A Clean Energy Consumption Assessment Method for Optimizing and Regulating Water Storage Flexibility

Abstract

This invention relates to a method for evaluating the clean energy consumption of water-storage flexible resource optimization and regulation, belonging to the field of multi-energy complementary scheduling technology of water, wind, solar, storage, and nuclear power. It includes: constructing a water-storage flexible resource optimization and regulation model with the objective function of minimizing the sum of squared deviations of remaining load; using the firefly algorithm to solve the optimization and regulation model for different types of power sources such as water, wind, solar, storage, and nuclear power, considering their operating boundary conditions; constructing an evaluation index system for clean energy consumption of water-storage flexible resource optimization and regulation from the perspectives of clean development, system stability, and safety and reliability; proposing calculation methods for each evaluation index based on its meaning; and using the analytic hierarchy process (AHP) to perform comprehensive analysis and calculation based on the calculation results of each evaluation index to determine the clean energy consumption index of water-storage flexible resource optimization and regulation, and thereby judge the clean energy consumption level of the multi-energy complementary system under the effect of water-storage flexible resource optimization and regulation.

Description

Technical Field

[0001] This invention belongs to the field of multi-energy complementary scheduling technology of water, wind, solar, storage and nuclear power, and specifically relates to a clean energy consumption assessment method for optimizing the regulation of water storage flexibility resources. Background Technology

[0002] In recent years, wind power and photovoltaic power have developed rapidly. However, the randomness, fluctuation, and intermittency of their power generation output directly restrict the large-scale development and utilization of wind and solar power. Optimizing the regulation of hydropower and pumped storage power stations to smooth out fluctuations in wind and solar power output is beneficial for promoting the development and utilization of clean energy resources and contributing to the construction of new power systems. Since the scheduling of a multi-energy complementary system involving hydropower, wind, solar, and storage is a complex optimization problem involving multiple coupled factors, the assessment of clean energy consumption under the optimized regulation of hydropower and storage flexibility resources is inevitably more complex, and there are currently no mature assessment methods domestically or internationally. Therefore, to achieve accurate assessment of clean energy consumption under the optimized regulation of hydropower and storage flexibility resources, and to provide technical support for the multi-energy complementary scheduling operation and optimized allocation of response capacity of clean power sources such as hydropower, wind, solar, storage, and nuclear power, it is urgent to research a method for assessing clean energy consumption under the optimized regulation of hydropower and storage flexibility resources. This method would enable the assessment of clean energy consumption in the scheduling operation mode of multi-energy complementary systems and provide key technical support for the integrated operation of hydropower, wind, solar, and storage clean energy bases. Summary of the Invention

[0003] The purpose of this invention is to solve the technical problems existing in the prior art, and to address the lack of a clean energy consumption assessment index system and assessment method, by providing a clean energy consumption assessment method based on the flexible resource optimization and regulation of water storage, so as to achieve accurate assessment of the clean energy consumption level of water, wind, solar and storage.

[0004] To achieve the above objectives, the technical solution of the present invention is: a method for evaluating the absorption of clean energy through flexible water storage resource optimization, comprising the following steps:

[0005] S1. Construct a water storage flexibility resource optimization and regulation model with the objective function of minimizing the sum of squared deviations of the remaining load;

[0006] S2. For the operating boundary conditions of different types of power supplies, the firefly algorithm is used to solve the optimization and adjustment model.

[0007] S3. Construct an evaluation index system for clean energy consumption based on the flexible resource regulation of water storage;

[0008] S4. Based on the meaning of different indicators, propose the calculation methods for each evaluation indicator;

[0009] S5. Based on the calculation results of various evaluation indicators, the analytic hierarchy process (AHP) is used to conduct a comprehensive analysis and calculation to determine the clean energy consumption index of water storage flexibility resource optimization and regulation. Based on this, the clean energy consumption level of the multi-energy complementary system under the water storage flexibility resource optimization and regulation effect is judged.

[0010] In one embodiment of the present invention, in step S1, a water storage flexibility resource optimization and regulation model is constructed with the objective function of minimizing the sum of squared deviations of the remaining load, based on the operating principles of clean energy sources including wind power, photovoltaics, conventional hydropower, pumped storage, and nuclear power.

[0011] In one embodiment of the present invention, in step S3, a clean energy consumption evaluation index system is constructed from aspects including clean development, system stability, and safety and reliability, to optimize the regulation of water storage flexibility resources.

[0012] In one embodiment of the present invention, step S1 involves constructing a water storage flexibility resource optimization and regulation model with the objective function of minimizing the sum of squared deviations of the remaining load. This specifically includes the following steps:

[0013] S11. Calculate the sum of power generation output of power plants of wind power, photovoltaic power, conventional hydropower, pumped storage and nuclear power at the same time. The output of pumped storage power plants under pumping conditions is calculated as the negative value of pumping power.

[0014] S12. Based on the load demand process curve of the power grid, subtract the load demand at the corresponding time from the total power generation output of hydropower, wind power, solar power, energy storage and nuclear power to calculate the remaining load of the power grid at the corresponding time. This process is repeated until the remaining load at all times within the calculation period is obtained. The sum of squared deviations of the remaining load sequence is used as the objective function of the hydropower flexible resource optimization and regulation model.

[0015] S13. Consider the power output constraints of wind power, photovoltaic power, conventional hydropower, pumped storage, and nuclear power; the reservoir capacity constraints and flow constraints of hydropower and pumped storage; and various types of constraints, including the switching constraints between pumping and power generation operating conditions of pumped storage power stations.

[0016] In one embodiment of the present invention, step S2, which uses the firefly algorithm to solve the optimization and adjustment model, specifically includes the following steps:

[0017] S21. Initialize basic algorithm parameters: Set the number of fireflies n, maximum attraction β0, light intensity absorption coefficient γ, step size factor α, and maximum number of iterations or search accuracy ε.

[0018] S22. Randomly initialize the positions of the fireflies and calculate the objective function value of each firefly as its maximum fluorescence brightness I0.

[0019] S23. Calculate the relative brightness I of fireflies in the population.il and attraction β il The direction of firefly movement is determined by the phase brightness;

[0020]

[0021] In the formula: I il I represents the fluorescence intensity of firefly i relative to firefly l. i,0 β represents the maximum fluorescence intensity of firefly i, i.e., the fluorescence intensity at its position r = 0, which is determined by the objective function value; il β represents the attraction of firefly i to firefly l; i,0 γ represents the maximum attraction of firefly i, i.e., the attraction at r = 0; γ is the light intensity absorption coefficient; r il For fireflies i, i.e. x i With fireflies l i x l The distance between them; d represents the spatial dimension; x i,j x l,j They are firefly i and x respectively. i and fireflies l that is x l The coordinates of the j-th component in d-dimensional space;

[0022] S24. Update the spatial position of fireflies and randomly move fireflies in the best position.

[0023]

[0024] In the formula: Let i be the firefly calculated for the t-th search; α t Let be the step size factor calculated for the t-th search; It is a random factor that follows a Gaussian or uniform distribution;

[0025] S25. Based on the updated position of the firefly, recalculate the brightness of the firefly, i.e., calculate the objective function value after the position update.

[0026] S26. Compare the objective function value of the firefly after the position update with the historical best solution to determine the current best solution;

[0027] S27. If the maximum number of iterations or search precision ε is not satisfied, return to step S23 and perform the next search calculation; if the maximum number of iterations or search precision ε is satisfied, proceed to step S28.

[0028] S28. Output the power generation process of each power station, the water level process and the flow process of hydropower and pumped storage power stations corresponding to the optimal solution.

[0029] In one embodiment of the present invention, step S3 involves constructing a clean energy consumption evaluation index system based on the optimization and regulation of water storage flexibility resources, considering aspects such as clean development, system stability, and safety and reliability. This specifically includes the following steps:

[0030] S31. The assessment indicators for clean development include the proportion of clean energy installed capacity, clean energy power generation, clean energy curtailment, clean energy power generation utilization hours, clean energy power generation utilization rate, and average channel utilization rate.

[0031] S32. The evaluation indicators for system stability include the coefficient of variation of total clean energy output, the maximum ramp rate of total clean energy output, the coefficient of variation of nuclear power output, the coefficient of variation of reservoir water level, and the coefficient of variation of outflow.

[0032] S33. Evaluation indicators for safety and reliability include the proportion of power supply capacity that can be flexibly adjusted, daily power margin, daily minimum power margin, maximum load shedding depth, minimum margin for increasing output, and minimum margin for decreasing output.

[0033] In one embodiment of the present invention, step S4 proposes calculation methods for each evaluation indicator based on the meaning of different indicators, specifically including the following steps:

[0034] S41. The installed capacity ratio of clean energy is equal to the ratio of the installed capacity of clean power sources such as hydropower, wind power, solar power, energy storage, and nuclear power to the installed capacity of all power sources in the power system. The power generation of clean energy is equal to the sum of the power generation of clean power sources such as hydropower, wind power, solar power, energy storage, and nuclear power during the calculation period. The power curtailment of clean energy is equal to the sum of the power that clean power sources such as hydropower, wind power, solar power, energy storage, and nuclear power failed to connect to the grid during the calculation period. The utilization hours of clean energy power generation are equal to the ratio of the total power generation of each type of clean energy to its installed capacity. The utilization rate of clean energy power generation is equal to the ratio of the actual grid-connected power of each type of clean energy to the power that can be generated (if there is no curtailment, the utilization rate is 100%). The average channel utilization rate is the ratio of the average load of transmission during the calculation period to the rated transmission capacity of the channel. Proceed to step S42.

[0035] S42. The coefficient of variation of total clean energy output is equal to the ratio of the mean square of the total power generation output sequence of clean power sources such as hydropower, wind power, solar power, energy storage, and nuclear power within the calculation period to its mean value. The maximum ramp rate of total clean energy output is equal to the maximum value of the ratio of the change in total power generation output of clean power sources such as hydropower, wind power, solar power, energy storage, and nuclear power within the calculation period to the total power generation output of that period (if the total power generation output increases in adjacent periods, it is an upward ramp; if the total power generation output decreases, it is a downward ramp). The coefficient of variation of nuclear power output is equal to the ratio of the mean square of the nuclear power output sequence within the calculation period to its mean value. The coefficient of variation of reservoir water level is equal to the ratio of the mean square of the water level sequence of each reservoir within the calculation period to its mean value. The coefficient of variation of outflow is equal to the ratio of the mean square of the outflow sequence of each reservoir within the calculation period to its mean value. Proceed to step S43.

[0036] S43. The installed capacity ratio of flexible power sources is equal to the ratio of the installed capacity of flexible power sources such as hydropower and pumped storage to the installed capacity of all power sources in the power system. The daily power margin is equal to the maximum daily power generation (including daily purchased power) minus the daily power consumption (including daily transmitted power). The daily minimum power margin is equal to the minimum difference between the total power generation output (including power purchased from tie lines) and the power load (including power transmitted from tie lines) during each period of the day. The maximum load shedding depth is equal to the maximum difference between the power load (including power transmitted from tie lines) and the total power generation output (including power purchased from tie lines) during periods of insufficient power generation output. The minimum margin for increasing power output is equal to the minimum value after the power output of flexible power sources such as hydropower and pumped storage can be increased during the calculation period. The minimum margin for decreasing power output is equal to the minimum value after the power output of flexible power sources such as hydropower and pumped storage can be decreased during the calculation period.

[0037] In one embodiment of the present invention, in step S5, based on the calculation results of various evaluation indicators, a comprehensive analysis and calculation is performed using the analytic hierarchy process (AHP) to determine the clean energy consumption index for the optimized regulation of water storage flexibility resources. This specifically includes the following steps:

[0038] S51. Normalize the calculation results of each evaluation indicator and convert them into dimensionless physical quantities.

[0039] S52. Construct a judgment matrix using the analytic hierarchy process (AHP) to determine the weight coefficients of each sub-evaluation indicator.

[0040] S53. Multiply the normalized results of each evaluation indicator by the corresponding weight coefficient, and sum them up one by one to obtain the clean energy consumption index for water storage flexibility resource optimization and regulation.

[0041] The present invention also provides a clean energy consumption assessment system for optimizing and regulating water storage flexibility resources, including a memory, a processor, and computer program instructions stored in the memory and executable by the processor. When the processor executes the computer program instructions, it can implement the steps described above.

[0042] The present invention also provides a computer-readable storage medium having stored thereon computer program instructions that can be executed by a processor, wherein when the processor executes the computer program instructions, it can implement the steps of the method described above.

[0043] Compared with existing technologies, this invention has the following advantages: Addressing the lack of a clean energy consumption assessment index system and assessment methods, this invention establishes a water storage flexibility resource optimization and regulation model, and uses the firefly algorithm to solve the optimization and regulation model; it constructs a clean energy consumption assessment index system that includes elements such as clean development, system stability, and safety and reliability, and proposes calculation methods for each assessment index; it uses the analytic hierarchy process (AHP) for comprehensive analysis and calculation to obtain the clean energy consumption index for water storage flexibility resource optimization and regulation, thus achieving an accurate assessment of the clean energy consumption level. Attached Figure Description

[0044] Figure 1 This is a flowchart of a clean energy consumption assessment process for optimizing and regulating water storage flexibility resources, as described in this invention. Detailed Implementation

[0045] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings.

[0046] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of this application. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0047] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0048] This invention provides a method for evaluating the absorption of clean energy through optimized regulation of water storage resources, comprising the following steps:

[0049] S1. Construct a water storage flexibility resource optimization and regulation model with the objective function of minimizing the sum of squared deviations of the remaining load;

[0050] S2. For the operating boundary conditions of different types of power supplies, the firefly algorithm is used to solve the optimization and adjustment model.

[0051] S3. Construct an evaluation index system for clean energy consumption based on the flexible resource regulation of water storage;

[0052] S4. Based on the meaning of different indicators, propose the calculation methods for each evaluation indicator;

[0053] S5. Based on the calculation results of various evaluation indicators, the analytic hierarchy process (AHP) is used to conduct a comprehensive analysis and calculation to determine the clean energy consumption index of water storage flexibility resource optimization and regulation. Based on this, the clean energy consumption level of the multi-energy complementary system under the water storage flexibility resource optimization and regulation effect is judged.

[0054] The following is a detailed implementation process of the present invention.

[0055] Because the scheduling of a multi-energy complementary system involving hydropower, wind power, solar power, and energy storage is a complex optimization problem involving multiple coupled factors, the assessment of clean energy consumption under the flexible resource optimization and regulation of hydropower and energy storage is inevitably more complex, and there is currently no mature assessment method available domestically or internationally. Therefore, in order to achieve accurate assessment of clean energy consumption under the flexible resource optimization and regulation of hydropower and energy storage, and to provide technical support for the multi-energy complementary scheduling operation and response capacity optimization of clean power sources such as hydropower, wind power, solar power, energy storage, and nuclear power, it is urgent to study a method for assessing clean energy consumption under the flexible resource optimization and regulation of hydropower and energy storage. This method will enable the assessment of clean energy consumption under the scheduling and operation mode of multi-energy complementary systems, and provide key technical support for the integrated operation of hydropower, wind power, solar power, and energy storage clean energy bases.

[0056] like Figure 1 As shown in the example, this invention proposes a method for evaluating the absorption of clean energy through flexible water storage resource optimization, comprising the following steps:

[0057] S1. Combining the operating principles of clean energy sources such as wind power, photovoltaics, conventional hydropower, pumped storage, and nuclear power, construct a water storage flexibility resource optimization and regulation model with the objective function of minimizing the sum of squared deviations of the remaining load, and execute step S2.

[0058] S2. For the operating boundary conditions of different types of power sources such as hydropower, wind power, solar power, energy storage, and nuclear power, the firefly algorithm is used to solve the optimization and regulation model, and then step S3 is executed.

[0059] S3. Based on clean development, system stability, safety and reliability, construct a clean energy consumption assessment index system for flexible water storage resource optimization and regulation, and execute step S4.

[0060] S4. Based on the meaning of different indicators, propose the calculation method for each evaluation indicator, and proceed to step S5.

[0061] S5. Based on the calculation results of various evaluation indicators, the analytic hierarchy process (AHP) is used to conduct a comprehensive analysis and calculation to determine the clean energy consumption index of water storage flexibility resource optimization and regulation. Based on this, the clean energy consumption level of the multi-energy complementary system under the water storage flexibility resource optimization and regulation effect is judged.

[0062] Furthermore, in step S1, constructing a water storage flexibility resource optimization and regulation model with the objective function of minimizing the sum of squared deviations of the remaining load includes the following steps:

[0063] S11. Calculate the sum of power generation output of wind power, photovoltaic power, conventional hydropower, pumped storage, nuclear power and other types of power plants at the same time. Among them, the output of pumped storage power plants under pumping conditions is calculated as the negative value of pumping power. Execute step S12.

[0064] S12. Based on the load demand process curve of the power grid, subtract the load demand at the corresponding moment from the total power generation output of hydropower, wind power, solar power, energy storage and nuclear power to calculate the remaining load of the power grid at that moment. Repeat this process until the remaining load at all moments in the calculation period is obtained. Then, use the sum of squared deviations of the remaining load sequence as the objective function of the model and execute step S13.

[0065] S13. Consider various types of constraints, including power generation output constraints for wind power, photovoltaic power, conventional hydropower, pumped storage, and nuclear power; reservoir capacity constraints and flow constraints for hydropower and pumped storage; and constraints on the switching between pumping and power generation operating conditions of pumped storage power stations.

[0066] Furthermore, in step S2, the firefly algorithm is used to solve the optimization and adjustment model, including the following steps:

[0067] S21. Initialize the basic parameters of the algorithm. Set the number of fireflies n, the maximum attraction β0, the light intensity absorption coefficient γ, the step size factor α, the maximum number of iterations or the search accuracy ε, and execute step S22.

[0068] S22. Randomly initialize the positions of the fireflies, calculate the objective function value of the fireflies as their respective maximum fluorescence brightness I0, and execute step S23.

[0069] S23. Calculate the relative brightness I of fireflies in the population. il and attraction β il The direction of firefly movement is determined based on phase brightness, and step S24 is executed.

[0070]

[0071] In the formula: I il I represents the fluorescence intensity of firefly i relative to firefly l. i,0 β represents the maximum fluorescence intensity of firefly i, i.e., the fluorescence intensity at its own position (r = 0), determined by the objective function value; il β represents the attraction of firefly i to firefly l; i,0 γ represents the maximum attraction of firefly i, i.e., the attraction at r = 0; γ is the light intensity absorption coefficient; r il For firefly i (i.e. x) i ) and fireflies l (i.e. x l The distance between ); d represents the spatial dimension; x i,j x l,jLet be the coordinates of firefly i and firefly l in the j-th dimension of the d-dimensional space, respectively.

[0072] S24. Update the spatial position of the fireflies, randomly move the fireflies in the best position, and execute step S25.

[0073]

[0074] In the formula: Let i be the firefly calculated for the first search; α is the step size factor; It is a random factor that follows a Gaussian or uniform distribution.

[0075] S25. Based on the updated position of the firefly, recalculate the brightness of the firefly (i.e., calculate the objective function value after the position update), then proceed to step S26.

[0076] S26. Compare the objective function value of the firefly after the position update with the historical best solution to determine the current best solution, and then proceed to step S27.

[0077] S27. If the maximum number of iterations or search precision ε is not satisfied, return to step S23 and perform the next search calculation; if the maximum number of iterations or search precision ε is satisfied, proceed to step S28.

[0078] S28. Output the power generation process of each power station, the water level process and the flow process of hydropower and pumped storage power stations corresponding to the optimal solution.

[0079] Furthermore, in step S3, a clean energy consumption assessment index system for flexible resource optimization and regulation of water storage is constructed from the perspectives of clean development, system stability, and safety and reliability. This includes the following steps:

[0080] S31. The main evaluation indicators for clean development include the proportion of clean energy installed capacity, clean energy power generation, clean energy curtailment, clean energy power generation utilization hours, clean energy power generation utilization rate, and average channel utilization rate. Execution steps are outlined in S32.

[0081] S32. The main evaluation indicators for system stability include the total output variation coefficient of clean energy, the maximum ramp rate of total output of clean energy, the output variation coefficient of nuclear power, the water level variation coefficient of reservoir, and the outflow variation coefficient. Execute step S33.

[0082] S33. The main evaluation indicators for safety and reliability include the proportion of flexibly adjustable power supply capacity, daily power margin, daily minimum power margin, maximum load shedding depth, minimum margin for increasing output, and minimum margin for decreasing output.

[0083] Furthermore, in step S4, based on the meaning of different indicators, the calculation methods for each evaluation indicator are proposed, including the following steps:

[0084] S41. The installed capacity ratio of clean energy is equal to the ratio of the installed capacity of clean power sources such as hydropower, wind power, solar power, energy storage, and nuclear power to the installed capacity of all power sources in the power system. The power generation of clean energy is equal to the sum of the power generation of clean power sources such as hydropower, wind power, solar power, energy storage, and nuclear power during the calculation period. The power curtailment of clean energy is equal to the sum of the power that clean power sources such as hydropower, wind power, solar power, energy storage, and nuclear power failed to connect to the grid during the calculation period. The utilization hours of clean energy power generation are equal to the ratio of the total power generation of each type of clean energy to its installed capacity. The utilization rate of clean energy power generation is equal to the ratio of the actual grid-connected power of each type of clean energy to the power that can be generated (if there is no curtailment, the utilization rate is 100%). The average channel utilization rate is the ratio of the average load of transmission during the calculation period to the rated transmission capacity of the channel. Proceed to step S42.

[0085] S42. The coefficient of variation of total clean energy output is equal to the ratio of the mean square of the total power generation output sequence of clean power sources such as hydropower, wind power, solar power, energy storage, and nuclear power within the calculation period to its mean value. The maximum ramp rate of total clean energy output is equal to the maximum value of the ratio of the change in total power generation output of clean power sources such as hydropower, wind power, solar power, energy storage, and nuclear power within the calculation period to the total power generation output of that period (if the total power generation output increases in adjacent periods, it is an upward ramp; if the total power generation output decreases, it is a downward ramp). The coefficient of variation of nuclear power output is equal to the ratio of the mean square of the nuclear power output sequence within the calculation period to its mean value. The coefficient of variation of reservoir water level is equal to the ratio of the mean square of the water level sequence of each reservoir within the calculation period to its mean value. The coefficient of variation of outflow is equal to the ratio of the mean square of the outflow sequence of each reservoir within the calculation period to its mean value. Proceed to step S43.

[0086] S43. The installed capacity ratio of flexible power sources is equal to the ratio of the installed capacity of flexible power sources such as hydropower and pumped storage to the installed capacity of all power sources in the power system. The daily power margin is equal to the maximum daily power generation (including daily purchased power) minus the daily power consumption (including daily transmitted power). The daily minimum power margin is equal to the minimum difference between the total power generation output (including power purchased from tie lines) and the power load (including power transmitted from tie lines) during each period of the day. The maximum load shedding depth is equal to the maximum difference between the power load (including power transmitted from tie lines) and the total power generation output (including power purchased from tie lines) during periods of insufficient power generation output. The minimum margin for increasing power output is equal to the minimum value after the power output of flexible power sources such as hydropower and pumped storage can be increased during the calculation period. The minimum margin for decreasing power output is equal to the minimum value after the power output of flexible power sources such as hydropower and pumped storage can be decreased during the calculation period.

[0087] Furthermore, in step S5, based on the calculation results of various evaluation indicators, a comprehensive analysis and calculation is performed using the analytic hierarchy process (AHP) to determine the clean energy consumption index for optimizing and regulating water storage flexibility resources, including the following steps:

[0088] S51. Normalize the calculation results of each evaluation indicator and convert them into dimensionless physical quantities. Then execute step S52.

[0089] S52. Construct a judgment matrix using the analytic hierarchy process (AHP) to determine the weight coefficients of each sub-evaluation indicator, and then proceed to step S53.

[0090] S53. Multiply the normalized results of each evaluation indicator by the corresponding weight coefficient, and sum them up one by one to obtain the clean energy consumption index for water storage flexibility resource optimization and regulation.

[0091] In summary, addressing the lack of a comprehensive evaluation index system and methodology for clean energy consumption, this paper establishes a water storage flexibility resource optimization and regulation model and uses the firefly algorithm to solve the optimization and regulation model. A clean energy consumption evaluation index system incorporating elements such as clean development, system stability, and safety and reliability is constructed, and calculation methods for each evaluation index are proposed. The analytic hierarchy process (AHP) is employed for comprehensive analysis and calculation to obtain a clean energy consumption index for water storage flexibility resource optimization and regulation, thereby achieving an accurate assessment of the clean energy consumption level.

[0092] The present invention also provides a clean energy consumption assessment system for optimizing and regulating water storage flexibility resources, including a memory, a processor, and computer program instructions stored in the memory and executable by the processor. When the processor executes the computer program instructions, it can implement the steps described above.

[0093] The present invention also provides a computer-readable storage medium having stored thereon computer program instructions that can be executed by a processor, wherein when the processor executes the computer program instructions, it can implement the steps of the method described above.

[0094] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0095] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0096] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0097] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0098] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for evaluating the absorption of clean energy through flexible water storage resource optimization, characterized in that, Includes the following steps: S1. Construct a water storage flexibility resource optimization and regulation model with the objective function of minimizing the sum of squared deviations of the remaining load; S2. For the operating boundary conditions of different types of power supplies, the firefly algorithm is used to solve the optimization and adjustment model. S3. Construct an evaluation index system for clean energy consumption based on the flexible resource regulation of water storage; S4. Based on the meaning of different indicators, propose the calculation methods for each evaluation indicator; S5. Based on the calculation results of various evaluation indicators, the analytic hierarchy process is used to conduct comprehensive analysis and calculation to determine the clean energy consumption index of water storage flexibility resource optimization and regulation, and based on this, to judge the clean energy consumption level of the multi-energy complementary system under the water storage flexibility resource optimization and regulation effect. In step S1, a water storage flexibility resource optimization and regulation model is constructed with the objective function of minimizing the sum of squared deviations of the remaining load. This specifically includes the following steps: S11. Calculate the sum of power generation output of power plants of wind power, photovoltaic power, conventional hydropower, pumped storage and nuclear power at the same time. The output of pumped storage power plants under pumping conditions is calculated as the negative value of pumping power. S12. Based on the load demand process curve of the power grid, subtract the load demand at the corresponding time from the total power generation output of hydropower, wind power, solar power, energy storage and nuclear power to calculate the remaining load of the power grid at the corresponding time. This process is repeated until the remaining load at all times within the calculation period is obtained. The sum of squared deviations of the remaining load sequence is used as the objective function of the hydropower flexible resource optimization and regulation model. S13. Consider the power output constraints of wind power, photovoltaic power, conventional hydropower, pumped storage, and nuclear power; the reservoir capacity constraints and flow constraints of hydropower and pumped storage; and various types of constraints, including the switching constraints between pumping and power generation operating conditions of pumped storage power stations. In step S3, a clean energy consumption assessment index system is constructed based on aspects including clean development, system stability, and safety and reliability. This specifically includes the following steps: S31. The assessment indicators for clean development include the proportion of clean energy installed capacity, clean energy power generation, clean energy curtailment, clean energy power generation utilization hours, clean energy power generation utilization rate, and average channel utilization rate. S32. The evaluation indicators for system stability include the coefficient of variation of total clean energy output, the maximum ramp rate of total clean energy output, the coefficient of variation of nuclear power output, the coefficient of variation of reservoir water level, and the coefficient of variation of outflow. S33. Evaluation indicators for safety and reliability include the proportion of flexibly adjustable power supply capacity, daily power margin, daily minimum power margin, maximum load shedding depth, minimum margin for increasing output, and minimum margin for decreasing output. In step S5, based on the calculation results of various evaluation indicators, the analytic hierarchy process (AHP) is used for comprehensive analysis and calculation to determine the clean energy consumption index for the optimized regulation of water storage flexibility resources. This specifically includes the following steps: S51. Normalize the calculation results of each evaluation indicator and convert them into dimensionless physical quantities. S52. Construct a judgment matrix using the analytic hierarchy process (AHP) to determine the weight coefficients of each sub-evaluation indicator. S53. Multiply the normalized results of each evaluation indicator by the corresponding weight coefficient, and sum them up one by one to obtain the clean energy consumption index for water storage flexibility resource optimization and regulation.

2. The method for evaluating the clean energy consumption of water storage flexibility resource optimization and regulation according to claim 1, characterized in that, In step S1, a water storage flexibility resource optimization and regulation model is constructed by combining the operating principles of clean energy sources including wind power, photovoltaics, conventional hydropower, pumped storage, and nuclear power, with the objective function being the minimum sum of squared deviations of the remaining load.

3. The method for evaluating the clean energy consumption of water storage flexibility resource optimization and regulation according to claim 1, characterized in that, In step S2, the firefly algorithm is used to solve the optimization and adjustment model, which specifically includes the following steps: S21. Initialize basic algorithm parameters: Set the number of fireflies n, maximum attraction β0, light intensity absorption coefficient γ, step size factor α, and maximum number of iterations or search accuracy ε. S22. Randomly initialize the positions of the fireflies and calculate the objective function value of each firefly as its maximum fluorescence brightness I0. S23. Calculate the relative brightness I of fireflies in the population. il and attraction β il The direction of firefly movement is determined by the phase brightness; In the formula: I il I represents the fluorescence intensity of firefly i relative to firefly l. i,0 β represents the maximum fluorescence intensity of firefly i, i.e., the fluorescence intensity at its position r = 0, which is determined by the objective function value; il β represents the attraction of firefly i to firefly l; i,0 γ represents the maximum attraction of firefly i, i.e., the attraction at r = 0; γ is the light intensity absorption coefficient; r il For fireflies i, i.e. x i With fireflies l i x l The distance between them; d represents the spatial dimension; x i,j x l,j They are firefly i and x respectively. i and fireflies l that is x l The coordinates of the j-th component in d-dimensional space; S24. Update the spatial position of fireflies and randomly move fireflies in the best position. In the formula: Let i be the firefly calculated for the t-th search; α t Let be the step size factor calculated for the t-th search; It is a random factor that follows a Gaussian or uniform distribution; S25. Based on the updated position of the firefly, recalculate the brightness of the firefly, i.e., calculate the objective function value after the position update. S26. Compare the objective function value of the firefly after the position update with the historical best solution to determine the current best solution; S27. If the maximum number of iterations or search precision ε is not satisfied, return to step S23 and perform the next search calculation; if the maximum number of iterations or search precision ε is satisfied, proceed to step S28. S28. Output the power generation process of each power station, the water level process and the flow process of hydropower and pumped storage power stations corresponding to the optimal solution.

4. The method for evaluating the clean energy consumption of water storage flexibility resource optimization and regulation according to claim 1, characterized in that, In step S4, the calculation methods for each evaluation indicator are proposed based on the meaning of different indicators, specifically including the following steps: S41. The installed capacity of clean energy is equal to the ratio of the installed capacity of clean power sources such as hydropower, wind power, solar power, energy storage, and nuclear power to the installed capacity of all power sources in the power system. The power generation of clean energy is equal to the sum of the power generation of clean power sources such as hydropower, wind power, solar power, energy storage, and nuclear power during the calculation period. The amount of clean energy wasted is equal to the sum of the amount of clean power sources such as hydropower, wind power, solar power, energy storage, and nuclear power that failed to be connected to the grid during the calculation period. The number of hours of clean energy power generation utilization is equal to the ratio of the total power generation of each type of clean energy to its installed capacity. The utilization rate of clean energy power generation is equal to the ratio of the actual amount of clean energy connected to the grid to the amount of clean energy that can be generated. If there is no wasted power, the utilization rate of power generation is 100%. The average channel utilization rate is the ratio of the average load of power transmission during the calculation period to the rated transmission capacity of the channel. S42. The coefficient of variation of total clean energy output is equal to the ratio of the mean square of the total power generation output sequence of hydropower, wind power, solar power, energy storage and nuclear power within the calculation period to its mean value. The maximum ramp rate of total clean energy output is equal to the maximum value of the ratio of the change in total power generation output of hydropower, wind power, solar power, energy storage and nuclear power within the calculation period to the total power generation output within that period. If the total power generation output increases in adjacent periods, it is an upward ramp; if the total power generation output decreases, it is a downward ramp. The coefficient of variation of nuclear power output is equal to the ratio of the mean square of the nuclear power output sequence within the calculation period to its mean value. The coefficient of variation of reservoir water level is equal to the ratio of the mean square of the water level sequence of each reservoir within the calculation period to its mean value. The coefficient of variation of outflow is equal to the ratio of the mean square of the outflow sequence of each reservoir within the calculation period to its mean value. S43. The installed capacity ratio of flexible power sources is equal to the ratio of the installed capacity of hydropower and pumped storage flexible power sources to the installed capacity of all power sources in the power system. The daily power margin is equal to the maximum daily power generation minus the daily power consumption. The daily minimum power margin is equal to the minimum difference between the total power generation output and the power load in each period of the day. The maximum load shedding depth is equal to the maximum difference between the power load and the total power generation output during the period when the power generation output is insufficient. The minimum margin for increasing power output is equal to the minimum value after the hydropower and pumped storage flexible power sources can increase their output in the calculation period. The minimum margin for decreasing power output is equal to the minimum value after the hydropower and pumped storage flexible power sources can decrease their output in the calculation period.

5. A clean energy consumption assessment system for water storage flexibility resource optimization and regulation, characterized in that, It includes a memory, a processor, and computer program instructions stored in the memory and executable by the processor, which, when executed by the processor, enable the implementation of the steps of the method as described in any one of claims 1-4.

6. A computer-readable storage medium having stored thereon computer program instructions executable by a processor, wherein when the processor executes the computer program instructions, it is able to implement the steps of the method as described in any one of claims 1-4.

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