Multi-objective optimization-based drainage basin cascade power station combined scheduling and water level control method

Abstract

The invention discloses a drainage basin cascade power station combined dispatching and water level control method based on multi-objective optimization, and relates to cascade reservoir dispatching and data processing technologies, and the method comprises the steps: collecting drainage basin data, and building a multi-objective optimization model containing power generation benefits, ecological flow and flood control safety; an improved non-dominated sorting genetic algorithm is adopted to solve the multi-objective optimization model, and a Pareto optimal solution set is generated; selecting an optimal scheduling scheme from the Pareto optimal solution set based on a dynamic weight decision method, introducing a water level dynamic control mechanism, and adjusting a water level control threshold value of each cascade power station; and establishing a water level-output coupling feedback model, correcting the scheduling scheme in real time in a rolling optimization mode, and outputting a power generation plan and a gate opening control instruction of each power station. According to the method, through deep fusion of dynamic water level control and multi-target optimization, the conflict problem of power generation, ecology and flood control targets in cascade power station scheduling is solved, and the precision of the reservoir optimization scheduling method is improved.

Description

Technical Field

[0001] The present application relates to the technical fields of cascade reservoir scheduling and data processing, and in particular to a method for joint scheduling and water level control of cascade power stations in a river basin based on multi-objective optimization. Background Art

[0002] With the gradual formation of large-scale hydropower station reservoir groups, there are various functional synergies and interest coordination relationships among the various departments involved in reservoir scheduling. The functional cross-linking and interest conflicts are serious. The status and role of reservoir scheduling are becoming more and more prominent. How to maximize the benefits of reservoirs has always been one of the main directions of research on reservoir group scheduling.

[0003] Since there are complex hydroelectric connections between reservoirs, the optimal dispatching problem of a reservoir group is actually a large-scale dynamic, complex and nonlinear optimization problem. The optimal dispatching of a cascade reservoir group is due to its large number of reservoirs, large scale, complex coupling relationships between reservoirs, numerous constraints to be considered, and increased uncertainty, which makes the high-dimensional, nonlinear, and coupled characteristics of the basin cascade joint optimal dispatching more prominent, and the constraints are more difficult to handle, resulting in a high complexity of the algorithm. Therefore, it is necessary to propose improvements to the reservoir optimal dispatching method to improve the calculation accuracy. Summary of the invention

[0004] The embodiment of the present application provides a method for joint scheduling and water level control of cascade power stations in a river basin based on multi-objective optimization. Through the deep integration of dynamic water level control and multi-objective optimization, the conflict problem among power generation, ecology and flood prevention objectives in the scheduling of cascade power stations is solved, and the accuracy of the reservoir optimization scheduling method is improved.

[0005] The embodiment of the present application proposes a method for joint dispatching and water level control of cascade power stations in a river basin based on multi-objective optimization, comprising the following steps:

[0006] S1. Collect basin hydrological data, power station operation parameters and environmental constraint indicators, and establish a multi-objective optimization model including power generation benefits, ecological flow and flood control safety;

[0007] S2, using an improved non-dominated sorting genetic algorithm (NSGA-II) to solve the multi-objective optimization model and generate a Pareto optimal solution set;

[0008] S3, based on the dynamic weight decision method, select the optimal dispatching scheme from the Pareto optimal solution set, and introduce the water level dynamic control mechanism to adjust the water level control threshold of each cascade power station according to the collected hydrological data;

[0009] S4. Establish a water level-output coupling feedback model, make real-time corrections to the dispatching plan through rolling optimization, and output the power generation plan and gate opening control instructions of each power station.

[0010] Optionally, the environmental constraint indicators include the minimum downstream ecological flow constraint, the flood control limit water level constraint, and the water depth requirement water level constraint for shipping. Among them, the ecological flow constraint adopts a segmented dynamic threshold control method.

[0011] Optionally, solving the multi-objective optimization model by using the improved non-dominated sorting genetic algorithm (NSGA-II) includes:

[0012] Introducing an adaptive parameter adjustment mechanism in the crossover and mutation operations, where the crossover probability Pc satisfies Pc = 0.6 - 0.9, and the mutation probability range Pm satisfies Pm = 0.01 - 0.1;

[0013] Adopting the elitist retention strategy to maintain population diversity.

[0014] Optionally, introducing a water level dynamic control mechanism to adjust the water level control thresholds of each cascade power station according to real-time hydrological data includes:

[0015] Establishing a three-dimensional relationship surface of water level - storage capacity - output;

[0016] According to the established three-dimensional relationship surface, combined with the runoff forecast data for a specified future duration, adjusting the allowable fluctuation range of the operating water levels of each power station.

[0017] Optionally, establishing a water level - output coupling feedback model includes:

[0018]

[0019] Among them, E is the objective function of the water level - output coupling feedback model, N is the prediction time domain length, α and β represent the tracking weight coefficients of output and water level, is the reference output according to the Pareto optimal solution set at time k, is the target water level under the water level dynamic control mechanism at time k, and min is the minimization function.

[0020] Optionally, establishing the water level - output coupling feedback model further includes:

[0021] Setting the following constraint conditions:

[0022] Z min ≤Z k|t ≤Z max

[0023]

[0024] Q k|t ≥Q eco

[0025] Among them, ΔP maxIndicates the maximum output change rate in adjacent time periods, Q eco Indicates the minimum ecological discharge flow, Q k|t Indicates the discharge flow in the k-th time period.

[0026] Optionally, the real-time correction of the scheduling plan by the rolling optimization method includes:

[0027] Adopt a time window sliding window, perform optimization calculations every set time duration, and feedback the actual operation data to the model to correct the model parameters.

[0028] The method of the present application solves the conflict problem among power generation, ecology and flood control objectives in cascade hydropower station scheduling through the deep integration of dynamic water level control and multi-objective optimization, and improves the accuracy of the reservoir optimization scheduling method.

[0029] The above description is only an overview of the technical solution of the present application. In order to be able to understand the technical means of the present application more clearly, it can be implemented in accordance with the content of the specification. And in order to make the above and other purposes, features and advantages of the present application more obvious and understandable, the following specifically describes the embodiments of the present invention. Description of the Drawings

[0030] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. And throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0031] Figure 1 Is the basic process schematic of the joint scheduling and water level control method of cascade hydropower stations in the basin based on multi-objective optimization of this embodiment. Detailed Embodiments

[0032] The following will describe the exemplary embodiments of the present disclosure in more detail with reference to the drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art.

[0033] The embodiment of the present application proposes a joint scheduling and water level control method for cascade hydropower stations in a basin based on multi-objective optimization, as Figure 1 shown, including the following steps:

[0034] S1. Collect the hydrological data of the basin, the operation parameters of the power station, and the environmental constraint indicators, and establish a multi-objective optimization model including power generation benefits, ecological flow, and flood control safety. In a specific example, the collected data includes hydrological data: rainfall, inflow, downstream water level (accuracy ±0.1 m); power station parameters: unit efficiency curve, gate opening - flow relationship table; environmental constraints: segmented threshold of ecological flow (for example, ≥50 m 3 / s during the dry season, ≥80 m 3 / s during the wet season). In some embodiments, the environmental constraint indicators include the minimum downstream ecological flow constraint, the flood control limit water level constraint, and the navigation depth requirement water level constraint, and the ecological flow constraint adopts a segmented dynamic threshold control method.

[0035] S2. Use the improved non-dominated sorting genetic algorithm (NSGA-II) to solve the multi-objective optimization model and generate a Pareto optimal solution set. In a specific example, using the improved non-dominated sorting genetic algorithm (NSGA-II) to solve the multi-objective optimization model includes:

[0036] Introduce an adaptive parameter adjustment mechanism in the crossover and mutation operations. The crossover probability Pc satisfies Pc = 0.6 - 0.9, and the mutation probability range Pm satisfies Pm = 0.01 - 0.1. Adopt the elitist retention strategy to maintain population diversity. The method of the present application can improve the uniformity of the Pareto front distribution by introducing the adaptive crossover and mutation probabilities and the elitist retention strategy.

[0037] S3. Select the optimal scheduling plan from the Pareto optimal solution set based on the dynamic weight decision method, and introduce a water level dynamic control mechanism to adjust the water level control threshold of each cascade power station according to the collected hydrological data.

[0038] S4. Establish a water level-output coupling feedback model, and perform real-time correction on the scheduling plan through rolling optimization, and output the power generation plan and gate opening control instructions of each power station.

[0039] In some examples, introducing a water level dynamic control mechanism to adjust the water level control threshold of each cascade power station according to real-time hydrological data includes:

[0040] Establish a three-dimensional relationship surface of water level - reservoir capacity - output;

[0041] According to the established three-dimensional relationship surface, combined with the runoff forecast data for a specified future duration, adjust the allowable fluctuation range of the operating water level of each power station. For example, combined with the runoff forecast data for the next 72 hours, dynamically adjust the allowable fluctuation range of the operating water level of each power station by ±0.3 - 0.5 meters.

[0042] In some examples, establishing a water level-output coupling feedback model includes:

[0043]

[0044] Among them, E is the objective function of the water level-output coupling feedback model, N is the length of the prediction time domain, and α, β represent the tracking weight coefficients of output and water level. is the reference output according to the Pareto optimal solution set at time period k. is the target water level under the dynamic water level control mechanism at time period k, and min is the minimization function.

[0045] In some examples, establishing the water level-output coupling feedback model further includes:

[0046] Setting the following constraint conditions:

[0047] Z min ≤Z k|t ≤Z max

[0048]

[0049] Q k|t ≥Q eco

[0050] Among them, ΔP max represents the maximum output change rate between adjacent time periods, Q eco represents the minimum ecological discharge for downstream, and Q k|t represents the discharge at time period k.

[0051] In some examples, the real-time correction of the scheduling plan by the rolling optimization method includes:

[0052] Adopting a time window sliding window, performing optimization calculations every set time duration, and feeding back the actual operation data to the model to correct the model parameters. For example, adopting the time window sliding technology, performing optimization calculations once every 6 hours, with the optimization time period length of 24 - 168 hours, and feeding back the actual operation data to adjust the model parameters.

[0053] The method of this application solves the conflict problem of power generation, ecology and flood control objectives in cascade hydropower station scheduling through the deep integration of dynamic water level control and multi-objective optimization. This application further introduces the two-way influence in the time series into the static water level-output relationship, and realizes closed-loop correction by real-time adjusting the water level threshold through the deviation feedback between the actual output and the target value. Further, through the long-term scheduling plan (generated by NSGA-II) and the short-term rolling optimization of the water level-output coupling feedback model, the accuracy of the reservoir optimal scheduling method is improved.

[0054] The embodiment of this application also proposes a cascade hydropower station joint scheduling and water level control system based on multi-objective optimization, including:

[0055] A data acquisition module, configured to obtain rainfall, runoff, water level, and unit status parameters in real time;

[0056] A multi-objective optimization module, integrated with objective functions including maximizing power generation, achieving the highest ecological flow compliance rate, and minimizing flood control risks;

[0057] A dynamic water level control module, configured with an adaptive adjustment algorithm for water level thresholds based on fuzzy logic;

[0058] A human-machine interaction module, providing a three-dimensional visualization interface to display optimization results and control instructions.

[0059] In a specific example, the data acquisition module includes a meteorological and hydrological monitoring unit, a unit vibration monitoring unit, and a water quality monitoring unit. The data of each unit is aligned by timestamp and stored in a distributed database.

[0060] In a specific example, the dynamic water level control module is configured with an abnormal condition handling mechanism. For example, when it is monitored that the actual water level deviates from the set value by more than ±0.8 meters, an emergency dispatching plan is automatically triggered and a warning signal is issued.

[0061] Furthermore, although exemplary embodiments have been described herein, their scope includes any and all embodiments based on the present disclosure having equivalent elements, modifications, omissions, combinations (e.g., solutions where various embodiments intersect), adaptations, or alterations. It is not limited to the examples described in this specification or during the implementation of this application, and the examples will be construed as non-exclusive.

[0062] The above description is intended to be illustrative rather than restrictive. For example, the above examples (or one or more of their solutions) can be used in combination with each other. For example, those of ordinary skill in the art can use other embodiments when reading the above description.

[0063] The above embodiments are only exemplary embodiments of the present disclosure. Those skilled in the art can make various modifications or equivalent replacements to the present invention within the essence and protection scope of the present disclosure, and such modifications or equivalent replacements should also be regarded as falling within the protection scope of the present invention.

Claims

1. A method for joint dispatching and water level control of cascade power stations in a river basin based on multi-objective optimization, characterized in that: The following steps are involved: S1. Collect basin hydrological data, power station operation parameters and environmental constraint indicators, and establish a multi-objective optimization model including power generation benefits, ecological flow and flood control safety; S2, using an improved non-dominated sorting genetic algorithm (NSGA-II) to solve the multi-objective optimization model and generate a Pareto optimal solution set; S3, based on the dynamic weight decision method, select the optimal dispatching scheme from the Pareto optimal solution set, and introduce the water level dynamic control mechanism to adjust the water level control threshold of each cascade power station according to the collected hydrological data; S4. Establish a water level-output coupling feedback model, make real-time corrections to the dispatching plan through rolling optimization, and output the power generation plan and gate opening control instructions of each power station.

2. The method for joint dispatching and water level control of cascade power stations in a river basin based on multi-objective optimization according to claim 1, characterized in that: The environmental constraint indicators include minimum downstream ecological flow constraint, flood control limit water level constraint, and navigation depth requirement water level constraint, among which the ecological flow constraint adopts a segmented dynamic threshold control method.

3. The method for joint dispatching and water level control of cascade power stations in a river basin based on multi-objective optimization according to claim 2, characterized in that: The improved non-dominated sorting genetic algorithm (NSGA-II) is used to solve the multi-objective optimization model, including: An adaptive parameter adjustment mechanism is introduced in the crossover mutation operation, the crossover probability Pc satisfies Pc=0.6~0.9, and the mutation probability range Pm satisfies Pm=0.01~0.1; An elite retention strategy is adopted to maintain population diversity.

4. The method for joint dispatching and water level control of cascade power stations in a river basin based on multi-objective optimization according to claim 3, characterized in that: Introducing a dynamic water level control mechanism to adjust the water level control thresholds of each cascade power station based on real-time hydrological data includes: Establish a three-dimensional relationship surface between water level, reservoir capacity and output; According to the established three-dimensional relationship surface and combined with the runoff forecast data for a specified period of time in the future, the allowable fluctuation range of the operating water level of each power station is adjusted.

5. The method for joint dispatching and water level control of cascade power stations in a river basin based on multi-objective optimization according to claim 4, characterized in that: Establishing the water level-output coupling feedback model includes: Among them, E is the objective function of the water level-output coupling feedback model, N is the prediction time domain length, α, β represent the output and water level tracking weight coefficients, is the reference output of the Pareto optimal solution set in period k, is the target water level under the dynamic water level control mechanism in time period k, and min is the minimization function.

6. The method for joint dispatching and water level control of cascade power stations in a river basin based on multi-objective optimization according to claim 5, characterized in that: Establishing the water level-output coupling feedback model also includes: Set the following constraints: WITH min ≤Z k|t ≤Z max Q k|t ≥Q eco Among them, ΔP max Indicates the maximum output change rate in adjacent time periods, Q eco represents the minimum downstream ecological flow, Q k|t represents the downstream flow in period k.

7. The method for joint dispatching and water level control of cascade power stations in a river basin based on multi-objective optimization according to claim 6, characterized in that: The real-time modification of the scheduling plan through rolling optimization includes: A time window sliding window is used to perform optimization calculations every set time, and the actual operation data is fed back to the model to correct the model parameters.

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