Reservoir ecological scheduling sequential decision-making method based on positive extrinsic property

Abstract

The invention discloses a reservoir ecological scheduling sequential decision-making method based on positive extrinsic performance, and aims to optimize a reservoir scheduling strategy so as to improve an ecological protection effect on a downstream river in a dry season. The method comprises the following steps: firstly, calculating a positive extrinsic index (PEI), and quantifying improvement benefits of reservoir scheduling on flow and a fish habitat environment; and then establishing a remaining period ecological benefit function, comprehensively considering the ecological benefits of the current time period and the future time period, and carrying out recursive optimization by adopting a dynamic planning or approximate dynamic planning algorithm. State variables such as reservoir capacity, runoff and time periods are discretized, an optimal scheduling strategy of each time period is generated through recursion calculation, and termination of the recursion process is judged by setting convergence conditions. The method not only can meet the water supply, power generation and irrigation requirements of the reservoir, but also can effectively improve the health level of a river ecosystem.

Description

Technical Field

[0001] The invention relates to the technical field of reservoir ecological management, and in particular to a sequential decision-making method for reservoir ecological dispatching based on positive externalities. Background Art

[0002] The ecological regulation of reservoirs plays a vital role in maintaining the health of river ecosystems. The regulation of reservoirs should not only meet human water demand and power generation tasks, but also take into account ecological environmental protection. Especially in the dry season, reasonable ecological regulation can maintain a suitable aquatic environment, ensure the survival and reproduction of fish, and promote the balance and sustainable development of the entire ecosystem. Therefore, how to achieve the active regulation of reservoirs on river ecological environment while meeting the needs of comprehensive utilization of water resources is one of the important research topics of reservoir regulation today.

[0003] However, the existing reservoir ecological regulation methods have some shortcomings. First, traditional regulation methods are usually regulated based on certain fixed ecological flow thresholds. Although they can guarantee the most basic ecological water demand, they fail to fully consider the positive externalities of reservoir regulation on the ecosystem. For example, only regulating by flow thresholds may ignore the impact of reservoirs on fish aquatic habitats at different times, which is not conducive to the growth and reproduction of fish. Secondly, most traditional methods are limited to optimizing the benefits of the current stage during the regulation period, while ignoring the long-term ecological benefits of the remaining period, resulting in the inability to achieve the best balance between ecological and economic benefits from a global perspective.

[0004] In response to the above problems, the present invention proposes a sequential decision-making method for reservoir ecological regulation based on positive externalities. This method introduces the concept of positive ecological externalities of reservoir regulation to quantify the benefits of reservoir regulation on the improvement of fish aquatic habitats; at the same time, by introducing the residual period ecological positive externality benefit function, it achieves a balanced optimization of long-term and short-term ecological benefits. This method can dynamically optimize the sequential regulation strategy of the reservoir in different time periods, maximize the positive ecological externalities in the current period and the residual period, and thus provide a scientific basis and technical support for the protection of river ecosystems.

[0005] To this end, we design a sequential decision-making method for reservoir ecological operation based on positive externalities to solve the above problems. Summary of the invention

[0006] In view of the shortcomings of existing reservoir ecological scheduling methods, the present invention introduces positive externalities and residual period benefit functions to propose a sequential decision-making method for reservoir ecological scheduling. The method aims to effectively improve the flow during the dry season by optimizing reservoir scheduling, thereby providing a more suitable habitat for aquatic organisms such as fish and improving the health and sustainability of river ecosystems. Compared with traditional scheduling methods, the present invention not only takes into account the ecological benefits of the current period, but also introduces the positive ecological externality benefits of the residual period, which can achieve a balanced optimization of the ecological benefits of the current period and the residual period, and ultimately achieve a win-win goal of ecological protection and water resource utilization.

[0007] To achieve the above object, the present invention provides the following technical solution: a sequential decision-making method for reservoir ecological regulation based on positive externalities, the method comprising the following steps:

[0008] Step 1: Calculate the ecological positive externality index of reservoir operation to obtain the improvement benefits of reservoir operation on the ecological environment in the current period, that is, the positive externality benefit. This positive externality benefit index is used to quantify the degree of improvement of reservoir operation on dry season flow and aquatic habitat;

[0009] Step 2: Based on the positive externality benefits obtained in step 1, establish the residual ecological benefit function, comprehensively consider the ecological benefits of the current period and the future period, and maximize the positive ecological externalities of reservoir operation in different periods;

[0010] Step 3: Based on the residual ecological benefit function established in step 2, the reservoir capacity, runoff and time period are discretized to generate a finite set of state variables, laying the foundation for subsequent recursive optimization calculations;

[0011] Step 4: Based on the discretized state variables in step 3, a dynamic programming or approximate dynamic programming algorithm is used to recursively calculate the immediate positive externality benefits and residual ecological benefits of each period, and optimize the reservoir operation strategy for each period;

[0012] Step 5: In the recursive calculation process of step 4, a convergence judgment condition is set. When the change of the benefit value of the recursive calculation is less than a predetermined threshold, the recursive process is judged to have converged and further calculation is stopped;

[0013] Step 6: Based on the convergence judgment of step 5, output the optimal dispatching plan for each period during the reservoir dispatching period to ensure that the reservoir dispatching achieves the maximum positive ecological externality benefit during the entire dispatching period and optimizes the ecological benefits of the remaining period.

[0014] As a preferred technical solution of the present invention, the calculation formula of the reservoir ecological positive externality index (PEI) is:

[0015]

[0016] Among them, Q res is the average flow rate in the dry season after reservoir regulation, Q nat is the average flow rate during the dry season under natural conditions, Q avg The average flow rate for the whole year.

[0017] As a preferred technical solution of the present invention, the recursive formula of the remaining period ecological benefit function is:

[0018]

[0019] Among them, F t (v t ,q t ) is the comprehensive benefit function of the current period t, v t is the storage capacity in time period t, q t is the runoff in period t, PEI(v t ,v t+1 ,q t ) is the immediate positive externality benefit, is the runoff state transition probability, h t+1 (v t+1 ,q t+1 ) is the expected positive externality benefit in the next period.

[0020] As a preferred technical solution of the present invention, the runoff state transition probability It is derived from the historical runoff data of the reservoir and calculated based on the probability distribution of historical data or models.

[0021] As a preferred technical solution of the present invention, the state variables include: storage capacity v t : Discretized into several possible values ​​between the upper and lower limits of reservoir capacity; runoff q t : Discretized into several intervals according to the probability distribution of historical data or models; Time period t: Discrete processing according to the time step (such as ten days, days).

[0022] As the preferred technical solution of the present invention, the storage capacity v t can be discretized into 10 or more values, the runoff q t Divide into 5 or more intervals based on historical traffic data.

[0023] As a preferred technical solution of the present invention, the recursively calculated immediate positive externality benefit is calculated by the following formula:

[0024]

[0025] Among them, Q res (t) is the dry season flow after scheduling in the current period, Q nat (t) is the natural low-water flow, Qavg (t) is the average annual flow rate.

[0026] As a preferred technical solution of the present invention, the expected positive externality benefits of the remaining period are calculated recursively by the following formula:

[0027]

[0028] This formula is used to evaluate the positive externality benefits in the next period and ensure the maximization of the positive externality benefits in each period through dynamic recursive calculation.

[0029] As a preferred technical solution of the present invention, the convergence judgment condition of the recursive process is that when the benefit value calculated by recursion changes less than the set threshold value ∈ in multiple iterations, the recursive calculation is stopped. The convergence judgment formula is:

[0030]

[0031] Among them, δ is the number of iterations, and ∈ is the set convergence threshold.

[0032] As a preferred technical solution of the present invention, the optimal scheduling scheme is the optimal reservoir capacity scheduling strategy for each time period, which ensures the optimization of flow during the dry season, improves the suitability of the fish habitat, and enhances the ecological benefits of the remaining period.

[0033] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention effectively optimizes the ecological dispatching strategy of the reservoir by introducing the positive externality index (PEI) and the residual ecological benefit function, especially in the dry season, which can significantly improve the flow conditions of the downstream rivers and enhance the living environment of organisms such as fish and aquatic plants. This method not only solves the ecological protection problem in the current period through dynamic optimization, but also achieves the global optimization of ecological benefits in multiple time periods, which greatly improves the health and sustainability of the ecosystem. Taking the Three Gorges Reservoir as an example, the optimized dispatching scheme increases the flow rate in the dry season from 5000m 3 / s increased to 8500m 3 / s, the positive externality benefit increased by 43.75%, greatly improving the river ecosystem.

[0034] Another major advantage of the present invention is that it can balance ecological needs while ensuring economic benefits. For example, in irrigation reservoirs or hydropower stations, traditional scheduling methods are difficult to meet the needs of production water and ecological protection at the same time. The present invention uses a dynamic programming recursive algorithm, combined with historical runoff data and future time period needs, to dynamically optimize the reservoir scheduling strategy to ensure that while meeting agricultural irrigation, urban water supply or power generation needs, the positive ecological externality benefits are maximized. In this way, while taking into account economic goals, the ecological benefits are also significantly improved. For example, in the embodiment, the optimized scheduling of a certain city reservoir increased the ecological benefits by 35% and ensured the normal operation of water supply and irrigation.

[0035] The technical solution of the present invention is applicable to reservoir scheduling scenarios of different scales and functions. Whether it is a large reservoir, a small irrigation reservoir, or a sensitive area such as a wetland protection area, the optimal scheduling solution suitable for different needs can be generated by discretizing state variables, recursive optimization, and convergence judgment. The patented method has strong versatility and can flexibly adapt to complex water resource scheduling needs to achieve comprehensive optimization of different goals. For example, in the case of a wetland protection area, the positive externality benefits were increased by 50% through optimized scheduling, significantly improving the habitat of aquatic plants and birds. This flexibility makes the present invention have significant promotion value in a wide range of practical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a flowchart of a sequential decision-making method for reservoir ecological regulation based on positive externalities proposed by the present invention;

[0037] Figure 2 This is the ecological optimization scheduling result of the outflow (upper) and water level (lower) of the Three Gorges Reservoir every ten days. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] The following is combined with Figure 1 and Figure 2 And multiple embodiments are described in detail for specific implementations of the present invention.

[0040] Example 1: Application of the sequential decision-making method for reservoir ecological regulation based on positive externalities in the Three Gorges Reservoir

[0041] The reservoir operation is optimized from June 2021 to May 2022. During this period, the operation plan is generated at the ten-day scale. The ten-day dam site (inflow) flow of the Three Gorges Reservoir from 1961 to 2020 is collected for the calculation of positive ecological externalities and runoff transfer probability.

[0042] (1) Calculation of the positive ecological externalities of the Three Gorges Reservoir

[0043] In order to measure the improvement effect of the Three Gorges Reservoir operation on the dry season flow and ecosystem, a positive externality index (PEI) is defined. The PEI index reflects the increase in the dry season flow after reservoir operation relative to the natural dry season flow, and its calculation formula is:

[0044]

[0045] In the formula, Q res is the average flow rate in the dry season after reservoir operation; Q nat is the average flow rate during the natural dry season, i.e. 5,000m 3 / s; Q avg is the average annual flow, which is 13,000m 3 / s.

[0046] (2) Establishing the basic recursive formula for the residual benefits of positive externalities

[0047] In the dispatch of the Three Gorges Reservoir, the residual benefit function takes positive externalities as the goal and uses dynamic programming to optimize the dispatch of each period. The basic recursive formula is as follows:

[0048]

[0049] In the formula, F t (v t ,q t ) is the comprehensive benefit function, PEI t (v t ,v t+1 ,q t ) is the immediate positive externality benefit in the current period, indicating that v t 、Storage capacity for the next period v t+1 , and runoff q t The expected positive externality benefits in the remaining period h t+1 (v t+1 ,q t+1 ) is used to evaluate the potential positive external benefits in the next period. The state transition probability of runoff P( q t+1,j ∣q t ) It is calculated based on the ten-day dam site (inflow) flow data of the Three Gorges Reservoir from 1961 to 2020.

[0050] (3) Discrete state variables

[0051] In order to simplify the calculation, the state variables of the Three Gorges Reservoir are discretized:

[0052] Storage capacityv t : In the range of 17.1 billion to 39.3 billion m3, it is discretely divided into 10 storage capacity values, namely: 17.1 billion, 19 billion, 20.9 billion, 22.8 billion, 24.7 billion, 26.6 billion, 28.5 billion, 30.4 billion, 32.3 billion, 39.3 billion m3 3 .

[0053] run-off q t :According to the historical runoff data of the Three Gorges, the 5000m 3 / s to 50,000m 3 The runoff interval of / s is divided into 5 discrete values, namely: 5,000m 3 / s、15,000m 3 / s、25,000m 3 / s、35,000m 3 / s、50,000m 3 / s.

[0054] Period t: Discretized into ten days, 3 periods per month, and 36 periods per year.

[0055] (4) Sequential optimization based on recursive calculation

[0056] The optimal decision for reservoir operation is calculated by dynamic programming in each period. The immediate positive externality benefit formula is:

[0057]

[0058] in, Q res,t is the average flow rate during the dry season after scheduling, which is affected by the reservoir capacity. v t and v t+1 The impact of Q nat,t The natural low-water flow (5,000m 3 / s); Q av g The annual average flow (13,000m 3 / s).

[0059] The calculation formula for the expected positive external benefits during the remaining period is:

[0060]

[0061] The recursive process will be carried out step by step from May 2022 (the 36th period) to June 2021 (the 1st period) to ensure that the positive ecological externality benefits are maximized in each period.

[0062] (5) Convergence determination

[0063] In the recursive process, when the benefit value calculated by recursion changes less than a predetermined threshold ∈ in multiple iterations, it can be considered to have converged:

[0064]

[0065] By setting the convergence condition ∈＝10 -4 , when the change of the benefit function in multiple iterations is less than the threshold, the reservoir operation decision converges to the optimal operation plan.

[0066] (6) Output the optimal scheduling plan:

[0067] After convergence, the optimal dispatching plan for each period is output. According to the calculation results, during the dry season from November 2021 to May 2022, the average flow after dispatching will increase from the natural 5,000m 3 / s increased to 8,500m 3 / s, the positive externality benefit increased by 43.75%, and the residual ecological positive externality benefit function reached its maximum value.

[0068] This optimized scheduling plan provides a scientific basis for the ecological scheduling of the Three Gorges Reservoir, which can maximize the improvement of fish habitat and enhance the overall ecological benefits.

[0069] Example 2: Ecological dispatch of a city’s drinking water source reservoir

[0070] This example introduces how to use a positive externality scheduling decision method in a city’s drinking water source reservoir to balance the needs of urban water supply and ecological protection.

[0071] Calculate the positive ecological externality index: Calculate the positive externality of the reservoir operation using the formula:

[0072]

[0073] Among them, Q res is the dry season flow after the current reservoir regulation, Q nat The natural dry season flow (40m 3 / s),Q avg The average annual flow (110m 3 / s). Through calculation, it is found that the positive externality during the dry season increases by 35%.

[0074] Recursive formula for ecological benefits during the remaining period: To optimize the ecological benefits of the current and future periods, the recursive formula is:

[0075] State variable discretization:

[0076]

[0077] The recursive process depends on the storage capacity v of the current period. t and runoff qt state, ensuring that the positive externality benefits in each period are maximized.

[0078] Discretize the reservoir capacity and runoff state: reservoir capacity v t Discretized into 10 possible values; runoff q t According to the historical flow data, it is discretized into 5 intervals: 40m 3 / s,80m 3 / s,120m 3 / s,160m 3 / s,200m 3 / s.

[0079] Optimization results: Dynamic programming recursive calculation optimizes the scheduling plan, maximizes ecological benefits without affecting water supply, and ultimately optimizes the flow rate during the dry season to a level that is more suitable for the downstream ecological environment.

[0080] Example 3: Ecological dispatching of a hydropower station reservoir during dry season

[0081] Conflict between power generation and ecological needs: In a large hydropower station reservoir, ecological scheduling during the dry season needs to find a balance between power generation and downstream ecosystem protection. The reservoir is mainly used for power generation during the dry season, but the downstream ecosystem, especially the fish spawning area, requires a certain flow rate. Preliminary calculations show that the natural dry season flow rate is 450m 3 / s, and the flow rate after power generation scheduling is 600m 3 / s.

[0082] Calculation of positive externality benefits: Calculate the positive ecological externality benefits through the PEI formula:

[0083]

[0084] Among them, Q res The flow rate after scheduling (600m 3 / s),Q nat The natural dry season flow (450m 3 / s),Q avg The average annual flow (1500m 3 / s). Preliminary calculations show that the scheduling scheme increases positive externalities by 25%.

[0085] Recursive calculation: Use recursive formulas to optimize reservoir power generation scheduling to ensure that power generation needs and ecological needs are taken into account:

[0086]

[0087] The recursive process is optimized time period by time to ensure maximum protection of the downstream fish spawning environment during the power generation period.

[0088] Final effect: The optimized scheduling scheme increased the downstream river flow by 30% during the dry season, while maintaining a balance between power generation efficiency and ecological protection.

[0089] Example 4: Reservoir Operation in Coastal Wetland Conservation Area

[0090] In reservoirs upstream of coastal wetland reserves, the protection of wetland ecosystems depends on the ecological flow regulation of the reservoirs.

[0091] Wetland ecological status: The upstream reservoir regulation has a great impact on the aquatic plants and bird habitats in the wetland. Historical data show that the natural dry season flow is 200m 3 / s, and the traffic after scheduling is 350m 3 / s.

[0092] Calculation of positive externality benefits: The benefits of scheduling on improving the wetland environment are calculated through the positive externality index (PEI):

[0093]

[0094] Q res =350m 3 / s,Q nat =200m 3 / s,Q avg =800m 3 / s. Calculations show that the positive externalities after scheduling are increased by 40%.

[0095] Recursive optimization scheduling scheme: Construct a recursive formula that takes into account the ecological benefits of the current and future periods:

[0096]

[0097] Dynamic planning optimizes ecological water replenishment scheduling in different time periods to ensure that the wetland can obtain sufficient flow during the dry season.

[0098] Implementation effect: The optimized scheduling scheme provides a more stable water source for the wetland, and the positive ecological externality benefits are increased by 50%. The health of aquatic plants in the wetland ecosystem has been significantly improved, and the bird habitat has been protected.

[0099] According to monitoring data, the number and diversity of birds in the wetland have increased, and the ecosystem has been effectively restored.

[0100] Example 5: Optimization of small reservoirs in mountainous areas for agricultural irrigation and ecological regulation

[0101] This example introduces a small reservoir located in a mountainous area. The reservoir is mainly used for agricultural irrigation. In the dry season, it is necessary to ensure the water demand of crops. At the same time, it is also necessary to provide necessary ecological water for downstream rivers to ensure the living environment of aquatic organisms. Conflict between irrigation and ecological needs: The reservoir faces a conflict between irrigation demand and ecological flow guarantee during the dry season. The natural dry season flow is 15m 3 / s, the flow rate after reservoir regulation during irrigation is only 10m 3 / s, making it difficult to meet the needs of the downstream ecosystem.

[0102] Calculation of positive externality benefits: The positive externality benefits of irrigation scheduling on the downstream ecosystem are calculated using the PEI formula:

[0103]

[0104] Among them, Q res is the flow rate after reservoir regulation (15m 3 / s),Q nat is the natural dry season flow (10m 3 / s),Q avg The average annual flow (50m 3 / s). After calculation, the positive externality benefits of irrigation scheduling have been initially increased by 20%.

[0105] Dynamic programming optimization scheduling: In order to balance agricultural irrigation and downstream ecological needs, the residual ecological benefit function is constructed, and dynamic programming is used to optimize reservoir scheduling. The recursive formula is:

[0106]

[0107] During the optimization process, the irrigation demand and downstream ecological flow are balanced for different time periods to ensure that the ecological benefits are improved while ensuring agricultural production.

[0108] Implementation effect: The optimized scheduling scheme has increased the positive externality benefits of reservoir scheduling on the ecosystem, while the irrigation water supply has not been affected. Through optimized scheduling, the positive externality benefits of the ecosystem have increased by 35%, the downstream aquatic population has returned to normal, and the crop water supply in the irrigation area has also reached a good state.

[0109] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in the field. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments, or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A sequential decision-making method for reservoir ecological regulation based on positive externalities, characterized in that: The method comprises the following steps: Step 1: Calculate the ecological positive externality index of reservoir operation to obtain the improvement benefit of reservoir operation on the ecological environment in the current period, that is, the positive externality benefit; Step 2: Based on the positive externality benefits obtained in step 1, establish the residual ecological benefit function; Step 3: Based on the residual ecological benefit function established in step 2, the reservoir capacity, runoff and time period are discretized to generate a finite set of state variables; Step 4: Based on the discretized state variables in step 3, a dynamic programming or approximate dynamic programming algorithm is used to recursively calculate the immediate positive externality benefits and residual ecological benefits of each period, and optimize the reservoir operation strategy for each period; Step 5: In the recursive calculation process of step 4, a convergence judgment condition is set. When the change of the benefit value of the recursive calculation is less than a predetermined threshold, the recursive process is judged to have converged and further calculation is stopped; Step 6: Based on the convergence judgment of step 5, output the optimal dispatching plan for each period during the reservoir dispatching period, so as to maximize the positive ecological externality benefits of reservoir dispatching during the entire dispatching period and optimize the ecological benefits of the remaining period.

2. The sequential decision-making method for reservoir ecological regulation based on positive externalities according to claim 1 is characterized in that: The calculation formula of the reservoir ecological positive externality index (PEI) is: Among them, Q res is the average flow rate in the dry season after reservoir regulation, Q nat is the average flow rate during the dry season under natural conditions, Q avg The average flow rate for the whole year.

3. The sequential decision-making method for reservoir ecological regulation based on positive externalities according to claim 1 is characterized in that: The recursive formula of the remaining period ecological benefit function is: Among them, F t (v t ,q t ) is the comprehensive benefit function of the current period t, v t is the storage capacity in time period t, q t is the runoff in period t, PEI(v t , v t+1 ,q t ) is the immediate positive externality benefit, is the runoff state transition probability, h t+1 (v t+1 ,q t+1 ) is the expected positive externality benefit in the next period.

4. The sequential decision-making method for reservoir ecological regulation based on positive externalities according to claim 3 is characterized in that: The runoff state transition probability It is derived from the historical runoff data of the reservoir and calculated based on the probability distribution of historical data or models.

5. The sequential decision-making method for reservoir ecological regulation based on positive externalities according to claim 3 is characterized in that: The state variables include: Storage capacity t : Discretize into several possible values ​​between the upper and lower limits of storage capacity; Runoffq t :Discrete into several intervals according to the probability distribution of historical data or models; Time period t: Discrete processing according to the time step (such as ten days, day).

6. The sequential decision-making method for reservoir ecological regulation based on positive externalities according to claim 5 is characterized in that: Storage capacity t can be discretized into 10 or more values, the runoff q t Divide into 5 or more intervals based on historical traffic data.

7. The sequential decision-making method for reservoir ecological regulation based on positive externalities according to claim 1 is characterized in that: The immediate positive externality benefit calculated recursively is calculated by the following formula: Among them, Q res (t) is the dry season flow after scheduling in the current period, Q nat (t) is the natural low-water flow, Q avg (t) is the average annual flow rate.

8. The sequential decision-making method for reservoir ecological regulation based on positive externalities according to claim 1 is characterized in that: The expected positive externality benefits of the remaining period are calculated recursively using the following formula: This formula is used to evaluate the positive externality benefits in the next period and ensure the maximization of the positive externality benefits in each period through dynamic recursive calculation.

9. The sequential decision-making method for reservoir ecological regulation based on positive externalities according to claim 1 is characterized in that: The convergence judgment condition of the recursive process is that when the benefit value of the recursive calculation changes less than the set threshold ∈ in multiple iterations, the recursive calculation is stopped. The convergence judgment formula is: Among them, δ is the number of iterations, and ∈ is the set convergence threshold.

10. The sequential decision-making method for reservoir ecological regulation based on positive externalities according to claim 1 is characterized in that: The optimal scheduling scheme is the optimal storage capacity scheduling strategy for each time period.

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