Source, storage and load cooperative scheduling method and system and storage medium

Abstract

The invention relates to the technical field of data processing, in particular to a source storage load collaborative scheduling method and system and a storage medium, and the method comprises the steps: obtaining to-be-optimized source storage load data of a user side energy system; performing load division on the to-be-optimized source storage load data to obtain multiple types of divided data; constructing a multi-target source storage load optimization function based on the multiple types of divided data; determining an optimization result corresponding to the multi-target source storage load optimization function; and determining a scheduling scheme of the to-be-optimized source storage and load data based on the optimization result. According to the method, through collaborative optimization of multiple types of divided data, the new energy utilization rate can be improved, the electric charge cost is reduced, and the stability and the anti-risk capability of the system are effectively improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a source-storage-load collaborative scheduling method, system and storage medium in the field of data processing technology. Background Art

[0002] Optimization methods in related technologies face numerous technical challenges in the coordinated optimization of sources, storage, and loads, particularly in high-altitude renewable energy systems. First, the randomness and volatility of renewable energy generation complicates the charging and discharging scheduling of energy storage systems. At high altitudes, photovoltaic output is significantly affected by variations in temperature, radiation intensity, and wind speed, making it difficult to accurately predict and optimize energy storage management. Furthermore, scheduling strategies in related technologies often employ single-objective optimization or weighted summation methods, making it difficult to balance multiple objectives such as user electricity costs, power supply reliability, and voltage stability while ensuring renewable energy consumption. Furthermore, existing optimization methods lack refined load control and primarily target flexible loads, failing to fully tap the potential of adjustable resources such as energy storage equipment and electric vehicles. This results in insufficient demand-side response capabilities and an inability to effectively address fluctuations in renewable energy output. High altitude environments further exacerbate the difficulty of managing the state of charge (SOC) of energy storage systems. Traditional methods struggle to optimize the high SOC range, impacting the energy storage lifespan and system economics. In addition, the current scheduling method has a slow convergence speed, uneven distribution of solutions, lacks the robustness to adapt to complex environments, and is difficult to cope with system optimization needs under power grid fluctuations and extreme climatic conditions. Summary of the Invention

[0003] The purpose of the present invention is to provide a source-storage-load coordinated scheduling method, system, and storage medium. The technical solutions adopted are as follows:

[0004] In a first aspect, an embodiment of the present invention provides a source-storage-load coordinated scheduling method, the method comprising:

[0005] Obtain the source, storage and load data to be optimized of the user-side energy system;

[0006] Performing load division on the source-storage load data to be optimized to obtain multiple types of divided data;

[0007] Constructing a multi-objective source-load optimization function based on the multi-class divided data;

[0008] Determining an optimization result corresponding to the multi-objective source-load optimization function;

[0009] Based on the optimization result, a scheduling scheme for the source storage load data to be optimized is determined.

[0010] In a second aspect, a source-storage-load coordinated scheduling system is provided, the system comprising:

[0011] An acquisition module is used to obtain the source storage and load data to be optimized of the user-side energy system;

[0012] A partitioning module, configured to partition the source-storage load data to be optimized to obtain multiple types of partitioned data;

[0013] A construction module, configured to construct a multi-objective source-storage-load optimization function based on the multiple types of divided data;

[0014] A first determining module is used to determine the optimization result corresponding to the multi-objective source-load optimization function;

[0015] The second determining module is configured to determine a scheduling scheme for the source storage load data to be optimized based on the optimization result.

[0016] In a third aspect, a computer program product is provided, comprising: a computer program code, which, when executed on a computer, enables the computer to execute the method in the first aspect or any one of the possible implementations of the first aspect.

[0017] In a fourth aspect, a computer-readable storage medium is provided, which stores a computer program code. When the computer program code runs on a computer, the computer executes the method in the first aspect or any possible implementation method described in the first aspect.

[0018] The present invention has the following beneficial effects: by obtaining the source-storage-load data to be optimized of the user-side energy system, the source-storage-load data to be optimized is load divided to obtain multiple categories of divided data for load modeling, thereby improving the flexibility of load scheduling. Afterwards, a multi-objective source-storage-load optimization function is constructed based on the multiple categories of divided data, and the optimization results corresponding to the multi-objective source-storage-load optimization function are determined; thereby, the scheduling scheme for the source-storage-load data to be optimized is determined through the optimization results. In this way, by analyzing the optimization results corresponding to the multi-objective source-storage-load optimization function, the limitations of single-objective optimization can be avoided. By collaboratively optimizing multiple categories of divided data, the utilization rate of new energy can be improved, the electricity cost can be reduced, and the stability and risk resistance of the system can be effectively improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions and advantages of the embodiments of the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0020] Figure 1This is a schematic diagram of the implementation process of a source-storage-load collaborative scheduling method provided by an embodiment of the present invention;

[0021] Figure 2 This is another implementation flow diagram of a source-storage-load collaborative scheduling method provided by an embodiment of the present invention;

[0022] Figure 3 This is another implementation flow diagram of a source-storage-load collaborative scheduling method provided by an embodiment of the present invention;

[0023] Figure 4 This is a schematic diagram of the structure of a source-storage-load coordinated scheduling system provided by an embodiment of the present invention;

[0024] Figure 5 It is a structural diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0025] To further illustrate the technical means and effectiveness of the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, describes in detail the specific implementation, structure, features, and effectiveness of a source-storage-load coordinated scheduling method proposed in accordance with the present invention. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics of one or more embodiments may be combined in any suitable manner.

[0026] In the description of the embodiments of the present invention, unless otherwise specified, " / " means or, for example, A / B can mean A or B: "and / or" in the text is only a description of the association relationship of associated objects, indicating that there can be three relationships, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present invention, "multiple" refers to two or more than two.

[0027] In the following, the terms "first" and "second" are used for descriptive purposes only and should not be understood to imply or suggest relative importance or implicitly indicate the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features.

[0028] Unless otherwise defined, all technical and scientific terms used in the embodiments of the present invention have the same meanings as commonly understood by one of ordinary skill in the art to which the present invention belongs.

[0029] In related technologies, with the widespread application of new energy power generation and distributed energy storage systems, achieving efficient coordinated optimization of source, storage, and load in extreme environments at high altitudes is a key issue in improving grid stability and energy economy. Taking the Lago Co Salt Lake lithium mine in Ali Prefecture, Tibet as an example, this area is far away from the main power grid and needs to rely on photovoltaic and energy storage systems for independent power supply. However, extreme climatic conditions such as low temperature, low oxygen, and high wind speed in high-altitude areas pose severe challenges to the stability of photovoltaic power generation, energy storage charging and discharging efficiency, and long-term operation of equipment. The output of new energy is significantly affected by the weather, the management of the energy storage state of charge (SOC) is complex, and the production load in the mining area fluctuates greatly. Therefore, accurate source storage and load optimization scheduling is crucial.

[0030] The optimization methods in related technologies still have limitations when dealing with multi-objective optimization problems of high-altitude new energy systems. Traditional linear programming methods have high computational complexity and are difficult to simultaneously optimize multiple objectives such as electricity prices, power quality control, and stable power supply. Although some heuristic algorithms can find the best solution, the distribution of solutions is uneven and the convergence is slow, which affects the stability of the scheduling scheme. Especially in high-altitude environments, energy storage systems need to overcome the influence of extreme temperatures, ensure a high SOC available range, and achieve rapid black start and stable operation of new energy power systems. However, most current methods tend to focus on a single goal, such as reducing electricity prices or improving energy storage utilization, and fail to fully consider factors such as new energy consumption, grid reliability, and user load response, resulting in insufficient applicability and practicality of scheduling schemes.

[0031] Therefore, innovative optimization methods that integrate intelligent scheduling, load forecasting, and renewable energy characteristic modeling are urgently needed to improve the accuracy and adaptability of source-storage-load coordinated optimization. In high-altitude renewable energy systems, grid-connected energy storage, advanced multi-objective optimization algorithms, and intelligent scheduling strategies should be combined to achieve optimal scheduling under multiple constraints, improve the operational stability and economic efficiency of renewable energy systems, and ensure the long-term safe and efficient operation of a high proportion of renewable energy in an independent power grid environment.

[0032] Based on this, embodiments of the present invention propose a coordinated source-storage-load scheduling method. This method achieves intelligent scheduling of user-side energy systems by optimizing renewable energy consumption, energy storage charge and discharge scheduling, and flexible load management. First, in the data acquisition and preprocessing phase, information on renewable energy generation, energy storage state of charge, user load demand, and electricity prices is acquired. This data is then formatted, outliers are removed, and normalized to provide unified data input for optimized scheduling. Subsequently, in the load modeling phase, based on the adjustable characteristics of user loads, loads are classified into rigid loads, curtailable loads, shiftable loads, and electric vehicle loads. Operational constraints and optimization variables are then set for each load to enhance load scheduling flexibility. In the optimization objective setting phase, a multi-objective source-storage-load optimization mathematical model is constructed, with the optimization objectives of minimizing user electricity costs, reducing voltage deviation, and shortening outage duration. This model comprehensively considers power balance, energy storage state of charge, grid voltage stability, and demand-side response strategies. In the optimization solution phase, a multi-objective genetic algorithm is employed, utilizing Pareto solution set screening, fast non-dominated sorting, and congestion control strategies to ensure balanced and diverse optimization results. During the optimization process, the population is first initialized and its fitness value is calculated. A non-dominated sorting algorithm is then used to select the Pareto optimal solution. Congestion control is used to ensure a uniform distribution of solutions. Finally, a crossover and mutation strategy is used to optimize the population individuals and enhance search capabilities. During the scheduling plan generation phase, the optimization results are used to formulate a new energy consumption strategy, energy storage system charging and discharging plans, flexible load scheduling schemes, and electric vehicle charging optimization plans.

[0033] The following describes in detail a specific scheme of the source-storage-load coordinated scheduling method provided by the present invention in conjunction with the accompanying drawings. Figure 1 , which shows a schematic diagram of an implementation flow of a source-storage-load coordinated scheduling method provided by an embodiment of the present invention, the method comprising:

[0034] 101. Obtain source-storage-load data to be optimized of the user-side energy system.

[0035] Here, the source-storage-load data to be optimized is data obtained after standardization processing, and the source-storage-load data to be optimized has a consistent format. In some possible implementations, the new energy output, energy storage state of charge, user load demand, and electricity price information of the user-side energy system are first obtained; then, the new energy output, energy storage state of charge, user load demand, and electricity price information are subjected to data standardization processing to obtain the source-storage-load data to be optimized.

[0036] For example, the system first obtains information on renewable energy output, energy storage charge status, user load demand and electricity price, and then performs data standardization to ensure the quality of data input for optimized scheduling. Specifically, the optimized scheduling cycle is set to T, and the data collected includes renewable energy power generation power P PV(t), energy storage system state of charge SOC(t) and its charge and discharge power P ESS (t), user load demand P L (t), time-of-use electricity price C L (t), demand response electricity price C DR (t) and the remaining on-grid electricity price C sell (t). To ensure the consistency and comparability of the data, all variables were converted to the interval [0,1] using the normalization method. The normalization formula is: Among them, X is the original data, X′ is the normalized data; X min and X max are the minimum and maximum values of the data, respectively.

[0037] 102. Load partitioning is performed on the source load storage data to be optimized to obtain multiple types of partitioned data.

[0038] Here, user loads are obtained from the source load storage data to be optimized, and the user loads are divided into multiple categories of divided data based on the adjustable characteristics of the user loads. In some possible implementations, user loads are first determined from the source load storage data to be optimized; then, the user loads are divided into multiple categories to obtain the multiple categories of divided data. Here, by determining the adjustable characteristics of the user loads, the user loads are divided into rigid loads, curtailable loads, movable loads, and electric vehicle loads, thereby obtaining the multiple categories of divided data. In this way, by dividing the user loads based on their adjustable characteristics, it is possible to accurately construct a load model.

[0039] In some possible implementations, this can be achieved by Figure 2 The steps shown divide the user load:

[0040] 201. Determine the maximum curtailable power, schedulable time window, and maximum state of charge of the user load demand.

[0041] 202 , based on the maximum curtailable power, the schedulable time window, and the maximum state of charge, divide the user load demand into multiple load categories to obtain the multiple categories of divided data.

[0042] In the above steps 201 and 202, the user load is divided into rigid load, curtailable load, movable load and electric vehicle load according to the adjustability of the user load, and mathematical models are established for each of them.

[0043] In some possible implementations, the constraints and optimization of the curtailable load, the translatable load, the electric vehicle load, and the rigid load may be achieved through the following process:

[0044] First, based on the maximum curtailable power, the curtailable load is determined; that is, the curtailable load is constrained by the maximum curtailable power.

[0045] Secondly, based on the schedulable time window, the shiftable load is determined.

[0046] Here, the shiftable load refers to the load whose running time can be adjusted, so the shiftable load is constrained by the schedulable time window.

[0047] Thirdly, based on the maximum state of charge, the load of the electric vehicle is determined.

[0048] Here, the state of charge maximum value includes: a maximum state of charge value and a minimum state of charge value, and the electric vehicle load is constrained by the maximum value and the minimum value.

[0049] Finally, a rigid load is determined based on the difference between the user load demand, the curtailable load, the shiftable load, and the electric vehicle load.

[0050] Here, the multiple types of classified data include: the curtailable load, the movable load, the electric vehicle load, and the rigid load. The rigid load can be obtained by subtracting the curtailable load, the movable load, and the electric vehicle load from the user load demand.

[0051] Among them, the rigid load P fix (t) represents the basic load that cannot be adjusted, as shown in formula (1):

[0052] P fix (t) = P L (t)-P cut (t)-P trans (t)-P EV (t) (1);

[0053] Among them, P cut (t) represents the load that can be reduced, P trans (t) represents the translation load, P EV (t) represents the electric vehicle charging load, P L (t) represents the user load demand. For the load that can be reduced, the power reduction needs to meet the following constraints, as shown in formula (2):

[0054] 0≤P cut (t)≤P cut,max (2);

[0055] In addition, the load reduction compensation can be reduced according to the demand f cut (t) Response electricity price calculation, the specific expression is shown in formula (3):

[0056] fcut (t) = C DR (t)·P cut (t). (3);

[0057] Shiftable loads refer to loads whose operating time can be adjusted, such as electric water heaters and washing machines. Their operating time t must meet the schedulable time window, as shown in formula (4):

[0058] P trans (t) = P trans (t+Δt),t∈[T start ,T end ] (4);

[0059] Where T start and T end The earliest and latest start times of the load are respectively. The scheduling of electric vehicle loads needs to consider the battery state of charge constraint. The battery load state SOC of electric vehicle loads EV (t), as shown in formula (5):

[0060] SOC EV,min ≤SOC EV (t)≤SOC EV,max (5);

[0061] Among them, SOC EV,min Indicates the maximum and minimum battery load status, SOC EV,max This represents the maximum battery load state, ensuring that the battery does not overcharge or over-discharge during the charging process. Thus, during the load modeling phase, based on the adjustable characteristics of user loads, loads are divided into rigid loads, curtailable loads, shiftable loads, and electric vehicle loads. Operating constraints and optimization variables are then set for each load type to improve load scheduling flexibility.

[0062] 103. Construct a multi-objective source-load optimization function based on the multiple types of divided data.

[0063] Here, by setting the optimization target and combining the optimization correlation parameters of the energy system, a multi-objective source-storage-load optimization function is constructed. The optimal solution is calculated through the multi-objective source-storage-load optimization function to obtain the best solution for source-storage-load collaborative optimization. In some possible implementations, the above step 103 can be achieved by Figure 3 The steps shown achieve:

[0064] 301 : Determine optimization-related parameters of the energy system based on the multiple types of divided data.

[0065] Here, since the multiple categories of classified data include: the reducible load, the movable load, the electric vehicle load and the rigid load; the grid-purchased power, on-grid power, time-of-use electricity price, surplus on-grid power price, and compensation costs for the reducible load of the energy system can be obtained through the multiple categories of classified data; that is, the associated parameters are optimized, including: the grid-purchased power, on-grid power, time-of-use electricity price, surplus on-grid power price, compensation costs for the reducible load, etc. of the energy system.

[0066] 302 : Constrain the optimization-related parameters based on a preset optimization objective to obtain the multi-objective source-load optimization function.

[0067] Here, we use the preset optimization objectives as a reference and combine them with the optimization-related parameters to construct a multi-objective source-storage-load optimization function. By constraining the optimization-related parameters with the preset optimization objectives, we can construct a more accurate multi-objective source-storage-load optimization function.

[0068] In some possible implementations, during the optimization target setting phase, minimizing user electricity costs, reducing voltage deviation, and shortening power outage duration are preset optimization targets. Taking into account power balance, energy storage charge state, grid voltage stability, and demand-side response strategies, a multi-objective source-storage-load optimization mathematical model (i.e., a multi-objective source-storage-load optimization function) is constructed. The economic optimization target aims to reduce user electricity purchase costs. The multi-objective source-storage-load optimization function is expressed as shown in Formula (6):

[0069]

[0070] Among them, P grid (t) is the power purchased from the grid, P sell (t) is the amount of electricity connected to the grid, f cut (t) is the compensation cost for load reduction. In addition, to ensure power quality, the optimization goal needs to minimize voltage deviation to maintain voltage stability. Its mathematical expression is shown in formula (7):

[0071] f2=minmax|V(t)-V N | (7);

[0072] Where V(t) is the voltage at the user side at time t, V N is the rated voltage. In order to improve power supply reliability, the power outage time needs to be reduced during the optimization scheduling process. The optimization target is defined as shown in formula (8):

[0073]

[0074] Wherein, S(t)=1 indicates that the power supply is normal, and S(t)=0 indicates that the power supply is insufficient.

[0075] 104. Determine an optimization result corresponding to the multi-objective source-load optimization function.

[0076] Here, a multi-objective genetic algorithm is used to solve the multi-objective source-load optimization function. The fitness value of each objective is calculated and classified by fast non-dominated sorting to obtain the optimal solution set. Finally, the optimal solution set is homogenized to obtain the optimization result.

[0077] In some possible implementations, the above step 104 may be implemented by the following steps 141 to 143 (not shown):

[0078] 141. Determine the fitness value of each target based on the multi-target source-load optimization function.

[0079] 142. Classify the fitness value of each target using fast non-dominated sorting to obtain an optimal solution set of the fitness value of each target.

[0080] 143. Perform homogenization processing on the optimal solution set to obtain the optimization result.

[0081] In the above steps 141 to 143, the population is initialized based on the multi-objective genetic algorithm, and the fitness value of each individual is calculated; the population is sorted using the fast non-dominated sorting algorithm to screen the Pareto optimal solution set; then, the congestion control strategy is used to ensure the uniform distribution of solutions and avoid the convergence problem of local optimal solutions, and the crossover and mutation operation is performed to update the population individuals and improve the optimization search capability; finally, the termination condition is set to obtain the final Pareto optimal solution set, and the final optimization scheduling plan is determined based on the fuzzy comprehensive evaluation method.

[0082] In some possible implementations, a multi-objective genetic algorithm is used for solving the problem, and Pareto solution set screening, fast non-dominated sorting, and crowding control strategies are used to ensure the balance and diversity of the optimization results. First, the optimization population is randomly initialized and the fitness value of each individual is calculated; then, the population is classified using fast non-dominated sorting to screen the Pareto optimal solution set, and the crowding control strategy is used to ensure the uniform distribution of Pareto solutions, that is, to achieve uniform processing of the optimal solution set and avoid the population from converging to the local optimum too early. The crossover and mutation operation is used to improve the search capability, where the crossover operator uses extended middle crossover (SBX) and the mutation operator uses polynomial mutation (PM), which are defined by the following formulas (9) and (10), respectively:

[0083] x′=α·x1+(1-α)·x2 (9);

[0084] x′=x+σ·(x max -xmin ) (10);

[0085] Here, x1 and x2 are the two parent individuals, α is the crossover coefficient, and σ is the variation range. The optimization algorithm iteratively optimizes the population through Pareto selection, ultimately obtaining the multi-objective optimal solution set. The final scheduling solution is then selected using a fuzzy comprehensive evaluation method. This multi-objective genetic algorithm optimizes the results, employing Pareto solution selection, fast non-dominated sorting, and congestion control strategies to ensure global and uniform distribution of the optimization results.

[0086] 105. Based on the optimization result, determine a scheduling scheme for the source storage load data to be optimized.

[0087] Here, through the optimization results, new energy consumption strategies, energy storage charging and discharging plans, flexible load scheduling plans and electric vehicle charging optimization plans are formulated to ensure the economy and safety of the system.

[0088] In some possible implementations, based on the optimization results, a new energy consumption plan is formulated for smoothly matching the new energy output curve with the user load curve, a flexible load scheduling plan is formulated for characterizing the load operation time, an energy storage system charging and discharging plan is formulated for characterizing the charging and discharging power of the energy system, and an electric vehicle charging optimization plan is formulated for characterizing the electric vehicle charging period; and through the new energy consumption plan, the flexible load scheduling plan, the energy storage system charging and discharging plan and the electric vehicle charging optimization plan, a scheduling plan for the source storage and load data to be optimized is obtained. Among them: the steps for formulating the new energy consumption plan are:

[0089] Step 1: Calculate the matching degree between the output of photovoltaic and other new energy sources and the load demand, and increase the proportion of self-generation and self-use of new energy sources;

[0090] Step 2: Coordinate the charging and discharging scheduling of the energy storage system to ensure a smooth match between the new energy output curve and the user load curve;

[0091] Step 3: Optimize the surplus grid-connected power to reduce the wind and solar power curtailment rate and improve the utilization rate of new energy.

[0092] The steps for formulating the energy storage system charging and discharging plan are as follows:

[0093] Step 1: Energy storage is charged during periods of low electricity prices and released during periods of high electricity prices to reduce user electricity costs.

[0094] Step 2: When there is excess renewable energy generation, energy storage equipment is prioritized for charging to reduce the amount of renewable energy curtailed.

[0095] Step 3: When a power grid failure occurs, reasonably dispatch energy storage discharge to prioritize the electricity demand of rigid loads.

[0096] The steps for formulating a flexible load dispatching plan are as follows:

[0097] Step 1: Adjust the operating power of the load that can be reduced to ensure that the user's electricity demand is met while reducing the load;

[0098] Step 2: Optimize the operating time of the shiftable load to avoid the high electricity price period and improve the economic efficiency of electricity use;

[0099] Step 3: Develop an electric vehicle charging plan, optimize the charging start time and charging power, and reduce the impact on the power grid.

[0100] The steps for developing an EV charging optimization plan are as follows: Step 1: Arrange EV charging during the low electricity price period to reduce charging costs;

[0101] Step 2: Give priority to using renewable energy electricity to charge electric vehicles during the peak period of renewable energy power generation to increase the renewable energy consumption rate;

[0102] Step 3: Optimize the charging scheduling of multiple electric vehicles to reduce the impact of charging load on grid stability.

[0103] During the scheduling plan generation process, the optimization results are used to formulate a new energy consumption strategy, energy storage system charging and discharging plan, flexible load scheduling plan, and electric vehicle charging optimization plan. The new energy consumption strategy aims to increase the proportion of self-generation and self-use of new energy, optimize the energy storage charging and discharging sequence, match the new energy output curve with the user load demand, and improve the utilization rate of renewable energy. The energy storage scheduling plan comprehensively considers electricity price fluctuations and load demand to formulate the optimal charging and discharging plan, as shown in formulas (11) and (12):

[0104] 0≤P ESS,c (t)≤P ESS,max (11);

[0105] -P ESS,max ≤P ESS,d (t)≤0 (12);

[0106] Among them, P ESS，c (t) is the energy storage charging power, P ESS，d (t) is the energy storage discharge power, P ESS，max The maximum charge and discharge power of the energy storage system. Optimizing flexible load scheduling can reduce load power and the operating time of shiftable loads, improving system economics. Electric vehicle charging optimization combines user travel needs with grid load conditions to rationally schedule charging times, minimizing impact on the grid while ensuring user charging needs.

[0107] In an embodiment of the present invention, by obtaining the source-load-storage data to be optimized of the user-side energy system, load division is performed on the source-load-storage data to be optimized, and multiple categories of divided data are obtained to perform load modeling and improve the flexibility of load scheduling. Afterwards, a multi-objective source-load-storage optimization function is constructed based on the multiple categories of divided data, and the optimization results corresponding to the multi-objective source-load-storage optimization function are determined; thereby, the scheduling scheme for the source-load-storage data to be optimized is determined based on the optimization results. In this way, by analyzing the optimization results corresponding to the multi-objective source-load-storage optimization function, the limitations of single-objective optimization can be avoided. By collaboratively optimizing multiple categories of divided data, the utilization rate of new energy can be improved, the electricity cost can be reduced, and the stability and risk resistance of the system can be effectively improved.

[0108] The embodiment of the present invention provides a source storage load coordinated scheduling system, please refer to Figure 4 , which shows a schematic diagram of the structure of a source-storage-load coordinated scheduling system provided by an embodiment of the present invention. The system 400 includes:

[0109] An acquisition module 401 is used to acquire source-storage-load data to be optimized of a user-side energy system;

[0110] A partitioning module 402 is configured to partition the source load data to be optimized to obtain multiple types of partitioned data;

[0111] A construction module 403 is configured to construct a multi-objective source-load optimization function based on the multiple types of divided data;

[0112] A first determining module 404 is configured to determine an optimization result corresponding to the multi-objective source-load optimization function;

[0113] The second determining module 405 is configured to determine a scheduling scheme for the source load data to be optimized based on the optimization result.

[0114] In some possible implementations, the acquisition module 401 is also used to obtain the new energy output, energy storage charge status, user load demand and electricity price information of the user-side energy system; and perform data standardization processing on the new energy output, the energy storage charge status, the user load demand and the electricity price information to obtain the source-storage-load data to be optimized.

[0115] In some possible implementations, the partitioning module 402 is further configured to determine user loads in the source storage load data to be optimized;

[0116] The user load is divided into multiple load categories to obtain the multiple categories of divided data.

[0117] In some possible implementations, the division module 402 is also used to determine the maximum curtailable power, schedulable time window, and maximum state of charge of the user load demand; based on the maximum curtailable power, schedulable time window, and maximum state of charge, the user load demand is divided into multiple load categories to obtain the multiple categories of divided data.

[0118] In some possible implementations, the division module 402 is further used to determine the curtailable load based on the maximum curtailable power; determine the shiftable load based on the schedulable time window; determine the electric vehicle load based on the maximum state of charge; determine the rigid load based on the difference between the user load demand, the curtailable load, the shiftable load and the electric vehicle load; wherein the multiple categories of divided data include: the curtailable load, the shiftable load, the electric vehicle load and the rigid load.

[0119] In some possible implementations, the construction module 403 is further used to determine the optimization-related parameters of the energy system based on the multiple categories of divided data; constrain the optimization-related parameters based on preset optimization objectives to obtain the multi-objective source-storage-load optimization function.

[0120] In some possible implementations, the first determination module 404 is further used to determine the fitness value of each target based on the multi-target source-load optimization function; classify the fitness value of each target using fast non-dominated sorting to obtain the optimal solution set of the fitness value of each target; and homogenize the optimal solution set to obtain the optimization result.

[0121] In some possible implementations, the second determination module 405 is further used to formulate, based on the optimization results, a new energy consumption plan for smoothly matching the new energy output curve and the user load curve, a flexible load scheduling plan for characterizing the load operation time, an energy storage system charging and discharging plan for characterizing the charging and discharging power of the energy system, and an electric vehicle charging optimization plan for characterizing the electric vehicle charging period; based on the new energy consumption plan, the flexible load scheduling plan, the energy storage system charging and discharging plan, and the electric vehicle charging optimization plan, a scheduling plan for the source-storage-load data to be optimized is obtained.

[0122] Optionally, the transmission medium can be a wired link (for example, but not limited to, coaxial cable, optical fiber and digital subscriber line (DSL)) or a wireless link (for example, but not limited to, wireless Fidelity (WIFI), Bluetooth and mobile device network). It should be noted that the system provided in the above embodiment is only illustrated by the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above. In addition, the method embodiments provided in the above embodiments belong to the same concept. The specific implementation process is detailed in the method embodiments, which will not be repeated here.

[0123] Figure 5 FIG. 1 is a schematic diagram of the structure of a computer device provided by an embodiment of the present invention. For example, Figure 5 As shown, the computer device 500 includes: a memory 501, a processor 502, and a computer program 503 stored in the memory 501 and running on the processor 502, wherein when the processor 502 executes the computer program 503, the computer device can execute any one of the source-storage-load collaborative scheduling methods introduced above.

[0124] In addition, an embodiment of the present invention also protects a system, which may include a memory and a processor, wherein the memory stores executable program code, and the processor is used to call and execute the executable program code to perform a source-storage-load collaborative scheduling method provided by an embodiment of the present invention. This embodiment can divide the system into functional modules according to the above method example. For example, it can correspond to each functional module, or two or more functions can be integrated into one processing module. The above integrated module can be implemented in the form of hardware. It should be noted that the division of modules in this embodiment is schematic, which is only a logical function division. There may be other division methods in actual implementation. It should be noted that all relevant contents of each step involved in the above method embodiment can be referred to the functional description of the corresponding functional module, and will not be repeated here.

[0125] It should be understood that the system provided in this embodiment is used to execute the above-mentioned source-storage-load collaborative scheduling method, and therefore can achieve the same effect as the above-mentioned implementation method. In the case of an integrated unit, the system may include a processing module and a storage module. Specifically, when the system is applied to a device, the processing module can be used to control and manage the actions of the device. The storage module can be used to support the device in executing mutual program codes, etc. Specifically, the processing module can be a processor or a controller, which can implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the contents disclosed in the present invention. The processor can also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of digital signal processing (DSP) and a microprocessor, etc., and the storage module can be a memory.

[0126] In addition, the system provided by an embodiment of the present invention may specifically be a chip, component, or module. The chip may include a connected processor and memory; wherein the memory is used to store instructions. When the processor calls and executes the instructions, the chip can execute the source-storage-load coordinated scheduling method provided in the above embodiment. This embodiment also provides a computer-readable storage medium, which stores computer program code. When the computer program code is executed on a computer, it causes the computer to execute the above-mentioned method steps to implement the source-storage-load coordinated scheduling method provided in the above embodiment.

[0127] This embodiment also provides a computer program product. When the computer program product is executed on a computer, it causes the computer to execute the above-mentioned steps to implement a source-storage-load coordinated scheduling method provided in the above embodiment. The system, computer-readable storage medium, computer program product, or chip provided in this embodiment are all used to execute the corresponding method provided above. Therefore, the beneficial effects that can be achieved can refer to the beneficial effects of the corresponding method provided above and will not be repeated here. Through the description of the above embodiments, those skilled in the art will understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual application, the above-mentioned functions can be distributed to different functional modules as needed, that is, the internal structure of the system is divided into different functional modules to complete all or part of the functions described above. In the embodiments provided by the present invention, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not implemented. On the other hand, the mutual coupling or direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection through some interface, system or unit, which may be electrical, mechanical or other forms.

[0128] It should be noted that the above-mentioned order of the embodiments of the present invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-task processing and parallel processing are also possible or may be advantageous. The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other. Each embodiment focuses on the differences from other embodiments. The above content is only a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or replacements within the technical scope disclosed by the present invention, which should be covered within the scope of protection of the present invention.

Claims

1. A source-storage-load coordinated scheduling method, characterized in that: The method comprises: Obtain the source, storage and load data to be optimized of the user-side energy system; Performing load division on the source-storage load data to be optimized to obtain multiple types of divided data; Constructing a multi-objective source-load optimization function based on the multi-class divided data; Determining an optimization result corresponding to the multi-objective source-load optimization function; Based on the optimization result, a scheduling scheme for the source storage load data to be optimized is determined.

2. A source-storage-load coordinated scheduling method according to claim 1, characterized in that: The obtaining of the source-storage-load data to be optimized of the user-side energy system includes: Obtaining the new energy output, energy storage charge status, user load demand and electricity price information of the user-side energy system; Data standardization is performed on the new energy output, the energy storage charge state, the user load demand and the electricity price information to obtain the source storage load data to be optimized.

3. The source-storage-load coordinated scheduling method according to claim 1, characterized in that: The load division of the source storage load data to be optimized is performed to obtain multiple types of divided data, including: Determining user loads in the source storage load data to be optimized; The user load is divided into multiple load categories to obtain the multiple categories of divided data.

4. The source-storage-load coordinated scheduling method according to claim 3, characterized in that: The performing multi-class load classification on the user load to obtain the multi-class classified data includes: Determine the maximum curtailable power, dispatchable time window, and maximum state of charge value of the user load demand; Based on the maximum curtailable power, the schedulable time window, and the maximum state of charge, the user load demand is divided into multiple load categories to obtain the multiple categories of divided data.

5. The source-storage-load coordinated scheduling method according to claim 4, characterized in that: The multi-class load classification of the user load demand based on the maximum curtailable power, the schedulable time window, and the maximum state of charge value to obtain the multi-class classified data includes: determining a curtailable load based on the maximum curtailable power; Determining a shiftable load based on the dispatchable time window; determining the electric vehicle load based on the maximum state of charge value; The rigid load is determined based on the difference between the user load demand, the curtailable load, the movable load and the electric vehicle load; wherein the multiple categories of divided data include: the curtailable load, the movable load, the electric vehicle load and the rigid load.

6. The source-storage-load coordinated scheduling method according to claim 1, characterized in that: The constructing of a multi-objective source-load optimization function based on the multi-class divided data includes: Determining optimized associated parameters of the energy system based on the multiple types of divided data; The optimization-related parameters are constrained based on preset optimization objectives to obtain the multi-objective source-storage-load optimization function.

7. The source-storage-load coordinated scheduling method according to claim 1, characterized in that: Determining the optimization result corresponding to the multi-objective source-load optimization function includes: Determining the fitness value of each target based on the multi-target source-storage optimization function; Using fast non-dominated sorting to classify the fitness value of each target, and obtain an optimal solution set of the fitness value of each target; The optimal solution set is homogenized to obtain the optimization result.

8. The source-storage-load coordinated scheduling method according to claim 1, characterized in that: Determining a scheduling scheme for the source storage load data to be optimized based on the optimization result includes: Based on the optimization results, formulate a new energy consumption plan for smoothly matching the new energy output curve with the user load curve, a flexible load scheduling plan for characterizing the load operation time, an energy storage system charging and discharging plan for characterizing the charging and discharging power of the energy system, and an electric vehicle charging optimization plan for characterizing the electric vehicle charging period; Based on the new energy consumption plan, the flexible load scheduling plan, the energy storage system charging and discharging plan and the electric vehicle charging optimization plan, a scheduling plan for the source-storage-load data to be optimized is obtained. Obtaining the user's travel demand and current load status; Formulate an electric vehicle charging optimization plan based on the user's travel needs and the current load status; Based on the optimization results, the maximum charge and discharge power of the energy system, the load power that can be reduced, and the operating time of the load that can be shifted are obtained; Determining energy storage charging power and energy storage discharging power based on the maximum charging and discharging power; Based on the energy storage charging power and the energy storage discharging power, a scheduling plan for the source storage load data to be optimized is formulated.

9. A source-storage-load coordinated scheduling system, characterized in that: The system comprises: An acquisition module is used to obtain the source storage and load data to be optimized of the user-side energy system; A partitioning module, configured to partition the source-storage load data to be optimized to obtain multiple types of partitioned data; A construction module, configured to construct a multi-objective source-storage-load optimization function based on the multiple types of divided data; A first determining module is used to determine the optimization result corresponding to the multi-objective source-load optimization function; The second determining module is configured to determine a scheduling scheme for the source storage load data to be optimized based on the optimization result.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer program code, and when the computer program code runs on a computer, the computer is enabled to execute the source-storage-load coordinated scheduling method according to any one of claims 1 to 8.

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