An ecological friendly hydroelectric system optimal scheduling method for fish population

Abstract

The application discloses a kind of fish population ecological friendly hydroelectric system optimal scheduling method.In the application, all core ecological parameters are based on offline fitting solution and solidification storage based on conventional fish census data, without calling any hydrodynamic simulation model in online scheduling, and each node fish population damage value can be quickly obtained directly through linear accounting formula, the overall model belongs to linear programming category, and the solving speed is fast, convergent and stable, can be seamlessly embedded into day-ahead scheduling and intra-day rolling scheduling process, fully meet the timeliness requirement of power dispatching, set independent red line for each ecological node through fish population comprehensive damage value, realize the accurate limit of local river section;At the same time, introduce marginal ecological damage coefficient as incremental sensitivity index, avoid potential ecological impact caused by load fluctuation in advance.At the same time, control parameters are dynamically switched according to spawning period and non-spawning period, and different threshold values are implemented for different sensitive grades of river section, which effectively improves the fineness of ecological protection.

Description

Technical Field

[0001] This invention belongs to the field of water ecological protection technology, specifically an optimal scheduling method for a fish-friendly hydropower system. Background Technology

[0002] Against the backdrop of rapid construction of new power systems, hydropower, with its advantages of flexible start-up and shutdown, excellent regulation performance, and low operating costs, has become a core clean energy source for peak shaving, frequency regulation, and backup in the power system. It is also a key support for ensuring the balance of regional power grid supply and demand and smoothing fluctuations in renewable energy output. At present, most industry research on optimal dispatch of hydropower systems focuses on improving the economic benefits of hydropower generation, optimizing power flow distribution, and reducing unit operation and maintenance losses. The dispatch decision-making process only considers power operation indicators such as unit equipment constraints, transmission line capacity, and power grid load supply and demand, generally ignoring the anomalies in river hydrological environment caused by deep intraday peak shaving and frequent start-up and shutdown of hydropower. The output regulation of hydropower stations directly changes the downstream river discharge, causing sudden rises and falls in water level and disorderly fluctuations in flow velocity, disrupting the natural river hydrological rhythm, and seriously interfering with the normal reproduction, habitat, and migration activities of native fish in the basin.

[0003] Current eco-friendly hydropower dispatching schemes primarily rely on coupling hydrodynamic numerical models and habitat suitability models to build simulation systems. These systems indirectly evaluate the negative impacts of hydropower dispatching on downstream fish populations by simulating changes in parameters such as river depth, flow velocity, and water area under different power generation conditions. However, these simulation models are complex in architecture and have high parameter tuning requirements. They not only require the collection of massive amounts of basic data on river topography, hydrology, meteorology, and fish species habits, but also involve lengthy simulations for each operating condition. Furthermore, the models exhibit significant nonlinear characteristics, making them difficult to integrate directly into optimal power flow dispatching models. They are also unsuitable for time-sensitive power dispatching scenarios such as day-ahead dispatching and intraday rolling dispatching, thus limiting their practicality and applicability.

[0004] However, existing ecological scheduling technologies still have two core shortcomings: First, they lack standardized and lightweight quantitative indicators, making it impossible to accurately characterize the relationship between the power output of a single hydropower station and the ecological loss of fish populations in different river sections, and making it difficult to distinguish the differences in ecological damage among different units and at different power generation periods; Second, existing schemes only carry out ecological management from the overall level of the entire basin, without being detailed to each downstream branch river section and ecologically sensitive node, making it impossible to achieve differentiated and precise management, and making it very easy to cause ecological overload problems in local sensitive habitats.

[0005] In summary, existing technologies struggle to balance scheduling timeliness, ecological control precision, and engineering implementation difficulty. There is an urgent need to design a two-layer ecological protection-oriented optimal scheduling method for hydropower that is based on measured survey data, has simple modeling, and is highly efficient in solving problems. Summary of the Invention

[0006] The purpose of this invention is to provide an optimal scheduling method for a fish-friendly hydropower system in order to solve the problems mentioned above.

[0007] The technical solution adopted in this invention is as follows: an optimal scheduling method for a fish-friendly hydropower system, comprising the following steps:

[0008] S1: Select each hydropower station to be dispatched in the target watershed, collect two types of basic data, and calculate the ecological impact factor of the hydropower station based on fish survey data;

[0009] S2: Based on the ecological impact factors obtained in Step 1, and combined with segmented fish survey data, the proportion of ecological damage from a single hydropower station in different downstream river sections is quantified, and the constrained least squares method is used to solve for the ecological allocation coefficient of the river section.

[0010] S3: Integrate the ecological impact factors and allocation coefficients obtained from the previous steps, establish linear ecological constraints, construct an ecological perception optimal power flow model, and complete the coordinated scheduling of hydropower.

[0011] In a preferred embodiment, in step S1, during the data collection and operating condition classification process, each hydropower station to be dispatched within the target basin is selected, and two types of basic data are collected: one is the historical operating data of the hydropower station, including the unit's power output and average daily power generation at different times; the other is the fish survey data of the designated monitoring section downstream, with the survey indicators prioritizing the number of fish eggs, the number of larvae, and the total number of adult fish; combined with the fish's reproductive habits, all data are divided into two major operating conditions: the fish spawning period and the non-spawning period, and abnormal data samples such as floods, extreme droughts, and large-scale human fishing are removed.

[0012] Determining the baseline stock of fish populations specifically includes:

[0013] Historical survey data were selected from areas where hydropower stations were nearly shut down and rivers maintained natural flow.

[0014] The total number of fish eggs, larvae, and adult fish at the monitoring section under this operating condition is taken as the baseline stock of the fish population, denoted as . The baseline stock represents the natural steady-state number of fish in the downstream river channel without any artificial power generation disturbance.

[0015] In a preferred embodiment, during step S1, the average daily power generation of the hydropower station under different power generation conditions is retrieved during the process of solving the ecological impact factors. and the actual stock of fish in the downstream area during the corresponding period Power generation scheduling alters river discharge, leading to a decrease in downstream fish populations. To quantify the ecological loss associated with power generation, we first calculate the difference in fish populations between the natural state and the power generation conditions. This yields the fish population loss caused by power generation disturbances, calculated using the following formula:

[0016]

[0017] In the formula: This represents the loss of fish population, expressed in tails. The baseline fish population is defined as the number of fish without any power generation disturbance; the unit is fish. The measured fish population at the downstream monitoring section under specific power generation conditions is expressed in tails. Extensive historical data has verified an approximately linear correlation between hydropower generation and fish loss within the regular dispatch interval. Therefore, using power generation as the independent variable and fish loss as the dependent variable, a univariate linear regression is performed using the well-known least squares method. The linear regression expression is as follows:

[0018]

[0019] In the formula, the slope of the fitted straight line is the ecological impact factor corresponding to the hydropower station. The unit is tails / MWh; the physical meaning of this parameter is the average number of fish lost downstream due to the hydropower station producing 1MWh of electricity. Ecological impact factors corresponding to the fish spawning season and non-spawning season were fitted and stored offline for subsequent use by the scheduling model.

[0020] In a preferred embodiment, in step S2, the ecological nodes are divided based on the river's geographical distribution, the location of fish survey monitoring sections, and the distribution characteristics of fish habitats. The downstream main stream and tributaries of the hydropower station are divided into several independent ecological nodes, and periodic fish surveys are conducted separately for each ecological node to obtain the actual fish loss observation values ​​for each node. ; where subscript Refers to the different ecological node numbers in the downstream division, subscript The sample number refers to the different historical working conditions collected, used to distinguish the fish loss data corresponding to different times and different river sections.

[0021] In a preferred embodiment, in step S2, a constrained least squares model is constructed with the objective of minimizing the fitting error of fish population loss during the coefficient solution model construction process. The objective function is the sum of squared errors between the measured fish population loss and the theoretically allocated loss at each ecological node under multiple sets of historical samples, and the specific formula is as follows:

[0022]

[0023] In the formula: This represents the overall fitting error; This represents the total number of historical samples. This represents the total number of downstream ecological nodes. It is a collection of hydropower stations within the river basin; For the first Ecological nodes under group sample Actual fish losses were measured. The ecological allocation coefficient is to be solved. These are the ecological impact factors of the hydropower station that are solidified in step one. For the first Group of samples of hydropower stations Average daily power generation. The optimization variable is the hydropower-river section ecological allocation coefficient. .

[0024] In a preferred embodiment, in step S2, the constraint formula for solving and solidifying the coefficients is as follows:

[0025]

[0026] In the formula: This constraint represents that the total ecological damage generated by a single hydropower station is fully distributed to all downstream ecological nodes, and the sum of the allocation coefficients is always equal to 1. The least squares model with the above constraints is solved using a solver to obtain the allocation coefficients for each ecological node corresponding to all hydropower stations. This model is a convex quadratic programming problem with equality constraints. The load ecological sensitivity index under the optimal solution, i.e., the node marginal ecological damage coefficient, can be derived through the KKT optimality condition. This index, short for Locational Marginal Ecological Impact (LMEI), is used to characterize the amount of fish population loss in the entire basin when the load of a single ecological node increases by one unit of active power. The calculation formula is as follows:

[0027]

[0028] In the formula: For ecological nodes The corresponding node marginal ecological damage coefficient; this indicator is calculated solely based on the allocation coefficient obtained in this step and the ecological impact factor in step one, without requiring additional simulation modeling. Finally, the ecological allocation coefficients and node marginal ecological damage coefficients corresponding to different periods are stored offline and will not be updated during the online scheduling phase.

[0029] In a preferred embodiment, in step S3, the ecological impact factor from step S1 is incorporated into the calculation of fish population damage values ​​at each ecological node. Step S2 Ecological Allocation Coefficient and the real-time output of hydropower stations The comprehensive damage value of fish populations at each downstream ecological node is calculated in real time using a linear accounting formula, as follows:

[0030]

[0031] In the formula: For ecological nodes The corresponding comprehensive fish damage value, in units of fish, is linearly represented by the formula, which characterizes the degree of damage to fish populations in different river sections caused by the power output of each hydropower station. The aforementioned node fish damage value belongs to the stock-type average ecological evaluation index, used for the control of the ecological total red line of the river section; it is used in conjunction with the node marginal ecological damage coefficient (LMEI) that has been solved offline in step two. This index is an incremental sensitivity evaluation index, used to predict the potential ecological risks brought about by load fluctuations. The two work together to achieve a two-layer ecological protection scheduling.

[0032] In a preferred embodiment, in step S3, an eco-friendly optimal power flow scheduling model is established with the global optimization objective of minimizing the sum of hydropower operation and maintenance costs and fish population ecological costs. The objective function formula is:

[0033]

[0034] In the formula: The total cost of the system; For hydroelectric power station The power generation and operation and maintenance cost function is a well-known, commonly used model in the field of hydropower dispatching. It is obtained by fitting historical operation and maintenance records of hydropower stations and water consumption characteristic data of generating units, and generally adopts a quadratic cost function form: In the formula These are the cost characteristic coefficients pre-fitted for the h-th hydropower station; This is the ecological cost conversion factor, representing the unit price for ecological restoration and resource compensation per fish, expressed in yuan / fish. It is used to convert the biological loss of fish populations into monetary costs, achieving unified optimization of power generation costs and ecological damage costs. The factor can be tiered based on the type of fish in the watershed and whether it is a protected species: 30-80 yuan / fish for common economic fish; 100-300 yuan / fish for native common fish in the watershed; and 400-600 yuan / fish for rare and protected fish. The value represents the damage to fish populations at each ecological node.

[0035] In a preferred embodiment, step S3, which involves simultaneously configuring conventional hydropower scheduling constraints and newly added ecological constraints, specifically includes:

[0036] (1) Active power balance constraint: This constraint is used to ensure real-time balance between active power supply and demand in the power grid; whereby... The total active power output of all power sources in the power system other than the hydropower station optimized and regulated by this invention includes external power sources such as thermal power, photovoltaic power, wind power and energy storage. This invention only focuses on hydropower as the optimization object, and the output of other power sources is regarded as known fixed parameters.

[0037] (2) Upper and lower limits of hydropower station output constraints: This constraint is used to limit the power output range of a single hydropower station, taking into account both the upper limit of the unit's equipment capacity and the minimum discharge flow requirement of the river channel;

[0038] (3) Line transmission capacity constraints: This constraint is used to avoid overload and exceeding limits in transmission lines and to ensure the safe operation of power network transmission.

[0039] (4) Ecological red line constraints at nodes: This constraint is used to control the ecological damage value of a single river section and avoid devastating destruction of fish populations in local sensitive habitats;

[0040] (5) Overall ecological constraints: This constraint limits the total loss of fish populations at the overall watershed level, achieving macro-level control over the total ecological volume of the entire watershed;

[0041] (6) Node-based marginal ecological early warning constraints: This constraint matches the marginal ecological damage coefficient of the aforementioned nodes to manage incremental risks; combined with the existing ecological red line constraint, it limits the load growth of highly ecologically sensitive nodes, avoids sudden ecological damage to fish populations caused by load fluctuations from the source, and completes a two-layer ecological protection of existing damage and incremental risks.

[0042] in: For the electricity load of ecological nodes; These are the upper and lower limits of the power output of the hydropower station; These are the line transmission power and maximum transmission capacity, respectively. This represents the upper limit of ecological damage at a single node. The total value of the ecological red line is preset for the entire watershed; This represents the marginal ecological damage coefficient corresponding to the ecological node. The threshold for marginal ecological damage is preset for each ecological node and set differently according to the sensitivity level of the river section habitat. The threshold is lowered for sensitive river sections during the spawning period to strengthen ecological management.

[0043] In a preferred embodiment, in step S3, during the actual scheduling process, it is first determined whether the current operating period belongs to the fish spawning season or not, and the offline solidified ecological impact factors, ecological allocation coefficients, and node marginal ecological damage coefficients under the corresponding operating conditions are retrieved; the above-mentioned pre-parameters are embedded into the ecologically-aware optimal power flow scheduling model, and the complete optimization model is solved using a commercial convex optimization solver. Since all ecological constraints in this invention are linearized, the overall model belongs to the category of linear programming, which has the characteristics of fast solution speed and strong convergence stability, and can be adapted to various scheduling scenarios such as day-ahead scheduling and intraday rolling scheduling. After the model is solved, the optimal power output command of each hydropower station that takes into account both power generation and maintenance costs and the ecological protection of fish populations in the basin is output, and the command is sent to the corresponding hydropower station control units to complete the intelligent collaborative scheduling of cascade / independent hydropower stations.

[0044] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0045] 1. In this invention, all core ecological parameters are solved offline based on conventional fish survey data and stored in a fixed manner. During online scheduling, there is no need to call any hydrodynamic simulation model. The fish damage value of each node can be quickly obtained directly through linear calculation formula. The overall model belongs to the category of linear programming, with fast solution speed and stable convergence. It can be seamlessly embedded into the day-ahead scheduling and intraday rolling scheduling process, fully meeting the timeliness requirements of power dispatch.

[0046] 2. In this invention, independent red lines are set for each ecological node based on the comprehensive fish population damage value, achieving precise limits for local river sections. Simultaneously, a marginal ecological damage coefficient is introduced as an incremental sensitivity indicator to proactively mitigate potential ecological impacts caused by load fluctuations. Furthermore, control parameters are dynamically switched between spawning and non-spawning periods, implementing differentiated threshold management for river sections with different sensitivity levels, effectively improving the precision of ecological protection.

[0047] 3. In this invention, the relevant parameters are entirely based on existing survey data, requiring no additional monitoring equipment. The indicators are clearly defined, and the operation is simple. It is applicable to both independent hydropower stations and cascade hydropower station groups, and is compatible with complex power systems containing multiple types of power sources. Technicians only need to adjust a few coefficients according to local fish species and protection levels to quickly adapt the application. The engineering implementation cost is low, and it has broad promotional value. Attached Figure Description

[0048] Figure 1 This is a schematic diagram illustrating the process principle of the optimal scheduling method for the fish-friendly hydropower system of the present invention. Detailed Implementation

[0049] 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 embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0050] Example:

[0051] Reference Figure 1 An optimal scheduling method for a fish-friendly hydropower system includes the following steps:

[0052] S1: Select each hydropower station to be dispatched in the target watershed, collect two types of basic data, and calculate the ecological impact factor of the hydropower station based on fish survey data;

[0053] S2: Based on the ecological impact factors obtained in Step 1, and combined with segmented fish survey data, the proportion of ecological damage from a single hydropower station in different downstream river sections is quantified, and the constrained least squares method is used to solve for the ecological allocation coefficient of the river section.

[0054] S3: Integrate the ecological impact factors and allocation coefficients obtained from the previous steps, establish linear ecological constraints, construct an ecological perception optimal power flow model, and complete the coordinated scheduling of hydropower.

[0055] In step S1, during the data collection and operational condition classification process, each hydropower station to be dispatched within the target basin is selected, and two types of basic data are collected: one is the historical operation data of the hydropower station, including the unit's power output and average daily power generation at different times; the other is the fish survey data of the designated monitoring section downstream, with priority given to the number of fish eggs, the number of larvae, and the total number of adult fish. In combination with the fish's reproductive habits, all data are divided into two major operational conditions: the fish spawning period and the non-spawning period, and abnormal data samples such as floods, extreme droughts, and large-scale human fishing are removed.

[0056] Determining the baseline stock of fish populations specifically includes:

[0057] Historical survey data were selected from areas where hydropower stations were nearly shut down and rivers maintained natural flow.

[0058] The total number of fish eggs, larvae, and adult fish at the monitoring section under this operating condition is taken as the baseline stock of the fish population, denoted as . The baseline stock represents the natural steady-state number of fish in the downstream river channel without any artificial power generation disturbance.

[0059] In step S1, during the process of solving for the ecological impact factors, the average daily power generation of the hydropower station under different power generation conditions is retrieved. and the measured stock of fish in the downstream area during the corresponding period Power generation scheduling alters river discharge, leading to a decrease in downstream fish populations. To quantify the ecological loss associated with power generation, we first calculate the difference in fish populations between the natural state and the power generation conditions to obtain the fish population loss caused by power generation disturbances. The calculation formula is as follows:

[0060]

[0061] In the formula: This represents the loss of fish population, expressed in tails. The baseline fish population is defined as the number of fish without any power generation disturbance; the unit is fish. The measured fish population at the downstream monitoring section under specific power generation conditions is expressed in tails. Extensive historical data has verified an approximately linear correlation between hydropower generation and fish loss within the regular dispatch interval. Therefore, using power generation as the independent variable and fish loss as the dependent variable, a univariate linear regression is performed using the well-known least squares method. The linear regression expression is as follows:

[0062]

[0063] In the formula, the slope of the fitted straight line is the ecological impact factor corresponding to the hydropower station. The unit is tails / MWh; the physical meaning of this parameter is the average number of fish lost downstream due to the hydropower station producing 1MWh of electricity. Ecological impact factors corresponding to the fish spawning season and non-spawning season were fitted and stored offline for subsequent use by the scheduling model.

[0064] In step S2, the ecological nodes are divided based on the river's geographical distribution, the location of fish survey monitoring sections, and the distribution characteristics of fish habitats. The downstream main stream and tributaries of the hydropower station are divided into several independent ecological nodes. Periodic fish surveys are conducted separately for each ecological node to obtain the actual fish loss observation values ​​for each node. ; where subscript Refers to the different ecological node numbers in the downstream division, subscript The sample number refers to the different historical working conditions collected, used to distinguish the fish loss data corresponding to different times and different river sections.

[0065] In step S2, a constrained least squares model is constructed to minimize the fitting error of fish population loss during the coefficient solution process. The objective function is the sum of squared errors between the measured fish population loss and the theoretically allocated loss at each ecological node under multiple historical samples. The specific formula is as follows:

[0066]

[0067] In the formula: This represents the overall fitting error; This represents the total number of historical samples. This represents the total number of downstream ecological nodes. It is a collection of hydropower stations within the river basin; For the first Ecological nodes under group sample Actual fish losses were measured. The ecological allocation coefficient is to be solved. These are the ecological impact factors of the hydropower station that are solidified in step one. For the first Group of samples of hydropower stations Average daily power generation. The optimization variable is the hydropower-river section ecological allocation coefficient. .

[0068] In step S2, the constraint formulas for solving and solidifying the coefficients are as follows:

[0069]

[0070] In the formula: This constraint represents that the total ecological damage generated by a single hydropower station is fully distributed to all downstream ecological nodes, and the sum of the allocation coefficients is always equal to 1. The least squares model with the above constraints is solved using a solver to obtain the allocation coefficients for each ecological node corresponding to all hydropower stations. This model is a convex quadratic programming problem with equality constraints. The load ecological sensitivity index under the optimal solution, i.e., the node marginal ecological damage coefficient, can be derived through the KKT optimality condition. This index, short for Locational Marginal Ecological Impact (LMEI), is used to characterize the amount of fish population loss in the entire basin when the load of a single ecological node increases by one unit of active power. The calculation formula is as follows:

[0071]

[0072] In the formula: For ecological nodes The corresponding node marginal ecological damage coefficient; this indicator is calculated solely based on the allocation coefficient obtained in this step and the ecological impact factor in step one, without requiring additional simulation modeling. Finally, the ecological allocation coefficients and node marginal ecological damage coefficients corresponding to different periods are stored offline and will not be updated during the online scheduling phase.

[0073] In step S3, the calculation of fish population damage values ​​at each ecological node is combined with the ecological impact factors from step S1. Step S2 Ecological Allocation Coefficient and the real-time output of hydropower stations The comprehensive damage value of fish populations at each downstream ecological node is calculated in real time using a linear accounting formula, as follows:

[0074]

[0075] In the formula: For ecological nodes The corresponding comprehensive fish damage value, in units of fish, is linearly represented by the formula, which characterizes the degree of damage to fish populations in different river sections caused by the power output of each hydropower station. The aforementioned node fish damage value belongs to the stock-type average ecological evaluation index, used for the control of the ecological total red line of the river section; it is used in conjunction with the node marginal ecological damage coefficient (LMEI) that has been solved offline in step two. This index is an incremental sensitivity evaluation index, used to predict the potential ecological risks brought about by load fluctuations. The two work together to achieve a two-layer ecological protection scheduling.

[0076] In step S3, an eco-friendly optimal power flow scheduling model is established with the global optimization objective of minimizing the sum of hydropower operation and maintenance costs and fish population ecological costs. The objective function formula is as follows:

[0077]

[0078] In the formula: The total cost of the system; For hydroelectric power station The power generation and operation and maintenance cost function is a well-known, commonly used model in the field of hydropower dispatching. It is obtained by fitting historical operation and maintenance records of hydropower stations and water consumption characteristic data of generating units, and generally adopts a quadratic cost function form: In the formula These are the cost characteristic coefficients pre-fitted for the h-th hydropower station; This is the ecological cost conversion factor, representing the unit price for ecological restoration and resource compensation per fish, expressed in yuan / fish. It is used to convert the biological loss of fish populations into monetary costs, achieving unified optimization of power generation costs and ecological damage costs. The factor can be tiered based on the type of fish in the watershed and whether it is a protected species: 30-80 yuan / fish for common economic fish; 100-300 yuan / fish for native common fish in the watershed; and 400-600 yuan / fish for rare and protected fish. The value represents the damage to fish populations at each ecological node.

[0079] In step S3, the synchronous configuration of conventional hydropower dispatch constraints and newly added ecological constraints specifically includes:

[0080] (1) Active power balance constraint: This constraint is used to ensure real-time balance between active power supply and demand in the power grid; whereby... The total active power output of all power sources in the power system other than the hydropower station optimized and regulated by this invention includes external power sources such as thermal power, photovoltaic power, wind power and energy storage. This invention only focuses on hydropower as the optimization object, and the output of other power sources is regarded as known fixed parameters.

[0081] (2) Upper and lower limits of hydropower station output constraints: This constraint is used to limit the power output range of a single hydropower station, taking into account both the upper limit of the unit's equipment capacity and the minimum discharge flow requirement of the river channel;

[0082] (3) Line transmission capacity constraints: This constraint is used to avoid overload and exceeding limits in transmission lines and to ensure the safe operation of power network transmission.

[0083] (4) Ecological red line constraints at nodes: This constraint is used to control the ecological damage value of a single river section and avoid devastating destruction of fish populations in local sensitive habitats;

[0084] (5) Overall ecological constraints: This constraint limits the total loss of fish populations at the overall watershed level, achieving macro-level control over the total ecological volume of the entire watershed;

[0085] (6) Node-based marginal ecological early warning constraints: This constraint matches the marginal ecological damage coefficient of the aforementioned nodes to manage incremental risks; combined with the existing ecological red line constraint, it limits the load growth of highly ecologically sensitive nodes, avoids sudden ecological damage to fish populations caused by load fluctuations from the source, and completes a two-layer ecological protection of existing damage and incremental risks.

[0086] in: For the electricity load of ecological nodes; These are the upper and lower limits of the power output of the hydropower station; These are the line transmission power and maximum transmission capacity, respectively. This represents the upper limit of ecological damage at a single node. The total value of the ecological red line is preset for the entire watershed; This represents the marginal ecological damage coefficient corresponding to the ecological node. The threshold for marginal ecological damage is preset for each ecological node and set differently according to the sensitivity level of the river section habitat. The threshold is lowered for sensitive river sections during the spawning period to strengthen ecological management.

[0087] In step S3, during the actual scheduling process, it is first determined whether the current operating period belongs to the spawning season or non-spawning season of fish. The corresponding offline solidified ecological impact factors, ecological allocation coefficients, and node marginal ecological damage coefficients are retrieved. These pre-set parameters are embedded into the ecologically-aware optimal power flow scheduling model, and the complete optimization model is solved using a commercial convex optimization solver. Since all ecological constraints in this invention are linearized, the overall model belongs to the category of linear programming, possessing the characteristics of fast solution speed and strong convergence stability, and can be adapted to various scheduling scenarios such as day-ahead scheduling and intraday rolling scheduling. After the model is solved, the optimal power output command for each hydropower station, balancing power generation and maintenance costs with the ecological protection of fish populations in the basin, is output. The command is then sent to the corresponding hydropower station control units, completing the intelligent collaborative scheduling of cascade / independent hydropower stations.

[0088] In summary, this invention significantly reduces the difficulty of constructing and solving ecological scheduling models, and substantially improves computational efficiency. Existing mainstream technologies rely excessively on coupled hydrodynamic numerical simulations and habitat suitability models. These methods require the collection of extensive basic data, such as river cross-section topography, hydrological boundary conditions, and fish swimming ability curves, and each simulation can take several hours, failing to meet the practical time constraints of day-ahead and intraday rolling scheduling in power systems. This invention completely abandons this complex approach, instead relying on fish field survey data routinely conducted by various watershed management agencies. Ecological impact factors are directly obtained through least squares fitting, and allocation coefficients are solved using a constrained least squares model. All core ecological parameters are solved offline and stored in a single, fixed manner. During the online scheduling phase, no repeated simulation calculations are required; simply substituting the real-time output of the hydropower station into the linear calculation formula quickly yields the fish damage values ​​at each node. The overall model falls under the category of linear programming, offering fast solution speed and strong convergence stability. It can be seamlessly embedded into existing power dispatching systems, fundamentally solving the technical bottleneck of the difficulty in engineering the implementation of ecological models.

[0089] This invention constructs a complete ecological protection hierarchy, from total quantity control to incremental early warning, and from the entire basin to local nodes. Traditional scheduling schemes only set single, overall ecological constraints, failing to distinguish the sensitivity differences of different river sections during fish breeding, feeding, and overwintering periods. This can easily lead to local ecological collapse in individual spawning grounds or rare habitats due to overloading. This invention, on the one hand, uses the comprehensive fish population damage value to implement stock red line control for each ecological node, ensuring that the absolute loss in any sensitive river section does not exceed the threshold. On the other hand, it introduces the node marginal ecological damage coefficient as a sensitivity indicator for incremental dimensions. This coefficient can quantify the marginal ecological cost of the entire basin caused by the increase in load at a single node, and based on this, incremental early warning constraints are set to limit the load growth rate in highly sensitive areas from the source. The two work together to enable scheduling decisions to control the total loss while avoiding the potential ecological impact of sudden load fluctuations. At the same time, this invention establishes different parameter systems for spawning and non-spawning periods, and the control standards are dynamically adjusted according to the fish life cycle, achieving a precise match between protection efforts and actual ecological needs.

[0090] This invention possesses excellent versatility and a very low reproduction threshold, making it suitable for widespread application in various river basins. The calculation of the proposed ecological impact factors and allocation coefficients relies entirely on routine fish survey data, requiring no additional monitoring equipment or specialized experiments. The physical meaning of the indicators is clear, and relevant technical personnel can independently complete parameter solving and model configuration after simple training. This solution is not only applicable to the daily scheduling of a single independent hydropower station but can also be flexibly extended to cascade hydropower station groups and complex power systems containing multiple types of power sources such as thermal power, photovoltaic, and wind power. Addressing the differences in fish species composition and protection levels across different river basins, users only need to adjust the ecological cost conversion coefficient and the sensitivity threshold of each node according to the actual situation to quickly adapt to local ecological protection needs without any modifications to the main structure of the model. The technology is highly replicable, with low engineering implementation costs, providing a universal green scheduling solution for river basins with different levels of hydropower development and varying ecological protection requirements.

[0091] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the term "comprising" or any other variations thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0092] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An optimal scheduling method for a fish-friendly hydropower system, characterized by: The method includes the following steps: S1: Select each hydropower station to be dispatched in the target watershed, collect two types of basic data, and calculate the ecological impact factor of the hydropower station based on fish survey data; S2: Based on the ecological impact factors obtained in Step 1, and combined with segmented fish survey data, the proportion of ecological damage from a single hydropower station in different downstream river sections is quantified, and the constrained least squares method is used to solve for the ecological allocation coefficient of the river section. S3: Integrate the ecological impact factors and allocation coefficients obtained from the previous steps, establish linear ecological constraints, construct an ecological perception optimal power flow model, and complete the coordinated scheduling of hydropower.

2. The optimal scheduling method for a fish-friendly hydropower system according to claim 1, characterized in that: In step S1, during the data collection and operating condition classification process, each hydropower station to be dispatched in the target basin is selected, and two types of basic data are collected: one is the historical operating data of the hydropower station, including the unit's power output and average daily power generation at different times; the other is the fish survey data of the designated monitoring section downstream, with the survey indicators prioritizing the number of fish eggs, the number of larvae, and the total number of adult fish. Determining the baseline stock of fish populations specifically includes: Historical survey data were selected from areas where hydropower stations were nearly shut down and rivers maintained natural flow. The total number of fish eggs, larvae, and adult fish at the monitoring section under this operating condition is taken as the baseline stock of the fish population, denoted as . The baseline stock represents the natural steady-state number of fish in the downstream river channel without any artificial power generation disturbance.

3. The optimal scheduling method for a fish-friendly hydropower system according to claim 1, characterized in that: In step S1, during the process of solving for the ecological impact factors, the average daily power generation of the hydropower station under different power generation conditions is retrieved. and the measured stock of fish in the downstream area during the corresponding period Power generation scheduling alters river discharge, leading to a decrease in downstream fish populations. To quantify the ecological loss associated with power generation, we first calculate the difference in fish populations between the natural state and the power generation conditions to obtain the fish population loss caused by power generation disturbances. The calculation formula is as follows: In the formula: This represents the loss of fish population, expressed in tails. The baseline fish population is defined as the number of fish without any power generation disturbance; the unit is fish. The measured fish population at the downstream monitoring section under specific power generation conditions is expressed in tails. Verification based on extensive historical data shows that hydropower generation and fish loss within the regular dispatching interval exhibit an approximately linear correlation. Therefore, using power generation as the independent variable and fish loss as the dependent variable, a univariate linear regression fitting was performed using the well-known least squares method. The linear fitting expression is as follows: In the formula, the slope of the fitted straight line is the ecological impact factor corresponding to the hydropower station. The unit is fish / MWh; the physical meaning of this parameter is the average number of fish lost downstream due to the hydropower station producing 1MWh of electricity.

4. The optimal scheduling method for a fish-friendly hydropower system according to claim 1, characterized in that: In step S2, the ecological nodes are divided based on the river's geographical distribution, the location of fish survey monitoring sections, and the distribution characteristics of fish habitats. The downstream main stream and tributaries of the hydropower station are divided into several independent ecological nodes. Periodic fish surveys are conducted separately for each ecological node to obtain the actual fish loss observation values ​​for each node. ; where subscript Refers to the different ecological node numbers in the downstream division, subscript The sample number refers to the different historical working conditions collected, used to distinguish the fish loss data corresponding to different times and different river sections.

5. The optimal scheduling method for a fish-friendly hydropower system according to claim 1, characterized in that: In step S2, a constrained least squares model is constructed to minimize the fitting error of fish population loss during the coefficient solution process. The objective function is the sum of squared errors between the measured fish population loss and the theoretically allocated loss at each ecological node under multiple historical samples. The specific formula is as follows: In the formula: This represents the overall fitting error; This represents the total number of historical samples. This represents the total number of downstream ecological nodes. It is a collection of hydropower stations within the river basin; For the first Ecological nodes under group sample Actual fish losses were measured. The ecological allocation coefficient is to be solved. These are the ecological impact factors of the hydropower station that are solidified in step one. For the first Group of samples of hydropower stations Average daily power generation; the optimization variable is the hydropower-river section ecological allocation coefficient. .

6. The optimal scheduling method for a fish-friendly hydropower system according to claim 1, characterized in that: In step S2, the constraint formula for solving and solidifying the coefficients is as follows: In the formula: This constraint represents that the total ecological damage generated by a single hydropower station is fully distributed to all downstream ecological nodes, and the sum of the allocation coefficients is always equal to 1; The solver is used to solve the above-mentioned constraint least squares model to obtain the allocation coefficients for each ecological node corresponding to all hydropower stations; This model is a convex quadratic programming problem with equality constraints, and the load ecological sensitivity index under the optimal solution, i.e., the node marginal ecological damage coefficient, can be derived through the KKT optimality condition; This index is used to characterize the amount of fish loss in the entire basin when the load of a single ecological node increases by one unit of active power, and the calculation formula is as follows: In the formula: For ecological nodes The corresponding node marginal ecological damage coefficient; this indicator is calculated solely based on the allocation coefficient obtained in this step and the ecological impact factor in step one, without the need for additional simulation modeling; finally, the ecological allocation coefficients and node marginal ecological damage coefficients corresponding to different periods are uniformly stored offline and will not be updated during the online scheduling phase.

7. The optimal scheduling method for a fish-friendly hydropower system according to claim 1, characterized in that: In step S3, the calculation of fish population damage values ​​at each ecological node is combined with the ecological impact factors from step S1. Step S2 Ecological Allocation Coefficient and the real-time output of hydropower stations The comprehensive damage value of fish populations at each downstream ecological node is calculated in real time using a linear accounting formula, as follows: In the formula: For ecological nodes The corresponding overall damage value to the fish population, in units of fish; This formula linearly characterizes the degree of damage to fish populations in different river sections caused by the power output of each hydropower station; the aforementioned fish population damage values ​​at the nodes belong to the stock-type average ecological evaluation indicators, which are used for the control of the total ecological red line of the river section; the node marginal ecological damage coefficient (LMEI) that has been solved offline in step two is used in conjunction with this formula.

8. The optimal scheduling method for a fish-friendly hydropower system according to claim 1, characterized in that: In step S3, an eco-friendly optimal power flow scheduling model is established with the global optimization objective of minimizing the sum of hydropower operation and maintenance costs and fish population ecological costs. The objective function formula is as follows: In the formula: The total cost of the system; For hydroelectric power station The power generation and operation and maintenance cost function is a well-known, commonly used model in the field of hydropower dispatching. It is obtained by fitting historical operation and maintenance records of hydropower stations and water consumption characteristic data of generating units, and generally adopts a quadratic cost function form: In the formula These are the cost characteristic coefficients pre-fitted for the h-th hydropower station; This is the ecological cost conversion factor, representing the unit price for ecological restoration and resource compensation per fish, expressed in yuan / fish. It is used to convert the biological loss of fish populations into monetary costs, achieving unified optimization of power generation costs and ecological damage costs. The factor can be tiered based on the type of fish in the watershed and whether it is a protected species: 30-80 yuan / fish for common economic fish; 100-300 yuan / fish for native common fish in the watershed; and 400-600 yuan / fish for rare and protected fish. The value represents the damage to fish populations at each ecological node.

9. The optimal scheduling method for a fish-friendly hydropower system according to claim 1, characterized in that: In step S3, the synchronous configuration of conventional hydropower scheduling constraints and newly added ecological constraints specifically includes: (1) Active power balance constraint: This constraint is used to ensure real-time balance between active power supply and demand in the power grid; whereby... The total active power output of all power sources in the power system other than the hydropower station optimized and regulated by this invention, including external power sources such as thermal power, photovoltaic power, wind power and energy storage; (2) Upper and lower limits of hydropower station output constraints: This constraint is used to limit the power output range of a single hydropower station; (3) Line transmission capacity constraints: This constraint is used to prevent overload and exceeding limits in transmission lines; (4) Ecological red line constraints at nodes: This constraint is used to control the ecological damage value of a single river section; (5) Overall ecological constraints: This constraint limits the total loss of fish populations at the overall watershed level; (6) Node-based marginal ecological early warning constraints: This constraint is matched with the aforementioned node marginal ecological damage coefficient for incremental risk management; in: For the electricity load of ecological nodes; These are the upper and lower limits of the power output of the hydropower station; These are the line transmission power and maximum transmission capacity, respectively. This represents the upper limit of ecological damage at a single node. The total value of the ecological red line is preset for the entire watershed; This represents the marginal ecological damage coefficient corresponding to the ecological node. Preset marginal ecological damage thresholds for each ecological node.

10. The optimal scheduling method for a fish-friendly hydropower system according to claim 1, characterized in that: In step S3, during the actual scheduling process, it is first determined whether the current running segment belongs to the spawning period or non-spawning period of fish, and the ecological impact factors, ecological allocation coefficients and node marginal ecological damage coefficients that have been fixed offline under the corresponding working conditions are retrieved; the above-mentioned pre-parameters are embedded into the ecological perception optimal power flow scheduling model, and the complete optimization model is solved by relying on a commercial convex optimization solver.

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