-term optimal scheduling model for hydropower station groups considering multi-grid peak-shaving under the constraints of hydropower station and DC coupling operation, effectively realizing multi-grid peak-shaving and ensuring the safe operation of the power grid. The aforementioned scholars have conducted extensive research and achieved corresponding results on the optimal scheduling of cascade reservoirs under the consideration of grid load demand. However, existing research still has some problems when considering the response of the power generation side to grid demand. The scheduling models for the power generation side to grid demand response are mostly based on peak-shaving models, aiming to reduce the peak-to-valley difference of surplus load to achieve peak shaving and valley filling. However, the peak-shaving model itself prioritizes ensuring grid demand and fails to consider the actual needs of hydropower stations from the perspective of the power generation side. Besides responding to grid load demand, hydropower first needs to ensure the safe and stable operation of the power station, and secondly, it needs to achieve its own economic benefits. These factors are not reflected in the peak-shaving model. While the peak-shaving model has a certain peak-shaving effect during peak periods, it ignores the consistency between hydropower and grid load trends at other times. When reporting their output plans, power stations often expect a high degree of matching between their output plans and grid load trends. This not only better meets grid load demand but also improves power generation utilization, reduces reservoir water discharge, and thus improves economic efficiency. Improving the matching degree between hydropower output and grid load changes is a win-win measure that can not only improve the power generation utilization rate and economic benefits of power plants, but also match grid demand over more time periods and promote the safe and stable operation of the grid. Summary of the Invention

[0003] The purpose of this invention is to overcome the above-mentioned shortcomings and provide a reservoir scheduling method that considers grid load changes. It improves the traditional cascade reservoir power generation model by introducing a reward and penalty factor for grid load demand satisfaction. This allows the method to consider both the power generation benefit requirements of cascade power stations and the load change requirements of the grid. By using a single-objective optimization scheduling model, it takes into account the needs of both the grid side and the power generation side, thereby improving the model's effectiveness without increasing the computational load of the model solution.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a reservoir scheduling method considering changes in power grid load, which includes the following steps:

[0005] Step (1): Determine the water levels of the cascade reservoirs at the beginning and end of the scheduling period as boundary conditions for scheduling calculation; collect the inflow of the reservoirs and the interval inflow between each reservoir during the scheduling period as input for scheduling calculation; determine the water level, flow, and output constraints of the reservoirs during the scheduling period based on the actual operation requirements of the reservoirs, and collect the basic characteristic curve data of each reservoir; the objective function is an improved function considering the grid load demand satisfaction rate, the fitness is the objective function value, the decision variable is the water level in each time period, input the constraints in the cascade reservoir scheduling model, and use the particle swarm algorithm to solve the model.

[0006] Step (2): Initialize the time period, let T be the number of calculation time periods in the entire scheduling period; randomly generate an initial population of N individuals, each individual representing the water level process of a cascade reservoir, determine whether the water level process meets the constraints, if it does, proceed to the next step; if it does not meet the constraints, then perform water level correction.

[0007] Step (3): Locate the reservoir's water level and capacity curve to obtain the capacity change ΔV(i) for the i-th time period. Calculate the outflow Q(i) for the time period based on the inflow I(i) and the reservoir's water balance equation. Determine whether the outflow meets the constraints. If it does, proceed to the next step; otherwise, perform flow correction.

[0008] Step (4): Calculate the downstream water level Z based on the discharge capacity curve and the outflow rate Q(i) for the time period. xy (i) The upstream water level Z of the reservoir sy (i) and downstream water level Z xy (i) The head H(i) of the hydropower station in the i-th time period can be calculated;

[0009] Step (5): Calculate the output N(i) for this period based on the outflow Q(i) and the hydropower station head H(i); determine whether the output meets the constraints. If it does, proceed to the next step; if it does not, adjust the output.

[0010] Step (6): Return to step (3) to calculate the output of each time period and obtain the entire output process of the reservoir;

[0011] Step (7): Calculate the interval flow between power stations based on the known inflow and outflow data, and calculate the time-period outflow Q from the previous power station based on the interval flow and the time-period outflow Q from the previous power station. ck Calculate the inflow of the next-level power station, return to step (3), and calculate the power output process of each level of reservoir in the cascade reservoir;

[0012] Step (8): Calculate the total output process of the cascade reservoirs and calculate the total output change ΔN(i) for each time period; calculate the load change ΔP(i) based on the load process P(i) on the power grid side; calculate the ratio of output change to load change. The reward / penalty factor λ for each time period is obtained through the transformation function. i Multiply by the output for the corresponding time period to calculate the adaptability of the cascade reservoirs;

[0013] Step (9): Iteratively update the population according to the particle swarm optimization algorithm and output the optimal solution as the cascade reservoir scheduling scheme.

[0014] Preferably, in step (1), the power grid load demand satisfaction rate is defined as:

[0015] When the output of a hydropower station and the trend of grid load change are consistent, the grid load demand is said to be satisfied; this is expressed by the following formula:

[0016]

[0017] In the formula, N i+1 N i : Hydropower station output; P i+1 P i : Grid-side load; i: The i-th time period;

[0018] Furthermore, the grid load demand satisfaction rate is defined as the proportion of the total time period in which the grid load demand is satisfied, i.e.:

[0019]

[0020] In the formula, η: grid load demand satisfaction rate, t: number of time periods in which grid load demand is satisfied, and T: total number of time periods;

[0021] By calculating the grid load demand satisfaction rate, we can directly see the matching situation between changes in hydropower output and grid load over a certain period of time. The higher the grid load demand satisfaction rate, the higher the consistency between changes in hydropower output and grid load over the time period. Conversely, the lower the grid load demand satisfaction rate, the weaker the consistency between changes in hydropower output and grid load over the time period. This indicates that the output plan reported by the hydropower station cannot match the grid load situation well. Therefore, when the grid side issues a power generation plan, the power generation resources of the hydropower station cannot be efficiently utilized. Thus, when constructing a reservoir scheduling model, it is necessary to fully consider the grid load demand satisfaction rate in order to maximize the utilization of power generation resources.

[0022] More preferably, in step (1), the improved function considering the grid load demand satisfaction rate is:

[0023] In reservoir scheduling models, if the benefits of power plants need to be considered, the objective function is usually to maximize the total power generation, i.e.:

[0024]

[0025] Where, E i : Power generation in the i-th time period; T: Total number of time periods;

[0026] To maximize the utilization of power plant resources, it is necessary to consider the grid load demand satisfaction rate. The objective function for maximizing power generation is improved by introducing a reward / penalty factor λ for grid load demand satisfaction. This improves the objective function by rewarding power generation during periods when grid load demand is met, thus increasing the objective function value for those periods. Conversely, it penalizes power generation during periods when grid load demand is not met, thus decreasing the objective function value for those periods. Based on these functions, the improved objective function expression is as follows:

[0027]

[0028] In the formula, λ i The reward / penalty factor for the i-th time period; to achieve the effect of the reward / penalty factor, it needs to be calculated based on the power grid load satisfaction rate. The calculation formula is as follows:

[0029]

[0030] In the formula: f(·) is the transformation function; it indicates that in the first time period, since the load on the grid side and the power output of the power station have not yet started to change, no reward or penalty is applied to the objective function value, that is, the reward and penalty factor is taken as 1; in each subsequent time period, the value of the reward and penalty factor is calculated based on the ratio of the change in power output of the power station to the change in grid load through the transformation function.

[0031] With the grid-side load as a known input, the power plant output process is optimized. Therefore, when i≠0, the reward / penalty factor λ is a function of the output change, i.e.:

[0032] λ i =h(ΔN) (6);

[0033] Set different conversion functions and perform multiple verification comparisons under the same conditions; when At that time, the power output change of the power plant is inconsistent with the change of the grid load and The smaller the value, the greater the difference between the power plant output change and the grid load change, thus penalizing the objective function, i.e., setting... And in Monotonically increasing within the interval; when When the power plant output changes in line with the grid load change, and the power plant output change is less than the grid load change, The closer the value is to 1, the stronger the consistency between the power plant's output changes and the grid load changes. The closer the value is to 1, the greater the reward for the objective function. And in Monotonically increasing within the interval; when When the power plant output change coincides with the grid load change, but the power plant output change is greater than the grid load change, it is necessary to reduce the reward on the objective function, i.e., set... exist Monotonically decreasing within the interval and The relationship between the ratio of hydropower station output to grid load variation and the transformation function can be used to establish λ i The transformation function f(·) provides the theoretical basis.

[0034] More preferably, in step (1), the constraint condition of the improved function considering the grid load demand satisfaction rate is:

[0035] (1) Water balance constraint:

[0036] V i =V i-1 +(I i -Q i )·Δi (7);

[0037] In the formula, V i and V i-1 Let I represent the reservoir capacity at the end of time period i and time period i-1, respectively; i Let Q be the average inflow rate of the reservoir during the i-th time period. i Δi represents the average outflow from the reservoir during the i-th time period; Δi is the duration of a single time period.

[0038] (2) Water level constraint:

[0039]

[0040] In the formula, and These represent the minimum and maximum water level limits of the reservoir in the i-th time period, respectively.

[0041] (3) Flow constraints:

[0042]

[0043] In the formula, and These represent the minimum and maximum outflow limits of the reservoir in the i-th time period, respectively.

[0044] (4) Output constraint:

[0045]

[0046] In the formula, and These are the minimum and maximum output limits of the power station in the i-th time period, respectively;

[0047] (5) Water level / flow rate variation constraints:

[0048]

[0049] In the formula, ΔZ and ΔQ are the maximum variation constraints of water level and flow rate in adjacent time periods, respectively;

[0050] (6) Boundary value constraints:

[0051]

[0052] In the formula, Z start and Z end These represent the initial and final water levels of the reservoir during the scheduling period.

[0053] Preferably, in step (1), the specific process of solving the model using the particle swarm optimization algorithm is as follows:

[0054] Particle swarm optimization (PSO) updates and optimizes the algorithm based on the search paths of memorized individuals and their adaptation to the environment, through information sharing within the population. This allows each individual to move closer to the optimal solution. Each individual is called a particle, and the position of each particle represents a set of feasible solutions to the problem. The particle's velocity, its optimal position on its own path, and its global optimal position in the population determine the particle's update direction. The update formulas for particle velocity and position are as follows:

[0055] v j (k+1)=wv j (k)+c1r1(p j -xj (k))+c2r2(p g -x j (k)) (13);

[0056] x ) (k+1)=x j (k)+v j (k+1) (14);

[0057] In the formula, v j Let x represent the velocity of the j-th particle. j Let p represent the position of the j-th particle, k represent the iteration number, w, c1, and c2 be constants, r1 and r2 be random numbers uniformly distributed in (0, 1), and p j p represents the optimal position of the j-th particle along its path. g This indicates the optimal position within the entire population.

[0058] Beneficial effects of this invention:

[0059] 1. In the optimized scheduling of cascade reservoirs, this invention comprehensively considers the demand from both the hydropower station side and the power grid side. For most models, the power generation, residual load variance, and load factor are all between the power generation model and the peak-shaving model. Regarding the power grid demand satisfaction rate index proposed in this invention, most models outperform the power generation model and the peak-shaving model in various typical years. This indicates that the model proposed in this patent, considering the power generation on the power generation side, can better match the load changes on the power grid side and improve water resource utilization.

[0060] 2. This invention improves the traditional cascade reservoir power generation model by introducing a reward and penalty factor for meeting grid load demand. This allows the model to consider both the power generation benefit requirements of the cascade power stations and the load change requirements of the grid. By using a single-objective optimization scheduling model, the model can take into account the needs of both the grid side and the power generation side, thereby improving the model's effectiveness without increasing the computational load of the model solution.

[0061] 3. This invention makes up for the lack of consideration of the overall trend of power grid changes and the lack of consideration of power plant benefits when power plants carry out peak shaving; the concept of power grid load demand satisfaction rate proposed in this invention is used to characterize the consistency between reservoir output and power grid load change process, which enhances the consistency between hydropower dispatching scheme and load change during off-peak periods. Attached Figure Description

[0062] Figure 1 This is a flowchart illustrating a reservoir scheduling method that takes into account changes in power grid load.

[0063] Figure 2 A schematic diagram of the transformation function graph;

[0064] Figure 3 This is a schematic diagram of the transformation function graph;

[0065] Figure 4 A ranking chart of different models and indicators in 2021;

[0066] Figure 5 A ranking chart of different models and indicators in 2008;

[0067] Figure 6 This is a ranking chart of different models and indicators in 2011. Detailed Implementation

[0068] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0069] Example 1: As Figure 1 As shown, a reservoir scheduling method considering changes in power grid load includes the following steps:

[0070] Step (1): Determine the water levels of the cascade reservoirs at the beginning and end of the scheduling period as boundary conditions for scheduling calculation; collect the inflow of the reservoirs and the interval inflow between each reservoir during the scheduling period as input for scheduling calculation; determine the water level, flow, output and other constraints of the reservoirs during the scheduling period according to the actual operation requirements of the reservoirs, and collect the basic characteristic curve data of each reservoir; the objective function is an improved function considering the grid load demand satisfaction rate, the fitness is the objective function value, the decision variable is the water level in each time period, input the constraints in the cascade reservoir scheduling model, and use the particle swarm algorithm to solve the model.

[0071] The definition, objective function, and constraints of the power grid load demand satisfaction rate are as follows:

[0072] 1. Power grid load demand satisfaction rate

[0073] Hydropower stations need to rely on grid load forecast data when submitting their power output plans. Ideally, the power station's power output plan should align with the grid load trend to significantly improve power generation utilization. Therefore, the grid load trend is often more important than the accuracy of the load forecast. Consequently, it is crucial to construct a reasonable dispatch model using grid load changes. Based on this, the concept of grid load demand satisfaction rate is proposed.

[0074] When the output of a hydropower station and the trend of grid load change are consistent, the grid load demand is said to be satisfied. This can be expressed by the following formula.

[0075]

[0076] In the formula, N i+1 N i : Hydropower station output; Pi+1 P i : Grid-side load; i: i-th time period.

[0077] Furthermore, the grid load demand satisfaction rate is defined as the proportion of the total time period in which the grid load demand is satisfied, i.e.:

[0078]

[0079] In the formula, η: the grid load demand satisfaction rate, t: the number of time periods in which the grid load demand is satisfied, and T: the total number of time periods.

[0080] By calculating the grid load demand satisfaction rate, we can directly observe the matching between changes in hydropower output and grid load over a given period. A higher grid load demand satisfaction rate indicates a higher consistency between hydropower output and grid load changes over that period. Conversely, a lower rate indicates a weaker consistency, suggesting that the hydropower station's reported output plan does not adequately match the grid load situation. Consequently, the hydropower station's power generation resources cannot be efficiently utilized when the grid issues its power generation plan. Therefore, when constructing a reservoir scheduling model, it is crucial to fully consider the grid load demand satisfaction rate to maximize the utilization of power generation resources.

[0081] 2. Objective function

[0082] In reservoir scheduling models, if the benefits of power plants need to be considered, the objective function is usually to maximize the total power generation, i.e.:

[0083]

[0084] Where, E i : Power generation in the i-th time period, T: Total number of time periods.

[0085] To maximize the utilization of power plant resources, the grid load demand satisfaction rate needs to be considered. The objective function for maximizing power generation is improved by introducing a grid load demand satisfaction reward / penalty factor λ (hereinafter referred to as the "reward / penalty factor"). This makes the objective function have the following effects: during periods when grid load demand is satisfied, power generation is rewarded, i.e., the objective function value for that period is increased; during periods when grid load demand is not satisfied, power generation is penalized, i.e., the objective function value for that period is decreased. Based on these functions, the improved objective function expression is as follows:

[0086]

[0087] In the formula, λ i: The reward / penalty factor for the i-th time period. To achieve the effect of the reward / penalty factor, it needs to be calculated based on the grid load satisfaction rate. The calculation formula is as follows:

[0088]

[0089] In the formula: f(·) is the transformation function. This indicates that in the first time period, since the load on the grid side and the power plant output have not yet started to change, no reward or penalty is applied to the objective function value, i.e., the reward and penalty factor is set to 1; in each subsequent time period, the value of the reward and penalty factor is calculated based on the ratio of the change in power plant output to the change in grid load through the transformation function.

[0090] With the grid-side load as a known input, the power plant output process is optimized. Therefore, when i≠0, the reward / penalty factor λ is a function of the output change, i.e.:

[0091] λ i =h(ΔN) (6)

[0092] The form of the transformation function has a significant impact on the model optimization results; therefore, it is necessary to set different transformation functions and perform multiple verification and comparison tests under the same conditions. At that time, the power output change of the power plant is inconsistent with the change of the grid load and The smaller the value, the greater the difference between the power plant output change and the grid load change, thus penalizing the objective function, i.e., setting... And in Monotonically increasing within the interval; when When the power plant output changes in line with the grid load change, and the power plant output change is less than the grid load change, The closer the value is to 1, the stronger the consistency between the power plant's output changes and the grid load changes. The closer the value is to 1, the greater the reward for the objective function. And in Monotonically increasing within the interval; when When the power plant output change coincides with the grid load change, but the power plant output change is greater than the grid load change, it is necessary to reduce the reward on the objective function, i.e., set... exist Monotonically decreasing within the interval and The relationship between the ratio of hydropower station output to grid load variation and the transformation function (as shown in Table 1) can be used to establish λ i The transformation function f(·) provides the theoretical basis.

[0093] Table 1. Ratio of power plant output change to grid load change

[0094]

[0095] Based on the above approach, ten sets of transformation functions were set up for simulation experiments. Let... The transformation function forms and graphs are shown in Table 2. Figure 2 and 3 As shown.

[0096] Table 2 Expressions of the Conversion Function

[0097]

[0098] 3. Constraints

[0099] (1) Water balance constraint:

[0100] V i =V i-1 +(I i -Q i )·Δi (7);

[0101] In the formula, V i and V i-1 Let I represent the reservoir capacity at the end of time period i and time period i-1, respectively; i Let Q be the average inflow rate of the reservoir during the i-th time period. i Δi represents the average outflow from the reservoir during the i-th time period; Δi is the duration of a single time period.

[0102] (2) Water level constraint:

[0103]

[0104] In the formula, and These represent the minimum and maximum water level limits of the reservoir in the i-th time period, respectively.

[0105] (3) Flow constraints:

[0106]

[0107] In the formula, and These represent the minimum and maximum outflow limits of the reservoir in the i-th time period, respectively.

[0108] (4) Output constraint:

[0109]

[0110] In the formula, and These are the minimum and maximum output limits of the power station in the i-th time period, respectively;

[0111] (5) Water level / flow rate variation constraints:

[0112]

[0113] In the formula, ΔZ and ΔQ are the maximum variation constraints of water level and flow rate in adjacent time periods, respectively;

[0114] (6) Boundary value constraints:

[0115]

[0116] In the formula, Z start and Z end These represent the initial and final water levels of the reservoir during the scheduling period.

[0117] In step (1), the specific process of solving the model using the particle swarm optimization algorithm is as follows:

[0118] Particle swarm optimization (PSO) updates and optimizes the algorithm based on the search paths of memorized individuals and their adaptation to the environment, through information sharing within the population. This allows each individual to move closer to the optimal solution. Each individual is called a particle, and the position of each particle represents a set of feasible solutions to the problem. The particle's velocity, its optimal position on its own path, and its global optimal position in the population determine the particle's update direction. The update formulas for particle velocity and position are as follows:

[0119] v j (k+1)=wv j (k)+c1r1(p j -x j (k))+c2r2(p g -x j (k)) (13);

[0120] x ) (k+1)=x j (k)+v j (k+1) (14);

[0121] In the formula, v j Let x represent the velocity of the j-th particle. j Let p represent the position of the j-th particle, k represent the iteration number, w, c1, and c2 be constants, r1 and r2 be random numbers uniformly distributed in (0, 1), and p j p represents the optimal position of the j-th particle along its path. g This indicates the optimal position within the entire population.

[0122] Step (2): Initialize the time period, let T be the number of calculation time periods in the entire scheduling period; randomly generate an initial population of N individuals, each individual representing the water level process of a cascade reservoir, and determine whether the water level process meets the constraints. If it does, proceed to the next step; if it does not, perform water level correction.

[0123] Step (3): Locate the reservoir's water level-capacity curve to obtain the reservoir capacity change ΔV(i) for the i-th time period. Calculate the outflow Q(i) for the time period based on the inflow I(i) and the reservoir's water balance equation. Determine whether the outflow meets the constraints. If it does, proceed to the next step; otherwise, perform flow correction.

[0124] Step (4): Calculate the downstream water level Z based on the discharge capacity curve and the outflow rate Q(i) for the time period. xy (i) The upstream water level Z of the reservoir sy (i) and downstream water level Z xy (i) The head H(i) of the hydropower station in the i-th time period can be calculated.

[0125] Step (5): Calculate the power output N(i) for this period based on the outflow Q(i) and the hydropower station head H(i). Determine whether the power output meets the constraints. If it does, proceed to the next step; otherwise, adjust the power output.

[0126] Step (6): Return to Step 3 to calculate the output of each time period and obtain the entire output process of the reservoir.

[0127] Step (7): Calculate the interval flow between power stations based on the known inflow and outflow data, and calculate the time-period outflow Q from the previous power station based on the interval flow and the time-period outflow Q from the previous power station. ck Calculate the inflow of the next-level power station, return to Step 3, and calculate the power output of each level of the cascade reservoirs.

[0128] Step (8): Calculate the total output process of the cascade reservoirs and calculate the total output change ΔN(i) for each time period; calculate the load change ΔP(i) based on the load process P(i) on the power grid side; calculate the ratio of output change to load change. The reward / penalty factor λ for each time period is obtained through the transformation function. i Multiplying the output by the corresponding time period, the adaptability of the cascade reservoirs is calculated.

[0129] Step (9): Iteratively update the population according to the particle swarm optimization algorithm and output the optimal solution as the cascade reservoir scheduling scheme.

[0130] Example 2: Cascade power stations A, B, and C, along with the connected Hubei power grid, were selected as case studies. 2021, 2008, and 2011 were chosen as typical years representing severe load fluctuations, moderate load fluctuations, and balanced load fluctuations, respectively, serving as the study period, with a ten-day time scale. A power generation model maximizing output and a peak-shaving model minimizing surplus load variance were introduced, and the reservoir scheduling model considering grid load demand satisfaction rate proposed in this patent were compared. The Lightning Search Algorithm (LSA) was used to solve the three reservoir scheduling models. To avoid randomness in the results, each model was solved twenty times under the same conditions, and the average value was calculated. Power generation, surplus load variance, load factor, and grid demand satisfaction rate were selected as four evaluation indicators. The calculated results are shown in the table and figures. Figure 4-6 In Table 3-5, the new model (*) represents the reservoir scheduling model that considers the grid load demand satisfaction rate and is constructed using the transformation function (*).

[0131] Table 3. Values ​​of various indicators for different scheduling models in 2021

[0132]

[0133] Table 4. Values ​​of various indicators for different scheduling models in 2008

[0134]

[0135] Table 5. Values ​​of various indicators for different scheduling models in 2011

[0136]

[0137] Of the four indicators, power generation, load factor, and grid demand satisfaction rate are benefit-oriented indicators, meaning the higher their values, the better the effect. The mean square error of surplus load is a cost-oriented indicator, meaning the lower its value, the better the effect. In 2021, except for new models 3 and 7, the power generation of the other new models fell between the power generation model and the peak-shaving model. The mean square error of surplus load and load factor of all the new models also fell between the power generation model and the peak-shaving model. For the grid demand satisfaction rate indicator proposed in this paper, all the new models outperformed the power generation model, except for new models 2 and 8, which outperformed the peak-shaving model. In 2008, the values ​​of power generation, mean square error of surplus load, and load factor of all the new models fell between power generation and mean square error of surplus load. The ranking of grid demand satisfaction was similar to that of 2021, meaning all the new models outperformed the power generation model, except for new models 2 and 8, which outperformed the peak-shaving model. In 2011, except for new models 4 and 7, the power generation of the other new models was between that of the power generation model and the peak shaving model. The mean square error of surplus load and the load factor of all the new models were between those of the power generation model and the peak shaving model. The grid demand satisfaction rate of all the new models was better than that of the power generation model. Except for new model 8, the other new models were better than the peak shaving model in this aspect.

[0138] In summary, this model comprehensively considers the demand from both the hydropower station side and the power grid side in the optimal scheduling of cascade reservoirs. For most models, the power generation, surplus load variance, and load factor fall between the power generation model and the peak-shaving model. Regarding the power grid demand satisfaction rate index proposed in this paper, most models outperform both the power generation model and the peak-shaving model in various typical years, indicating that the model proposed in this patent, considering power generation on the power generation side, can better match changes in power grid load and improve water resource utilization.

[0139] The above embodiments are merely preferred technical solutions of the present invention and should not be considered as limitations on the present invention. The scope of protection of the present invention should be limited to the technical solutions described in the claims, including equivalent substitutions of the technical features described in the claims. That is, equivalent substitutions and improvements within this scope are also within the scope of protection of the present invention.

Claims

1. A reservoir scheduling method considering changes in power grid load, characterized in that: It includes the following steps: Step (1): Determine the water levels of the cascade reservoirs at the beginning and end of the scheduling period as boundary conditions for scheduling calculation; collect the inflow of the reservoirs and the interval inflow between each reservoir during the scheduling period as input for scheduling calculation; determine the water level, flow, and output constraints of the reservoirs during the scheduling period based on the actual operation requirements of the reservoirs, and collect the basic characteristic curve data of each reservoir; the objective function is an improved function considering the grid load demand satisfaction rate, the fitness is the objective function value, the decision variable is the water level in each time period, input the constraints in the cascade reservoir scheduling model, and use the particle swarm algorithm to solve the model. Step (2): Initialize the time period, let T be the number of calculation time periods in the entire scheduling period; randomly generate an initial population of N individuals, each individual representing the water level process of a cascade reservoir, determine whether the water level process meets the constraints, if it does, proceed to the next step; if it does not meet the constraints, then perform water level correction. Step (3): Locate the reservoir's water level and capacity curve to obtain the capacity change ΔV(i) for the i-th time period. Calculate the outflow Q(i) for the time period based on the inflow I(i) and the reservoir's water balance equation. Determine whether the outflow meets the constraints. If it does, proceed to the next step; otherwise, perform flow correction. Step (4): Calculate the downstream water level Z based on the discharge capacity curve and the outflow rate Q(i) for the time period. xy (i) The upstream water level Z of the reservoir sy (i) and downstream water level Z xy (i) The head H(i) of the hydropower station in the i-th time period can be calculated; Step (5): Calculate the output N(i) for this period based on the outflow Q(i) and the hydropower station head H(i); determine whether the output meets the constraints. If it does, proceed to the next step; if it does not, adjust the output. Step (6): Return to step (3) to calculate the output of each time period and obtain the entire output process of the reservoir; Step (7): Calculate the interval flow between power stations based on the known inflow and outflow data, and calculate the time-period outflow Q from the previous power station based on the interval flow and the time-period outflow Q from the previous power station. ck Calculate the inflow of the next-level power station, return to step (3), and calculate the power output process of each level of reservoir in the cascade reservoir; Step (8): Calculate the total output process of the cascade reservoirs and calculate the change in total output ΔN(i) for each time period; Calculate the load change ΔP(i) based on the load process P(i) on the grid side; Calculate the ratio of output change to load change. The reward / penalty factor λ for each time period is obtained through the transformation function. i Multiply by the output for the corresponding time period to calculate the adaptability of the cascade reservoirs; Step (9): Iteratively update the population according to the particle swarm optimization algorithm and output the optimal solution as the cascade reservoir scheduling scheme.

2. The reservoir scheduling method considering power grid load changes according to claim 1, characterized in that: In step (1), the power grid load demand satisfaction rate is defined as: When the output of a hydropower station and the trend of grid load change are consistent, the grid load demand is said to be satisfied; this is expressed by the following formula: In the formula, N i+1 N i : Hydropower station output; P i+1 P i : Grid-side load; i: The i-th time period; Furthermore, the grid load demand satisfaction rate is defined as the proportion of the total time period in which the grid load demand is satisfied, i.e.: In the formula, η: the grid load demand satisfaction rate, t: the number of time periods in which the grid load demand is satisfied, and T: the total number of time periods.

3. A reservoir scheduling method considering power grid load changes according to claim 2, characterized in that: In step (1), the improved function considering the grid load demand satisfaction rate is: In reservoir scheduling models, if the benefits of power plants need to be considered, the objective function is usually to maximize the total power generation, i.e.: Where, E i : Power generation in the i-th time period; T: Total number of time periods; To maximize the utilization of power plant generation resources, it is necessary to consider the grid load demand satisfaction rate. Therefore, the objective function for maximizing power generation is improved by introducing a reward / penalty factor λ for grid load demand satisfaction. i This allows the objective function to have the following effects: during periods when grid load demand is met, the generation during those periods is rewarded, i.e., the objective function value for those periods is increased; during periods when grid load demand is not met, the generation during those periods is penalized, i.e., the objective function value for those periods is decreased. Based on these functions, the improved objective function expression is as follows: In the formula, λ i The reward / penalty factor for the i-th time period; to achieve the effect of the reward / penalty factor, it needs to be calculated based on the power grid load satisfaction rate. The calculation formula is as follows: In the formula: f(·) is the transformation function; it indicates that in the first time period, since the load on the grid side and the power output of the power station have not yet started to change, no reward or penalty is applied to the objective function value, that is, the reward and penalty factor is taken as 1; In each subsequent time period, the value of the reward / penalty factor is calculated based on the ratio of power plant output change to grid load change using a conversion function; Set different conversion functions and perform multiple verification comparisons under the same conditions; when At that time, the power output change of the power plant is inconsistent with the change of the grid load and The smaller the value, the greater the difference between the power plant output change and the grid load change, thus penalizing the objective function, i.e., setting... And in Monotonically increasing within the interval; when When the power plant output changes in line with the grid load change, and the power plant output change is less than the grid load change, The closer the value is to 1, the stronger the consistency between the power plant's output changes and the grid load changes. The closer the value is to 1, the greater the reward for the objective function. And in Monotonically increasing within the interval; when When the power plant output change coincides with the grid load change, but the power plant output change is greater than the grid load change, it is necessary to reduce the reward on the objective function, i.e., set... exist Monotonically decreasing within the interval and 4. A reservoir scheduling method considering power grid load changes according to claim 3, characterized in that: In step (1), the constraints of the improved function considering the grid load demand satisfaction rate are as follows: (1) Water balance constraint: V i =V i-1 +(I i -Q i )·Δi (7); In the formula, V i and V i-1 Let I represent the reservoir capacity at the end of time period i and time period i-1, respectively; i Let Q be the average inflow rate of the reservoir during the i-th time period. i Let be the average outflow from the reservoir during the i-th time period; Δi represents the duration of a single time period; (2) Water level constraint: In the formula, and These represent the minimum and maximum water level limits of the reservoir in the i-th time period, respectively. (3) Flow constraints: In the formula, and These represent the minimum and maximum outflow limits of the reservoir in the i-th time period, respectively. (4) Output constraint: In the formula, and These are the minimum and maximum output limits of the power station in the i-th time period, respectively; (5) Water level / flow rate variation constraints: In the formula, ΔZ and ΔQ are the maximum variation constraints of water level and flow rate in adjacent time periods, respectively; (6) Boundary value constraints: In the formula, Z start and Z end These represent the initial and final water levels of the reservoir during the scheduling period.

5. A reservoir scheduling method considering power grid load changes according to claim 1, characterized in that: In step (1), the specific process of solving the model using the particle swarm optimization algorithm is as follows: Particle swarm optimization (PSO) updates and optimizes the algorithm based on the search paths of memorized individuals and their adaptation to the environment, through information sharing within the population. This allows each individual to move closer to the optimal solution. Each individual is called a particle, and the position of each particle represents a set of feasible solutions to the problem. The particle's velocity, its optimal position on its own path, and its global optimal position in the population determine the particle's update direction. The update formulas for particle velocity and position are as follows: v j (k+1)=wv j (k)+c1r1(p j -x j (k))+c2r2(p g -x j (k)) (13); x j (k+1)=x j (k)+v j (k+1) (14); In the formula, v j Let x represent the velocity of the j-th particle. j Let p represent the position of the j-th particle, k represent the iteration number, w, c1, and c2 be constants, r1 and r2 be random numbers uniformly distributed in (0, 1), and p j p represents the optimal position of the j-th particle along its path. g This indicates the optimal position within the entire population.

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