Step hydropower station multi-objective optimization method considering incoming water uncertainty

Abstract

The invention discloses a stepped hydropower station multi-objective optimization method considering incoming water uncertainty, and relates to the technical field of hydropower station optimization scheduling. Comprising the steps of considering water balance between cascade hydropower stations, and establishing an optimal scheduling model of the cascade hydropower stations; based on the short-term and long-term allocation targets, combining the short-term and long-term allocation targets by adopting a layered optimization method; constructing a random dynamic optimization model of the uncertainty influence of the incoming water side based on normal distribution; a deterministic dynamic programming method is used for preliminary solving, a preliminary solving result is used as an initial state of random dynamic programming, and inflow uncertainty is optimized. According to the invention, the overall operation efficiency and robustness of the system are improved, the optimal utilization of water resources is realized, and a brand new solution is provided for the optimal scheduling of the stepped hydropower station.

Description

Technical Field

[0001] The invention relates to the technical field of optimal dispatching of hydropower stations, in particular to a multi-objective optimization method for a cascade hydropower station taking into account uncertainty of incoming water. Background Art

[0002] With global climate change and the continuous growth of energy demand, hydropower stations, as an important form of clean energy, have gradually occupied an important position in the power system. A cascade hydropower station system refers to multiple hydropower stations arranged in cascades on a river. By jointly dispatching these hydropower stations, the efficiency of water resource utilization can be improved and electricity demand at different times can be met. However, the optimal dispatching of cascade hydropower stations faces many challenges, especially the impact of the uncertainty of water inflow on system dispatching.

[0003] Traditional hydropower station dispatching methods usually rely on deterministic water inflow forecasting models, which are difficult to accurately reflect the volatility and uncertainty in actual situations when dealing with short-term and long-term hydrological data. In addition, traditional dispatching methods mainly focus on the optimization of a single objective, such as maximizing power generation or minimizing operating costs, while ignoring the need for multi-objective optimization. With the increasing complexity of the power market, single-objective optimization methods have been unable to meet actual needs, and an optimization method that can simultaneously consider multiple objectives is urgently needed. In the dispatching process of cascade hydropower stations, it is necessary to consider two important indicators: supply adequacy and peak-shaving capacity. Supply adequacy aims to ensure that hydropower stations can fully utilize water resources and reduce water abandonment while meeting electricity demand; peak-shaving capacity requires hydropower stations to be able to cope with load fluctuations, reduce the burden on the power grid, and achieve peak shaving and valley filling. However, these two goals are often contradictory and difficult to take into account in actual dispatching.

[0004] The uncertainty of water inflow is one of the main challenges faced in the scheduling of cascade hydropower stations. The changes in natural hydrological conditions and the unpredictability of climate make the water inflow highly uncertain in time and space. This uncertainty not only affects the power generation capacity of hydropower stations, but also has an adverse impact on the stability and economic benefits of the power grid. In order to effectively deal with this uncertainty, the scheduling model needs to have a certain degree of robustness and be able to maintain good scheduling performance under different water inflow scenarios.

[0005] Most existing studies focus on single-objective optimization scheduling, lacking a systematic approach to multi-objective optimization and water inflow uncertainty. At the same time, traditional deterministic scheduling models are difficult to deal with uncertainty problems in actual operations, which can easily lead to scheduling results deviating from expectations, affecting the reliability and economy of the power system. Therefore, there is an urgent need for a new scheduling method that can comprehensively consider multi-objective optimization and water inflow uncertainty to improve the overall operating efficiency of cascade hydropower stations. Summary of the invention

[0006] In view of the problems existing in the existing traditional deterministic scheduling model, the present invention is proposed.

[0007] Therefore, the problem to be solved by the present invention is to comprehensively consider multi-objective optimization and water inflow uncertainty to improve the overall operating efficiency of the cascade hydropower station.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0009] In the first aspect, an embodiment of the present invention provides a multi-objective optimization method for a cascade hydropower station taking into account the uncertainty of water inflow, which includes: considering the water balance between the cascade hydropower stations, establishing an optimization scheduling model for the cascade hydropower station; based on short-term and long-term allocation goals, adopting a hierarchical optimization method to combine the short-term and long-term allocation goals; constructing a random dynamic optimization model influenced by the uncertainty of the water inflow side based on the normal distribution; using a deterministic dynamic programming method for a preliminary solution, and using the result of the preliminary solution as the initial state of the random dynamic programming to optimize the uncertainty of the water inflow.

[0010] As a preferred solution of the multi-objective optimization method of the cascade hydropower station considering the uncertainty of water supply described in the present invention, wherein: the establishment of the optimization scheduling model of the cascade hydropower station includes: the water balance equation for the i-th hydropower station at time t is:

[0011] V i,t+1 =V i,t +(Q in,i,t -Q out,i,t -Q spill,i,t )×Δt+Q lateral,i,t ×Δt

[0012] Among them, V i,t+1 and V i,t are the reservoir capacities at time t+1 and time t, respectively, Q in,i,t is the inflow to the i-th hydropower station at time t, Q out,i,t is the power generation flow of the ith hydropower station at time t, Q spill,i,t is the water discharge of the ith hydropower station at time t, Q lateral,i,t is the water inflow of the ith hydropower station at time t, Δt is the time step; for hydropower stations other than the first station, the inflow is:

[0013] Q in,i,t =Q out,i-1,t +Q spill,i-1,t +Q lateral,i-1,t

[0014] Among them, Q out,i-1,t is the power generation flow of the i-1th hydropower station at time t, Q spill,i-1,t is the abandoned water flow of the i-1th hydropower station at time t, Qlateral,i-1,t is the water inflow of the interval where the i-1th hydropower station is located at time t; a hydropower generation function is established to describe the relationship between power generation, head and flow:

[0015] P i,t =δ×g×ρ×Q out,i,t ×H i,t

[0016] Among them, P i,t is the power generation, δ is the power generation efficiency, g is the gravitational acceleration, ρ is the density of water, H i,t For the water head.

[0017] As a preferred solution of the multi-objective optimization method of the cascade hydropower station considering the uncertainty of water supply described in the present invention, wherein: the establishment of the optimization scheduling model of the cascade hydropower station also includes: setting two optimization objectives of supply adequacy and peak load regulation capacity according to actual operation requirements, wherein the supply adequacy objective function is:

[0018]

[0019] Among them, w n Represents the weight coefficient, reflecting the relative importance of each target, R A,n represents the actual value of the nth target, α m,n represents the influence coefficient of the mth control variable on the nth target, R m,n represents the value of the mth control variable under the nth target, and λ is a tuning parameter used to balance the priorities between targets. represents the mean value of the mth control variable;

[0020] The peak load capacity objective function is:

[0021]

[0022] Among them, M is the total time scale of optimized scheduling, and K is the total number of hydropower stations.

[0023] As a preferred solution of the multi-objective optimization method of the cascade hydropower station considering the uncertainty of water supply described in the present invention, the following constraints are included in the optimization scheduling model of the cascade hydropower station: there is a time delay in water flow transmission between different hydropower stations, specifically as follows:

[0024]

[0025] in, is the time t+τ i,j The inflow to the i-th hydropower station, τ i,j is the water flow delay time from hydropower station j to hydropower station i, Q out,j,tis the power generation flow of the jth hydropower station at time t, Q spill,j,t is the abandoned water flow of the j-th hydropower station at time t, Q lateral,j,t is the water flow in the interval where the j-th hydropower station is located at time t; the total output of the power generation flow of each hydropower station in each time period is between the upper and lower limits; the reservoir capacity of each hydropower station has upper and lower limits in each time period, and considers the capacity requirements at the initial and final moments, including:

[0026] V min,i ≤V i,t ≤V max,i

[0027] V i,0 =V initial,i

[0028] V i,T =V final,i

[0029] Among them, V min，i and V max，i are the lower and upper limits of the reservoir capacity of the i-th hydropower station, V i，t The reservoir capacity of the ith hydropower station at time t, V i，0 is the reservoir capacity of the i-th hydropower station at the initial time (time 0), V initial，i is the initial capacity value of the i-th hydropower station, V i，T is the reservoir capacity of the i-th hydropower station at the end of the day (time T), V final，i is the terminal capacity value of the i-th hydropower station; the discharge flow of each hydropower station has upper and lower limits in each time period, and includes the consideration of abandoned water flow, including:

[0030]

[0031] in, are the minimum and maximum downstream flows of the i-th hydropower station respectively.

[0032] As a preferred scheme of the multi-objective optimization method of cascade hydropower stations considering the uncertainty of incoming water as described in the present invention, wherein: the hierarchical optimization method is used to combine short-term and long-term allocation goals, including the following steps: the short-term goals include ensuring that the power supply in the short term meets the demand and optimizing the output of the hydropower station; the long-term goals include optimizing the long-term use of water resources, considering the balance between the dry season and the wet season and ensuring the water demand of the downstream ecological environment; the upper model includes long-term water resource allocation optimization, with an annual cycle, considering the interannual changes in hydrology, and optimizing the annual operation plan of each cascade hydropower station, and the upper objective function is to maximize the annual power generation and minimize the waste of water resources; the lower model includes short-term supply and peak-shaving optimization, with a day or week as a cycle, based on the constraints of the upper model, optimizes the short-term output plan of each hydropower station, and the lower objective function is to maximize the supply adequacy and peak-shaving capacity.

[0033] As a preferred solution of the multi-objective optimization method of the cascade hydropower station considering the uncertainty of water inflow described in the present invention, the random dynamic optimization model based on the normal distribution for the influence of uncertainty on the water inflow side includes the following steps: Assume that the interval water inflow of each hydropower station obeys the normal distribution, that is: Q i(t) ~N(ε i(t) ),σ i(t)2 ; Among them, Q i(t) is the interval water inflow of the i-th hydropower station at time t, ε i(t) is the mean, σ i(t)2 is the variance; based on historical data and weather forecasts, multiple groups of water inflow scenarios are generated, each of which represents a possible water inflow situation:

[0034] Q i,s(t) =ε i(t) +σ i(t) ×Z s

[0035] Among them, Q i,s(t) is the interval water inflow of the ith hydropower station at time t under the sth scenario, Z s is a standard normal distribution random number; the deterministic water balance equation is modified into a random form:

[0036] V i,s(t+1) =V i,s(t) +[I i,s(t) -q i,s(t) -S i,s(t) +Q i,s(t) ]×Δt

[0037] Among them, V i,s(t+1) is the reservoir capacity of the i-th hydropower station at time t+1 in state s, I i,s(t) is the inflow of the i-th hydropower station at time t under state s, q i,s(t)is the outflow of the i-th hydropower station at time t under state s, S i,s(t) is the evaporation and other loss flow of the i-th hydropower station at time t under state s, Q i,s(t) is the water discharge of the ith hydropower station at time t under state s, s represents the sth scenario, and Δt is the time step; the objective function of the stochastic dynamic optimization model affected by uncertainty is: min E[minG 1 (s)]+E[minG 2 (s)]; where E[] represents the expected value, minG 1 (s) and minG 2 (s) are the supply adequacy and peak-shaving capacity objective functions under the s-th scenario respectively.

[0038] As a preferred solution of the multi-objective optimization method of the cascade hydropower station considering the uncertainty of water inflow described in the present invention, wherein: the stochastic dynamic optimization model based on the normal distribution for the influence of uncertainty on the water inflow side also includes: introducing risk constraints:

[0039]

[0040]

[0041] Among them, α and β are reliability levels, and Prob is probability.

[0042] In the second aspect, an embodiment of the present invention provides a multi-objective optimization system for a cascade hydropower station taking into account the uncertainty of incoming water, which includes a cascade water balance module, which is used to consider the water balance between cascade hydropower stations and establish an optimization scheduling model for cascade hydropower stations; a hierarchical optimization scheduling module, which is used to combine short-term and long-term allocation goals based on short-term and long-term allocation goals using a hierarchical optimization method; a random dynamic optimization module, which is used to construct a random dynamic optimization model influenced by the uncertainty of the incoming water side based on a normal distribution; and a dynamic programming solution module, which is used to perform a preliminary solution using a deterministic dynamic programming method, and use the result of the preliminary solution as the initial state of the random dynamic programming to optimize the uncertainty of the incoming water.

[0043] In a third aspect, an embodiment of the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the computer program instructions are executed by the processor, the steps of the multi-objective optimization method for a cascade hydropower station taking into account the uncertainty of incoming water as described in the first aspect of the present invention are implemented.

[0044] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program instructions are executed by a processor, the steps of the multi-objective optimization method for a cascade hydropower station taking into account the uncertainty of incoming water as described in the first aspect of the present invention are implemented.

[0045] The beneficial effects of the present invention are as follows: through the coordinated work of multiple hydropower stations, the present invention has achieved remarkable technical effects in terms of supply adequacy, peak-shaving capacity and water inflow uncertainty. This collaborative optimization method not only improves the overall operating efficiency and robustness of the system, but also achieves the optimal utilization of water resources, providing a new solution for the optimal scheduling of cascade hydropower stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0047] Figure 1 The present invention is a flowchart of a multi-objective optimization method for a cascade hydropower station taking into account uncertainty of incoming water in one embodiment of the present invention.

[0048] Figure 2 Schematic diagram of the discharge flow of the hydropower station under scenario 1.

[0049] Figure 3 This is the total power output diagram of the hydropower station under scenario 1.

[0050] Figure 4 This is the capacity optimization diagram of the hydropower station reservoir under scenario 1.

[0051] Figure 5 This is a comparison chart of the original system load and the remaining load after optimization in scenario 1.

[0052] Figure 6 This is the discharge flow diagram of the hydropower station under scenario 2.

[0053] Figure 7 This is the total power output diagram of the hydropower station under scenario 2.

[0054] Figure 8 This is the capacity optimization diagram of the hydropower station reservoir under scenario 2.

[0055] Fig. 9 This is a comparison chart of the original system load and the remaining load after optimization in scenario 2.

[0056] Fig.10 This is the discharge flow diagram of the hydropower station in scenario 3.

[0057] Fig.11 This is the total power output diagram of the hydropower station in scenario 3.

[0058] Fig.12This is the capacity optimization diagram of the hydropower station reservoir under scenario 3.

[0059] Fig.13 This is a comparison chart of the original system load and the remaining load after optimization in scenario 3.

[0060] Fig.14 This is the discharge flow diagram of the hydropower station under scenario 4.

[0061] Fig.15 This is the total power output diagram of the hydropower station under scenario 4.

[0062] Fig.16 This is the capacity optimization diagram of the hydropower station reservoir under scenario 4.

[0063] Fig.17 This is a comparison chart of the original system load and the remaining load after optimization in scenario 4. DETAILED DESCRIPTION

[0064] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the accompanying drawings.

[0065] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0066] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0067] Example 1

[0068] Reference Figure 1 , which is the first embodiment of the present invention, and provides a multi-objective optimization method for a cascade hydropower station considering the uncertainty of incoming water, including:

[0069] S1: Considering the water balance among cascade hydropower stations, an optimal scheduling model for cascade hydropower stations is established.

[0070] Establishing an optimal scheduling model for cascade hydropower stations includes: first, determining the number and spatial distribution of cascade hydropower stations, drawing a water system map of cascade hydropower stations, marking the relative position and flow direction of each hydropower station, and collecting characteristic parameters of each hydropower station, such as installed capacity, reservoir capacity, power generation efficiency, etc.

[0071] Preferably, the water balance equation for the i-th hydropower station at time t is:

[0072] V i,t+1 =V i,t +(Q in,i,t -Q out,i,t -Q spill,i,t )×Δt+Q lateral,i,t ×Δt

[0073] Among them, V i,t+1 and V i,t are the reservoir capacities at time t+1 and time t, respectively, Q in,i,t is the inflow to the i-th hydropower station at time t, Q out,i,t is the power generation flow of the ith hydropower station at time t, Q spill,i,t is the water discharge of the ith hydropower station at time t, Q lateral,i,t is the water inflow of the ith hydropower station at time t, and Δt is the time step.

[0074] Considering the cascade effect, for the hydropower station that is not the first station, the inflow is:

[0075] Q in,i,t =Q out,i-1,t +Q spill,i-1,t +Q lateral,i-1,t

[0076] Among them, Q out，i-1，t is the power generation flow of the i-1th (upstream) hydropower station at time t, Q spill，i-1，t is the water discharge of the i-1th (upstream) hydropower station at time t, Q lateral，i-1，t is the water inflow of the section where the i-1th (upstream) hydropower station is located at time t.

[0077] Furthermore, a hydropower generation function is established to describe the relationship between power generation, head and flow:

[0078] P i,t =δ×g×ρ×Q out,i,t ×H i,t

[0079] Among them, P i,t is the power generation, δ is the power generation efficiency, g is the gravitational acceleration, ρ is the density of water, H i,t For the water head.

[0080] Furthermore, according to the actual operation requirements, two optimization objectives, supply adequacy and peak load regulation capacity, are set, where the supply adequacy objective function is:

[0081]

[0082] Among them, w nRepresents the weight coefficient, reflecting the relative importance of each target, R A,n represents the actual value of the nth target, α m,n represents the influence coefficient of the mth control variable on the nth target, R m,n represents the value of the mth control variable under the nth target, and λ is a tuning parameter used to balance the priorities between targets. It represents the average value of the mth control variable, which mainly considers the minimization of the weighted sum of squares of the system's remaining load and the volatility of power generation.

[0083] The peak load capacity objective function is:

[0084]

[0085] Among them, M is the total scale of the optimized scheduling time, which is 1 day in the present invention, and K is the total number of hydropower stations; by minimizing the peak-to-valley difference of the power supply load, the optimization of the peak-shaving capacity is ensured. The first half represents the maximum value of the difference between the actual value and the sum of each control variable among all targets, and the second half represents the minimum value of the difference between the actual value and the sum of each control variable among all targets.

[0086] Among them, the optimal dispatching model of cascade hydropower stations includes the following constraints:

[0087] The output of each hydroelectric unit has upper and lower limits in each time period;

[0088] Considering the capacity coupling and water storage effect of each reservoir, that is, the water balance equation of the i-th hydropower station at time t;

[0089] There is a time delay in water flow transmission between different hydropower stations, as follows:

[0090]

[0091] in, is the time t+τ i,j The inflow to the i-th hydropower station, τ i,j is the water flow delay time from hydropower station j to hydropower station i, Q out,j,t is the power generation flow of the jth hydropower station at time t, Q spill,j,t is the abandoned water flow of the j-th hydropower station at time t, Q lateral,j,t is the water inflow of the section where the j-th hydropower station is located at time t.

[0092] The power generation flow of each hydropower unit has upper and lower limits in each time period, and the total output of each hydropower station in each time period is between the upper and lower limits, as follows:

[0093]

[0094] Among them, Pi,j,t is the output of the j-th unit of hydropower station i in time period t, are the total minimum and maximum output of the i-th hydropower station in time period t; n 0 is the total number of units in the i-th hydropower station.

[0095] The reservoir capacity of each hydropower station has upper and lower limits in each time period, and takes into account the capacity requirements at the initial and final moments, including:

[0096] V min,i ≤V i,t ≤V max,i

[0097] V i,0 =V initial,i

[0098] V i,T =V final,i

[0099] Among them, V min，i and V max，i are the lower and upper limits of the reservoir capacity of the i-th hydropower station, V i，t The reservoir capacity of the ith hydropower station at time t, V i，0 is the reservoir capacity of the i-th hydropower station at the initial time (time 0), V initial，i is the initial capacity value of the i-th hydropower station, V i，T is the reservoir capacity of the i-th hydropower station at the end of the day (time T), V final，i is the final capacity value of the i-th hydropower station.

[0100] The discharge flow of each hydropower station has upper and lower limits in each time period, and includes consideration of abandoned water flow, including:

[0101]

[0102] in, are the minimum and maximum downstream flows of the i-th hydropower station respectively.

[0103] By setting the above constraints, efficient collaboration among hydropower stations can be achieved, and the operation strategies of hydropower stations can be optimized at different times and spaces to ensure the stability of system power supply and the improvement of peak load regulation capacity.

[0104] It can be seen that through the coordinated scheduling of various hydropower stations in time and space, the system can respond to load fluctuations more flexibly. Compared with the traditional single hydropower station optimization method, the present invention can reasonably allocate water resources in different time periods and realize efficient cooperation between hydropower stations, thereby greatly improving the operating efficiency of the entire hydropower system.

[0105] S2: Based on the short-term and long-term allocation goals, a hierarchical optimization method is used to combine the short-term and long-term allocation goals.

[0106] Preferably, short-term goals include ensuring that electricity supply meets demand in the short term and optimizing hydropower station output to cope with peaks and troughs in electricity demand.

[0107] Long-term goals include optimizing long-term water resource use, considering the balance between dry and wet seasons, and ensuring the water needs of the downstream ecological environment.

[0108] Furthermore, a hierarchical optimization model is established:

[0109] The upper model includes long-term water resources allocation optimization, taking one year as the cycle, taking into account the interannual changes in hydrology, and optimizing the annual operation plan of each cascade hydropower station. The optimization algorithm uses dynamic programming or genetic algorithm.

[0110] The upper objective function is to maximize annual power generation and minimize water waste. The objective function is expressed as:

[0111]

[0112] Among them, P i,t1 is the power generation of the ith hydropower station at time t1, Δt1 is the time interval, ∝ is the weight coefficient of water resource waste, N 1 is the total number of hydropower stations, T 1 is the total number of time steps, divided according to the optimization cycle, and t1 is the specific time point.

[0113] Furthermore, long-term model constraints include:

[0114]

[0115] Among them, V min,i and V max,i are the upper and lower limits of the reservoir capacity of the i-th hydropower station respectively.

[0116] The lower-level model includes short-term supply and peak-shaving optimization, with a daily or weekly cycle. Based on the constraints of the upper-level model, it optimizes the short-term output plan of each hydropower station. The optimization algorithm adopts linear programming or nonlinear programming methods.

[0117] The lower objective function is to maximize the supply adequacy and peak load regulation capacity. The objective function is expressed as:

[0118]

[0119] Among them, ω t1 is the weight of time t1 (reflecting the peak and trough of electricity demand).

[0120] Furthermore, short-term model constraints include:

[0121] P i,min ≤P i,t ≤P i,max

[0122]

[0123] Among them, D t1 is the power demand at time t1.

[0124] Through the above-mentioned hierarchical optimization method, it is possible to combine short-term and long-term goals and dynamically adjust the output plan of each hydropower station to ensure that the power supply meets the demand, while optimizing the long-term utilization of water resources to achieve the effect of peak regulation and ecological environmental protection.

[0125] S3: Construct a stochastic dynamic optimization model for the uncertainty impact on the incoming water side based on normal distribution.

[0126] Assume that the interval water flow of each hydropower station follows a normal distribution, that is: Q i(t) ~N(ε i(t) ),σ i(t)2

[0127] Among them, Q i(t) is the interval water inflow of the i-th hydropower station at time t, ε i(t) is the mean, σ i(t)2 is the variance.

[0128] Based on historical data and weather forecasts, multiple water inflow scenarios are generated, each representing a possible water inflow situation:

[0129] Q i,s(t) =ε i(t) +σ i(t) ×Z s

[0130] Among them, Q i,s(t) is the interval water inflow of the ith hydropower station at time t under the sth scenario, Z s is a standard normally distributed random number.

[0131] Modify the deterministic water balance equation into a stochastic form:

[0132] V i,s(t+1) =V i,s(t) +[I i,s(t) -q i,s(t) -S i,s(t) +Q i,s(t) ]×Δt

[0133] Among them, V i,s(t+1) is the reservoir capacity of the i-th hydropower station at time t+1 in state s, I i,s(t)is the inflow of the i-th hydropower station at time t under state s, q i,s(t) is the outflow of the i-th hydropower station at time t under state s, S i,s(t) is the evaporation and other loss flow of the i-th hydropower station at time t under state s, Q i,s(t) is the water discharge of the ith hydropower station at time t under state s, the subscript s represents the sth scenario, and Δt is the time step.

[0134] Constructing a stochastic dynamic optimization model:

[0135] Among them, the objective function is: min E[minG 1 (s)]+E[minG 2 (s)];

[0136] Among them, E[] represents the expected value, minG 1 (s) and minG 2 (s) are the supply adequacy and peak-shaving capacity objective functions under the s-th scenario respectively.

[0137] Optionally, modify the deterministic constraints to be stochastic constraints, for example:

[0138]

[0139] In order to ensure the reliability of the scheduling plan, risk constraints are introduced:

[0140]

[0141] Among them, α and β are reliability levels, usually 0.9 or 0.95, and Prob is probability.

[0142] The stochastic dynamic optimization model is solved by using stochastic dynamic programming or Monte Carlo simulation combined with optimization algorithms (such as genetic algorithm and particle swarm algorithm).

[0143] Optionally, emergency dispatch plans can be designed for extreme drought or flood situations, including giving priority to ecological water use and power supply to important users in drought situations, and appropriately lowering power generation targets; in flood situations, flood discharge can be increased to ensure reservoir safety while maximizing power generation benefits.

[0144] Taking into account the uncertainty of water inflow, the present invention constructs a random dynamic optimization model of water inflow based on normal distribution. On this basis, the optimization model can effectively respond to changes under different water inflow conditions. Even in extreme weather or unforeseen hydrological changes, the system can still maintain efficient operation and avoid power supply instability caused by water resource fluctuations.

[0145] S4: Use the deterministic dynamic programming method to perform a preliminary solution, and use the result of the preliminary solution as the initial state of the stochastic dynamic programming to optimize the uncertainty of the water inflow.

[0146] Specifically, determine a preliminary long-term water resource allocation plan to ensure that electricity supply meets basic needs.

[0147] Obtain the characteristic parameters of each hydropower station: installed capacity, reservoir capacity, power generation efficiency, etc., obtain the inflow flow, power generation flow, abandoned water flow, interval water inflow, etc. of each hydropower station.

[0148] The upper-level optimization model is constructed using the deterministic dynamic programming method, and the upper-level model is solved using dynamic programming or genetic algorithm to obtain a preliminary long-term water resources allocation plan, including the optimal power generation plan and reservoir capacity adjustment plan for each hydropower station at each time step.

[0149] The solution of deterministic dynamic programming is used as the initial state of stochastic dynamic programming, and the preliminary deployment plan is input as the initial condition for the next step of optimization.

[0150] Further optimize the dispatching plan, consider the uncertainty of water inflow, and ensure the reliability and robustness of the system, including: assuming that the interval water inflow of each hydropower station follows a normal distribution, generate multiple sets of water inflow scenarios based on historical data and weather forecasts, modify the deterministic water balance equation into a random form, use the random dynamic programming method combined with Monte Carlo simulation, solve the optimization model through genetic algorithm or particle swarm algorithm, and consider risk constraints in the solution process, compare the random dynamic programming optimization results with the initial state, and adjust the preliminary allocation plan according to the optimization results to form a closed loop.

[0151] It can be seen that the present invention realizes a smooth transition from determinism to randomness, fully utilizes the fast solution advantage of the deterministic model, and at the same time improves the model's adaptability to the uncertainty of incoming water.

[0152] Furthermore, the present embodiment also provides a multi-objective optimization system for a cascade hydropower station taking into account the uncertainty of incoming water, including a cascade water balance module, which is used to consider the water balance between cascade hydropower stations and establish an optimization scheduling model for cascade hydropower stations; a hierarchical optimization scheduling module, which is used to combine short-term and long-term allocation goals based on short-term and long-term allocation goals using a hierarchical optimization method; a random dynamic optimization module, which is used to construct a random dynamic optimization model influenced by the uncertainty of the incoming water side based on a normal distribution; and a dynamic programming solution module, which is used to perform a preliminary solution using a deterministic dynamic programming method, and use the result of the preliminary solution as the initial state of the random dynamic programming to optimize the uncertainty of the incoming water.

[0153] This embodiment also provides a computer device, which is suitable for the multi-objective optimization method of cascade hydropower stations considering the uncertainty of water inflow, including a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute computer-executable instructions to implement the multi-objective optimization method of cascade hydropower stations considering the uncertainty of water inflow as proposed in the above embodiment.

[0154] The computer device may be a terminal, and the computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covering the display screen, or a key, trackball or touchpad provided on the housing of the computer device, or an external keyboard, touchpad or mouse, etc.

[0155] This embodiment also provides a storage medium on which a computer program is stored. When the program is executed by a processor, the multi-objective optimization method for a cascade hydropower station taking into account the uncertainty of incoming water as proposed in the above embodiment is implemented.

[0156] In summary, the present invention has achieved significant technical effects in terms of supply adequacy, peak load regulation capacity and water inflow uncertainty through the collaborative work of multiple hydropower stations. This collaborative optimization method not only improves the overall operating efficiency and robustness of the system, but also achieves the optimal utilization of water resources, providing a new solution for the optimal scheduling of cascade hydropower stations.

[0157] Example 2

[0158] Reference Figure 2 to Figure 17 , which is the second embodiment of the present invention, provides a multi-objective optimization method for a cascade hydropower station taking into account the uncertainty of incoming water. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0159] The uncertainty impact on the water supply side refers to the volatility of the water supply, which makes the hydropower station face challenges in terms of energy output and economic benefits. The example mentioned above refers to setting up four operating scenarios, which are:

[0160] Scenario 1: Target 1 optimization, that is, the objective function is only set to minimize the total amount of remaining load;

[0161] Scenario 2: Target 2 optimization, that is, the objective function is only set to minimize the peak-to-valley difference of the remaining load;

[0162] Scenario 3: Multi-objective optimization with comprehensive consideration of objective 1 and objective 2, that is, a comprehensive optimization mode that weighs the two objectives;

[0163] Scenario 4: Consider the uncertainty optimization of the water supply side based on the multi-objective optimization framework of Scenario 3, that is, random optimization.

[0164] Figures 2 to 17 Comparison of the system's original load and optimized residual load, total output of hydropower units, downstream flow, and reservoir capacity optimization curves under four scenarios.

[0165] Reference Figures 2 to 5 :The comparison between the original load and the optimized residual load shows that in scenario 1, the water supply optimization of the cascade hydropower station in each period is different. The reason is that in this scenario, the system optimization goal is to minimize the total residual load of the system, which requires the cascade hydropower station to optimize the water discharge in each period in different periods. The water discharge in each period is related to the interactive water volume and water inflow of the coupled hydropower station, which makes the optimization value of the load reduction in each period different. And the total power output of the hydropower station and the optimization curve of the load reduction correspond to each period, which shows the effectiveness of the results. It can be analyzed from the optimization curve of the hydropower station reservoir that the hydropower station reservoir has the initial and final capacity requirements. The capacity change shows that the hydropower station reservoir has the phenomenon of water storage and water discharge. Its optimization trend is roughly from 7:00 to 13:00, showing a significant increase, and from 13:00 to 17:00, showing a significant decrease. There is a small range of fluctuation in the remaining intervals, which is due to the coupling of the hydropower group within the cascade hydropower station.

[0166] Same as scenario 1 analysis, refer to Figures 6 to 9 According to the original load and the optimized residual load curve, the peak-to-valley difference of the residual load curve is optimized to the minimum. According to the characteristic analysis of the original load curve, the optimization process is roughly to supply the load in the two peak intervals as much as possible, thereby reducing the peak-to-valley difference and utilizing the peak-shaving capacity of the cascade hydropower station.

[0167] According to the total output of the hydropower units, it can be seen that there is a large output from 6:00 to 15:00 and from 19:00 to 21:00, which is consistent with the above analysis. According to the reservoir capacity optimization curve, the system reservoir has been storing water from 0:00 to 6:00, and the system reservoir is discharging water from 6:00 to 12:00. The reason is that from 6:00 onwards, the system faces the first peak load section, and the reservoir needs to cooperate with the hydropower units to discharge water.

[0168] according to Figures 10 to 13According to the comparison between the original load and the optimized residual load curve, the peak-to-valley difference of the residual load curve is larger than that of scenario 2, and the peak base plane is higher, but there is a "deep drop" in the load in several time periods in the middle. The reason is that the cascade hydropower station needs to balance the optimization of the two objective functions in this scenario. Although the peak-shaving capacity is sacrificed in the "deep drop" section, a certain water supply capacity is exerted.

[0169] Comparing with the optimization curve of total output of hydropower units and reservoir capacity, the curve has a higher floating frequency than scenario 1 and scenario 2. The reason is the optimization result of the cascade hydropower station when looking for the "deep water supply" interval.

[0170] Reference Figures 14 to 17 ,The analysis of scenario 4 is similar to scenario 3, both belong to the category of multi-objective optimization. The difference is that scenario 4 involves the uncertainty factor of water inflow, which will not be repeated here.

[0171] Through the collaborative work of multiple hydropower stations, the present invention has achieved remarkable technical effects in terms of supply adequacy, peak-shaving capacity and water inflow uncertainty. This collaborative optimization method not only improves the overall operating efficiency and robustness of the system, but also achieves the optimal utilization of water resources, providing a new solution for the optimal scheduling of cascade hydropower stations.

[0172] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A multi-objective optimization method for cascade hydropower stations considering water inflow uncertainty, characterized by: include, Considering the water balance between cascade hydropower stations, an optimal dispatching model for cascade hydropower stations is established; Based on the short-term and long-term allocation goals, a hierarchical optimization method is used to combine short-term and long-term allocation goals; Construct a stochastic dynamic optimization model for the uncertainty impact on the incoming water side based on normal distribution; A deterministic dynamic programming method is used to perform a preliminary solution, and the result of the preliminary solution is used as the initial state of the stochastic dynamic programming to optimize the uncertainty of the incoming water.

2. The multi-objective optimization method for cascade hydropower stations considering water inflow uncertainty as claimed in claim 1, characterized in that: The establishment of the optimal dispatching model for cascade hydropower stations includes: The water balance equation for the i-th hydropower station at time t is: V i,t+1 =V i,t +(Q in,i,t -Q out,i,t -Q spill,i,t )×Δt+Q lateral,i,t ×Δt Among them, V i,t+1 and V i,t are the reservoir capacities at time t+1 and time t, respectively, Q in,i,t is the inflow to the i-th hydropower station at time t, Q out,i,t is the power generation flow of the ith hydropower station at time t, Q spill,i,t is the water discharge of the ith hydropower station at time t, Q lateral,i,t is the water inflow of the ith hydropower station at time t, and Δt is the time step; For hydropower stations other than the first station, the inflow is: Q in,i,t =Q out,i-1,t +Q spill,i-1,t +Q lateral,i-1,t Among them, Q out,i-1,t is the power generation flow of the i-1th hydropower station at time t, Q spill,i-1,t is the abandoned water flow of the i-1th hydropower station at time t, Q lateral,i-1,t is the water inflow of the interval where the i-1th hydropower station is located at time t; Establish a hydropower generation function to describe the relationship between power generation, head and flow: P i,t =δ×g×ρ×Q out,i,t ×H i,t Among them, P i,t is the power generation, δ is the power generation efficiency, g is the gravitational acceleration, ρ is the density of water, H i,t For the water head.

3. The multi-objective optimization method for cascade hydropower stations considering water inflow uncertainty as claimed in claim 2, characterized in that: The establishment of the optimal dispatching model for cascade hydropower stations also includes: According to the actual operation demand, two optimization objectives, supply adequacy and peak load regulation capacity, are set. The supply adequacy objective function is: Among them, w n Represents the weight coefficient, reflecting the relative importance of each target, R A,n represents the actual value of the nth target, α m,n represents the influence coefficient of the mth control variable on the nth target, R m,n represents the value of the mth control variable under the nth target, and λ is a tuning parameter used to balance the priorities between targets. represents the mean value of the mth control variable; The peak load regulation capability objective function is: Among them, M is the total time scale of optimized scheduling, and K is the total number of hydropower stations.

4. The multi-objective optimization method for cascade hydropower stations considering water inflow uncertainty as claimed in claim 3, characterized in that: The optimal dispatching model of cascade hydropower stations contains the following constraints: There is a time delay in water flow transmission between different hydropower stations, as follows: in, is the time t+τ i,j The inflow to the i-th hydropower station, τ i,j is the water flow delay time from hydropower station j to hydropower station i, Q out,j,t is the power generation flow of the jth hydropower station at time t, Q spill,j,t is the water discharge of the jth hydropower station at time t, Q lateral,j,t is the water inflow in the section where the j-th hydropower station is located at time t; The total output of each hydropower station in each time period is between the upper and lower limits; The reservoir capacity of each hydropower station has upper and lower limits in each time period, and takes into account the capacity requirements at the initial and final moments, including: In mini ≤V i,t ≤V max,i V i,0 =V initial,i V i,T =V final,i Among them, V min,i and V max,i are the lower and upper limits of the reservoir capacity of the i-th hydropower station, V i,t time The reservoir capacity of the i-th hydropower station, V i,0 is the reservoir capacity of the i-th hydropower station at the initial time (time 0), V initial,i is the initial capacity value of the i-th hydropower station, V i,T is the reservoir capacity of the i-th hydropower station at the end of the day (time T), V final,i is the final capacity value of the i-th hydropower station; The discharge flow of each hydropower station has upper and lower limits in each time period, and includes consideration of abandoned water flow, including: in, and are the minimum and maximum downstream flows of the i-th hydropower station respectively.

5. The multi-objective optimization method for cascade hydropower stations considering water inflow uncertainty as claimed in claim 4, characterized in that: The hierarchical optimization method for combining short-term and long-term deployment goals includes the following steps: The short-term goals include ensuring that electricity supply meets demand in the short term and optimizing the output of hydropower plants; The long-term goals include optimizing long-term water resource use, considering the balance between dry and wet seasons and ensuring water demand for downstream ecological environment; The upper model includes long-term water resource allocation optimization, taking an annual cycle, considering the interannual variation of hydrology, and optimizing the annual operation plan of each cascade hydropower station. The upper objective function is to maximize annual power generation and minimize water resource waste; The lower-level model includes short-term supply and peak-shaving optimization, with a daily or weekly cycle. Based on the constraints of the upper-level model, it optimizes the short-term output plan of each hydropower station. The lower-level objective function is to maximize the supply adequacy and peak-shaving capacity.

6. The multi-objective optimization method for cascade hydropower stations considering water inflow uncertainty as claimed in claim 5, characterized in that: The stochastic dynamic optimization model for the uncertainty impact on the water side based on normal distribution includes the following steps: Assume that the interval water inflow of each hydropower station follows a normal distribution, that is: Among them, Q i(t) is the interval water inflow of the i-th hydropower station at time t, ε i(t) is the mean, is the variance; Based on historical data and weather forecasts, multiple water inflow scenarios are generated, each representing a possible water inflow situation: Q i,s(t) =e i(t) +s i(t) ×Z s Among them, Q i,s(t) is the interval water inflow of the ith hydropower station at time t under the sth scenario, Z s is a standard normal distribution random number; Modify the deterministic water balance equation into a stochastic form: V i,s(t+1) =V i,s(t) +[I i,s(t) -q i,s(t) -S i,s(t) +Q i,s(t) ]×Δt Among them, V i,s(t+1) is the reservoir capacity of the i-th hydropower station at time t+1 in state s, I i,s(t) is the inflow of the i-th hydropower station at time t under state s, q i,s(t) is the outflow of the i-th hydropower station at time t under state s, S i,s(t) is the evaporation and other loss flow of the i-th hydropower station at time t under state s, Q i,s(t) is the water discharge of the ith hydropower station at time t under state s, s represents the sth scenario, and Δt is the time step; The objective function of the stochastic dynamic optimization model affected by uncertainty is: min E[minG1(s)]+E[minG2(s)]; Where E[] represents the expected value, minG1(s) and minG2(s) are the supply adequacy and peak-shaving capacity objective functions under the sth scenario, respectively.

7. The multi-objective optimization method for cascade hydropower stations considering water inflow uncertainty as claimed in claim 6, characterized in that: The stochastic dynamic optimization model for the uncertainty impact on the water side constructed based on normal distribution also includes: Introducing risk constraints: Among them, α and β are reliability levels, and Prob is probability.

8. A multi-objective optimization system for a cascade hydropower station considering water inflow uncertainty, based on the multi-objective optimization method for a cascade hydropower station considering water inflow uncertainty as claimed in any one of claims 1 to 7, characterized in that: Also includes, Cascade water balance module, which is used to consider the water balance between cascade hydropower stations and establish an optimal scheduling model for cascade hydropower stations; Hierarchical optimization scheduling module, which is used to combine short-term and long-term allocation goals based on the short-term and long-term allocation goals by adopting hierarchical optimization method; Stochastic dynamic optimization module, used to construct a stochastic dynamic optimization model for the uncertainty impact on the incoming water side based on normal distribution; The dynamic programming solution module is used to perform a preliminary solution using a deterministic dynamic programming method, and use the result of the preliminary solution as the initial state of the random dynamic programming to optimize the uncertainty of the incoming water.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the multi-objective optimization method for a cascade hydropower station taking into account the uncertainty of incoming water as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the multi-objective optimization method for a cascade hydropower station taking into account the uncertainty of incoming water as described in any one of claims 1 to 7 are implemented.

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