Reservoir scheduling method considering power grid load change

Abstract

The invention discloses a reservoir scheduling method considering power grid load change, which makes up for the deficiency that a power station lacks consideration on the overall change trend of a power grid and lacks consideration on the benefits of the power station during peak regulation. A concept of a power grid load demand satisfaction rate is provided and is used for representing a consistency strength relationship between reservoir output and a power grid load change process; a power grid demand satisfaction reward and punishment factor is introduced into a traditional reservoir power generation scheduling model, ten groups of conversion functions are designed, the reward and punishment factor is calculated by using a reservoir power generation process and a power grid load change process, and a cascade reservoir scheduling model considering a power grid load demand satisfaction rate is constructed; according to the method, the double-side requirements of the power grid side and the power generation side are considered, and the consistency of the hydropower dispatching scheme and the load change in the non-peak period is enhanced.

Description

Technical Field

[0001] The invention relates to the technical field of reservoir dispatching methods, in particular to a reservoir dispatching method that takes into account changes in power grid load. Background Art

[0002] The optimal dispatching of cascade hydropower stations is related to the safe operation of the power grid. It is a typical high-dimensional, multi-stage, nonlinear, and discrete problem. The purpose is to respond to the changing demand of the power grid load in a balanced and accurate manner during the dispatching period and improve the utilization rate of power generation. For the study on the optimal dispatching of cascade reservoirs considering the load demand of the power grid, Liu Benxi constructed a coordinated dispatching model of large and small hydropower based on a decision tree, which effectively improved the utilization rate of the transmission power grid channel; Wang Jiayang adopted load deviation smoothing technology to reduce the deviation between load and power generation plan, ensured the synchronous and rapid distribution of load deviations between cascade reservoirs in multiple time periods, and effectively adjusted the preparation of power generation dispatching plan; Cheng Xiong proposed a short-term dispatching method for small and medium-sized river basin cascade power stations in response to peak-shaving needs, which effectively increased the cascade power generation, responded to the peak-shaving needs of the power grid, and realized the efficient use of water resources for power generation; Zhou Binbin established a short-term optimal dispatching model for hydropower stations considering multi-grid peak-shaving under the constraints of hydropower station and DC coupling operation, which effectively realized multi-grid peak-shaving and ensured the safe operation of the power grid. The above scholars have done a lot of research on the optimal dispatching of cascade reservoirs under the consideration of grid load demand and have achieved corresponding results. However, there are still some problems in the existing research when considering the response of the power generation side to the grid demand. The dispatching model of the power generation side to the grid demand is mainly based on the peak-shaving model, the goal is to reduce the peak-to-valley difference of the residual load to achieve the purpose of peak-shaving and valley-filling. However, the peak-shaving model itself takes the guarantee of grid demand as the primary task, and fails to consider the actual needs of the hydropower station from the perspective of the power generation side. In addition to responding to the load demand of the grid, hydropower first needs to ensure the safe and stable operation of the power station, and secondly, it needs to achieve the economic benefits of the power station itself. These factors are not reflected in the peak-shaving model. The peak-shaving model has a certain peak-shaving effect during the peak period, but ignores the consistency of the hydropower and grid load change trends in other periods. When reporting the output plan, the power station often expects the output plan to be highly matched with the grid load change trend, which can not only better meet the grid load demand, but also improve the power generation utilization rate, reduce the amount of water abandoned by the reservoir, and thus improve the economic benefits. 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 of power stations and enhance the economic benefits of power stations, but also match grid demand in more time periods and promote safe and stable operation of the grid. Summary of the invention

[0003] The purpose of the present invention is to overcome the above-mentioned shortcomings, provide a reservoir scheduling method that takes into account the changes in power grid load, improve the traditional cascade reservoir power generation model, introduce power grid load demand satisfaction reward and punishment factors, so that it can take into account the power generation efficiency needs of cascade power stations while taking into account the load change needs of the power grid, use a single-objective optimization scheduling model, take into account both the grid side and the power generation side, and improve the model utility without increasing the calculation amount of the model solution.

[0004] In order to solve the above technical problems, the technical solution adopted by the present invention is: a reservoir dispatching method considering the change of power grid load, which comprises the following steps:

[0005] Step (1): Determine the water level of the cascade reservoirs at the beginning and end of the dispatch period as the boundary condition of the dispatch calculation; collect the inflow of the reservoirs during the dispatch period and the interval inflow between the reservoirs as the input of the dispatch calculation; determine the water level, flow and output constraints of the reservoirs during the dispatch 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, the constraints in the cascade reservoir dispatch model are input, and the particle swarm algorithm is used 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 represents the water level process of a cascade reservoir, and judge whether the water level process meets the constraint conditions. If so, proceed to the next step; if not, perform water level correction;

[0007] Step (3): Find the water level and storage capacity curve of the reservoir, and obtain the storage capacity change ΔV(i) in the i-th period. Calculate the outflow Q(i) in the period according to the inflow I(i) and the reservoir water balance equation; determine whether the outflow meets the constraint conditions. If so, proceed to the next step; if not, perform flow correction;

[0008] Step (4): Calculate the downstream water level Z according to the discharge capacity curve and the outflow flow Q(i) during the period xy (i), the water level Z upstream of the reservoir sy (i) and downstream water level Z xy (i) The water head H(i) of the hydropower station in the i-th period can be calculated;

[0009] Step (5): Calculate the output N(i) of the period according to the outflow Q(i) and the water head H(i) of the hydropower station; determine whether the output meets the constraint conditions. If so, proceed to the next step; if not, perform output correction;

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

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

[0012] Step (8): Calculate the total output process of the cascade reservoirs and calculate the total output change ΔN(i) in each period; calculate the load change ΔP(i) according to the load process P(i) on the grid side and calculate the ratio of the output change to the load change Calculate the reward and punishment factor λ for each period through the conversion function i , multiplied by the output of the corresponding period, the fitness of the cascade reservoirs is calculated;

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

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

[0015] Definition: When the output of the hydropower station and the load of the power grid are consistent, the load demand of the power grid is said to be met; it can be expressed as follows:

[0016]

[0017] Where, N: hydropower station output; P: grid load; i: i-th time period;

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

[0019]

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

[0021] By calculating the grid load demand satisfaction rate, we can directly see the matching situation of hydropower output changes and grid load changes within a period of time; the greater the grid load demand satisfaction rate, the higher the consistency of hydropower output changes and grid load changes within the time range; on the contrary, the lower the grid load demand satisfaction rate, the weaker the consistency of hydropower output changes and grid load changes within the time range, indicating that the output plan reported by the hydropower station cannot match the grid load situation well, and when the grid side issues the power generation plan, the power generation resources of the hydropower station cannot be efficiently utilized; therefore, when constructing the reservoir scheduling model, it is necessary to fully consider the grid load demand satisfaction rate 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 the reservoir operation model, if the power station benefits need to be considered, the maximum total power generation is usually used as the objective function, that is:

[0024]

[0025] In the formula, E i : Power generation in the ith period, T: total number of time periods;

[0026] In order to maximize the utilization of power generation resources of power plants, it is necessary to consider the grid load demand satisfaction rate; improve the objective function of maximum power generation, introduce the grid load demand satisfaction reward and penalty factor λ, so that the objective function has the following utility: in the period when the grid load demand is met, the power generation in this period is rewarded, that is, the objective function value of this period is increased; in the period when the grid load demand is not met, the power generation in this period is punished, that is, the objective function value of this period is reduced; based on the above functions, the improved objective function expression is as follows:

[0027]

[0028] In the formula, λ i : The reward and punishment factor of the i-th period; In order to achieve the effect of the reward and punishment factor, it is necessary to calculate the reward and punishment factor through the grid load satisfaction rate. The calculation formula is as follows:

[0029]

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

[0031] The grid-side load is used as a known quantity input to optimize the power station output process. Therefore, when i≠0, the reward and penalty factor λ is a function of the load change, that is:

[0032] λ i =h(ΔN) (6);

[0033] Set different conversion functions to perform multiple verification comparisons under the same conditions; When the power station output change is inconsistent with the grid load change and The smaller the value is, the greater the difference between the power station output change and the grid load change is, and the objective function is penalized, that is, And in monotonically increasing within the interval; when When the power station output change is consistent with the grid load change and the power station output change is smaller than the grid load change, The closer it is to 1, the stronger the consistency between the power station output change and the grid load change is, and the objective function is rewarded, and the closer it is to 1, the greater the reward. And in monotonically increasing within the interval; when When , that is, the power plant output change is consistent with the grid load change, but the power plant output change is greater than the value of the grid load change, so it is necessary to reduce the reward of the objective function, that is, set exist Monotonically decreasing in the interval The relationship between the change ratio of the hydropower station output and the grid load and the change of the conversion function can be used to establish λ i The conversion function f(·) provides a 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 constraints:

[0036] V t =V t-1 +(I t -Q t )·Δt (7);

[0037] Where V t and V t-1 are the storage capacity of the reservoir at the end of the t period and the t-1 period respectively; I t is the average inflow of the reservoir in period t, Q t is the average outflow of the reservoir in the t period; Δt is the duration of a single period;

[0038] (2) Water level constraint:

[0039]

[0040] In the formula, and are the minimum and maximum water level limits of the reservoir in period t, respectively;

[0041] (3) Traffic Constraints:

[0042]

[0043] In the formula, and are the minimum and maximum outflow limits of the reservoir in period t respectively;

[0044] (4) Output constraints:

[0045]

[0046] In the formula, and are the minimum and maximum output limits of the power station in the tth period respectively;

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

[0048]

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

[0050] (6) Boundary value constraints:

[0051]

[0052] In the formula, Z start and Z end are the initial and final water levels of the reservoir during the dispatching period, respectively.

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

[0054] The particle swarm algorithm updates and optimizes the search path of the memory individual and the degree of adaptation to the environment through information sharing within the population, so that each individual moves closer to the optimal solution; each individual is called a particle, and the position of each particle is a set of feasible solutions to the problem to be optimized. The particle's speed, the optimal position in its own path, and the global optimal position in the population determine the particle's update direction; the particle speed and position update formula are as follows:

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

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

[0057] In the formula, v i represents the velocity of the ith particle, x i represents the position of the ith particle, k represents the number of iterations, w, c1, c2 are constants, r1, r2 are random numbers uniformly distributed in (0, 1), p i represents the best position of the ith particle in the path, p g represents the best position in the entire population.

[0058] Beneficial effects of the present invention:

[0059] 1. In the optimization scheduling of cascade reservoirs, the present invention comprehensively considers the demands of the hydropower station side and the power grid side. For most models, the power generation, residual load mean square error, and load rate are between the power generation model and the peak-shaving model; for the index of the power grid demand satisfaction rate proposed in the present invention, most models are better than the power generation model and the peak-shaving model in various typical years, indicating that the model proposed in this patent can better match the load changes on the power grid side and improve the utilization rate of water resources under the premise of considering the power generation on the power generation side.

[0060] 2. The present invention improves the traditional cascade reservoir power generation model by introducing a grid load demand satisfaction reward and punishment factor, so that it can take into account the load change requirements of the grid while considering the power generation efficiency requirements of the cascade power stations. It uses a single-objective optimization scheduling model to take into account the requirements of both the grid side and the power generation side, thereby improving the model utility without increasing the computational complexity of the model solution.

[0061] 3. The present invention makes up for the lack of consideration of the overall change trend of the power grid and the lack of consideration of the benefits of the power station when the power station performs peak load regulation; the concept of power grid load demand satisfaction rate proposed in the present invention is used to characterize the consistency strength relationship between the reservoir output and the power grid load change process, which enhances the consistency of the hydropower scheduling plan with the load changes during non-peak periods. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A schematic diagram of a flow chart of a reservoir dispatching method considering the load variation of the power grid;

[0063] Figure 2 It is a schematic diagram of the conversion function image;

[0064] Figure 3 It is a schematic diagram of the conversion function image 2;

[0065] Figure 4 This is a ranking chart of different indicators of different models in 2021;

[0066] Figure 5 This is a ranking chart of different indicators of different models in 2008;

[0067] Figure 6 This is a ranking chart of different indicators of different models in 2011. DETAILED DESCRIPTION

[0068] The present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments.

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

[0070] Step (1): Determine the water level of the cascade reservoirs at the beginning and end of the dispatching period as the boundary condition of the dispatching calculation; collect the inflow of the reservoirs during the dispatching period and the interval inflow between the reservoirs as the input of the dispatching calculation; determine the water level, flow, output and other constraints of the reservoirs during the dispatching period according to the actual operation needs 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, the constraints in the cascade reservoir dispatching model are input, and the particle swarm algorithm is used to solve the model.

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

[0072] 1. Grid load demand satisfaction rate

[0073] When a hydropower station reports its output plan, it needs to use the load forecast data of the power grid as support. If the change trend of the output plan reported by the hydropower station is consistent with the change trend of the power grid load, the power generation utilization rate of the power station can be greatly improved, which is the most ideal situation. Therefore, the change trend of the power grid load is often more important than the accuracy of the load forecast value. Therefore, it is urgent to use the change of the power grid load to build a reasonable dispatching model. Based on this, the concept of power grid load demand satisfaction rate is proposed.

[0074] Definition: When the output of the hydropower station and the load of the power grid change in the same trend, the load demand of the power grid is said to be met. It can be expressed by the following formula.

[0075]

[0076] Where, N: hydropower station output; P: grid side load; i: the i-th time period.

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

[0078]

[0079] Where, η: grid load demand satisfaction rate, t: number of time periods in which grid load demand is met, T: total number of time periods.

[0080] By calculating the grid load demand satisfaction rate, we can directly see the matching of hydropower output changes and grid load changes within a period of time. The greater the grid load demand satisfaction rate, the higher the consistency of hydropower output changes and grid load changes within the time range; on the contrary, the lower the grid load demand satisfaction rate, the weaker the consistency of hydropower output changes and grid load changes within the time range, indicating that the output plan reported by the hydropower station cannot match the grid load situation well. When the grid side issues the power generation plan, the power generation resources of the hydropower station cannot be efficiently utilized. Therefore, 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.

[0081] 2. Objective Function

[0082] In the reservoir operation model, if the power station benefits need to be considered, the maximum total power generation is usually used as the objective function, that is:

[0083]

[0084] In the formula, E i : Power generation in the ith period, T: total number of time periods.

[0085] In order to maximize the utilization of power generation resources in power plants, it is necessary to consider the grid load demand satisfaction rate. The objective function of maximum power generation is improved by introducing a grid load demand satisfaction reward and penalty factor λ (hereinafter referred to as the "reward and penalty factor") so that the objective function has the following utility: in the period when the grid load demand is met, the power generation in that period is rewarded, that is, the objective function value of that period is increased; in the period when the grid load demand is not met, the power generation in that period is punished, that is, the objective function value of that period is reduced. Based on the above functions, the improved objective function expression is as follows:

[0086]

[0087] In the formula, λ i : The reward and punishment factor of the i-th period. In order to achieve the effect of the reward and punishment factor, it is necessary to calculate the reward and punishment factor through the grid load satisfaction rate. The calculation formula is as follows:

[0088]

[0089] Where: f(·) is the conversion function. It indicates that in the first time period, since the grid load and power station output have not started to change, no reward or penalty is applied to the objective function value, that is, the reward or penalty factor is taken as 1; in each subsequent time period, the value of the reward or penalty factor is calculated based on the ratio of power station output change to grid load change through the conversion function.

[0090] The grid-side load is used as a known quantity input to optimize the power station output process. Therefore, when i≠0, the reward and penalty factor λ is a function of the load change, that is:

[0091] λ i =h(ΔN) (6)

[0092] The form of the conversion function has a great influence on the model optimization results, so it is necessary to set different conversion functions for multiple verification and comparison under the same conditions. When the power station output change is inconsistent with the grid load change and The smaller the value is, the greater the difference between the power station output change and the grid load change is, and the objective function is penalized, that is, 1 and in monotonically increasing within the interval; when When the power station output change is consistent with the grid load change and the power station output change is smaller than the grid load change, The closer it is to 1, the stronger the consistency between the power station output change and the grid load change is, and the objective function is rewarded, and the closer it is to 1, the greater the reward. 1 and in monotonically increasing within the interval; when When , that is, the power plant output change is consistent with the grid load change, but the power plant output change is greater than the value of the grid load change, so it is necessary to reduce the reward of the objective function, that is, set exist Monotonically decreasing in the interval The relationship between the change ratio of the hydropower station output and the grid load and the conversion function (as shown in Table 1) can be used to establish λ i The conversion function f(·) provides a theoretical basis.

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

[0094]

[0095] According to the above ideas, ten sets of conversion functions are set to conduct simulation experiments. The conversion function form and image are shown in Table 2. Figure 2 and 3 shown.

[0096] Table 2 Conversion function expressions

[0097]

[0098] 3. Constraints

[0099] (1) Water balance constraints

[0100] V t =V t-1 +(I t -Q t )·Δt (7)

[0101] Where V t and V t-1 are the storage capacity of the reservoir at the end of the t period and the t-1 period respectively; I t is the average inflow of the reservoir in period t, Q t is the average outflow of the reservoir in the tth period; Δt is the duration of a single period.

[0102] (2) Water level constraints

[0103]

[0104] In the formula, and are the minimum and maximum water level limits of the reservoir in period t, respectively.

[0105] (3) Traffic Constraints

[0106]

[0107] In the formula, and are the minimum and maximum outflow limits of the reservoir in the tth period respectively.

[0108] (4) Output constraints

[0109]

[0110] In the formula, and are the minimum and maximum output limits of the power station in the tth period respectively.

[0111] (5) Water level / flow range constraints

[0112]

[0113] Where ΔZ and ΔQ are the maximum fluctuation constraints of water level and flow in adjacent time periods, respectively.

[0114] (6) Boundary value constraints

[0115]

[0116] In the formula, Z start and Z end are the initial and final water levels of the reservoir during the dispatching period, respectively.

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

[0118] The particle swarm algorithm updates and optimizes the search path of the memory individual and the degree of adaptation to the environment through information sharing within the population, so that each individual moves closer to the optimal solution; each individual is called a particle, and the position of each particle is a set of feasible solutions to the problem to be optimized. The particle's speed, the optimal position in its own path, and the global optimal position in the population determine the particle's update direction; the particle speed and position update formula are as follows:

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

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

[0121] In the formula, v i represents the velocity of the ith particle, x i represents the position of the ith particle, k represents the number of iterations, w, c1, c2 are constants, r1, r2 are random numbers uniformly distributed in (0, 1), p i represents the best position of the ith particle in the path, p g represents the best position in 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 represents the water level process of a cascade reservoir, and judge whether the water level process meets the constraints. If so, proceed to the next step; if not, perform water level correction.

[0123] Step (3): Find the water level and storage capacity curve of the reservoir, and obtain the storage capacity change ΔV(i) in the i-th period. Calculate the outflow Q(i) in the period based on the inflow I(i) and the reservoir water balance equation. Determine whether the outflow meets the constraint conditions. If so, proceed to the next step; if not, perform flow correction.

[0124] Step (4): Calculate the downstream water level Z according to the discharge capacity curve and the outflow flow Q(i) during the period xy (i), the water level Z upstream of the reservoir sy (i) and downstream water level Z xy (i) The water head H(i) of the hydropower station in the i-th period can be calculated.

[0125] Step (5): Calculate the output N(i) of this period based on the outflow Q(i) and the water head H(i) of the hydropower station. Determine whether the output meets the constraint conditions. If so, proceed to the next step; if not, perform output correction.

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

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

[0128] Step (8): Calculate the total output process of the cascade reservoirs and calculate the total output change ΔN(i) in each period; calculate the load change ΔP(i) according to the load process P(i) on the grid side and calculate the ratio of the output change to the load change Calculate the reward and punishment factor λ for each period through the conversion function i , multiplied by the output of the corresponding period, the fitness of the cascade reservoirs is calculated.

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

[0130] Embodiment 2: Cascade power stations A, B, C and the connected Hubei power grid are selected as case study objects, and 2021, 2008, and 2011 are selected as typical years with severe load fluctuations, gentle load fluctuations, and moderate load fluctuations as the research period, with ten days as the time scale. The power generation model with the maximum power generation model and the peak-shaving model with the minimum mean square error of residual load are introduced, and compared with the reservoir scheduling model considering the grid load demand satisfaction rate proposed in this patent, and the three reservoir scheduling models are solved using the lightning search algorithm (LSA). In order to avoid the randomness of the results, various models are solved and calculated twenty times under the same conditions to obtain the average value, and four indicators of power generation, mean square error of residual load, load rate, and grid demand satisfaction rate are selected as evaluation indicators. The results are calculated as shown in the table and figure. Figure 4-6And in Table 3-5, the new model (*) represents the reservoir scheduling model that takes into account the grid load demand satisfaction rate and is constructed using the conversion function (*).

[0131] Table 3 Index values ​​of different scheduling models in 2021

[0132]

[0133] Table 4 Index values ​​of different scheduling models in 2008

[0134]

[0135] Table 5 Index values ​​of different scheduling models in 2011

[0136]

[0137] Among the four indicators, power generation, load factor, and grid satisfaction rate are benefit indicators, that is, the larger the value, the better the effect; residual load mean square error is a cost indicator, that is, the smaller the value, the better the effect. In 2021, except for new model 3 and new model 7, the power generation of other new models is between the power generation model and the peak-shaving model, and the residual load mean square error and load factor of all new models are between the power generation model and the peak-shaving model. For the indicator of grid demand satisfaction rate proposed in this paper, all new models are better than the power generation model, except for new model 2 and new model 8, other new models are better than the peak-shaving model. In 2008, the power generation, residual load mean square error, and load factor values ​​of all new models were between power generation and residual load mean square error, and the ranking of grid demand satisfaction was similar to that in 2021, that is, all new models were better than the power generation model, except for new model 2 and new model 8, other new models were better than the peak-shaving model. In 2011, except for new model 4 and new model 7, the power generation of other new models is between the power generation model and the peak-shaving model. The residual load mean square error and load rate of all new models are between the power generation model and the peak-shaving model. The grid demand satisfaction rate of all new models is better than that of the power generation model. Except for new model 8, the indicators of other new models are better than those of the peak-shaving model.

[0138] In summary, this model comprehensively considers the demands of the hydropower station side and the power grid side in the optimal dispatching of cascade reservoirs. For most models, the power generation, residual load mean square error, and load rate are between the power generation model and the peak-shaving model. For the indicators of the power grid demand satisfaction rate proposed in this paper, most models are better than the power generation model and the peak-shaving model in various typical years, indicating that the model proposed in this patent can better match the load changes on the power grid side and improve the utilization rate of water resources under the premise of considering the power generation on the power generation side.

[0139] The above embodiments are only preferred technical solutions of the present invention and should not be regarded as limiting the present invention. The protection scope of the present invention shall be the technical solutions recorded in the claims, including equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. A reservoir dispatching method considering power grid load changes, characterized in that: It includes the following steps: Step (1): Determine the water level of the cascade reservoirs at the beginning and end of the dispatch period as the boundary condition of the dispatch calculation; collect the inflow of the reservoirs during the dispatch period and the interval inflow between the reservoirs as the input of the dispatch calculation; determine the water level, flow and output constraints of the reservoirs during the dispatch 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, the constraints in the cascade reservoir dispatch model are input, and the particle swarm algorithm is used 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 represents the water level process of a cascade reservoir, and judge whether the water level process meets the constraint conditions. If so, proceed to the next step; if not, perform water level correction; Step (3): Find the water level and storage capacity curve of the reservoir, and obtain the storage capacity change ΔV(i) in the i-th period. Calculate the outflow Q(i) in the period according to the inflow I(i) and the reservoir water balance equation; determine whether the outflow meets the constraint conditions. If so, proceed to the next step; if not, perform flow correction; Step (4): Calculate the downstream water level Z according to the discharge capacity curve and the outflow flow Q(i) during the period xy (i), the water level Z upstream of the reservoir sy (i) and downstream water level Z xy (i) The water head H(i) of the hydropower station in the i-th period can be calculated; Step (5): Calculate the output N(i) of the period according to the outflow Q(i) and the water head H(i) of the hydropower station; determine whether the output meets the constraint conditions. If so, proceed to the next step; if not, perform output correction; Step (6): Return to step (3) to calculate the output of each period to obtain the entire output process of the reservoir; Step (7): Calculate the interval flow between power stations at each level based on the known inbound and outbound flow data, and calculate the outbound flow Q of the time period based on the interval flow and the previous power station ck Calculate the inflow flow of the next power station, return to step (3), and calculate the output process of each level of the cascade reservoirs; Step (8): Calculate the total output process of the cascade reservoirs and calculate the total output change ΔN(i) in each period; According to the load process P(i) on the power grid side, calculate the load change ΔP(i) and calculate the ratio of force change to load change Calculate the reward and punishment factor λ for each period through the conversion function i , multiplied by the output of the corresponding period, the fitness of the cascade reservoirs is calculated; Step (9): Iteratively update the population according to the particle swarm algorithm and output the optimal solution as the cascade reservoir scheduling plan.

2. A reservoir dispatching method considering power grid load changes according to claim 1, characterized in that: In step (1), the grid load demand satisfaction rate is defined as: Definition: When the output of the hydropower station and the load of the power grid are consistent, the load demand of the power grid is said to be met; it can be expressed as follows: Where, N: hydropower station output; P: grid side load; i: the i-th time period; Furthermore, the grid load demand satisfaction rate is defined as the proportion of the time period in which the grid load demand is satisfied to the total time period, that is: Where, η: grid load demand satisfaction rate, t: number of time periods in which grid load demand is satisfied, T: total number of time periods; By calculating the grid load demand satisfaction rate, we can directly see the matching situation of hydropower output changes and grid load changes within a period of time; the greater the grid load demand satisfaction rate, the higher the consistency of hydropower output changes and grid load changes within the time range; on the contrary, the lower the grid load demand satisfaction rate, the weaker the consistency of hydropower output changes and grid load changes within the time range, indicating that the output plan reported by the hydropower station cannot match the grid load situation well, and when the grid side issues the power generation plan, the power generation resources of the hydropower station cannot be efficiently utilized; therefore, when constructing the reservoir scheduling model, it is necessary to fully consider the grid load demand satisfaction rate to maximize the utilization of power generation resources.

3. A reservoir dispatching method considering power grid load changes according to claim 2, characterized in that: In the step (1), the improved function considering the grid load demand satisfaction rate is: In the reservoir operation model, if the power station benefits need to be considered, the maximum total power generation is usually used as the objective function, that is: In the formula, E i : Power generation in the ith period, T: total number of time periods; In order to maximize the utilization of power generation resources of power plants, it is necessary to consider the grid load demand satisfaction rate; improve the objective function of maximum power generation, introduce the grid load demand satisfaction reward and penalty factor λ, so that the objective function has the following utility: in the period when the grid load demand is met, the power generation in this period is rewarded, that is, the objective function value of this period is increased; in the period when the grid load demand is not met, the power generation in this period is punished, that is, the objective function value of this period is reduced; based on the above functions, the improved objective function expression is as follows: In the formula, λ i : The reward and punishment factor of the i-th period; in order to achieve the effect of the reward and punishment factor, it is necessary to calculate the reward and punishment factor through the grid load satisfaction rate. The calculation formula is as follows: Where: f(·) is the conversion function; it indicates that in the first time period, since the grid load and power station output have not started to change, no reward or penalty is given to the objective function value, that is, the reward or penalty factor is taken as 1; In each subsequent period, the value of the reward and penalty factor is calculated based on the ratio of power station output change to grid load change through the conversion function; The grid-side load is used as a known quantity input to optimize the power station output process. Therefore, when i≠0, the reward and penalty factor λ is a function of the load change, that is: λ i =h(ΔN) (6); Set different conversion functions to perform multiple verification comparisons under the same conditions; When the power station output change is inconsistent with the grid load change and The smaller the value is, the greater the difference between the power station output change and the grid load change is, and the objective function is penalized, that is, And in monotonically increasing within the interval; when When the power plant output change is consistent with the grid load change and the power plant output change is smaller than the grid load change, The closer it is to 1, the stronger the consistency between the power station output change and the grid load change is, and the objective function is rewarded, and the closer it is to 1, the greater the reward. And in monotonically increasing within the interval; when When , that is, the power plant output change is consistent with the grid load change, but the power plant output change is greater than the value of the grid load change, so it is necessary to reduce the reward of the objective function, that is, set exist Monotonically decreasing in the interval The relationship between the change ratio of the hydropower station output and the grid load and the change of the conversion function can be used to establish λ i The conversion function f(·) provides a theoretical basis.

4. A reservoir dispatching method considering power grid load changes according to claim 3, characterized in that: In the step (1), the constraint condition of the improved function considering the grid load demand satisfaction rate is: (1) Water balance constraints: V t =V t-1 +(I t -Q t )·Δt (7); Where V t and V t-1 are the storage capacity of the reservoir at the end of the t period and the t-1 period respectively; I t is the average inflow of the reservoir in period t, Q t is the average outflow of the reservoir in the t period; Δt is the duration of a single period; (2) Water level constraint: In the formula, and are the minimum and maximum water level limits of the reservoir in period t, respectively; (3) Traffic Constraints: In the formula, and are the minimum and maximum outflow limits of the reservoir in period t respectively; (4) Output constraints: In the formula, and are the minimum and maximum output limits of the power station in the tth period respectively; (5) Water level / flow range constraints: In the formula, ΔZ and ΔQ are the maximum fluctuation constraints of water level and flow in adjacent time periods respectively; (6) Boundary value constraints: In the formula, Z start and Z end are the initial and final water levels of the reservoir during the dispatching period, respectively.

5. A reservoir dispatching method considering power grid load changes according to claim 1, characterized in that: In step (1), the specific process of using the particle swarm algorithm to solve the model is as follows: The particle swarm algorithm updates and optimizes the search path of the memory individual and the degree of adaptation to the environment through information sharing within the population, so that each individual moves closer to the optimal solution; each individual is called a particle, and the position of each particle is a set of feasible solutions to the problem to be optimized. The particle's speed, the optimal position in its own path, and the global optimal position in the population determine the particle's update direction; the particle speed and position update formula are as follows: v i (k+1)=wv i (k)+c1r1(p i -x i (k))+c2r2(p g -x i (k)) (13); x i (k+1)=x i (k)+v i (k+1) (14); In the formula, v i represents the velocity of the ith particle, x i represents the position of the ith particle, k represents the number of iterations, w, c1, c2 are constants, r1, r2 are random numbers uniformly distributed in (0, 1), p i represents the best position of the ith particle in the path, p g represents the best position in the entire population.

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