Water-wind-light medium-and-long-term optimization scheduling method considering ecological target

Abstract

The invention relates to the crossing field of clean renewable energy utilization and reservoir scheduling, and discloses a water-wind-light medium-and-long-term optimization scheduling method considering an ecological target. The method comprises the following steps: (1) extracting hydrological variability and ecological targets suitable for floating roe incubation and river reach length through an IHA-RVA method and a hydrodynamic model; (2) establishing a water-wind-light medium-and-long-term optimization scheduling model considering an ecological target, and solving through an elk optimization algorithm; and (3) analyzing a collaborative competition relationship between ecology and a power generation target. According to the method, the ecological target is extracted by simulating the hydrodynamic conditions in the river channel through the hydrodynamic model, the water-wind-light medium-and-long-term optimization scheduling model considering the ecological target is established, solving is performed through the elk optimization algorithm, and the method has a guiding effect on the water-wind-light medium-and-long-term optimization scheduling considering the ecological target.

Description

Technical Field

[0001] This invention belongs to the interdisciplinary field of clean and renewable energy utilization and reservoir scheduling, and relates to a medium- and long-term optimized scheduling method for water, wind and solar power that takes into account ecological objectives. Background Technology

[0002] The impact mechanism of the ecological changes caused by the development of new energy sources and the changes in the operation mode of hydropower stations is unclear. Traditional long-term scheduling of water, wind and solar power has not taken into account ecological needs, posing a challenge to how to achieve a win-win situation for ecological and economic benefits.

[0003] In current medium- and long-term optimization scheduling of water, wind and solar power systems, ecological objectives are not considered in the objective function, resulting in an unclear mechanism of the impact of water, wind and solar power complementary systems on the ecology. Based on this, the present invention provides a medium- and long-term optimization scheduling method for water, wind and solar power systems that takes into account ecological objectives. Summary of the Invention

[0004] Purpose of the invention: In existing medium- and long-term optimization scheduling methods for water-wind-solar systems, the objective function does not consider ecological objectives, resulting in an unclear mechanism of the ecological impact of the water-wind-solar complementary system. This invention provides a medium- and long-term optimization scheduling method for water-wind-solar systems that considers ecological objectives. By adding ecological objectives extracted by the IHA-RVA method and hydrodynamic model, a new method is provided to adapt the medium- and long-term optimization scheduling of water-wind-solar systems to ecological needs.

[0005] Technical solution: A medium- to long-term optimization scheduling method for water, wind, and solar power that considers ecological objectives, comprising the following steps: (1) Extracting hydrological variability and ecological targets for suitable floating fish egg hatching and river length using the IHA-RVA method and hydrodynamic model. To clarify the ecological objectives required for long-term water, wind, and solar power scheduling, the impact of hydrological situation changes on fish resources was evaluated using the IHA-RVA method based on natural flow processes. To accommodate the flocculation process of the four major Chinese carps' eggs, a one-dimensional hydrodynamic model was constructed to simulate the hydrodynamic conditions within the river channel. The flow propagation in the river can be considered as a one-dimensional unsteady flow, and its evolution can be described by the Saint-Venant equations. The Saint-Venant equations consist of two partial differential equations: the continuity equation and the momentum equation.

[0006] ; ; In the formula: A represents the cross-sectional area of ​​the water passage; Q represents the river flow; q represents the lateral inflow or outflow per unit length of the river-type reservoir; x and t represent independent spatial and temporal variables; g represents gravitational acceleration; z represents the water depth of the cross-section; n represents the roughness coefficient; R represents the hydraulic radius. ; Indicates wetted perimeter.

[0007] The ecological objectives of the medium- and long-term optimal scheduling model for water, wind, and solar power, taking into account ecological goals, are as follows: 1) Minimal hydrological variability: ; In the formula: Y represents the overall hydrological variability; o,i Y represents the number of years in which the observed value of hydrological parameter i falls within the RVA range; e This represents the expected number of years falling within the RVA range (take half of the total number of years); I represents the number of hydrological parameters.

[0008] 2) The longest suitable river section for fish egg hatching is: ; In the formula: SRL represents the length of the river section suitable for fish egg hatching, in km; RLt represents the length of the river section suitable for fish egg hatching in time period t, in km.

[0009] (2) Establish a medium- and long-term optimization scheduling model for water, wind and light that takes into account ecological objectives, and solve it using the Père David's deer optimization algorithm. In step (1), the impact of hydrological situation changes on fish resources is evaluated by the IHA-RVA method, and the hydrodynamic model is used to simulate the hydrodynamic conditions in the river channel to extract ecological objectives. Two ecological objectives are obtained: the minimum hydrological variability and the maximum length of the suitable river section for fish egg hatching. At the same time, the maximum power generation of the water-wind-solar complementary system is taken as the economic objective.

[0010] The maximum power generation is: ; In the formula, This represents the total number of hours in the calculation period. Indicates the number of power plants; The output of the power plant during time period t; the output of the wind and solar power station during time period t; This indicates that the power output of the wind and solar power station is... The rate of power curtailment at that time To calculate the length of the time period; To contribute to the power plants.

[0011] The constraints are: water balance constraint; reservoir capacity constraint; outflow constraint; maximum output limit constraint; minimum ecological base flow constraint; and reservoir boundary condition constraint.

[0012] For this multi-objective optimization scheduling model, the Père David's deer optimization algorithm is used for solution. The Père David's deer optimization algorithm is derived from the breeding process of Père David's deer herds, which is mainly divided into the estrus season and the calving season.

[0013] 1) Estrus period: During the rutting season, the elk herd is divided into different families. Each family is led by a male elk, and the selection of the male is based on fitness value. Specifically, the male elk with the highest fitness value will receive the most females. The selection formula for the male elk is as follows: ; In the formula: B represents the number of male elk. Let be the fitness value of the j-th male elk.

[0014] The allocation of female elk is done using a roulette wheel selection method, based on the fitness scores of the male elk. The specific formula is as follows: ; In the formula: Let be the probability that the i-th male elk is selected.

[0015] 2) Farrowing period: During the breeding season, each family produces new offspring. Offspring generation is based on the attributes of the parents, using the following formula: ; In the formula: For the newly generated offspring, The location of the male elk; The location of the mother deer. It is a random number, in the range [0,1].

[0016] 3) Selection period: During the selection period, all family members (including male elk, female elk, and offspring) are merged, and the individual with the best fitness value is selected as the next generation of the population. The selection mechanism employs a (μ + λ) selection strategy, with the specific formula as follows: ; In the formula: The merged population is represented by EHS, which is the population size. The top function selects the EHS individual with the best fitness.

[0017] (3) Analyze the synergistic and competitive relationship between ecological and power generation goals. Correlation can effectively reflect the synergistic competition between ecological goals and economic benefits, provide a reference for the implementation of reservoir scheduling schemes, calculate the correlation coefficient r between optimization objectives in Pareto solution set, and analyze the synergistic competition between ecological goals and economic benefits.

[0018] This invention extracts ecological objectives by simulating hydrodynamic conditions in river channels using a hydrodynamic model, and establishes a medium- to long-term optimal scheduling model for water-wind-solar hybrid systems that takes ecological objectives into account. The model is solved using the elk optimization algorithm, which provides guidance for medium- to long-term optimal scheduling of water-wind-solar hybrid systems that take ecological objectives into account. This invention solves the shortcoming of water-wind-solar hybrid systems that lack consideration of ecological objectives, and provides a new approach to achieve a win-win situation for the economic and ecological benefits of water-wind-solar hybrid systems. Attached Figure Description

[0019] Figure 1 This is a flowchart of the method of the present invention.

[0020] Figure 2 This is a comparison of the DVA of the conventional scheduling scheme and the optimized scheduling scheme.

[0021] Figure 3 To optimize the flow rate satisfaction rate of the scheduling scheme in each month. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of this invention described below can be combined with each other as long as they do not conflict with each other.

[0023] An example of a long-term optimization scheduling method for water, wind, and solar power that considers ecological objectives includes the following main process: Figure 1 As shown, it includes the following steps: (1) Extracting hydrological variability and ecological targets for suitable floating fish egg hatching and river length using the IHA-RVA method and hydrodynamic model. To clarify the ecological objectives required for long-term water, wind, and solar power scheduling, the impact of hydrological situation changes on fish resources was evaluated using the IHA-RVA method based on natural flow processes. To accommodate the flocculation process of the four major Chinese carps' eggs, a one-dimensional hydrodynamic model was constructed to simulate the hydrodynamic conditions within the river channel. The flow propagation in the river can be considered as a one-dimensional unsteady flow, and its evolution can be described by the Saint-Venant equations. The Saint-Venant equations consist of two partial differential equations: the continuity equation and the momentum equation.

[0024] ; ; In the formula: A represents the cross-sectional area of ​​the water passage; Q represents the river flow; q represents the lateral inflow or outflow per unit length of the river-type reservoir; x and t represent independent spatial and temporal variables; g represents gravitational acceleration; z represents the water depth of the cross-section; n represents the roughness coefficient; R represents the hydraulic radius. ; Indicates wetted perimeter.

[0025] The ecological objectives of the medium- and long-term optimal scheduling model for water, wind, and solar power, taking into account ecological goals, are as follows: 1) Minimal hydrological variability: ; In the formula: Y represents the overall hydrological variability; o,i Y represents the number of years in which the observed value of hydrological parameter i falls within the RVA range; e This represents the expected number of years falling within the RVA range (take half of the total number of years); I represents the number of hydrological parameters.

[0026] 2) The longest suitable river section for fish egg hatching is: ; In the formula: SRL represents the length of the river section suitable for fish egg hatching, in km; RLt represents the length of the river section suitable for fish egg hatching in time period t, in km.

[0027] (2) Establish a medium- and long-term optimization scheduling model for water, wind and light that takes into account ecological objectives, and solve it using the Père David's deer optimization algorithm. In step (1), the impact of hydrological situation changes on fish resources is evaluated by the IHA-RVA method, and the hydrodynamic model is used to simulate the hydrodynamic conditions in the river channel to extract ecological objectives. Two ecological objectives are obtained: the minimum hydrological variability and the maximum length of the suitable river section for fish egg hatching. At the same time, the maximum power generation of the water-wind-solar complementary system is taken as the economic objective.

[0028] The maximum power generation is: ; In the formula, This represents the total number of hours in the calculation period. Indicates the number of power plants; The output of the power plant during time period t; the output of the wind and solar power station during time period t; This indicates that the power output of the wind and solar power station is... The rate of power curtailment at that time To calculate the length of the time period; To contribute to the power plants.

[0029] The constraints are: water balance constraint; reservoir capacity constraint; outflow constraint; maximum output limit constraint; minimum ecological base flow constraint; and reservoir boundary condition constraint.

[0030] For this multi-objective optimization scheduling model, the Père David's deer optimization algorithm is used for solution. The Père David's deer optimization algorithm is derived from the breeding process of Père David's deer herds, which is mainly divided into the estrus season and the calving season.

[0031] 1) Estrus period: During the rutting season, the elk herd is divided into different families. Each family is led by a male elk, and the selection of the male is based on fitness value. Specifically, the male elk with the highest fitness value will receive the most females. The selection formula for the male elk is as follows: ; In the formula: B represents the number of male elk. Let be the fitness value of the j-th male elk.

[0032] The allocation of female elk is done using a roulette wheel selection method, based on the fitness scores of the male elk. The specific formula is as follows: ; In the formula: Let be the probability that the i-th male elk is selected.

[0033] 2) Farrowing period: During the breeding season, each family produces new offspring. Offspring generation is based on the attributes of the parents, using the following formula: ; In the formula: For the newly generated offspring, The location of the male elk; The location of the mother deer. It is a random number, in the range [0,1].

[0034] 3) Selection period: During the selection period, all family members (including male elk, female elk, and offspring) are merged, and the individual with the best fitness value is selected as the next generation of the population. The selection mechanism employs a (μ + λ) selection strategy, with the specific formula as follows: ; In the formula: The merged population is represented by EHS, which is the population size. The top function selects the EHS individual with the best fitness.

[0035] (3) Analyze the synergistic and competitive relationship between ecological and power generation goals. Correlation can effectively reflect the synergistic competition between ecological goals and economic benefits, provide a reference for the implementation of reservoir scheduling schemes, calculate the correlation coefficient r between optimization objectives in Pareto solution set, and analyze the synergistic competition between ecological goals and economic benefits.

[0036] The above implementation method is compared with the conventional scheduling scheme as the preferred scheduling scheme, and the synergistic competitive relationship between ecological goals and economic benefits is analyzed. The proportions and comparison results are analyzed as follows: Taking Danjiangkou Reservoir as an example, the solar and wind power output is calculated using the photovoltaic radiation and wind speed around Danjiangkou Reservoir. The reservoir inflow at a ten-day scale from 1980 to 2020 is used to construct a hydrodynamic model using the topographic data of the Hanjiang River section from the Xiantao Station. Within a given scheduling period, a medium- to long-term optimized scheduling model for water, wind, and solar power is constructed with the ecological objectives of minimizing hydrological variability and maximizing the length of the suitable river section for fish egg hatching.

[0037] Comparison of DVA under optimal scheduling and conventional scheduling schemes, such as Figure 2 As shown, under the optimized scheduling scheme, the variability of indicators such as the number of flood reversals, the rate of water drop, and the number of high-flow peaks decreases; the flood rate, the monthly average flow, and the magnitude and duration of the annual extreme flow do not change significantly. Under the optimized scheduling scheme, the number of indicators with reduced variability is greater than the number of indicators with increased variability.

[0038] The monthly flow rate satisfaction rate of the optimal scheduling scheme is as follows: Figure 3 As shown, the velocity satisfaction rate for drifting eggs is relatively low, approximately 60-80%. From May to August, reservoir inflow is relatively abundant, but due to reservoir regulation, the amount of ecological water available for downstream use is limited, and some sections still fail to meet the velocity threshold. The velocity satisfaction rate in the studied river section changes significantly with the Han River as the dividing line. Upstream of the Han River, the velocity satisfaction rate can generally be maintained above 85%, while in the downstream section to the river mouth, the velocity satisfaction rate decreases, with some sections having a velocity satisfaction rate of less than 60%.

[0039] It is evident that the medium- and long-term optimization scheduling method for water-wind-solar systems that considers ecological objectives, provided by this invention, solves the shortcomings of water-wind-solar complementary systems that lack consideration of ecological objectives, and provides a new approach to achieve a win-win situation for the economic and ecological benefits of water-wind-solar complementary systems.

[0040] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the methods and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A medium- to long-term optimal scheduling method for water, wind, and solar power that considers ecological objectives, characterized in that: Includes the following steps: (1) The impact of hydrological situation changes on fish resources was evaluated by the IHA-RVA method and ecological objectives were extracted by simulating hydrodynamic conditions in the river channel using hydrodynamic models; (2) Establish a medium- and long-term optimization scheduling model for water, wind and light that takes into account ecological objectives, and solve it using the elk optimization algorithm; (3) Comparative analysis of the synergistic competition between ecological goals and economic benefits.

2. The method for medium- and long-term optimal scheduling of water, wind, and solar power considering ecological objectives as described in claim 1, characterized in that: In step (1), This involves constructing a one-dimensional hydrodynamic model to simulate hydrodynamic conditions within a river channel, treating the flow propagation in the river as a one-dimensional unsteady flow, the evolution of which is described by the Saint-Venant equations; the Saint-Venant equations include the continuity equation and the momentum equation: ； ； In the formula, A represents the cross-sectional area of ​​the water passage; Q represents the river flow; q represents the lateral inflow or outflow per unit length of the river-type reservoir; x and t represent independent spatial and temporal variables; g represents gravitational acceleration; z represents the water depth of the cross-section; n represents the roughness coefficient; and R represents the hydraulic radius. ; Indicates wetted perimeter.

3. The method for medium- and long-term optimal scheduling of water, wind, and solar power considering ecological objectives as described in claim 2, characterized in that: The ecological objectives include minimizing hydrological variability: ； In the formula, Y represents the overall hydrological variability; o,i Y represents the number of years in which the observed value of hydrological parameter i falls within the RVA range; e This represents the expected number of years falling within the RVA range, taken as half of the total number of years; I represents the number of hydrological parameters.

4. The method for medium- and long-term optimal scheduling of water, wind, and solar power considering ecological objectives as described in claim 3, characterized in that: The ecological objectives include maximizing the length of river sections suitable for fish egg hatching: ； In the formula, SRL represents the length of the river section suitable for fish egg hatching, in km; RL t The length of the river section suitable for fish egg hatching during time period t is expressed in km.

5. A medium- to long-term optimization scheduling method for water, wind, and solar power considering ecological objectives, as described in claim 4, is characterized in that: In step (2), the scheduling model takes maximizing the power generation of the hydro-wind-solar hybrid system as its economic objective, and the maximum power generation is: ； In the formula, This represents the total number of hours in the calculation period; Indicates the number of power plants; The output of the power plant during time period t; the output of the wind and solar power station during time period t; This indicates that the power output of the wind and solar power station is... The rate of power curtailment at that time To calculate the length of the time period; To contribute to the power plants; The constraints are: water balance constraint; reservoir capacity constraint; outflow constraint; maximum output limit constraint; minimum ecological base flow constraint; and reservoir boundary condition constraint.

6. A medium- to long-term optimization scheduling method for water, wind, and solar power considering ecological objectives, as described in claim 5, is characterized in that: The elk optimization algorithm includes: During the estrus cycle, the selection formula for male elk is as follows: ； In the formula: B represents the number of male elk. Let be the fitness value of the j-th male elk; The allocation of female elk is done using a roulette wheel selection method, based on the fitness scores of the male elk, as shown in the following formula: ； In the formula: Let be the probability that the i-th male elk is selected. During the breeding season, each family produces new offspring. The generation of offspring is based on the attributes of the parents, according to the following formula: ； In the formula: For the newly generated offspring, The location of the male elk; The location of the mother deer. It is a random number, in the range [0,1]. During the selection period, all family members are merged, and then the individual with the best fitness value is selected as the next generation population; the selection mechanism adopts a (μ+λ) selection strategy, as shown in the following formula: ； In the formula: The merged population is represented by EHS, which is the population size. The top function selects the EHS individual with the best fitness.

7. A medium- to long-term optimization scheduling method for water, wind, and solar power considering ecological objectives, as described in claim 1, is characterized in that: In step (3), the correlation coefficient r between the optimization objectives in the Pareto solution set is calculated to analyze the synergistic competitive relationship between ecological objectives and economic benefits.

Images