Optimized Dispatch Methods and Systems for Economic Operation of Photovoltaic and Energy Storage Power Stations

Abstract

This invention belongs to the field of power grid optimization dispatching and provides a day-ahead optimization dispatching method and system for the economical operation of photovoltaic and energy storage power plants. Based on day-ahead output forecasting results, a day-ahead dispatching model is built to solve for the optimal day-ahead output curve. The model aims to maximize the daily profit of the photovoltaic and energy storage power plant, taking into account the operational constraints of both photovoltaic and energy storage, and incorporating curtailment losses related to curtailment volume and grid connection prices. An improved genetic algorithm is used to solve the day-ahead dispatching model to obtain the optimized dispatching results for the photovoltaic and energy storage power plant. The improvements to the genetic algorithm include the introduction of an adaptive mechanism and an improved selection mechanism. This invention can effectively improve the economic efficiency of day-ahead dispatching for photovoltaic and energy storage power plants.

Description

Technical Field

[0001] This invention belongs to the field of power optimization dispatching, specifically relating to a day-ahead optimization dispatching method and system for the economical operation of photovoltaic and energy storage power stations. Background Technology

[0002] The statements in this section are merely background information related to the present invention and do not necessarily constitute prior art.

[0003] With the continuous development of the new energy industry, fossil energy is gradually being replaced by new energy sources such as wind and solar power. However, the inherent intermittency, volatility, and randomness of photovoltaic power generation mean that its direct grid connection may impact the safe and stable operation of the power grid, reduce power quality, and thus affect the economic benefits of photovoltaic power plants.

[0004] To improve the stability and dispatchability of photovoltaic (PV) power generation, configuring energy storage systems has become an effective means of addressing this issue. PV-storage power stations can rapidly store and release electrical energy, reducing grid-connected power fluctuations, improving plan tracking capabilities, and facilitating their participation in power market dispatch. Simultaneously, energy storage systems can participate in peak shaving and valley filling, absorbing excess PV power and increasing power station revenue. Therefore, PV-storage power stations have become a new type of economic entity in modern new energy power generation systems, characterized by complex functions and the ability to operate independently. Currently, however, practical PV-storage power stations lack effective coordinated operation and dispatch strategies, resulting in the following problems:

[0005] (1) In the optimized scheduling of photovoltaic and energy storage power stations, the economic indicators are not fully considered and fail to effectively reflect the actual economic performance of photovoltaic and energy storage power stations.

[0006] (2) The modeling of photovoltaic curtailment loss in the optimized scheduling of photovoltaic power stations is too simplified and fails to reflect the actual loss.

[0007] (3) In the optimization scheduling of photovoltaic and energy storage power stations, the energy storage charge range is generally set as a hard constraint of the upper and lower limits of the rated value. When the charge state reaches the upper limit, the photovoltaic power is abandoned; when the charge state reaches the lower limit, the peak regulation is withdrawn. This constraint not only limits the flexible adjustment capability of energy storage, but also easily causes the optimization problem to not converge. Summary of the Invention

[0008] To address the aforementioned problems, this invention proposes a day-ahead optimized scheduling method and system for the economical operation of photovoltaic-storage power plants. This invention can improve the economic efficiency of day-ahead scheduling for photovoltaic-storage power plants.

[0009] According to some embodiments, the present invention adopts the following technical solution:

[0010] A method for optimizing day-ahead scheduling of photovoltaic-storage power plants for economical operation includes the following steps:

[0011] Based on the day-ahead power output forecast, a day-ahead scheduling model is built to solve for the optimal day-ahead power output curve. The model aims to maximize the daily profit of the photovoltaic-storage power station and introduces curtailment losses related to curtailment volume and grid-connected electricity price.

[0012] Based on the operational requirements of photovoltaic and energy storage power stations, the constraints of the day-ahead scheduling model are determined;

[0013] An improved genetic algorithm was used to solve the day-ahead scheduling model, yielding optimized scheduling results for photovoltaic-storage power plants. The improvements to the genetic algorithm included: representing the initial feasible solutions of the day-ahead scheduling model as individuals in the genetic space using real-number encoding for population initialization; evaluating the quality of solutions and guiding evolution through a fitness function; employing a tournament mechanism to retain superior individuals, based on a standard tournament combined with fitness and abandonment loss; and introducing an adaptive mechanism to automatically adjust crossover and mutation probabilities based on the individual's current fitness value, minimum fitness value, and average fitness value.

[0014] As an alternative implementation, the process of the model aiming to maximize the daily profit of the photovoltaic-storage power station includes: ;

[0015] In the formula, , , and They are respectively The calculation includes the grid connection revenue, curtailment losses, energy storage operating costs, and soft-constraint costs of a photovoltaic-storage power station at each moment; among which, the grid connection revenue... Represented as:

[0016] ;

[0017] In the formula, and They are respectively Always plan power output and grid connection electricity price.

[0018] As an alternative implementation method, the process of introducing curtailment losses related to curtailed solar power volume and grid-connected electricity price includes: the loss function is expressed as:

[0019] ;

[0020] In the formula, and They are respectively At any given moment, the maximum photovoltaic output and the photovoltaic output coefficient; for The curtailment loss factor is related to the grid-connected electricity price. related:

[0021] .

[0022] As an alternative implementation method, the process of determining the constraints of the day-ahead dispatch model based on the operational requirements of the photovoltaic-storage power station includes: defining the constraints of the energy storage state of charge as soft constraints, allowing for moderate violations of certain constraints to absorb more curtailed photovoltaic power.

[0023] ;

[0024] In the formula, These are slack variables;

[0025] To ensure that the SOC limit is not seriously violated, a cost function is used to penalize it, namely:

[0026] ;

[0027] In the formula, This is the relaxation penalty coefficient.

[0028] As an alternative implementation method, the process of determining the constraints of the day-ahead scheduling model based on the operational requirements of the photovoltaic-storage power station includes: energy storage losses. It is related to energy storage output and state of charge. ;

[0029] In the formula, and These are the operating power cost and capacity cost of energy storage batteries, respectively. for Energy is stored and output at all times; when storing energy and discharging, Greater than zero; energy storage and charging. Less than zero; energy storage loss costs are represented using quadratic terms;

[0030] Its power balance constraints, photovoltaic output constraints, and energy storage constraints are as follows:

[0031] ;

[0032] ;

[0033] ;

[0034] ;

[0035] ;

[0036] In the formula, and To improve the charging and discharging efficiency of energy storage systems; This refers to the rated capacity of the energy storage system. This is the rated output power of the energy storage. and The initial and final states of charge of the stored energy are shown respectively. This refers to the tolerance range for the state of charge.

[0037] As an alternative implementation method, the population initialization process includes:

[0038] Energy storage output at different times Individuals as genetic space ,right Optimize energy storage output during specific time periods The remaining time periods make And force the initial population to satisfy energy balance constraints:

[0039] ;

[0040] Then in their respective intervals Internal random generation One value, and Each of the following constraints must be satisfied:

[0041] ;

[0042] ;

[0043] in and These represent the upper and lower limits of the initial population; then, the total current energy storage charge and discharge capacity is calculated. Sum of targets Then iterative adjustments are made; if According to the remaining space Proportional allocation difference ;like Adjustable space Reduced proportionally, corrected, and then adjusted again:

[0044] ;

[0045] ;

[0046] In the formula, For the corrected individuals, The difference between the current value and the target value, until... The process ends when all possibilities fall within the feasible interval.

[0047] As an alternative implementation method, the objective function is:

[0048]

[0049] The fitness function is:

[0050] ;

[0051] Among them, the penalty item for:

[0052] ;

[0053] In the formula, and These are the soft constraint penalty coefficient and the state of charge penalty coefficient, respectively.

[0054] As an alternative implementation, the tournament mechanism employed, based on a standard tournament combined with fitness and abandonment loss, includes the following process:

[0055] Based on the standard tournament selection, improvements were made by considering both fitness and wastage loss, as follows:

[0056] ;

[0057] In the formula, For the first The overall score of each individual, For fitness score, Score for wasted light. To mitigate the weighting of light loss, the generation with the highest overall score was selected as the parent generation for the first generation.

[0058] As an alternative implementation method, crossover probability The calculation is as follows:

[0059] ;

[0060] In the formula, Minimum crossover probability; The maximum crossover probability; For maximum fitness; Average fitness; Current fitness;

[0061] Mutation probability The calculation is as follows:

[0062] ;

[0063] In the formula, The minimum probability of mutation; This represents the highest mutation probability.

[0064] A day-ahead optimization dispatch system for the economic operation of photovoltaic and energy storage power plants includes:

[0065] The model building module is configured to build a day-ahead scheduling model based on the day-ahead power output prediction results to solve the optimal day-ahead power output curve. The model aims to maximize the daily profit of the photovoltaic-storage power station and introduces curtailment losses related to curtailment volume and grid-connected electricity price.

[0066] The constraint determination module is configured to determine the constraints of the day-ahead scheduling model based on the operational requirements of the photovoltaic-storage power station.

[0067] The solution module is configured to solve the day-ahead scheduling model using an improved genetic algorithm to obtain the optimized scheduling results of the photovoltaic-storage power station. The improvement of the genetic algorithm includes representing the initial feasible solution of the day-ahead scheduling model as individuals in the genetic space through real number encoding, initializing the population, evaluating the quality of the solution and guiding the evolution through the fitness function, and using a tournament mechanism to retain excellent individuals. The tournament mechanism is based on the standard tournament combined with fitness and abandoned light loss. An adaptive mechanism is introduced to automatically adjust the crossover probability and mutation probability based on the current fitness value, minimum fitness value and average fitness value of the individual.

[0068] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0069] This invention considers the impact of photovoltaic power generation and grid-connected electricity price fluctuations on the economic viability of photovoltaic-storage power stations, and the established curtailment loss model more effectively reflects the curtailment loss of photovoltaic-storage power stations.

[0070] This invention avoids the problems of insufficient photovoltaic curtailment and limited grid dispatching capabilities caused by setting the upper and lower limits of energy storage charge state to rated values ​​in traditional photovoltaic-storage power station dispatching methods. It further reduces the curtailment losses of photovoltaic-storage power stations and improves their economic efficiency.

[0071] This invention fully considers various economic indicators such as grid connection revenue of photovoltaic power stations, energy storage loss, and curtailment loss. Under the premise of meeting the requirements of safe and stable grid operation and active power dispatch, it can further optimize the power allocation strategy, which not only reduces photovoltaic curtailment loss during dispatch, but also reduces energy storage loss during operation.

[0072] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0073] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0074] Figure 1 This is a schematic diagram of the overall process of one embodiment;

[0075] Figure 2 This is a schematic diagram illustrating the day-ahead power output of a photovoltaic-storage power station according to one embodiment.

[0076] Figure 3 This is a schematic diagram of the solution process in one embodiment. Detailed Implementation

[0077] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0078] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. 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 invention pertains.

[0079] 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 scope of exemplary embodiments according to the invention. 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.

[0080] Where there is no conflict, the embodiments and features described in this application may be combined with each other.

[0081] Example 1

[0082] An intelligent scheduling method for the economic operation of photovoltaic-storage power plants based on an improved genetic algorithm is proposed. This embodiment introduces a curtailment loss model related to curtailment volume and grid connection price; it uses soft constraints of state of charge to replace traditional hard constraints, allowing for small-scale exceedances, thereby improving the energy storage's ability to absorb curtailment; based on this, it comprehensively considers grid connection revenue, curtailment loss, energy storage operating costs, and soft constraint costs to construct an economic operation scheduling model for photovoltaic-storage power plants; furthermore, this invention provides an improved genetic algorithm for efficiently solving the model, so as to achieve the economic optimization of day-ahead scheduling of photovoltaic-storage power plants.

[0083] The following is a detailed introduction.

[0084] According to relevant regulations, photovoltaic power plants are required to provide the dispatch center with the output curve for the next 24 hours one day before the dispatch date. Therefore, it is necessary to build a day-ahead dispatch model based on the day-ahead output prediction results to solve for the optimal day-ahead output curve.

[0085] like Figure 1 As shown, the following model is established with the objective of maximizing the daily profit of a photovoltaic-storage power station:

[0086]

[0087] In the formula, , , and They are respectively The grid connection revenue, curtailment losses, energy storage operating costs, and soft-constraint costs of a photovoltaic-storage power station at any given time; among which, such as Figure 2 As shown, grid connection benefits It can be represented as:

[0088]

[0089] In the formula, and They are respectively Always plan power output and grid connection electricity price.

[0090] Due to fluctuations in grid demand and capacity limitations of photovoltaic-storage power stations, not all photovoltaic power can be absorbed, often resulting in "curtailment" and economic losses. To quantify this loss, a curtailment loss function is introduced, expressed as:

[0091]

[0092] In the formula, and They are respectively At any given moment, the maximum photovoltaic output and the photovoltaic output coefficient; for The curtailment loss coefficient is given by the following formula, since the curtailment loss is also related to the grid-connected electricity price during actual operation.

[0093]

[0094] During operation, to prevent degradation stress caused by high charge or deep discharge states, the state of charge of energy storage is generally set to the following hard constraints:

[0095]

[0096] In the formula, for The constant state of charge of energy storage limits its ability to absorb "curtailed solar power" and can easily cause non-convergence in the optimization problem. This invention improves it to a soft constraint, allowing it to absorb more curtailed solar power even if certain constraints are violated.

[0097]

[0098] In the formula, is a slack variable, and its value range is [0, 0.2].

[0099] To ensure that the SOC limit is not seriously violated, a cost function is used to penalize it, namely:

[0100]

[0101] In the formula, The relaxation penalty coefficient

[0102] Energy storage loss Related to energy storage output and state of charge, etc., this invention adopts the following model to simplify the model:

[0103]

[0104] In the formula, and These are the operating power cost and capacity cost of energy storage batteries, respectively. for Energy is stored and output at all times; when storing energy and discharging, Greater than zero; energy storage and charging. Less than zero; since energy storage charging and discharging both produce certain losses, the energy storage loss cost is represented by a quadratic term.

[0105] When a photovoltaic-storage power station is in operation, the following power balance constraints, photovoltaic output constraints, and energy storage constraints must be met, as detailed below:

[0106]

[0107]

[0108]

[0109]

[0110]

[0111] In the formula, and To improve the charging and discharging efficiency of energy storage systems; This refers to the rated capacity of the energy storage system. This is the rated output power of the energy storage. and The initial and final states of charge of the stored energy are shown respectively. This refers to the tolerance range for the state of charge.

[0112] This model is a nonlinear programming model with inequality constraints. This invention provides an optimized scheduling algorithm for photovoltaic-storage power plants based on an improved genetic algorithm, such as... Figure 3 As shown, the details are as follows:

[0113] (1) Population initialization

[0114] This paper uses real number encoding to represent the energy storage output at different time periods. Individuals as genetic space , to represent the initial feasible solution of the target model. Due to the characteristics of photovoltaic power generation, energy storage only participates in scheduling during the effective photovoltaic period, therefore only for Optimize energy storage output during specific time periods In order to satisfy the scheduling instructions during other time periods, then... And force the initial population to satisfy the energy balance constraint, as shown in the following equation:

[0115]

[0116] Then in their respective intervals Internal random generation One value, and Each of the following constraints must be satisfied:

[0117]

[0118]

[0119] in and These represent the upper and lower limits of the initial population; then, the total current energy storage charge and discharge capacity is calculated. Sum of targets Then iterative adjustments are made; if According to the remaining space Proportional allocation difference ;like Adjustable space The scale was reduced, corrected, and then adjusted again.

[0120] ;

[0121] ;

[0122] In the formula, For the corrected individuals, The difference between the current value and the target value, until... The process ends when all possibilities fall within the feasible interval.

[0123] (2) Fitness function calculation

[0124] After population initialization, the quality of the solution needs to be evaluated and the evolution guided by the fitness function. Generally, a high fitness value corresponds to high profit and low constraint violation, making it more likely to be selected genetically. This paper defines fitness as...

[0125]

[0126] Among them, the penalty item for

[0127]

[0128] In the formula, and These are the soft constraint penalty coefficient and the state of charge penalty coefficient, respectively.

[0129] (3) Select operation

[0130] Based on the fitness function evaluation results, a tournament mechanism is used to retain superior individuals. To prioritize the inheritance of information from individuals with lower abandonment loss to the next generation, this paper improves upon the standard tournament selection by combining fitness and abandonment loss factors, as detailed below:

[0131]

[0132] In the formula, For the first The overall score of each individual, For fitness score, Score for wasted light. To mitigate the weighting of light loss, the generation with the highest overall score was selected as the parent generation for the first generation.

[0133] (4) Crossover and mutation operations

[0134] Crossover is a process of selecting two individuals from the new population and crossovering them with a certain probability to obtain a new individual; mutation is a process of randomly selecting one individual from the new population and mutating it with a certain probability to obtain a new individual.

[0135] By altering two individuals in the new population generated after the selection operation according to certain crossover and mutation probabilities, the population can be further evolved and made more diverse. In this invention, simulated binary crossover and polynomial mutation are used.

[0136] (5) Adaptive mechanism

[0137] To achieve a more efficient crossover and mutation rate and improve the algorithm's flexibility in responding to changes, an adaptive mechanism is introduced. Based on a comparative analysis of the individual's current fitness value, minimum fitness value, and average fitness value, an automatically adjusting crossover and mutation probability mechanism is employed. Crossover probability The calculation formula is as follows:

[0138]

[0139] In the formula, Minimum crossover probability; The maximum crossover probability; For maximum fitness; Average fitness; This represents the current fitness level.

[0140] Mutation probability The calculation formula is as follows:

[0141]

[0142] In the formula, The minimum probability of mutation; This represents the highest mutation probability.

[0143] Figure 2 The figure shows the optimized day-ahead power output of the photovoltaic-storage power station. As shown, the photovoltaic-storage power station effectively improves its economic efficiency while ensuring power balance through the flexible adjustment capability of energy storage. When the photovoltaic output exceeds the planned demand, the energy storage charges to absorb the "curtailment" of photovoltaic power. When the planned output exceeds the photovoltaic power generation capacity or during peak electricity price periods, the energy storage discharges to supplement the power gap and realize energy arbitrage. This strategy, by dynamically adjusting the upper and lower limits of the energy storage's state of charge, not only reduces the phenomenon of "curtailment" of photovoltaic power and ensures the stable operation of the power grid, but also maximizes the revenue of the photovoltaic-storage power station throughout the day.

[0144] Example 2

[0145] A day-ahead optimization dispatch system for the economic operation of photovoltaic and energy storage power plants includes:

[0146] The model building module is configured to build a day-ahead scheduling model based on the day-ahead power output prediction results to solve the optimal day-ahead power output curve. The model aims to maximize the daily profit of the photovoltaic-storage power station and introduces curtailment losses related to curtailment volume and grid-connected electricity price.

[0147] The constraint determination module is configured to determine the constraints of the day-ahead scheduling model based on the operational requirements of the photovoltaic-storage power station.

[0148] The solution module is configured to solve the day-ahead scheduling model using an improved genetic algorithm to obtain the optimized scheduling results of the photovoltaic-storage power station. The improvement of the genetic algorithm includes representing the initial feasible solution of the day-ahead scheduling model as individuals in the genetic space through real number encoding, initializing the population, evaluating the quality of the solution and guiding the evolution through the fitness function, and using a tournament mechanism to retain excellent individuals. The tournament mechanism is based on the standard tournament combined with fitness and abandoned light loss. An adaptive mechanism is introduced to automatically adjust the crossover probability and mutation probability based on the current fitness value, minimum fitness value and average fitness value of the individual.

[0149] Embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a completely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may be implemented in one or more computer-usable storage media (including, but not limited to, disk storage, etc.) containing computer-usable program code. CD - ROM It takes the form of a computer program product implemented on (such as optical memory, etc.).

[0150] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. 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 illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0151] 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.

[0152] 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.

[0153] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made by those skilled in the art without creative effort within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A day-ahead optimization scheduling method for the economical operation of photovoltaic-storage power stations, characterized in that, Includes the following steps: Based on the day-ahead power output forecast, a day-ahead scheduling model is built to solve for the optimal day-ahead power output curve. The model aims to maximize the daily profit of the photovoltaic-storage power station and introduces curtailment losses related to curtailment volume and grid-connected electricity price. Based on the operational requirements of photovoltaic-storage power stations, the process of determining the constraints of the day-ahead dispatch model includes: constraining the energy storage state of charge as a soft constraint, allowing it to moderately violate the constraint to absorb more curtailed photovoltaic power. ； In the formula, for Constant state of energy storage and charge. These are slack variables; To ensure that the SOC limit is not seriously violated, a cost function is used as a penalty, namely: ； In the formula, The relaxation penalty coefficient; The process of determining the constraints of the day-ahead dispatch model based on the operational requirements of photovoltaic-storage power stations includes: energy storage losses. It is related to energy storage output and state of charge. ; In the formula, and These are the operating power cost and capacity cost of energy storage batteries, respectively. for Energy is stored and output at all times; when storing energy and discharging, Greater than zero; energy storage and charging. Less than zero; energy storage loss costs are represented using quadratic terms; Its power balance constraints, photovoltaic output constraints, and energy storage constraints are as follows: ； ； ； ； ； In the formula, and To improve the charging and discharging efficiency of energy storage systems; This refers to the rated capacity of the energy storage system. This is the rated output power for energy storage; and The initial and final states of charge of the stored energy are shown respectively. This refers to the state-of-charge tolerance range. and They are respectively At any given time, the maximum photovoltaic output and the photovoltaic output coefficient; An improved genetic algorithm was used to solve the day-ahead scheduling model to obtain the optimized scheduling results of photovoltaic and energy storage power stations. The improvement of the genetic algorithm includes representing the initial feasible solution of the day-ahead scheduling model as individuals in the genetic space through real number encoding, initializing the population, evaluating the quality of the solution and guiding the evolution through the fitness function, and using a tournament mechanism to retain excellent individuals. The tournament mechanism adopted is based on the standard tournament combined with fitness and abandoned light loss. An adaptive mechanism is introduced to automatically adjust the crossover probability and mutation probability according to the current fitness value, minimum fitness value and average fitness value of the individual. The tournament mechanism adopted is based on a standard tournament combined with fitness and abandonment loss. The process includes: Based on the standard tournament selection, improvements were made by considering both fitness and wastage loss, as follows: ； In the formula, For the first The overall score of each individual, For fitness score, Score for wasted light. To avoid losing weight due to light loss, the one with the highest overall score was selected as the parent of the first generation. To improve global optimization capabilities, the crossover probability of the genetic algorithm... The calculation is as follows: ； In the formula, Minimum crossover probability; The maximum crossover probability; For maximum fitness; Average fitness; Current fitness; Mutation probability The calculation is as follows: ； In the formula, The minimum probability of mutation; This represents the highest mutation probability.

2. The day-ahead optimization scheduling method for economical operation of photovoltaic-storage power stations as described in claim 1, characterized in that, The process of the model, which aims to maximize the daily profit of a photovoltaic-storage power station, includes: ; In the formula, , , and They are respectively The calculation includes the grid connection revenue, curtailment losses, energy storage operating costs, and soft-constraint costs of a photovoltaic-storage power station at each moment; among which, the grid connection revenue... Represented as: ； In the formula, and They are respectively Always plan power output and grid connection electricity price.

3. The day-ahead optimization scheduling method for economic operation of photovoltaic-storage power stations as described in claim 1, characterized in that, it introduces... The process of curtailment losses related to curtailed solar power and grid-connected electricity prices includes: The loss function is expressed as: ； In the formula, and They are respectively At any given time, the maximum photovoltaic output and the photovoltaic output coefficient; for The curtailment loss factor is related to the grid-connected electricity price. related: 。 4. The day-ahead optimization scheduling method for economical operation of photovoltaic-storage power stations as described in claim 1, characterized in that, The population initialization process includes: Energy storage output at different times As individuals within the genetic space, Optimize energy storage output during specific time periods The remaining time periods make And force the initial population to satisfy energy balance constraints: ； Then in their respective intervals Internal random generation One value, and Each of the following constraints must be satisfied: ； ； in and These are the upper and lower limits of the initial population; then, the total current energy storage charge and discharge capacity is calculated. Sum of targets Then iterative adjustments are made; if According to the remaining space Proportional allocation difference ;like Adjustable space Reduced proportionally, corrected, and then adjusted again: ； ； In the formula, For the corrected individuals, The difference between the current value and the target value, until... The process ends when all possibilities fall within the feasible interval.

5. The day-ahead optimization scheduling method for economical operation of photovoltaic-storage power stations as described in claim 1, characterized in that, The fitness function is: ； In the formula, the penalty term for: ； In the formula, and These are the soft constraint penalty coefficient and the state of charge penalty coefficient, respectively.

6. A day-ahead optimization scheduling system for the economical operation of a photovoltaic-storage power station, employing the method described in claim 1, characterized in that, include: The model building module is configured to build a day-ahead scheduling model based on the day-ahead power output prediction results to solve the optimal day-ahead power output curve. The model aims to maximize the daily profit of the photovoltaic-storage power station and introduces curtailment losses related to curtailment volume and grid-connected electricity price. The constraint determination module is configured to determine the constraints of the day-ahead scheduling model based on the operational requirements of the photovoltaic-storage power station. The solution module is configured to solve the day-ahead scheduling model using an improved genetic algorithm to obtain the optimized scheduling results of the photovoltaic-storage power station. The improvement of the genetic algorithm includes representing the initial feasible solution of the day-ahead scheduling model as individuals in the genetic space through real number encoding, initializing the population, evaluating the quality of the solution and guiding the evolution through the fitness function, and using a tournament mechanism to retain excellent individuals. The tournament mechanism is based on the standard tournament combined with fitness and abandoned light loss. An adaptive mechanism is introduced to automatically adjust the crossover probability and mutation probability based on the current fitness value, minimum fitness value and average fitness value of the individual.

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