Multi-time-scale wind, light and water storage scheduling method based on improved sparrow algorithm

Abstract

The invention discloses a multi-time-scale wind-light-water storage scheduling method based on an improved sparrow algorithm. The method comprises the following steps: acquiring hour-level and short-term photovoltaic output prediction data, wind power output prediction data and load prediction data; based on photovoltaic output prediction data, wind power output prediction data and load prediction data, establishing a multi-time scale microgrid scheduling model including day-ahead economic scheduling and intra-day rolling optimization, the model including a first objective function about the day-ahead economic scheduling and a second objective function about the intra-day rolling scheduling; optimizing the first objective function and the second objective function by adopting an improved sparrow algorithm to obtain a first optimal solution and a second optimal solution; according to the first optimal solution and the second optimal solution, a corresponding position and fitness are output, the position is used for representing the optimal scheduling strategy, and the fitness is used for representing the minimum operation cost of the microgrid.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power dispatching, and in particular to a multi-time scale wind, solar and water storage dispatching method based on an improved sparrow algorithm. Background Art

[0002] With the widespread application of renewable energy sources such as wind power and photovoltaics, power system dispatch faces increasing challenges, mainly due to the unpredictability of these energy sources. In order to solve the problem of renewable energy consumption, strategies such as enhancing the complementarity of different power sources, integrating multiple variable resources, and maximizing the performance of hybrid systems are usually adopted to reduce the uncertainty of renewable energy. The large-scale application of energy storage equipment combined with renewable energy technology is an effective way to improve the absorption capacity of new energy and reduce overall energy consumption. By combining wind power generation, photovoltaic power generation, hydropower stations and energy storage stations with the power grid, the integration of renewable energy and system reliability can be improved.

[0003] The operation optimization of microgrids is an important factor to ensure their stability and has attracted extensive attention from scholars at home and abroad. Existing studies have shown that the use of distributed photovoltaic units for optimal scheduling can improve their economic efficiency, and an optimization model for multi-source network interaction and coordination has been proposed to address the uncertainty and randomness of distributed power generation. Other studies have also focused on the characteristics of wind farm energy storage and the impact of seasonal changes on integrated energy operations, and proposed different coordination optimization methods. However, most existing studies are limited to day-ahead scheduling, and fail to fully consider the impact of forecast errors of renewable energy and loads and sudden power fluctuations on microgrids, which may lead to optimization results that are inconsistent with reality and affect the operational stability of microgrids.

[0004] The complexity of scheduling strategies has prompted researchers to introduce a variety of optimization algorithms, including mathematical programming algorithms, intelligent evolutionary algorithms, and hybrid algorithms. Although linear programming and its variants have advantages in storage and computational efficiency, it is difficult to extract nonlinear features. Intelligent evolutionary algorithms, such as genetic algorithms, particle swarm optimization, differential evolution algorithms, and sparrow optimization algorithms, have become effective tools for finding optimal solutions due to their excellent performance and good integration with physical constraints. Summary of the invention

[0005] In view of the above-mentioned prior art, the present invention provides a multi-time-scale wind, solar and water storage scheduling method based on an improved sparrow algorithm, which mainly solves the technical problems existing in the above-mentioned background technology.

[0006] To achieve the above object, the technical solution of the embodiment of the present invention is implemented as follows:

[0007] A multi-time scale wind, solar and water storage scheduling method based on an improved sparrow algorithm, the method comprising the following steps:

[0008] Obtain hourly and short-term photovoltaic output forecast data, wind power output forecast data and load forecast data;

[0009] Based on photovoltaic output forecast data, wind power output forecast data and load forecast data, a multi-time scale microgrid dispatch model including day-ahead economic dispatch and intra-day rolling optimization is established, wherein the model includes a first objective function for day-ahead economic dispatch and a second objective function for intra-day rolling dispatch;

[0010] The improved sparrow algorithm is used to optimize the first objective function and the second objective function respectively to obtain the first optimal solution and the second optimal solution;

[0011] According to the first optimal solution and the second optimal solution, the corresponding position and fitness are output, the position is used to characterize the optimal scheduling strategy, and the fitness is used to characterize the minimum operating cost of the microgrid.

[0012] Preferably, the expression of the first objective function is:

[0013]

[0014] in, The revenue from the power grid selling electricity to users, The operation and maintenance costs of hydroelectric power stations and energy storage power stations, The cost of purchasing and selling electricity from the grid-connected network. Load imbalance costs.

[0015] Preferably, the model also includes constraints applicable to day-ahead economic scheduling and intraday rolling scheduling, and the constraints include:

[0016] Power balance constraints:

[0017] P LOAD,t =P WT,t +P PV,t +P HG,t +P SOC,t +P G,t

[0018] Among them, P LOAD,t is the total load power demand, P WT,t is the output of wind power, P PV,t is the photovoltaic output, P HG,t For hydropower output, P SOC,t For energy storage output, P G,t Contribute to the power grid;

[0019] Battery state of charge constraints:

[0020] V SOCmin ≤V SOC,t ≤VSOCmax

[0021] Where V SOC,t represents the state of charge of the tth battery, V SOCmin Indicates the minimum state of charge, V SOCmax Indicates the maximum state of charge;

[0022] Hydroelectric power station capacity limitation constraints:

[0023] V Hmin ≤V H,t ≤V Hmax

[0024] Where V H,t represents the output voltage of the tth hydroelectric power station, V Hmax Indicates the maximum output voltage of the hydroelectric power station, V Hmin Indicates the minimum output voltage of a hydroelectric power station.

[0025] Preferably, the second objective function is:

[0026]

[0027] in, is the deviation between the day-ahead dispatch and intraday dispatch of hydropower, energy storage, etc. within the dispatch interval of 1 hour, λ is the penalty coefficient, n is the number of dispatching equipment, P t,i is the power generation of the ith device in the tth time period, The power generation set for the device in the day-ahead scheduling.

[0028] Preferably, the improved sparrow algorithm is used to optimize the first objective function and the second objective function respectively, specifically including:

[0029] Generate an initial population of N sparrow individuals, set the population size and the maximum number of iterations, and use the first objective function and the second objective function as the objective functions of the sparrow population, and use the constraint conditions as the upper and lower limits of the search space of the sparrow population;

[0030] Use tent mapping to generate a set of chaotic sequences to initialize the positions of sparrows;

[0031] The fitness of the initialized sparrows is evaluated and sorted according to the size of the fitness. The top 20% of the sparrow population is named discoverers according to the fitness level, and the rest are named joiners. In addition, 10% to 20% of the entire population is selected as scouts.

[0032] The position update strategy is executed for each individual in the discoverer, joiner and scout respectively, and a dimension-by-dimension perturbation strategy is introduced in the iterative update process of each individual to enhance the ability to deviate from the local optimal value in subsequent iterations;

[0033] Determine whether the maximum number of iterations has been reached. If so, determine the individual with the best fitness and output its position and fitness.

[0034] Preferably, the finder's location update formula is:

[0035]

[0036] Among them, t represents the current iteration number, X i,j represents the position information of the i-th sparrow in the j-th dimension, where X best is the current global optimal position, α∈(0,1] is a random number, R2∈[0,1] represents the warning value, ST∈[0.5,1] represents the safety value, step is the random step size generated by Levy distribution, λ is the scaling factor of the step size, randn is a random number generated from the standard normal distribution, β is a constant, Q is a random number that follows the normal distribution, and L represents a 1×d matrix.

[0037] Preferably, the position update formula of the joiner is:

[0038]

[0039] Among them, X p is the optimal position currently occupied by the discoverer, X worst It represents the current worst global position, A represents a matrix, when i>n / 2, it means that the i-th participant with a lower fitness value has not obtained food and is in a very hungry state. At this time, it needs to fly to other places to find food to obtain more energy.

[0040] Preferably, the update formula for the scout's position update is:

[0041]

[0042] Among them, X best is the current global optimal position, β is the step size control parameter, which is a random number that obeys a normal distribution with a mean of 0 and a variance of 1, K∈[-1,1] is a random number, and f i is the fitness value of the current sparrow individual, f g and f w are the current global best and worst fitness values ​​respectively, and ε is the smallest constant to avoid zero in the denominator.

[0043] Preferably, the expression of the dimension-by-dimension perturbation strategy is:

[0044] X s+t -X s ~N(0,c 2 t)

[0045] Among them, c 2 t is the variance, t is the number of iterations, X s+t is the s+tth sparrow individual, X s is the sth sparrow individual, t and N are both constants.

[0046] The beneficial effects of the present invention are as follows: the present invention proposes a multi-time scale wind, solar and water storage optimization scheduling method based on an improved sparrow search algorithm, which establishes a multi-time scale scheduling model based on the prediction of photovoltaic, wind power and load demand. The day-ahead optimization strategy and the intraday optimization strategy are formulated by using the day-ahead prediction information and the real-time rolling prediction information. These optimization strategies are aimed at achieving low-cost operation and good operational stability of the microgrid. According to the operating cost function of the microgrid, the objective function of the day-ahead optimization scheduling is clarified. In addition, in order to ensure that the deviation between the intraday rolling plan and the day-ahead plan remains within a reasonable range, a correction term is introduced into the objective function of the intraday optimization scheduling.

[0047] Secondly, in order to improve the performance of the sparrow algorithm (SSA) in finding the optimal solution in the solution space of the objective function, the present invention introduces tent mapping, Levy flight strategy and Brownian motion. Tent mapping is used to perform chaos initialization, thereby increasing the quality of the initial solution; the Levy flight strategy introduces long jumps in the search process, so that the algorithm can jump out of the local optimum and explore the search space more effectively; the Brownian motion adds random disturbances, which enhances the algorithm's ability to avoid premature convergence and maintain population diversity. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flow chart of a multi-time-scale wind, solar and water storage optimization scheduling method based on an improved sparrow search algorithm in an embodiment of the present application;

[0049] Figure 2 A schematic diagram of multi-time scale microgrid energy scheduling in an embodiment of the present application;

[0050] Figure 3 This is a flow chart of the improved sparrow algorithm in the embodiment of the present application;

[0051] Figure 4 This is the wind, solar, and water storage microgrid model in the embodiment of this application. DETAILED DESCRIPTION

[0052] The technical solution of the present invention is further elaborated in detail below in conjunction with the drawings and specific embodiments of the specification. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present invention. The terms used in the specification of the present invention herein are only for the purpose of describing specific embodiments and are not intended to limit the present invention. In the following description, the expression "some embodiments" is related to a subset of all possible embodiments, but it should be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict.

[0053] In the following description, a large number of specific details are provided to provide a more thorough understanding of the present invention. However, it is apparent to those skilled in the art that the present invention can be implemented without one or more of these details. In other examples, in order to avoid confusion with the present invention, some technical features well known in the art are not described.

[0054] It should be understood that the present invention can be implemented in different forms and should not be interpreted as being limited to the embodiments proposed herein. On the contrary, providing these embodiments will make the disclosure thorough and complete, and the scope of the present invention will be fully conveyed to those skilled in the art. And the purpose of the terms used herein is only to describe specific embodiments and is not intended to be a limitation of the present invention. When used herein, the singular forms of "one", "one" and "said / the" are also intended to include plural forms, unless the context clearly indicates another way. It should also be understood that the terms "compose" and / or "include" when used in this specification determine the presence of the features, integers, steps, operations, elements and / or parts, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, parts and / or groups. When used herein, the term "and / or" includes any and all combinations of the relevant listed items.

[0055] It should also be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "inside", "outside", "left", "right" and similar expressions used herein are for illustrative purposes only and are not intended to be the only implementation method.

[0056] In order to fully understand the present invention, a detailed structure will be proposed in the following description to illustrate the technical solution proposed by the present invention. The optional embodiments of the present invention are described in detail as follows, but in addition to these detailed descriptions, the present invention may also have other implementations.

[0057] Please refer to the attached Figures 1 to 4 The present application provides a multi-time scale wind, solar and water storage scheduling method based on an improved sparrow algorithm, the method comprising the following steps:

[0058] S1. Obtain hourly and short-term photovoltaic output forecast data, wind power output forecast data and load forecast data;

[0059] S2. Based on the photovoltaic output forecast data, wind power output forecast data and load forecast data, a multi-time scale microgrid dispatch model including day-ahead economic dispatch and intraday rolling optimization is established. The model includes a first objective function for day-ahead economic dispatch and a second objective function for intraday rolling dispatch. The day-ahead economic dispatch has a cycle of 24 hours and a time interval of 1 hour. The goal is to minimize the daily operation cost of the microgrid system. The intraday rolling optimization is based on the day-ahead economic dispatch. Based on the short-term rolling forecast of renewable energy generation and load data, a rolling optimization is performed every 15 minutes to correct the output of energy storage and hydropower equipment in the next time interval, and consider power exchange with the upper-level power grid to achieve the best energy matching between distributed energy, energy storage and load.

[0060] S3, using the improved sparrow algorithm to optimize the first objective function and the second objective function respectively, to obtain a first optimal solution and a second optimal solution;

[0061] S4. According to the first optimal solution and the second optimal solution, the corresponding position and fitness are output, wherein the position is used to characterize the optimal scheduling strategy, and the fitness is used to characterize the minimum operating cost of the microgrid.

[0062] Furthermore, day-ahead dispatch mainly focuses on the economic issues of low-cost operation, including electricity sales revenue, penalty costs for wind and solar power abandonment, and interaction costs with the large power grid. Since the output of wind power and photovoltaic power generation is not affected by dispatch and is considered a fixed value, the cost of wind and solar power generation is not considered when determining the objective function. The optimal solution (minimum value) of the day-ahead economic dispatch objective function of the microgrid, i.e., the first objective function, is described as follows:

[0063]

[0064] in, The revenue from the power grid selling electricity to users, The operation and maintenance costs of hydroelectric power stations and energy storage power stations, The cost of purchasing and selling electricity from the grid-connected network. Load imbalance costs.

[0065] Operation and maintenance costs of hydroelectric power stations and energy storage power stations:

[0066]

[0067] where k s , k h are the operation and maintenance coefficients of energy storage and hydropower station power generation, P s,t With P h,t is the power generation of the energy storage power station and the hydropower station at time t.

[0068] Cost of purchasing and selling electricity for grid connection:

[0069]

[0070] Among them, c out,t With c in,t is the real-time electricity price sold and purchased at time t, P out,t With P in,t It is the amount of electricity sold and bought from the grid at time t. The amount sold is a negative number, which is equivalent to subtracting the cost.

[0071] The primary task of power grid dispatching is to match the power transmission with the power load. To ensure power balance, this penalty term is added to the cost function:

[0072]

[0073] where k im is the penalty coefficient for load imbalance, ΔP t It is the power difference between the transmission side and the consumption side.

[0074] Furthermore, dispatching must not only consider economic benefits, but also meet constraints such as power balance, upper and lower limits of output power, etc. Only by optimizing the objective function under these constraints can the reliability and safety of microgrid dispatching be ensured. Therefore, the model also includes constraints applicable to day-ahead economic dispatching and intraday rolling dispatching, and the constraints include:

[0075] Power balance constraints:

[0076] P LOAD,t =P WT,t +P PV,t +P HG,t +P SOC,t +P G,t

[0077] Among them, P LOAD,t is the total load power demand, P WT,t is the output of wind power, P PV,t is the photovoltaic output, P HG,t For hydropower output, P SOC,tFor energy storage output, P G,t Contribute to the power grid;

[0078] In order to ensure the reliable operation of the microgrid, each device operates at rated power:

[0079]

[0080] The above formula represents the upper and lower limit constraints of the hydropower generator set output, the charging and discharging power constraints of the energy storage power station, and the grid exchange power constraints. In the formula, P max and P min Respectively indicate the upper and lower limits of output power.

[0081] Overcharging and over-discharging will affect the service life of the battery and increase the overall operating cost of the microgrid, so it is necessary to constrain the battery's state of charge. The battery state of charge constraint is:

[0082] V SOCmin ≤V SOC,t ≤V SOCmax

[0083] Where V SOC,t represents the state of charge of the tth battery, V SOCmin Indicates the minimum state of charge, V SOCmax Indicates the maximum state of charge;

[0084] Reservoirs often have other functions such as water supply, irrigation, and ecological protection, and they need to ensure the maximum and minimum water levels. The capacity constraints of hydropower stations are:

[0085] V Hmin ≤V H,t ≤V Hmax

[0086] Where V H,t represents the output voltage of the tth hydroelectric power station, V Hmax Indicates the maximum output voltage of the hydroelectric power station, V Hmin Indicates the minimum output voltage of a hydroelectric power station.

[0087] Furthermore, in the intraday rolling scheduling stage, the scheduling time domain is 1h and the scheduling cycle is 15min. The day-ahead scheduling plan is revised based on more accurate forecast data. The economic principle of scheduling is consistent with the day-ahead target, but in order to make the day-ahead scheduling plan work, the objective function should add a correction term to prevent the intraday rolling plan from deviating too much from the day-ahead plan. Therefore, the second objective function representing the intraday rolling scheduling stage is:

[0088]

[0089] in, is the deviation between the day-ahead dispatch and intraday dispatch of hydropower, energy storage, etc. within the dispatch interval of 1 hour, λ is the penalty coefficient, n is the number of dispatching equipment, P t,i is the power generation of the ith device in the tth time period, The power generation set for the device in the day-ahead scheduling.

[0090] Preferably, the improved sparrow algorithm is used to optimize the first objective function and the second objective function respectively, that is, minF1 and minF2 are searched in the solution space. The Sparrow Search Algorithm (SSA) is an optimization algorithm based on swarm intelligence, and its principle is derived from the foraging and social behavior of sparrows. In the traditional sparrow algorithm, individual sparrows simulate foraging behavior and group collaboration to find the optimal solution, that is, the minimum operating cost of the microgrid and the corresponding optimal scheduling strategy.

[0091] Sparrow individuals are divided into two categories: foragers are responsible for finding food (searching), while guards observe the environment to avoid potential dangers. Individuals influence each other by constantly updating their positions and fitness, thus forming a dynamic group cooperation mechanism in the search space. The whole process emphasizes information sharing and collaborative optimization, so that the algorithm has strong global search capabilities and fast convergence, and is suitable for various complex optimization problems. The present invention introduces tent mapping chaos initialization, Levy flight strategy and dimension-by-dimension perturbation strategy to enhance the performance and exploration ability of the algorithm, and obtain the optimal day-ahead and intraday scheduling strategies.

[0092] Since the minimum value optimization of F1 and F2 is consistent in the algorithm operation process, the optimization algorithm of the present invention is described by taking the minimum value optimization process of F1 as an example, and F2 is not repeated. Therefore, the improved algorithm process is as follows:

[0093] S301, generating an initial population with N sparrow individuals, setting the population size and the maximum number of iterations, and using the first objective function and the second objective function as the objective function of the sparrow population, and using the constraint conditions as the upper and lower limits of the search space of the sparrow population;

[0094] The position of the i-th sparrow in the population can be described as x i =[P load,ti P s,ti P h,ti P out,ti P in,ti ΔP ti ].

[0095] Assuming that the total number of sparrows in the population is N, the position of the sparrows in the population can be represented by the following matrix:

[0096]

[0097] Then the fitness corresponding to the initial population is:

[0098]

[0099] Where F1(x i ) represents the components of the first objective function.

[0100] S302. Use tent mapping to generate a set of chaotic sequences for initializing the positions of sparrows. The initial diversification of the population can increase the optimization range of the population, thereby increasing the quality of the initial solution. Tent mapping is a simple piecewise linear mapping with chaotic characteristics, which is used to generate chaotic sequences. Its mathematical expression is as follows:

[0101]

[0102] Among them, when the parameter a is close to 0.5, the tent mapping will show more obvious chaotic characteristics.

[0103] S303, evaluate the fitness of the initialized sparrows, and sort them according to the size of the fitness, name the top 20% of the sparrow population as discoverers according to the fitness level, and the rest as joiners, and select 10% to 20% of the entire population as scouts;

[0104] The main responsibility of the discoverer is to search the solution space extensively to find new food sources (i.e. potential solutions to the optimization problem). They move randomly in the solution space, trying to find better solutions. The discoverer will update its position when it finds a new solution and record the fitness of the position to keep track of the best solution. When the discoverer finds a good food source, they will pass this information to other types of sparrows to promote information sharing within the group.

[0105] The joiner will follow the guide of the discoverer and move towards the food source found by the discoverer, trying to optimize its solution by using the information it has obtained. When approaching the food source found by the discoverer, the joiner will conduct a detailed local search to further improve the quality of the solution. The role of the joiner is to increase the diversity of the group by following and updating the position, so as to prevent the algorithm from falling into the local optimal solution.

[0106] The scouts' job is to conduct extensive exploration and find new potential food sources. They move more randomly, aiming to cover a wide area of ​​the solution space. Scouts collect information about the surrounding environment, evaluate their fitness, and decide whether to feed this information back to other sparrows. Scout early warning is a strategy in the sparrow algorithm that is used to send signals during the search process to alert other sparrows that the current solution may fall into a local optimum, prompting them to conduct more extensive exploration. In this way, the entire group can conduct global search more effectively, thereby improving the optimization effect of the algorithm.

[0107] S304, respectively executing the position update strategy for each individual among the discoverer, joiner and scout, and introducing a dimension-by-dimension perturbation strategy in the iterative update process of each individual to enhance the ability to deviate from the local optimal value in subsequent iterations;

[0108] The formula for updating the discoverer's position is:

[0109]

[0110] Where t represents the current iteration number. i,j represents the position information of the i-th sparrow in the j-th dimension, where X best is the current global optimal position. α∈(0,1] is a random number. R2∈[0,1] and ST∈[0.5,1] represent the warning value and safety value respectively. Step is a random step size generated using Levy distribution. λ is the scaling factor of the step size. Randn is a random number generated from a standard normal distribution. β is usually 1.5. Q is a random number that follows a normal distribution. L represents a 1×d matrix, in which all elements in the matrix are 1.

[0111] When R2<ST, it means that there are no predators around the foraging environment at this time, and the discoverer can perform extensive search operations. If R2≥ST, it means that some sparrows in the population have discovered the predator and have alerted other sparrows in the population. At this time, all sparrows need to quickly fly to other safe places to forage.

[0112] The position update formula of the joiner is:

[0113]

[0114] Among them, X p is the optimal position currently occupied by the discoverer, X worst It represents the current global worst position. A represents a 1×d matrix, in which each element is randomly assigned a value of 1 or -1, and A + =A T (AA T ) -1When i>n / 2, it means that the i-th participant with a lower fitness value has not obtained food and is in a very hungry state. At this time, it needs to fly to other places to find food in order to obtain more energy.

[0115] The update formula for the scout's position is:

[0116]

[0117] Where X best is the current global optimal position. β is the step size control parameter and is a random number that follows a normal distribution with a mean of 0 and a variance of 1. K∈[-1,1] is a random number, f i is the fitness value of the current sparrow individual. g and f w are the current global best and worst fitness values ​​respectively. ε is the smallest constant to avoid zero in the denominator.

[0118] When f i >f g When f i =f g This indicates that the sparrows in the group sense the threat posed by natural enemies and move to positions around other sparrows to reduce the possibility of being preyed upon.

[0119] After multiple iterations of the method for searching sparrows, the ideal sparrow is constantly approached by other sparrows, which makes it easy for individual sparrows to concentrate on the local optimal position for searching, resulting in local optimal stagnation. In order to solve this problem, this patent introduces a dimension-by-dimension perturbation strategy into the algorithm for searching a single sparrow (single iteration) to enhance the algorithm's ability to deviate from the local optimal value in subsequent iterations.

[0120] The Brownian motion formula is as follows:

[0121] X s+t -X s ~N(0,c 2 t)

[0122] Among them, c 2 t is the variance, t is the number of iterations, X s+t is the s+tth sparrow individual, X s is the sth sparrow individual, t and N are both constants, and the difference between the disturbance and the disturbance has a variance of c 2 The normal distribution of t, t is the number of iterations. The larger the variance is, the larger the movement distance is, and the more likely it is to jump out of the local optimal solution.

[0123] S305 , repeating individual fitness evaluation and position update until a termination condition is met, such as reaching a maximum number of iterations or a fitness change less than a threshold.

[0124] S306. Find the individual with the best fitness and output its position and fitness. Here, "position" refers to the optimal scheduling strategy, and "fitness" represents the minimum operating cost of the microgrid.

[0125] In summary, the multi-time scale dispatching strategy of microgrid can make rolling corrections on the basis of the day-ahead dispatching plan according to more accurate real-time forecast data, more effectively play the role of energy storage in peak shaving and valley filling, and improve the stability and economic benefits of microgrid. This strategy ensures that the microgrid can dynamically respond to changes in energy generation and load demand, thereby improving the overall system performance. The original sparrow search algorithm significantly improves its performance by introducing Levy flight strategy and Brownian motion. The Levy flight strategy introduces long jumps in the search process, enabling the algorithm to jump out of the local optimum and explore the search space more effectively. Brownian motion adds random perturbations, enhancing the algorithm's ability to avoid premature convergence and maintain population diversity. These improvements enable SSA to find better solutions in complex dispatching scenarios. In summary, the multi-time scale wind, solar, and water storage optimization dispatching strategy based on the improved sparrow search algorithm shows certain robustness and efficiency in microgrid management. By utilizing advanced optimization techniques and real-time forecast data, the strategy improves performance and economic benefits to a certain extent, providing some references and references for achieving a more sustainable and resilient energy system.

[0126] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. The protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A multi-time scale wind, solar and water storage scheduling method based on an improved sparrow algorithm, characterized in that: The method comprises the following steps: Obtain hourly and short-term photovoltaic output forecast data, wind power output forecast data and load forecast data; Based on photovoltaic output forecast data, wind power output forecast data and load forecast data, a multi-time scale microgrid dispatch model including day-ahead economic dispatch and intra-day rolling optimization is established, wherein the model includes a first objective function for day-ahead economic dispatch and a second objective function for intra-day rolling dispatch; The improved sparrow algorithm is used to optimize the first objective function and the second objective function respectively to obtain the first optimal solution and the second optimal solution; According to the first optimal solution and the second optimal solution, the corresponding position and fitness are output, the position is used to characterize the optimal scheduling strategy, and the fitness is used to characterize the minimum operating cost of the microgrid.

2. According to the multi-time scale wind, solar and water storage scheduling method based on the improved sparrow algorithm of claim 1, it is characterized in that: The expression of the first objective function is: in, The revenue from the power grid selling electricity to users, The operation and maintenance costs of hydroelectric power stations and energy storage power stations, The cost of purchasing and selling electricity from the grid-connected network. Load imbalance costs.

3. The multi-time scale wind, solar and water storage scheduling method based on the improved sparrow algorithm according to claim 2 is characterized in that: The model also includes constraints applicable to day-ahead economic dispatch and intraday rolling dispatch, which include: Power balance constraints: P LOAD,t =P WT,t +P PV,t +P HG,t +P SOC,t +P G,t Among them, P LOAD,t is the total load power demand, P WT,t is the output of wind power, P PV,t is the photovoltaic output, P HG,t For hydropower output, P SOC,t For energy storage output, P G,t Contribute to the power grid; Battery state of charge constraints: In SOCmin ≤V SOC,t ≤V SOCmax Where V SOC,t represents the state of charge of the tth battery, V SOCmin Indicates the minimum state of charge, V SOCmax Indicates the maximum state of charge; Hydroelectric power station capacity limitation constraints: In Hmin ≤V H,t ≤V Hmax Where V H,t represents the output voltage of the tth hydroelectric power station, V Hmax Indicates the maximum output voltage of the hydroelectric power station, V Hmin Indicates the minimum output voltage of a hydroelectric power station.

4. The multi-time scale wind, solar and water storage scheduling method based on the improved sparrow algorithm according to claim 3 is characterized in that: The second objective function is: in, is the deviation between the day-ahead dispatch and intraday dispatch of hydropower, energy storage, etc. within the dispatch interval of 1 hour, λ is the penalty coefficient, n is the number of dispatching equipment, P t,i is the power generation of the ith device in the tth time period, The power generation set for the device in the day-ahead scheduling.

5. The multi-time scale wind, solar and water storage scheduling method based on the improved sparrow algorithm according to claim 4 is characterized in that: The improved sparrow algorithm is used to optimize the first objective function and the second objective function respectively, specifically including: Generate an initial population of N sparrow individuals, set the population size and the maximum number of iterations, and use the first objective function and the second objective function as the objective functions of the sparrow population, and use the constraint conditions as the upper and lower limits of the search space of the sparrow population; Use tent mapping to generate a set of chaotic sequences to initialize the positions of sparrows; The fitness of the initialized sparrows is evaluated and sorted according to the size of the fitness. The top 20% of the sparrow population is named discoverers according to the fitness level, and the rest are named joiners. In addition, 10% to 20% of the entire population is selected as scouts. The position update strategy is executed for each individual in the discoverer, joiner and scout respectively, and a dimension-by-dimension perturbation strategy is introduced in the iterative update process of each individual to enhance the ability to deviate from the local optimal value in subsequent iterations; Determine whether the maximum number of iterations has been reached. If so, determine the individual with the best fitness and output its position and fitness.

6. The multi-time scale wind, solar and water storage scheduling method based on the improved sparrow algorithm according to claim 5 is characterized in that: The finder's position update formula is: Among them, t represents the current iteration number, X i,j represents the position information of the i-th sparrow in the j-th dimension, where X best is the current global optimal position, α∈(0,1] is a random number, R2∈[0,1] represents the warning value, ST∈[0.5,1] represents the safety value, step is the random step size generated by Levy distribution, λ is the scaling factor of the step size, randn is a random number generated from the standard normal distribution, β is a constant, Q is a random number that follows the normal distribution, and L represents a 1×d matrix.

7. The multi-time scale wind, solar and water storage scheduling method based on the improved sparrow algorithm according to claim 6 is characterized in that: The position update formula of the joiner is: Among them, X p is the optimal position currently occupied by the discoverer, X worst It represents the current worst global position, A represents a matrix, when i>n / 2, it means that the i-th participant with a lower fitness value has not obtained food and is in a very hungry state. At this time, it needs to fly to other places to find food to obtain more energy.

8. The multi-time scale wind, solar and water storage scheduling method based on the improved sparrow algorithm according to claim 7 is characterized in that: The update formula for the scout's position is: Among them, X best is the current global optimal position, β is the step size control parameter, which is a random number that obeys a normal distribution with a mean of 0 and a variance of 1, K∈[-1,1] is a random number, and f i is the fitness value of the current sparrow individual, f g and f w are the current global best and worst fitness values ​​respectively, and ε is the smallest constant to avoid zero in the denominator.

9. The multi-time scale wind, solar and water storage scheduling method based on the improved sparrow algorithm according to claim 8 is characterized in that: The expression of the dimension-by-dimension perturbation strategy is: X s+t -X s ~N(0,c 2 t) Among them, c 2 t is the variance, t is the number of iterations, X s+t is the s+tth sparrow individual, X s is the sth sparrow individual, t and N are both constants.

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