Integrated optimization method of photovoltaic installed capacity and complementary dispatching rules based on water and electricity regulation

Abstract

The application provides a photovoltaic installed capacity and complementary scheduling rule integrated optimization method matched with water and electricity regulation, and belongs to the technical field of water and light scheduling. The method comprises the following steps: taking photovoltaic installed capacity and complementary scheduling rule parameters as decision variables, and establishing a photovoltaic installed capacity planning and scheduling rule integrated optimization model; selecting a multi-objective optimization algorithm to solve the photovoltaic installed capacity planning and scheduling rule integrated optimization model, and obtaining a Pareto solution set; selecting a multi-attribute decision method to select a balanced solution from the Pareto solution set, so as to output optimal photovoltaic installed capacity of a water and light complementary power station and a scheduling rule. The method considers the coupling between photovoltaic installed capacity planning and scheduling rule compilation, and further ensures the output of optimal photovoltaic installed capacity and complementary scheduling rule parameters.

Description

Technical Field

[0001] This invention relates to the field of hydropower scheduling technology, specifically to an integrated optimization method for photovoltaic installation and complementary scheduling rules that coordinate with hydropower regulation, a computer-readable storage medium, and an electronic device. Background Technology

[0002] Under the dual pressures of climate change and energy transition, clean energy sources, represented by hydropower, wind power, and solar power, have developed rapidly worldwide and are considered the mainstay of future energy systems due to their cleanliness, environmental friendliness, and renewability. However, the inherent random fluctuations of wind and solar power have led to prominent issues of "wind and solar curtailment," severely restricting the large-scale development and utilization of new energy sources. Determining the installed capacity of new energy sources within a multi-energy complementary system and formulating complementary dispatch rules are key issues that urgently need to be addressed in the current implementation of multi-energy complementary operation and management.

[0003] Runoff and photovoltaic (PV) output are significantly influenced by meteorological factors. Under climate change conditions, the results obtained from optimizing PV capacity planning and scheduling rules for hydro-PV hybrid systems based on historical data may be difficult to apply to future climate conditions. Furthermore, PV capacity planning is based on simulations using complementary scheduling rules, and the formulation of these rules also requires PV capacity as input; the two are mutually influential. However, most existing technologies separate PV capacity planning from scheduling rule formulation, leading to two problems: 1. They fail to consider the coupling between capacity planning and scheduling rule development, resulting in locally optimal planning results; 2. They fail to consider the impact of climate change on capacity planning and complementary scheduling, resulting in poor applicability of the optimization results under changing conditions. Summary of the Invention

[0004] The purpose of this invention is to provide an integrated optimization method for photovoltaic installation and complementary dispatch rules that coordinate with hydropower regulation, so as to at least solve the problems of existing technologies not considering climate change and poor optimization results caused by the coupling between capacity planning and dispatch rule compilation.

[0005] To achieve the above objectives, the first aspect of the present invention provides an integrated optimization method for photovoltaic installation and complementary dispatch rules in conjunction with hydropower regulation, comprising:

[0006] Using photovoltaic installed capacity and complementary dispatch rule parameters as decision variables, an integrated optimization model for photovoltaic installed capacity planning and dispatch rules is established.

[0007] A multi-objective optimization algorithm was used to solve the integrated optimization model of photovoltaic installed capacity planning and scheduling rules, and the Pareto solution set was obtained.

[0008] A multi-attribute decision method is used to select an equilibrium solution from the Pareto solution set.

[0009] In this method, photovoltaic installed capacity and complementary scheduling rule parameters are taken into account, and an integrated optimization model of photovoltaic installed capacity planning and scheduling rules is established with the two as decision variables, so that the planning results output by the solution model are optimal.

[0010] Optional, also includes:

[0011] Historical runoff data, historical solar radiation data, and historical temperature data of hydro-solar hybrid power stations are obtained and used as inputs to an integrated optimization model for photovoltaic installed capacity planning and scheduling rules.

[0012] Optional, also includes:

[0013] Using GCM data, hydrological and meteorological data for the study area under different future climate models and emission scenarios were obtained; based on this hydrological and meteorological data, as well as the hydrological model and photovoltaic power generation model, runoff and photovoltaic power generation data were calculated.

[0014] The calculated runoff and photovoltaic power output data are intended to enable the planning results output by the optimization model to adapt to future climate change.

[0015] Optionally, the hydrological and meteorological data includes precipitation, evaporation, temperature, and sunshine duration data. The calculation of runoff and photovoltaic output data based on this hydrological and meteorological data, as well as the hydrological model and photovoltaic output model, includes:

[0016] The parameters of the hydrological model were calibrated using historical data. Precipitation and evaporation data from the GCM model were then input into the calibrated hydrological model to obtain the future inflow sequence for the study area.

[0017] By inputting temperature and sunshine duration data from the GCM model into the photovoltaic power output model, the future photovoltaic power output sequence of the study area is obtained.

[0018] By extrapolating future climate conditions based on existing models, we can determine runoff and photovoltaic output scenarios under different climate patterns and emission scenarios.

[0019] Optionally, the objective function of the integrated optimization model for photovoltaic installed capacity planning and scheduling rules includes:

[0020] Objective function 1: Maximize the power consumption of the hydro-solar hybrid system.

[0021] Objective function 2: Maximize average power output during the dry season;

[0022] Objective function 3: Under the condition of climate change perturbation, the robustness of the water-solar complementary system is optimal.

[0023] Optionally, the objective function 3 is calculated based on the estimated runoff and photovoltaic power output data.

[0024] Optionally, the step of using a multi-objective optimization algorithm to solve the integrated optimization model of photovoltaic installed capacity planning and scheduling rules includes:

[0025] First, based on the implicit stochastic optimization method, the scheduling function form and the initial values ​​of the scheduling function parameters of the water-solar hybrid system are determined; then, the multi-objective cuckoo algorithm is used to optimize and solve the integrated optimization model of photovoltaic installed capacity planning and scheduling rules.

[0026] Optionally, the constraints of the integrated optimization model for photovoltaic installed capacity planning and scheduling rules include: water balance constraints, reservoir capacity constraints, downstream flow constraints, hydropower station processing constraints, and variable non-negativity constraints.

[0027] A second aspect of the present invention provides a computer-readable storage medium storing computer instructions that, when executed on a computer, cause the computer to perform the method described in the first aspect.

[0028] A third aspect of the present invention provides an electronic device, comprising: at least one processor and a memory;

[0029] The memory stores computer instruction execution instructions;

[0030] The at least one processor executes computer execution instructions stored in the memory, causing the electronic device to perform the method described in the first aspect.

[0031] Through the above technical solution, this invention establishes an integrated optimization model for photovoltaic installed capacity planning and scheduling rule compilation that comprehensively considers the economy, reliability and robustness of the water-solar complementary system. Theoretically, it can simultaneously output the optimal photovoltaic installed capacity and complementary scheduling rule parameters. Furthermore, the planning results can adapt to future climate change conditions, which is of great significance for guiding the operation and management of multi-energy complementary systems under climate change conditions.

[0032] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0033] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0034] Figure 1 This is a flowchart of an integrated optimization method for photovoltaic installation and complementary scheduling rules in conjunction with hydropower regulation, provided by one embodiment of the present invention;

[0035] Figure 2 This is a basic flowchart of the MOCS algorithm provided in one embodiment of the present invention. Detailed Implementation

[0036] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0037] Figure 1 This is a flowchart illustrating an integrated optimization method for photovoltaic installation and complementary scheduling rules in conjunction with hydropower regulation, provided by one embodiment of the present invention. Figure 1 As shown, this invention provides an integrated optimization method for photovoltaic installation and complementary dispatch rules in conjunction with hydropower regulation. The method includes:

[0038] S1: Using photovoltaic (PV) installed capacity and complementary scheduling rule parameters as decision variables, and considering the economy, reliability, and robustness of the hydro-PV complementary system, an integrated optimization model for PV installed capacity planning and scheduling rules is established. The established model considers both PV installed capacity and complementary scheduling rule parameters, thus enabling it to output optimal PV installed capacity and complementary scheduling rule parameters.

[0039] Specifically, in this embodiment, the set of decision variables can be represented as [x, a1, ..., a...]. 12 ,b1,…,b 12 ], where x is the photovoltaic installed capacity, a1…a 12 and b1…b 12 All are complementary scheduling rule parameters.

[0040] The scheduling function takes the following form:

[0041] V k =a k *EA k +b k (k = 1, 2, ..., 12); (1)

[0042] In equation (1), k is the number of the scheduling function; for example, k = 1 represents the scheduling function for January. k For the output variable of the scheduling function; EA k The input variable for the scheduling function is available energy; a k and b k These are the parameters of the linear scheduling function.

[0043] S2: Obtain historical runoff data, historical solar radiation data, and historical temperature data of the hydro-solar hybrid power station as input for the integrated optimization model of photovoltaic installed capacity planning and scheduling rules; calculate the photovoltaic output P based on the historical data. t s The calculation formula is as follows:

[0044]

[0045]

[0046] In equations (2) and (3): P t s χ represents the actual average output of photovoltaic power during time period t; χ represents the installed capacity of the photovoltaic power station. and T t These represent the solar radiation intensity and solar panel temperature at time t, respectively. and T stc The solar radiation intensity and air temperature under standard test conditions are 1000 W / m². 2 and 25℃; α p The temperature-power conversion factor is taken as -0.35% / ℃; T t air T represents the temperature at the weather station during time period t. noc This is the normal operating temperature of the solar panel, typically taken as 48℃±2℃.

[0047] S3: Estimate runoff and photovoltaic power output data under climate change conditions.

[0048] Specifically, in this embodiment, GCM (Global Climate Model) data is downscaled to obtain hydrological and meteorological data for different future climate models and emission scenarios in the study area; the hydrological and meteorological data includes precipitation, evaporation, temperature, sunshine duration, and other data.

[0049] Runoff and photovoltaic output data were calculated based on obtained hydrological and meteorological data, as well as existing hydrological and photovoltaic power generation models. The specific process is as follows:

[0050] The parameters of the hydrological model are calibrated using historically measured precipitation, evaporation, and runoff data. Precipitation and evaporation data from the GCM model are input into the calibrated hydrological model to obtain the future inflow runoff sequence. Temperature and sunshine duration data from the GCM model are input into the photovoltaic power output model to obtain the future photovoltaic power output sequence.

[0051] Based on this step, various scenarios of runoff and photovoltaic output under different climate models and emission scenarios can be determined.

[0052] It should be noted that there is no fixed order between steps S1, S2, and S3. The step order in this embodiment is only for the convenience of describing the technical solution. S2 can also be performed before S1, and S3 can also be performed as the first step, depending on the specific situation.

[0053] S4: A multi-objective optimization algorithm is used to solve the integrated optimization model of photovoltaic installed capacity planning and scheduling rules, and the Pareto solution set of the problem is obtained.

[0054] First, based on the implicit stochastic optimization method, the scheduling function form and the initial values ​​of the scheduling function parameters of the water-solar hybrid system are determined; then, the multi-objective Cuckoo Search (MOCS) algorithm is used to optimize and solve the established integrated model.

[0055] S5: Employ a multi-attribute decision-making method to select an equilibrium solution from the Pareto solution set that coordinates the economy, reliability, and robustness of the hydro-solar hybrid system, thereby outputting the optimal photovoltaic installed capacity and scheduling rules for the hydro-solar hybrid power station.

[0056] Specifically, in this embodiment, the objective function of the integrated optimization model for photovoltaic installed capacity planning and scheduling rules includes:

[0057] Objective function 1: Maximize the power consumption of the hydro-solar hybrid system.

[0058]

[0059] In equation (4), f1 is the total power generation during the complementary system scheduling period; t and T are the medium- and long-term scheduling period number and the total scheduling period, respectively; P t h P t s Let ΔT be the actual average power output of hydropower and the average power output of photovoltaic power station in time period t; t The scheduling period is long.

[0060] Hydropower output P t h Calculate using the following formula:

[0061] P t h =9.81ηR t H t (5)

[0062]

[0063] In equations (5) and (6), η is the comprehensive efficiency coefficient of the hydropower station; R tH represents the power generation flow rate through the turbine during the t-th scheduling period; t The average head for power generation; These represent the average water level upstream of the dam, the average tailwater level, and the head loss during time period t.

[0064] Objective function 2: Maximize average power output during the dry season:

[0065]

[0066] In the formula: f2 is the average output of the complementary system during the dry season; d and D are the long-term scheduling period number and the total number of the dry season period, respectively. The average actual output of hydropower and the average output of photovoltaic power station during the dry season of period d.

[0067] Objective function 3: Under climate change perturbation conditions, the robustness of the water-solar hybrid system is optimal.

[0068]

[0069]

[0070] In the formula: F(x) i,j This represents calculating the value of target i under scenario j; Represented as the target i value under baseline conditions, where D represents the design power generation and guaranteed output, respectively; i,j This is represented as deviation.

[0071] The constraints of the integrated optimization model for photovoltaic installed capacity planning and scheduling rules include:

[0072] Constraint 1: Water balance constraint:

[0073] V t+1 =V t +(Q in,t -Q out,t )ΔT t (10)

[0074] Constraint 2: Storage capacity constraint:

[0075] It represents the range of changes in reservoir capacity, and the reservoir capacity at each moment must be within a certain allowable range.

[0076] V min ≤V t ≤V max (11)

[0077] Constraint 3: Downflow constraint:

[0078] Q out,min ≤Q out,t ≤Qout,max (12)

[0079] Constraint 4: Hydropower Station Output Constraints:

[0080]

[0081] Constraint 5: Non-negativity constraint for variables:

[0082] All variables are greater than or equal to 0.

[0083] In equations (10) to (13): V t Q represents the reservoir capacity of the hydropower station during time period t. in,t The inflow to the reservoir of the hydropower station during time period t; V min and V max These are the lower and upper limits of the reservoir capacity of a hydropower station, respectively; Q out,min and Q out,max These are the minimum and maximum discharge flows of the hydropower station, respectively. and These are the lower and upper limits of the hydropower station's output, respectively.

[0084] Specifically, in this embodiment, the robustness calculation of objective function 3 requires the use of the runoff and photovoltaic power output data determined in S3 of this embodiment. The specific calculation method is as follows:

[0085] First, the total power generation calculated using different GCM models and under different emission scenarios is subtracted from the designed power generation, and then divided by the designed power generation to obtain a series. The value corresponding to the 90th percentile of this series is then calculated to obtain R1.

[0086] Secondly, the average power output during the dry season calculated using different GCM models and different emission scenarios is subtracted from the guaranteed power output and then divided by the guaranteed power output to obtain a series. The value corresponding to the 90th quantile of this series is then calculated to obtain R2.

[0087] Finally, the robustness index is chosen as the larger of R1 and R2.

[0088] The objective function of the model, derived from runoff and photovoltaic power output data under different climate models and emission scenarios, can enable the planning results output by the optimization model to adapt to future climate change.

[0089] Specifically, the basic process of the MOCS algorithm used in this embodiment is as follows: Figure 2 The steps are as follows:

[0090] Step (1) Initialization: Given the parameters required by the algorithm, such as the dimension m of the solution, the number of bird nests n, the probability of being discovered Pa, the maximum number of iterations N, the upper and lower bounds of the search domain ub and lb, the initial archive Archive is an empty set, the maximum capacity of the archive ArchiveMax, and the niche radius σ. share The iteration count is t = 0. An initial m×n bird nest location matrix X0 is randomly generated. (t) ;

[0091] If t <= N in step (2), then go to (3); otherwise, stop the calculation and output Archive.

[0092] Step (3) Position Update: The bird's nest position is updated using the Levy flight principle to obtain the new bird's nest position matrix X1. (t) ;

[0093]

[0094] Where α>0 represents the step size. This represents point-to-point multiplication;

[0095] Step (4) Fitness selection: Merge X0 (t) With X1 (t) We obtain an m×2n matrix X', calculate the fitness of X' according to equation (3), and sort them from largest to smallest. We select the first n corresponding solutions to form a new solution matrix, denoted as X2. (t) ;

[0096] Step (5) Random elimination: For X2 (t) Each solution X2 i (t) Assign a random number i, and randomly eliminate solutions according to the elimination probability Pa to obtain X3. (t) ;

[0097]

[0098] Step (6) Fitness selection: Merge X2 (t) With X3 (t) We obtain m×2n X”, calculate the fitness of X” according to equation (3), and sort them from largest to smallest. Select the first n corresponding solutions to form a new solution matrix, denoted as X4. (t) ;

[0099] Step (7) File update and preprocessing: X4 (t) It is directly merged into the archive, that is, Archive = [Archive, X4] (t) The Archive is deduplicated, and according to the Pareto dominance relation, dominated solutions are deleted, and only non-dominated solutions are retained;

[0100] Step (8) Determine: If the number of solutions in Archive is greater than ArchiveMax (overflow), go to step (9); otherwise go to step (10).

[0101] Step (9) Archive reduction: Reduce Archive according to the stepwise archive reduction method based on niche technology until the archive no longer overflows, then proceed to step (10);

[0102] Step (10) Let X0 (t) =X4 (t) , t = t + 1, go to step (2).

[0103] This invention also provides a computer-readable storage medium storing computer instructions. When these computer instructions are executed on a computer, the computer executes an integrated optimization method for photovoltaic installation and complementary scheduling rules that coordinates with hydropower regulation, as provided in this embodiment.

[0104] A third aspect of the present invention provides an electronic device, comprising: at least one processor and a memory; the memory storing computer instruction execution instructions; the at least one processor executing the computer execution instructions stored in the memory, causing the electronic device to execute an integrated optimization method for photovoltaic installation and complementary scheduling rules in conjunction with hydropower regulation provided in this embodiment.

[0105] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a microcontroller, chip, or processor to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0106] The optional embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details described above. Within the scope of the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the protection scope of the embodiments of the present invention. It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not further describe the various possible combinations.

[0107] Furthermore, various different embodiments of the present invention can be combined in any way, as long as they do not violate the spirit of the embodiments of the present invention, they should also be regarded as the content disclosed by the embodiments of the present invention.

Claims

1. An integrated optimization method for photovoltaic installation capacity and complementary dispatch rules in conjunction with hydropower regulation, characterized in that, include: Using photovoltaic (PV) installed capacity and complementary dispatch rule parameters as decision variables, an integrated optimization model for PV installed capacity planning and dispatch rules is established; where the set of decision variables is represented as follows. , For photovoltaic installed capacity, and All parameters are complementary scheduling rule parameters; the objective functions of the integrated optimization model for photovoltaic installed capacity planning and scheduling rules include: objective function 1: maximizing the grid-connected power of the hydro-solar complementary system; objective function 2: maximizing the average output during the dry season; objective function 3: achieving optimal robustness of the hydro-solar complementary system under climate change disturbance conditions; objective function 3 is calculated based on the estimated runoff and photovoltaic output data. Historical runoff data, historical solar radiation data, and historical temperature data of hydro-solar hybrid power stations are obtained as inputs to the integrated optimization model for photovoltaic installed capacity planning and scheduling rules. Hydrological and meteorological data for the study area under different future climate models and emission scenarios are obtained using GCM data; runoff and photovoltaic power output data are calculated based on the hydrological and meteorological data, as well as the hydrological model and photovoltaic power output model. A multi-objective optimization algorithm was used to solve the integrated optimization model of photovoltaic installed capacity planning and scheduling rules, and the Pareto solution set was obtained. A multi-attribute decision method is used to select an equilibrium solution from the Pareto solution set.

2. The integrated optimization method for photovoltaic installation and complementary dispatch rules in conjunction with hydropower regulation as described in claim 1, characterized in that, The hydrological and meteorological data include: precipitation, evaporation, temperature, and sunshine duration data; Based on the aforementioned hydrological and meteorological data, as well as the hydrological model and photovoltaic power output model, runoff and photovoltaic power output data are calculated, including: The parameters of the hydrological model were calibrated using historical data. Precipitation and evaporation data from the GCM model were then input into the calibrated hydrological model to obtain the future inflow sequence for the study area. By inputting temperature and sunshine duration data from the GCM model into the photovoltaic power output model, the future photovoltaic power output sequence of the study area is obtained.

3. The integrated optimization method for photovoltaic installation and complementary dispatch rules in conjunction with hydropower regulation as described in claim 1, characterized in that, The method of using a multi-objective optimization algorithm to solve the integrated optimization model of photovoltaic installed capacity planning and scheduling rules includes: First, based on the implicit stochastic optimization method, the scheduling function form and the initial values ​​of the scheduling function parameters of the water-solar hybrid system are determined; then, the multi-objective cuckoo algorithm is used to optimize and solve the integrated optimization model of photovoltaic installed capacity planning and scheduling rules.

4. The integrated optimization method for photovoltaic installation and complementary dispatch rules in conjunction with hydropower regulation as described in claim 1, characterized in that, The constraints of the integrated optimization model for photovoltaic installed capacity planning and scheduling rules include: water balance constraints, reservoir capacity constraints, outflow constraints, hydropower station processing constraints, and variable non-negativity constraints.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed on a computer, cause the computer to perform the method of any one of claims 1-4.

6. An electronic device, characterized in that, include: At least one processor and memory; The memory stores computer instruction execution instructions; The at least one processor executes computer execution instructions stored in the memory, causing the electronic device to perform the method of any one of claims 1-4.

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