Multi-target scheduling strategy selection method and system considering source load uncertainty

Abstract

The embodiment of the invention discloses a multi-target scheduling strategy selection method and system considering source load uncertainty. The method comprises the steps of obtaining target virtual power plant data; determining an evaluation target corresponding to the target virtual power plant data to obtain a plurality of evaluation targets; constructing a multi-target optimization scheduling model according to the plurality of evaluation targets; performing fuzzy processing on the multi-objective optimization scheduling model to obtain a satisfaction model; inputting the target virtual power plant data into the satisfaction model to obtain a plurality of scheduling strategies; and comprehensively evaluating the plurality of scheduling strategies according to the plurality of evaluation targets to obtain a plurality of evaluation results, selecting a target evaluation result from the plurality of evaluation results, and selecting a scheduling strategy corresponding to the target evaluation result to obtain a target optimal scheduling strategy. According to the embodiment of the invention, the method achieves the selection of a multi-target scheduling strategy which comprehensively considers the economy, energy and environment when the virtual power plant faces the uncertainty of the source load.

Description

Technical Field

[0001] The present application relates to the field of power system dispatching selection, and in particular to a multi-objective dispatching strategy selection method and system considering source and load uncertainty. Background Art

[0002] In the context of vigorously developing renewable energy, the concept of virtual power plant (VPP) was proposed. As an innovative form of energy management, VPP integrates distributed generation resources, energy storage equipment and controllable loads into a virtual unity, thus achieving effective management and scheduling of decentralized energy. However, the volatility and uncertainty of distributed generation resources and the ever-changing power load demand pose a huge challenge to the reliability and economy of VPP.

[0003] In the field of VPP optimal dispatch, some studies use robust optimization and stochastic optimization methods to reduce the risks brought by uncertainty, while others use forecasting technology to improve the forecast accuracy of renewable energy output and load demand. Although these methods have improved the dispatch efficiency of VPP to a certain extent, they often only focus on specific aspects, such as economy or reliability of power system, and lack comprehensive consideration of multiple objectives of economy, energy and environment, and it is difficult to maintain system reliability and economy at the same time.

[0004] Therefore, how to solve the problem of choosing economic, energy and environmental multi-objective scheduling strategies for virtual power plants when facing source and load uncertainties has become an urgent issue to be solved. Summary of the invention

[0005] The embodiment of the present application provides a method and system for selecting a multi-objective scheduling strategy that takes into account source-load uncertainty. By establishing a multi-objective scheduling model that takes into account economic, energy, and environmental factors, and adopting a fuzzy membership function method, the multi-objective scheduling model is converted into a comprehensive satisfaction model. At the same time, according to the actual operation requirements of the power system, constraints are set, and the comprehensive satisfaction model is solved to obtain multiple scheduling strategies. Based on the robust optimization model, scheduling strategies with satisfaction greater than a certain preset value are screened out from the multiple scheduling strategies to obtain multiple optimized scheduling strategies. Finally, the optimal optimized scheduling strategy is selected from the multiple optimized scheduling strategies, thereby achieving the maximization of the economy of the virtual power plant, the optimization of energy efficiency, and the minimization of environmental impact under the condition of source-load uncertainty, while ensuring the stability and reliability of the power system.

[0006] In a first aspect, an embodiment of the present application provides a multi-objective scheduling strategy selection method considering source-load uncertainty, including:

[0007] Obtain target virtual power plant data;

[0008] Determine an evaluation target corresponding to the target virtual power plant data to obtain a plurality of evaluation targets;

[0009] Constructing a multi-objective optimization scheduling model according to the multiple evaluation objectives;

[0010] Performing fuzzy processing on the multi-objective optimization scheduling model to obtain a satisfaction model;

[0011] Inputting the target virtual power plant data into the satisfaction model to obtain multiple scheduling strategies;

[0012] The multiple scheduling strategies are comprehensively evaluated according to the multiple evaluation targets to obtain multiple evaluation results, a target evaluation result is selected from the multiple evaluation results, and the scheduling strategy corresponding to the target evaluation result is selected to obtain the target optimal scheduling strategy.

[0013] In a second aspect, the embodiment of the present application provides a multi-objective scheduling strategy selection system considering source-load uncertainty, including: a data acquisition module, a model building module, a model processing module, a strategy solving module, and a strategy evaluation module, wherein:

[0014] The data acquisition module is used to acquire target virtual power plant data;

[0015] The data acquisition module is also used to determine the evaluation target corresponding to the target virtual power plant data to obtain multiple evaluation targets;

[0016] The model building module is used to build a multi-objective optimization scheduling model according to the multiple evaluation objectives;

[0017] The model processing module is used to perform fuzzy processing on the multi-objective optimization scheduling model to obtain a satisfaction model;

[0018] The strategy solving module is used to input the target virtual power plant data into the satisfaction model to obtain multiple scheduling strategies;

[0019] The strategy evaluation module is used to comprehensively evaluate the multiple scheduling strategies according to the multiple evaluation targets to obtain multiple evaluation results, select a target evaluation result from the multiple evaluation results, select the scheduling strategy corresponding to the target evaluation result, and obtain the target optimal scheduling strategy.

[0020] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps in the first aspect of the embodiment of the present application.

[0021] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps described in the first aspect of the embodiment of the present application.

[0022] In a fifth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application. The computer program product may be a software installation package.

[0023] By implementing the embodiments of the present application, target virtual power plant data is obtained; evaluation targets corresponding to the target virtual power plant data are determined to obtain multiple evaluation targets; a multi-objective optimization scheduling model is constructed according to the multiple evaluation targets; fuzzy processing is performed on the multi-objective optimization scheduling model to obtain a satisfaction model; the target virtual power plant data is input into the satisfaction model to obtain multiple scheduling strategies; the multiple scheduling strategies are comprehensively evaluated according to the multiple evaluation targets to obtain multiple evaluation results, a target evaluation result is selected from the multiple evaluation results, and the scheduling strategy corresponding to the target evaluation result is selected to obtain the target optimal scheduling strategy, thereby enabling the virtual power plant to select a multi-objective scheduling strategy that comprehensively considers economy, energy and environment when facing source and load uncertainty. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background technology, the drawings required for use in the embodiments of the present application or the background technology will be described below.

[0025] Figure 1 It is a flowchart of a multi-objective scheduling strategy selection method considering source-load uncertainty provided by an embodiment of the present application;

[0026] Figure 2 It is a structural schematic diagram of a multi-objective scheduling strategy selection system considering source-load uncertainty provided by an embodiment of the present application;

[0027] Figure 3 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0028] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0029] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0030] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0031] The following is an explanation of the relevant contents, concepts, meanings, technical issues, technical solutions, beneficial effects, etc. involved in the embodiments of the present application.

[0032] First, some terms involved in this application are explained:

[0033] Virtual power plant: Virtual power plant is a new type of power resource management and optimization method. It aggregates distributed power generation resources (such as wind power, solar photovoltaic), energy storage systems, controllable loads and electric vehicles and other distributed energy resources through advanced information and communication technologies and software systems, and realizes the coordinated and optimized operation of these resources, which helps to improve energy utilization efficiency and the stability of system operation.

[0034] Source-load uncertainty: Source-load uncertainty mainly refers to the uncertainty of power generation (power source) and power demand (load) in the power system. This uncertainty may come from the volatility of renewable energy (such as changes in wind speed and sunshine intensity), changes in load demand (affected by factors such as weather, time, and economic activities), forecast errors, equipment failures and maintenance, and market and price fluctuations. These uncertainties can be addressed through forecasting technology, scheduling strategies, and reserve resources.

[0035] Membership function: Membership function refers to the membership relationship of an element to a fuzzy set, which is used to describe the degree to which an element belongs to a fuzzy set. It mainly maps elements to the interval [0, 1], where 0 means that the element does not belong to the fuzzy set at all, 1 means that the element completely belongs to the fuzzy set, and the values ​​between 0 and 1 represent the different degrees to which the element belongs to the fuzzy set.

[0036] CRITIC weighting method: The CRITIC weighting method comprehensively measures the objective weight of indicators based on the comparative strength of evaluation indicators and the conflict between indicators. It takes into account the variability of indicators while taking into account the correlation between indicators. The larger the number, the more important it is. It fully utilizes the objective attributes of the data itself for scientific evaluation.

[0037] Self-Confident DoubleHierarchy Linguistic Preference Relation (SC-DHLPR) is a method used for group decision-making. It expresses the decision maker's preference through a double-level linguistic terminology set and assigns a confidence level to each double-level linguistic terminology (DHLT) to reflect the decision maker's degree of certainty in the preference. This method helps to more objectively reflect the actual preferences of individuals in the group decision-making process and assists in determining the decision maker's weight, thereby affecting the final decision result.

[0038] Confidence: Confidence usually refers to the degree of certainty of an individual in his or her own judgment or preference. In the decision-making and evaluation process, confidence can help quantify the level of trust an individual has in his or her own choice or evaluation, thereby giving appropriate weight to the individual's opinion in group decision-making.

[0039] TOPSIS method: The TOPSIS method (Technique for Order Preference by Similarity to Ideal Solution, abbreviated as TOPSIS) is a method that approximates the ideal solution ranking method, that is, the distance method between good and bad solutions. It is a commonly used comprehensive evaluation method, and its results can accurately reflect the gap between various evaluation schemes.

[0040] See also Figure 1 , Figure 1 : is a flow chart of a multi-objective scheduling strategy selection method considering source-load uncertainty provided by an embodiment of the present application, the method includes but is not limited to the following steps:

[0041] S101. Obtain target virtual power plant data.

[0042] Among them, the virtual power plant (VPP) is a new form of power resource management, and the target virtual power plant data refers to the specific information set used to build and optimize the virtual power plant operation model. The target virtual power plant data may include the following data: real-time power generation data of distributed power generation facilities (such as the output power of wind turbines and photovoltaics), storage capacity and charging and discharging status of energy storage equipment, demand response data of controllable loads (such as adjustable loads in the power system), and grid operation data (such as electricity price information and load demand forecasts).

[0043] Specifically, smart meters and sensors can be used to collect real-time data from distributed generation facilities, energy storage equipment, and controllable loads. These collected data can be integrated and preprocessed to form target virtual power plant data. By analyzing the target virtual power plant data, effective management and optimized scheduling of decentralized energy resources can be achieved.

[0044] S102: Determine the evaluation target corresponding to the target virtual power plant data to obtain multiple evaluation targets.

[0045] Specifically, in the operation and management of VPP, the evaluation objectives are key indicators to measure its performance and benefits, which usually include Virtual Power Plant Consolidated Profit (VPPCP), Renewable Energy Usage Rate (REUR) and Carbon Net Processing Capacity (CNPC). By analyzing the VPP scheduling strategy from multiple evaluation objectives, a comprehensive evaluation from the aspects of economy, environment and energy is ensured.

[0046] Among them, the comprehensive profit of the virtual power plant is the comprehensive profit of the VPP, which is mainly calculated from the operating income of the VPP and the costs incurred during operation. The calculation formula is as follows:

[0047] P VPPCP =R VPP -[C dev +C buy +C d-r ]

[0048] In the above formula, P VPPCP is the comprehensive profit of the virtual power plant. When solving the dispatch strategy, the maximum value is usually taken as the solution target; R VPP The income of VPP operators; dev is the equipment operating cost of VPP; C buy is the energy purchase cost of VPP; Cd-r is the demand response cost of VPP. The above costs are all costs incurred during the operation of VPP.

[0049] The income of VPP operators mainly includes the income generated from the sale of electricity to power grid companies, power generation groups, etc. and renewable energy subsidies. The calculation formula is as follows:

[0050]

[0051] In the above formula, R sell Revenue from the sale of electricity for VPP operators; R RE-sub is the renewable energy subsidy; T is the operation cycle of VPP scheduling; is the electricity price on the grid at time t; is the amount of electricity sold at time t; β RE-sub subsidizing prices for renewable energy; is the renewable energy power generation at time t.

[0052] As for the equipment operating cost, it mainly includes the charging and discharging cost of the energy storage system and the operating cost of wind turbines and photovoltaic equipment. The calculation formula is as follows:

[0053]

[0054] In the above formula, h es is the cost coefficient of the energy storage system, which is generally a preset default coefficient; P es,pro (t) is the energy release power of the energy storage system at time t; P es,abo (t) is the charging power of the energy storage system at time t; h pv is the cost coefficient of distributed photovoltaic equipment in VPP; h wind is the cost coefficient of distributed wind turbine equipment in VPP; P pv (t) is the actual power generation of the photovoltaic equipment at time t; P wind (t) is the actual power generation of the wind turbine equipment at time t.

[0055] As for the energy purchase cost, it mainly includes the energy cost consumed by the VPP during scheduling. The energy cost can include the cost of electricity and natural gas. The calculation formula of the energy purchase cost is as follows:

[0056]

[0057] In the above formula, h e,buy (t) is the grid electricity price at time t; P e,buy (t) is the power of purchased electric energy at time t; h g,buy (t) is the natural gas purchase price at time t; Pg,buy (t) is the gas purchase volume at time t.

[0058] As for the demand response cost, it mainly includes the cost incurred by the VPP when responding to the dispatch strategy, and its calculation formula is as follows:

[0059]

[0060] In the above formula, h d-r P (t) is the subsidy cost coefficient of the user load participating in the demand response in the VPP at time t. This cost coefficient is mainly determined according to the way the user participates in the demand response and is generally preset; d-r (t) is the power of user load participating in demand response at time t.

[0061] Among them, the renewable energy utilization rate is the utilization rate of renewable energy such as photovoltaics and wind turbines after VPP scheduling. The calculation formula is as follows:

[0062]

[0063] In the above formula, A RE is the utilization rate of renewable energy, which is expressed as a ratio. When solving the scheduling strategy, the maximum value is usually taken as the solution target; P RE,real is the actual use of renewable energy; P RE,all Total renewable energy output.

[0064] Among them, net carbon emissions are the difference between carbon dioxide emissions and absorption under the scheduling of VPP, which is used to represent the net emissions when VPP and carbon dioxide interact with each other. The calculation formula is as follows:

[0065]

[0066] In the above formula, is the net carbon emission. When solving the scheduling strategy, the minimum value is usually used as the solution target; C release is carbon emissions; C abs is the carbon absorption; is the natural gas usage at time t; ρ gas is the coefficient of carbon dioxide produced when using natural gas; u is the carbon fixation system, which can be an ecosystem such as green land, forests and lakes, used to absorb carbon dioxide; is the amount of carbon dioxide absorbed per unit area of ​​different carbon fixation systems at time t; s u is the area of ​​the carbon fixation system.

[0067] Specifically, after determining the evaluation target corresponding to the dispatch strategy of the VPP according to the target virtual power plant data and obtaining multiple evaluation targets, each evaluation target has a corresponding calculation formula, and the actual target value of each evaluation target can be obtained through these calculation formulas. At the same time, a multi-objective optimization dispatch model for determining the VPP can be constructed according to multiple evaluation targets. By solving the multi-objective optimization dispatch model, goals such as maximizing profits, maximizing the use of renewable energy, and minimizing carbon emissions can be achieved.

[0068] S103: construct a multi-objective optimization scheduling model according to the multiple evaluation objectives.

[0069] Specifically, the three key evaluation targets required for evaluating the VPP dispatch strategy are clearly defined: the comprehensive profit of the virtual power plant, the utilization rate of renewable energy, and the net carbon emissions. These three key evaluation targets are used as the basis for building a multi-objective optimization dispatch model. The multi-objective optimization dispatch model includes defining the objective function of each evaluation target and the constraints for each evaluation target. By solving the multi-objective optimization dispatch model, considering multiple evaluation targets and constraints, and finding the optimal or approximately optimal solution, the optimal VPP dispatch strategy that comprehensively considers multiple evaluation targets can be achieved.

[0070] Optionally, the above step S103, constructing a multi-objective optimization scheduling model according to the multiple evaluation objectives, may further include the following steps:

[0071] A31, obtaining an objective function corresponding to each of the multiple evaluation objectives to obtain multiple objective functions;

[0072] A32. Determine a constraint condition corresponding to each objective function according to the multiple objective functions to obtain multiple constraint conditions;

[0073] A33. Construct the multi-objective optimization scheduling model according to the multiple objective functions and the multiple constraints.

[0074] Specifically, when constructing a multi-objective optimization scheduling model, the objective function corresponding to each of the multiple evaluation objectives can be obtained to obtain multiple objective functions. The objective function is a calculation formula corresponding to each evaluation objective. For each objective function, when solving the scheduling strategy, the solution objectives are to maximize profits, maximize the use of renewable energy, and minimize carbon emissions.

[0075] Furthermore, the constraint conditions corresponding to each objective function are determined according to the multiple objective functions to obtain multiple constraint conditions, wherein the multiple constraint conditions include at least one of the following: power balance constraint, renewable energy output constraint, energy storage system related constraint, and demand response constraint.

[0076] The power balance constraint is used to ensure the balance between the power input and output of the VPP, that is, the total power generated by the VPP at any time (including renewable energy, energy storage system discharge, power purchase, etc.) is equal to the total power consumption of the VPP (including load demand, energy storage system charging, power sales to the grid, etc.). The constraint condition is as follows:

[0077] P pv (t)+P wind (t)+P d－r (t)+P e,buy (t)+P es,pro (t)-P es,abo (t) = P load,e (t)+P e,sell (t)

[0078] In the above formula, P pv (t) is the actual power generation of the photovoltaic equipment at time t; P wind (t) is the actual power generation of the wind turbine at time t; P d-r (t) is the power of user load participating in demand response at time t; P e,buy (t) is the power of purchased electric energy at time t; P es,pro (t) is the energy release power of the energy storage system at time t; P es,abo (t) is the charging power of the energy storage system at time t; P load,e (t) is the load demand power at time t; P e,sell (t) is the power sold to the power grid.

[0079] Among them, the renewable energy output constraint is used to ensure that the power generation of renewable energy such as photovoltaics and wind turbines in the VPP is within the predicted maximum power range. By predicting the maximum power range of renewable energy, the volatility and uncertainty of renewable energy can be effectively managed. The constraint condition is as follows:

[0080]

[0081] In the above formula, P pv,pre (t) is the predicted power generation of photovoltaic power in VPP at time t; P wind,pre (t) is the predicted power generation of the wind turbine in the VPP at time t.

[0082] Among them, the energy storage system related constraints include energy storage system capacity and charge state constraints, as well as energy storage system power and charge and discharge state constraints.

[0083] For the energy storage system capacity and state of charge constraints, the constraints are as follows:

[0084]

[0085] In the above formula, E(t) is the state of charge of the energy storage system at time t; η es is the charge and discharge coefficient of the energy storage system; P es,abo (t) is the charging power of the energy storage system at time t; P es,pro (t) is the energy storage system’s discharge power at time t; E(24) is the charge state of the energy storage system at the 24th time unit (usually one day is 24 time units); E i (0) is the initial charge state of the energy storage system at the beginning of the day; E min is the minimum state of charge of the energy storage system; E max It is the maximum charge state of the energy storage system under safe conditions.

[0086] For the energy storage system power and charge and discharge state constraints, the constraints are as follows:

[0087]

[0088] In the above formula, P es,max is the maximum charge and discharge power of the energy storage system. After converting the nonlinear constraints related to the energy storage system into linear constraints, P es,max It is usually represented by M in the big M method and is used to linearize nonlinear constraints; U es,abo (t) is the charging state of the energy storage system at time t, 1 means it is charging, and 0 means it is not charging; U es,pro (t) is the discharge state of the energy storage system at time t. 1 indicates discharging, and 0 indicates not discharging. The charging state and discharging state of the energy storage system cannot be 1 at the same time.

[0089] Among them, the demand response constraints include demand response capacity limitation constraints and demand response speed constraints.

[0090] For the demand response capacity limit constraint, the constraints are as follows:

[0091] {P d-r,valley,max (t)≤P d-r (t)≤P d-r,peak,max (t)

[0092] In the above formula, P d-r,valley,max (t) is the maximum power of the user in the VPP at time t participating in the valley-filling demand response (i.e., increasing electricity consumption when electricity demand is low); P d-r,peak,max (t) is the maximum power of the user's electricity load in the VPP at time t participating in the peak shaving demand response (i.e. reducing electricity consumption when the electricity demand is peak).

[0093] For the demand response speed constraint, the constraints are as follows:

[0094] -P d-r,limit ≤P d-r (t)-P d-r (t-1)≤P d-r,limit

[0095] In the above formula, P d-r,limit The maximum power change limit for electricity loads participating in demand response is generally set by system default or in advance.

[0096] Furthermore, multiple objective functions and multiple constraints are integrated into a multi-objective optimization scheduling model, which can simultaneously consider multiple evaluation objectives and multiple constraints, and then solve and find multiple or optimal feasible scheduling strategies to achieve a balance between the economy of the virtual power plant, the utilization rate of renewable energy and the environmental impact.

[0097] S104: Perform fuzzy processing on the multi-objective optimization scheduling model to obtain a satisfaction model.

[0098] Specifically, in multi-objective optimization problems, there is often uncertainty and ambiguity in the objective functions and constraints. Direct processing may make the model too complex or difficult to solve. Therefore, the multi-objective optimization scheduling model is fuzzy processed to obtain a single-objective optimization scheduling model, namely the satisfaction model, in order to improve the practicality and flexibility of the model.

[0099] Optionally, the above step S104, performing fuzzy processing on the multi-objective optimization scheduling model to obtain a satisfaction model, may also include the following steps:

[0100] A41, determining a membership function corresponding to each of the multiple objective functions to obtain multiple membership functions;

[0101] A42. Obtain weight coefficients corresponding to multiple evaluation targets to obtain multiple weight coefficients;

[0102] A43. Construct the satisfaction model according to the multiple membership functions and the multiple weight coefficients.

[0103] Specifically, the membership function corresponding to each of the multiple objective functions can be determined through a preset fuzzy processing tool to obtain multiple membership functions, which map the numerical value of the objective function to the interval [0,1], where 1 indicates that the objective function value reaches the optimal state, and 0 indicates that the objective function value reaches the worst state. Then, the weight coefficients corresponding to the multiple evaluation targets are obtained to obtain multiple weight coefficients, where the sum of the multiple weight coefficients is 1. A satisfaction model can be constructed based on multiple memberships and multiple weight coefficients, where the satisfaction model is as follows:

[0104]

[0105] In the above formula, Z is the satisfaction calculated by the satisfaction model; ψ1 is the weight coefficient corresponding to the comprehensive profit of the virtual power plant; χ(P VPPCP ) is the membership function corresponding to the comprehensive profit objective function of the virtual power plant; ψ2 is the weight coefficient corresponding to the utilization rate of renewable energy; χ2(A RE ) is the membership function corresponding to the renewable energy utilization rate objective function; ψ3 is the weight coefficient corresponding to the net carbon emissions; is the membership function corresponding to the carbon net emission objective function.

[0106] Optionally, the above step A41, determining the membership function corresponding to each of the multiple objective functions to obtain multiple membership functions, may also include the following steps:

[0107] B41, obtaining a reference objective function; the reference objective function is any objective function among the multiple objective functions;

[0108] B42. Determine the maximum value and the minimum value of the reference objective function according to the target virtual power plant data;

[0109] B43. Input the maximum value and the minimum value into a preset fuzzy membership function to obtain a membership function corresponding to a reference objective function.

[0110] Specifically, any one of the multiple objective functions is obtained as a reference objective function for the main analysis, and the data range of the reference objective function can be determined according to the target virtual power plant data, and the maximum and minimum values ​​of the reference objective function can be determined according to the data range.

[0111] Furthermore, the maximum value and the minimum value are input into the preset fuzzy membership function to obtain the membership function corresponding to the reference objective function, wherein, if the objective function corresponding to the comprehensive profit of the virtual power plant or the utilization rate of renewable energy is selected as the reference objective function, the larger the objective function value, the better the scheduling performance, and the ascending semi-Γ-type membership function can be used to perform fuzzy analysis on the reference objective function to obtain the membership function of the comprehensive profit of the virtual power plant or the utilization rate of renewable energy, and if the net carbon emissions are selected as the reference objective function, the smaller the objective function value, the better the scheduling performance, and the descending semi-Γ-type membership function can be used to perform fuzzy analysis on the reference objective function to obtain the membership function of the net carbon emissions.

[0112] Among them, the rising semi-Γ type membership function is as follows:

[0113]

[0114] In the above formula, k(f) is the membership function; f is the objective function value; fmax is the maximum value of the objective function.

[0115] Among them, the downward-rising semi-Γ-type membership function is as follows:

[0116]

[0117] In the above formula, f min is the minimum value of the objective function.

[0118] Optionally, the above step A42, obtaining weight coefficients corresponding to multiple evaluation targets to obtain multiple weight coefficients, may also include the following steps:

[0119] C41. Perform objective weight analysis on the multiple evaluation targets based on an objective weighting method to obtain multiple objective weights;

[0120] C42. Determine a decision weight corresponding to each of the multiple evaluation targets according to the confident two-layer language preference relationship to obtain multiple decision weights;

[0121] C43, multiplying the multiple objective weights and the multiple decision weights one by one to obtain multiple combined weights;

[0122] C44. Normalize the multiple combined weights to obtain the multiple weight coefficients.

[0123] Specifically, based on the objective weighting method, objective weight analysis is performed on multiple evaluation targets to obtain multiple objective weights. The objective weighting method can adopt the CRITIC weighting method, which mainly performs objective weight analysis based on the evaluation target values ​​of different evaluation objects in multiple evaluation targets, and determines the objective weight of each evaluation target by calculating the standard deviation and correlation coefficient of different evaluation targets. Then, the decision weight corresponding to each evaluation target in the multiple evaluation targets is determined according to the confident two-layer language preference relationship to obtain multiple decision weights, and the multiple objective weights and the multiple decision weights are combined, that is, the objective weights are multiplied by the decision weights one by one to obtain multiple combined weights. Finally, the multiple combined weights are normalized to obtain multiple weight coefficients, which are used to balance the importance of each evaluation target in the satisfaction model.

[0124] Optionally, the above step C42, determining the decision weight corresponding to each of the multiple evaluation targets according to the confident two-layer language preference relationship to obtain multiple decision weights, may also include the following steps:

[0125] D41, obtaining a preset dual-level language term set; the preset dual-level language term set includes a plurality of dual-level language terms;

[0126] D42, evaluating the multiple evaluation targets according to the preset two-level language term set to obtain multiple evaluation results;

[0127] D43, determining the confidence level corresponding to each of the multiple evaluation results to obtain multiple confidence levels;

[0128] D44. Perform a weight analysis on each of the multiple evaluation targets according to the multiple evaluation results and the multiple confidence levels to obtain multiple decision weights.

[0129] Specifically, a preset two-level language term set is obtained, and the preset two-level language term set includes multiple two-level language terms. The two-level language terms can be terms such as good, general, and poor, and each two-level language term corresponds to a weight coefficient. Multiple evaluation targets are evaluated according to the preset level language term set to obtain multiple evaluation results, that is, a weight coefficient is assigned to each evaluation target. Then, for each evaluation result, a confidence level can be assigned to each evaluation result, and the confidence level can be preset or determined by the decision maker's preference. A weight analysis is performed on each evaluation target in the multiple evaluation targets according to the multiple evaluation results and the multiple confidence levels, that is, the weight of each evaluation target is calculated according to the evaluation results and the confidence levels, that is, the decision weight, and the decision weight is used to reflect the relative importance of each evaluation target.

[0130] Optionally, the decision weight of each evaluation target may be obtained by a subjective weight analysis method, and the subjective weight analysis method may be a hierarchical analysis method or the like.

[0131] Optionally, considering the uncertainty of source and load, the satisfaction model can be robustly optimized so that the dispatch strategy solution is robust to multiple uncertainties under uncertain conditions. Furthermore, the wind turbine, photovoltaic output and load are taken as uncertain parameters, and a power difference model is constructed. The power difference model is used to analyze the uncertainty of the difference between the actual output and the predicted output.

[0132] Among them, the power difference model is as follows:

[0133]

[0134] In the above formula, is the predicted value of power difference; P e,t is the actual value of the power difference; α e is the fluctuation amplitude, which is a non-negative parameter used to quantify the power difference prediction value The uncertainty of is, the larger its value is, the higher the uncertainty of the predicted value is; is the uncertainty set of the power difference, indicating that the actual value of the power difference P e,t The value range of is the predicted value of photovoltaic output; is the predicted value of wind turbine output; is the load output prediction value.

[0135] Furthermore, a robust model can be set so that under the robust model, the satisfaction of the scheduling strategy solved according to the satisfaction model is not lower than the satisfaction threshold, that is, a feasible scheduling strategy can be obtained, and its robust model is as follows:

[0136]

[0137] In the above formula, α is the possible value of the fluctuation range, and the maximum value can be taken as the fluctuation range; f1 is the acceptable satisfaction; f0 is the optimal target value of the satisfaction model; β CO is the deviation factor, which is a parameter between 0 and 1, indicating the degree of deviation of f0 above f1.

[0138] Specifically, according to the power difference model and the robust model, it can be determined that the satisfaction model can obtain a feasible dispatch strategy under multiple uncertainties, that is, the power difference is When the range changes arbitrarily, the satisfaction of the scheduling strategy is not less than (1-β CO )f0, then the scheduling strategy is a feasible solution.

[0139] S105. Input the target virtual power plant data into the satisfaction model to obtain multiple scheduling strategies.

[0140] Specifically, the membership of different evaluation targets is first calculated. The evaluation target values ​​of different evaluation targets can be determined according to the target virtual power plant data. The evaluation target values ​​are input into the membership function corresponding to each evaluation target to obtain the membership corresponding to different evaluation targets. The satisfaction of the scheduling strategy obtained under the input parameters is obtained according to the weight coefficients corresponding to multiple evaluation targets. If the satisfaction is greater than the preset satisfaction threshold, the scheduling strategy can be determined to be a feasible scheduling strategy.

[0141] In one possible embodiment, different input parameters can be adjusted according to the satisfaction model. Since the weight coefficient corresponding to each evaluation target has been determined, but under different management, such as different power generation combinations and scheduling sequences, different charging and discharging strategies of energy storage systems, different demand response and load management strategies, and different power trading and price negotiation strategies, the membership value corresponding to each evaluation target is different, so multiple scheduling strategies can be obtained.

[0142] Optionally, the above step S105, inputting the target virtual power plant data into the satisfaction model to obtain multiple scheduling strategies, may also include the following steps:

[0143] A51. Input the target virtual power plant data into the multiple constraint conditions to obtain multiple reference scheduling strategies; each reference scheduling strategy includes reference evaluation target values ​​of the multiple evaluation targets;

[0144] A52. Select any reference scheduling strategy from the multiple reference scheduling strategies to obtain a target reference scheduling strategy;

[0145] A53, obtaining a reference evaluation target value of each evaluation target of the target reference scheduling strategy, and obtaining a plurality of reference evaluation target values;

[0146] A54, inputting the multiple reference evaluation target values ​​into the membership function corresponding to each evaluation target to obtain multiple membership values;

[0147] A55. Determine a target satisfaction level of the reference scheduling strategy according to the multiple membership values;

[0148] A56. When the target satisfaction is greater than a preset satisfaction threshold, determining that the target reference scheduling strategy is a feasible scheduling strategy;

[0149] A57. Determine the multiple scheduling strategies according to the feasible scheduling strategies.

[0150] Specifically, the target virtual power plant data is input into multiple constraints to obtain multiple reference scheduling strategies, each reference scheduling strategy includes reference evaluation target values ​​of multiple evaluation targets, any reference scheduling strategy is selected from the multiple reference scheduling strategies to obtain the target reference scheduling strategy, the reference evaluation target value of each evaluation target of the target reference scheduling strategy is obtained to obtain multiple reference evaluation target values, the multiple reference evaluation target values ​​are input into the membership function corresponding to each evaluation target to obtain multiple membership values, the target satisfaction of the reference scheduling strategy is determined according to the multiple membership values, when the target satisfaction is greater than the preset satisfaction threshold, the target reference scheduling strategy can be determined as a feasible scheduling strategy, and through the above calculation method, multiple scheduling strategies can be determined from multiple reference scheduling strategies.

[0151] S106. Comprehensively evaluate the multiple scheduling strategies according to the multiple evaluation targets to obtain multiple evaluation results, select a target evaluation result from the multiple evaluation results, select the scheduling strategy corresponding to the target evaluation result, and obtain the target optimal scheduling strategy.

[0152] Specifically, the optimized TOPSIS method can be used to evaluate each scheduling strategy to select the optimal scheduling strategy. The optimized TOPSIS method is used to comprehensively evaluate multiple scheduling strategies according to multiple evaluation objectives to obtain multiple evaluation results. The evaluation results are used to represent the pros and cons of different scheduling strategies. A target evaluation result is selected from the multiple evaluation results. The target evaluation result is closest to the optimal solution. The scheduling strategy corresponding to the target evaluation result is selected to obtain the target optimal scheduling strategy.

[0153] Optionally, the above step S106, comprehensively evaluating the multiple scheduling strategies according to the multiple evaluation targets to obtain multiple evaluation results, may also include the following steps:

[0154] A61. Constructing a target decision matrix according to the plurality of scheduling strategies; each scheduling strategy includes evaluation target values ​​of the plurality of evaluation targets;

[0155] A62, normalizing the target decision matrix to obtain a target standard matrix;

[0156] A63, constructing a weighted standard matrix according to the multiple weight coefficients and the target standard matrix;

[0157] A64, determining an ideal solution for each evaluation target according to the weighted norm matrix, and obtaining a positive ideal solution and a negative ideal solution; the positive ideal solution is the maximum value among multiple evaluation target values ​​corresponding to the evaluation target; the negative ideal solution is the minimum value among multiple evaluation target values ​​corresponding to the evaluation target;

[0158] A65, determining the distance between the evaluation target value corresponding to the evaluation target of each scheduling strategy in the multiple scheduling strategies and the positive ideal solution, and obtaining multiple first distances;

[0159] A66. Determine the distance between the evaluation target value corresponding to the evaluation target of each scheduling strategy in the multiple scheduling strategies and the negative ideal solution to obtain multiple second distances;

[0160] A67. Determine an evaluation result of each of the multiple scheduling strategies according to the multiple first distances and the multiple second distances to obtain multiple evaluation results.

[0161] Specifically, a target decision matrix is ​​constructed according to multiple scheduling strategies, the rows of the target decision matrix are each scheduling strategy, and the columns of the target decision matrix are the evaluation target values ​​of each evaluation. Then, in order to eliminate the influence of different evaluation target dimensions and magnitudes, the target decision matrix is ​​normalized to obtain a target standard matrix, and then the target standard matrix is ​​processed by applying the weight coefficient of each evaluation target, that is, each evaluation target value is multiplied by the weight coefficient corresponding to the evaluation target, and then a weighted standard matrix is ​​constructed.

[0162] Further, the ideal solution of each evaluation target is determined according to the weighted norm matrix, and a positive ideal solution and a negative ideal solution are obtained, wherein the positive ideal solution is the maximum value among the multiple evaluation target values ​​corresponding to the evaluation target, and the negative ideal solution is the minimum value among the multiple evaluation target values ​​corresponding to the evaluation target. Next, the distance between the evaluation target value corresponding to the evaluation target of each scheduling strategy in the multiple scheduling strategies and the positive ideal solution is determined to obtain multiple first distances, and the distance between the evaluation target value corresponding to the evaluation target of each scheduling strategy in the multiple scheduling strategies and the negative ideal solution is determined to obtain multiple second distances.

[0163] The calculation formula of the first distance is as follows:

[0164]

[0165] In the above formula, is the first distance, that is, the distance between each evaluation target in the scheduling strategy and the positive ideal solution; j is the jth evaluation target; m is the total number of m evaluation targets; z ij is the value of the jth evaluation target of the i-th scheduling strategy in the weighted norm matrix; is a positive ideal solution; n is a total of n scheduling strategies to be evaluated.

[0166] The calculation formula of the first distance is as follows:

[0167]

[0168] In the above formula, is the second distance, i.e., the distance between each evaluation target in the scheduling strategy and the negative ideal solution; is a negative ideal solution.

[0169] Furthermore, an evaluation result of each scheduling strategy among the multiple scheduling strategies is determined according to the multiple first distances and the multiple second distances. The evaluation result of the scheduling strategy is determined by the first distance and the second distance. The smaller the first distance is, the closer the distance to the positive ideal solution is, and the smaller the second distance is, the closer the distance to the negative ideal solution is. The scheduling strategy that is closest to the positive ideal solution and farthest from the negative ideal solution is optimal, that is, the evaluation result is the best.

[0170] The calculation formula of the evaluation result is as follows:

[0171]

[0172] In the above formula, C i is the evaluation result of the i-th scheduling strategy, and its value range is [0,1]. The closer it is to 1, the better the scheduling strategy.

[0173] Optionally, the above step A62, normalizing the target decision matrix to obtain a target standard matrix, may further include the following steps:

[0174] B61, inputting each evaluation target value in the target decision matrix into the membership function corresponding to the evaluation target to obtain multiple standard memberships;

[0175] B61. Construct the target standard matrix according to the multiple standard memberships.

[0176] Specifically, in order to reduce the impact of data dimensions on the calculation results, each matrix element in the target decision matrix can be normalized according to the membership function of each evaluation target, that is, each evaluation target value in the target decision matrix is ​​input into the membership function corresponding to the evaluation target to obtain multiple standard memberships. The value range of the standard membership is [0,1]. Therefore, the target standard matrix can be constructed based on multiple standard memberships.

[0177] In an embodiment of the present application, based on multiple evaluation results, a scheduling strategy that is closest to the positive ideal solution and farthest from the negative ideal solution can be selected as the target optimal scheduling strategy. The target optimal scheduling strategy has the highest satisfaction among multiple scheduling strategies and can maximize the overall benefits.

[0178] In summary, by implementing the embodiments of the present application, it is possible to obtain target virtual power plant data; determine the evaluation targets corresponding to the target virtual power plant data to obtain multiple evaluation targets; construct a multi-objective optimization scheduling model based on the multiple evaluation targets; perform fuzzy processing on the multi-objective optimization scheduling model to obtain a satisfaction model; input the target virtual power plant data into the satisfaction model to obtain multiple scheduling strategies; perform a comprehensive evaluation of the multiple scheduling strategies based on the multiple evaluation targets to obtain multiple evaluation results, select a target evaluation result from the multiple evaluation results, select the scheduling strategy corresponding to the target evaluation result, and obtain the target optimal scheduling strategy, thereby enabling the virtual power plant to select a multi-objective scheduling strategy that comprehensively considers the economy, energy, and environment when facing source and load uncertainties.

[0179] See also Figure 2 , Figure 2 1 is a structural diagram of a multi-objective scheduling strategy selection system considering source-load uncertainty provided by an embodiment of the present application. The multi-objective scheduling strategy selection system considering source-load uncertainty 200 includes: a data acquisition module 201, a model construction module 202, a model processing module 203, a strategy solving module 204, and a strategy evaluation module 205, wherein:

[0180] The data acquisition module 201 is used to acquire target virtual power plant data;

[0181] The data acquisition module 201 is also used to determine the evaluation target corresponding to the target virtual power plant data, and obtain multiple evaluation targets;

[0182] The model building module 202 is used to build a multi-objective optimization scheduling model according to the multiple evaluation objectives;

[0183] The model processing module 203 is used to perform fuzzy processing on the multi-objective optimization scheduling model to obtain a satisfaction model;

[0184] The strategy solving module 204 is used to input the target virtual power plant data into the satisfaction model to obtain multiple scheduling strategies;

[0185] The strategy evaluation module 205 is used to comprehensively evaluate the multiple scheduling strategies according to the multiple evaluation targets to obtain multiple evaluation results, select a target evaluation result from the multiple evaluation results, select the scheduling strategy corresponding to the target evaluation result, and obtain the target optimal scheduling strategy.

[0186] Optionally, in the aspect of constructing a multi-objective optimization scheduling model according to the multiple evaluation objectives, the model construction module 202 is further specifically used for:

[0187] Obtaining an objective function corresponding to each of the multiple evaluation objectives to obtain multiple objective functions;

[0188] Determine a constraint condition corresponding to each objective function according to the multiple objective functions to obtain multiple constraint conditions;

[0189] The multi-objective optimization scheduling model is constructed according to the multiple objective functions and the multiple constraint conditions.

[0190] Optionally, in the aspect of performing fuzzy processing on the multi-objective optimization scheduling model to obtain a satisfaction model, the model processing module 203 is further specifically used for:

[0191] Determine a membership function corresponding to each of the multiple objective functions to obtain multiple membership functions;

[0192] Obtain weight coefficients corresponding to multiple evaluation targets to obtain multiple weight coefficients;

[0193] The satisfaction model is constructed according to the multiple membership functions and the multiple weight coefficients.

[0194] Optionally, in determining the membership function corresponding to each objective function in the multiple objective functions to obtain multiple membership functions, the model processing module 203 is further specifically used to:

[0195] Obtaining a reference objective function; the reference objective function is any objective function among the multiple objective functions;

[0196] Determining the maximum value and the minimum value of the reference objective function according to the target virtual power plant data;

[0197] The maximum value and the minimum value are input into a preset fuzzy membership function to obtain a membership function corresponding to a reference objective function.

[0198] Optionally, in the aspect of obtaining weight coefficients corresponding to a plurality of evaluation targets and obtaining a plurality of weight coefficients, the model processing module 203 is further specifically used for:

[0199] Based on the objective weighting method, objective weight analysis is performed on the multiple evaluation targets to obtain multiple objective weights;

[0200] Determine a decision weight corresponding to each of the multiple evaluation targets according to the confident two-layer language preference relationship to obtain multiple decision weights;

[0201] Multiplying the multiple objective weights and the multiple decision weights one by one to obtain multiple combined weights;

[0202] The multiple combined weights are normalized to obtain the multiple weight coefficients.

[0203] Optionally, in determining the decision weight corresponding to each of the multiple evaluation targets according to the confident two-layer language preference relationship to obtain multiple decision weights, the model processing module 203 is further specifically used to:

[0204] Obtaining a preset two-level language term set; the preset two-level language term set includes a plurality of two-level language terms;

[0205] Evaluate the multiple evaluation targets according to the preset two-level language term set to obtain multiple evaluation results;

[0206] Determine the confidence level corresponding to each evaluation result in the multiple evaluation results to obtain multiple confidence levels;

[0207] A weight analysis is performed on each of the multiple evaluation targets according to the multiple evaluation results and the multiple confidence levels to obtain multiple decision weights.

[0208] Optionally, in the aspect of inputting the target virtual power plant data into the satisfaction model to obtain a plurality of scheduling strategies, the strategy solving module 204 is further specifically used for:

[0209] Inputting the target virtual power plant data into the multiple constraints to obtain multiple reference scheduling strategies; each reference scheduling strategy includes reference evaluation target values ​​of the multiple evaluation targets;

[0210] Select any reference scheduling strategy from the multiple reference scheduling strategies to obtain a target reference scheduling strategy;

[0211] Obtaining a reference evaluation target value of each evaluation target of the target reference scheduling strategy to obtain a plurality of reference evaluation target values;

[0212] Inputting the multiple reference evaluation target values ​​into the membership function corresponding to each evaluation target to obtain multiple membership values;

[0213] Determining a target satisfaction level of the reference scheduling strategy according to the multiple membership values;

[0214] When the target satisfaction is greater than a preset satisfaction threshold, determining that the target reference scheduling strategy is a feasible scheduling strategy;

[0215] The multiple scheduling strategies are determined according to the feasible scheduling strategies.

[0216] Optionally, in the step of comprehensively evaluating the multiple scheduling strategies according to the multiple evaluation targets to obtain multiple evaluation results, the strategy evaluation module 205 is further specifically used to:

[0217] Constructing a target decision matrix according to the plurality of scheduling strategies; each scheduling strategy includes evaluation target values ​​of the plurality of evaluation targets;

[0218] Normalizing the target decision matrix to obtain a target standard matrix;

[0219] constructing a weighted norm matrix according to the plurality of weight coefficients and the target norm matrix;

[0220] Determine the ideal solution of each evaluation target according to the weighted norm matrix to obtain a positive ideal solution and a negative ideal solution; the positive ideal solution is the maximum value of multiple evaluation target values ​​corresponding to the evaluation target; the negative ideal solution is the minimum value of multiple evaluation target values ​​corresponding to the evaluation target;

[0221] Determine the distance between the evaluation target value corresponding to the evaluation target of each scheduling strategy in the multiple scheduling strategies and the positive ideal solution to obtain multiple first distances;

[0222] Determine the distance between the evaluation target value corresponding to the evaluation target of each scheduling strategy in the multiple scheduling strategies and the negative ideal solution to obtain multiple second distances;

[0223] An evaluation result of each scheduling strategy in the multiple scheduling strategies is determined according to the multiple first distances and the multiple second distances to obtain multiple evaluation results.

[0224] Optionally, in the step of normalizing the target decision matrix to obtain a target standard matrix, the strategy evaluation module 205 is further specifically configured to:

[0225] Inputting each evaluation target value in the target decision matrix into the membership function corresponding to the evaluation target to obtain multiple standard memberships;

[0226] The target standard matrix is ​​constructed according to the multiple standard memberships.

[0227] The multi-objective scheduling strategy selection system 200 considering source-load uncertainty described in the present application can obtain target virtual power plant data; determine the evaluation target corresponding to the target virtual power plant data to obtain multiple evaluation targets; construct a multi-objective optimization scheduling model based on the multiple evaluation targets; fuzzy process the multi-objective optimization scheduling model to obtain a satisfaction model; input the target virtual power plant data into the satisfaction model to obtain multiple scheduling strategies; comprehensively evaluate the multiple scheduling strategies based on the multiple evaluation targets to obtain multiple evaluation results, select a target evaluation result from the multiple evaluation results, select the scheduling strategy corresponding to the target evaluation result, and obtain the target optimal scheduling strategy, thereby enabling the virtual power plant to select a multi-objective scheduling strategy that comprehensively considers economy, energy and environment when facing source-load uncertainty.

[0228] See also Figure 3 , Figure 3 : is a structural diagram of an electronic device provided in an embodiment of the present application, the electronic device may include a processor, a memory, a communication interface and one or more programs, the processor, the memory and the communication interface may be interconnected through a bus; the one or more programs are stored in the memory and configured to be executed by the processor; in the embodiment of the present application, the program includes instructions for executing the following steps:

[0229] Obtain target virtual power plant data;

[0230] Determine an evaluation target corresponding to the target virtual power plant data to obtain multiple evaluation targets;

[0231] Constructing a multi-objective optimization scheduling model according to the multiple evaluation objectives;

[0232] Performing fuzzy processing on the multi-objective optimization scheduling model to obtain a satisfaction model;

[0233] Inputting the target virtual power plant data into the satisfaction model to obtain multiple scheduling strategies;

[0234] The multiple scheduling strategies are comprehensively evaluated according to the multiple evaluation targets to obtain multiple evaluation results, a target evaluation result is selected from the multiple evaluation results, and the scheduling strategy corresponding to the target evaluation result is selected to obtain the target optimal scheduling strategy.

[0235] The electronic device described in the present application can obtain target virtual power plant data; determine the evaluation target corresponding to the target virtual power plant data to obtain multiple evaluation targets; construct a multi-objective optimization scheduling model based on the multiple evaluation targets; fuzzy process the multi-objective optimization scheduling model to obtain a satisfaction model; input the target virtual power plant data into the satisfaction model to obtain multiple scheduling strategies; comprehensively evaluate the multiple scheduling strategies based on the multiple evaluation targets to obtain multiple evaluation results, select a target evaluation result from the multiple evaluation results, select the scheduling strategy corresponding to the target evaluation result, and obtain the target optimal scheduling strategy, thereby enabling the virtual power plant to select a multi-objective scheduling strategy that comprehensively considers economy, energy and environment when facing source and load uncertainty.

[0236] An embodiment of the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0237] The embodiment of the present application also provides a computer program product, the computer program product includes a non-transitory computer-readable storage medium storing a computer program, the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes an electronic device.

[0238] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

[0239] The steps of the method or algorithm described in the embodiments of the present application can be implemented in hardware or by executing software instructions by a processor. The software instructions can be composed of corresponding software modules, and the software modules can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (electrically EPROM, EEPROM), registers, hard disks, mobile hard disks, read-only compact disks (CD-ROMs) or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC. In addition, the ASIC can be located in a terminal device or a management device. Of course, the processor and the storage medium can also be present in a terminal device or a management device as discrete components.

[0240] Those skilled in the art should be aware that in one or more of the above examples, the functions described in the embodiments of the present application can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiments of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrations. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0241] The modules / units included in the devices and products described in the above embodiments may be software modules / units or hardware modules / units, or may be partially software modules / units and partially hardware modules / units. For example, for the devices and products applied to or integrated in the chip, the modules / units included therein may all be implemented in the form of hardware such as circuits, or at least some of the modules / units may be implemented in the form of software programs, which run on the processor integrated inside the chip, and the remaining (if any) modules / units may be implemented in the form of hardware such as circuits; for the devices and products applied to or integrated in the chip module, the modules / units included therein may all be implemented in the form of hardware such as circuits, and different modules / units may be located in the same component (such as a chip, circuit module, etc.) or in different components of the chip module, or at least some of the modules / units may be implemented in the form of software programs. The software programs run on the processor integrated inside the chip, and the remaining (if any) modules / units may be implemented in the form of hardware such as circuits. It is implemented in the form of a software program, which runs on a processor integrated inside the chip module, and the remaining (if any) modules / units can be implemented in hardware such as circuits; for various devices and products applied to or integrated in the terminal equipment, the various modules / units contained therein can be implemented in hardware such as circuits, and different modules / units can be located in the same component (for example, chip, circuit module, etc.) or in different components in the terminal equipment, or, at least some modules / units can be implemented in the form of a software program, which runs on a processor integrated inside the terminal equipment, and the remaining (if any) modules / units can be implemented in hardware such as circuits.

[0242] The specific implementation methods described above further illustrate the purpose, technical solutions and beneficial effects of the embodiments of the present application. It should be understood that the above description is only the specific implementation method of the embodiments of the present application and is not intended to limit the protection scope of the embodiments of the present application. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solutions of the embodiments of the present application should be included in the protection scope of the embodiments of the present application.

Claims

1. A multi-objective scheduling strategy selection method considering source-load uncertainty, characterized in that: The method comprises: Obtain target virtual power plant data; Determine an evaluation target corresponding to the target virtual power plant data to obtain multiple evaluation targets; Constructing a multi-objective optimization scheduling model according to the multiple evaluation objectives; Performing fuzzy processing on the multi-objective optimization scheduling model to obtain a satisfaction model; Inputting the target virtual power plant data into the satisfaction model to obtain multiple scheduling strategies; The multiple scheduling strategies are comprehensively evaluated according to the multiple evaluation targets to obtain multiple evaluation results, a target evaluation result is selected from the multiple evaluation results, and the scheduling strategy corresponding to the target evaluation result is selected to obtain the target optimal scheduling strategy.

2. The method according to claim 1, characterized in that The constructing of a multi-objective optimization scheduling model according to the multiple evaluation objectives includes: Obtaining an objective function corresponding to each of the multiple evaluation objectives to obtain multiple objective functions; Determine a constraint condition corresponding to each objective function according to the multiple objective functions to obtain multiple constraint conditions; The multi-objective optimization scheduling model is constructed according to the multiple objective functions and the multiple constraint conditions.

3. The method according to claim 2, characterized in that The fuzzy processing is performed on the multi-objective optimization scheduling model to obtain a satisfaction model, including: Determine a membership function corresponding to each of the multiple objective functions to obtain multiple membership functions; Obtain weight coefficients corresponding to multiple evaluation targets to obtain multiple weight coefficients; The satisfaction model is constructed according to the multiple membership functions and the multiple weight coefficients.

4. The method according to claim 3, characterized in that The step of determining a membership function corresponding to each of the multiple objective functions to obtain multiple membership functions includes: Obtaining a reference objective function; the reference objective function is any objective function among the multiple objective functions; Determining the maximum value and the minimum value of the reference objective function according to the target virtual power plant data; The maximum value and the minimum value are input into a preset fuzzy membership function to obtain a membership function corresponding to a reference objective function.

5. The method according to claim 3 or 4, characterized in that The step of obtaining weight coefficients corresponding to a plurality of evaluation targets to obtain a plurality of weight coefficients includes: Based on the objective weighting method, objective weight analysis is performed on the multiple evaluation targets to obtain multiple objective weights; Determine a decision weight corresponding to each of the multiple evaluation targets according to the confident two-layer language preference relationship to obtain multiple decision weights; Multiplying the multiple objective weights and the multiple decision weights one by one to obtain multiple combined weights; The multiple combined weights are normalized to obtain the multiple weight coefficients.

6. The method according to claim 5, characterized in that The step of determining a decision weight corresponding to each of the multiple evaluation targets according to the confident two-layer language preference relationship to obtain multiple decision weights includes: Obtaining a preset two-level language term set; the preset two-level language term set includes a plurality of two-level language terms; Evaluate the multiple evaluation targets according to the preset two-level language term set to obtain multiple evaluation results; Determine the confidence level corresponding to each evaluation result in the multiple evaluation results to obtain multiple confidence levels; A weight analysis is performed on each of the multiple evaluation targets according to the multiple evaluation results and the multiple confidence levels to obtain multiple decision weights.

7. The method according to any one of claims 3 to 6, characterized in that: The target virtual power plant data is input into the satisfaction model to obtain multiple scheduling strategies, including: Inputting the target virtual power plant data into the multiple constraint conditions to obtain multiple reference scheduling strategies; each reference scheduling strategy includes reference evaluation target values ​​of the multiple evaluation targets; Select any reference scheduling strategy from the multiple reference scheduling strategies to obtain a target reference scheduling strategy; Obtaining a reference evaluation target value of each evaluation target of the target reference scheduling strategy to obtain a plurality of reference evaluation target values; Inputting the multiple reference evaluation target values ​​into the membership function corresponding to each evaluation target to obtain multiple membership values; Determining a target satisfaction level of the reference scheduling strategy according to the multiple membership values; When the target satisfaction is greater than a preset satisfaction threshold, determining that the target reference scheduling strategy is a feasible scheduling strategy; The multiple scheduling strategies are determined according to the feasible scheduling strategies.

8. The method according to claim 7, characterized in that The multiple scheduling strategies are comprehensively evaluated according to the multiple evaluation targets to obtain multiple evaluation results, including: Constructing a target decision matrix according to the plurality of scheduling strategies; each scheduling strategy includes evaluation target values ​​of the plurality of evaluation targets; Normalizing the target decision matrix to obtain a target standard matrix; constructing a weighted norm matrix according to the plurality of weight coefficients and the target norm matrix; Determine the ideal solution of each evaluation target according to the weighted norm matrix to obtain a positive ideal solution and a negative ideal solution; the positive ideal solution is the maximum value of multiple evaluation target values ​​corresponding to the evaluation target; the negative ideal solution is the minimum value of multiple evaluation target values ​​corresponding to the evaluation target; Determine the distance between the evaluation target value corresponding to the evaluation target of each scheduling strategy in the multiple scheduling strategies and the positive ideal solution to obtain multiple first distances; Determine the distance between the evaluation target value corresponding to the evaluation target of each scheduling strategy in the multiple scheduling strategies and the negative ideal solution to obtain multiple second distances; An evaluation result of each scheduling strategy in the multiple scheduling strategies is determined according to the multiple first distances and the multiple second distances to obtain multiple evaluation results.

9. The method according to claim 8, characterized in that The target decision matrix is ​​normalized to obtain a target standard matrix, including: Inputting each evaluation target value in the target decision matrix into the membership function corresponding to the evaluation target to obtain multiple standard memberships; The target standard matrix is ​​constructed according to the multiple standard memberships.

10. A multi-objective scheduling strategy selection system considering source-load uncertainty, characterized in that: The system includes: a data acquisition module, a model building module, a model processing module, a strategy solving module, and a strategy evaluation module, wherein: The data acquisition module is used to acquire target virtual power plant data; The data acquisition module is also used to determine the evaluation target corresponding to the target virtual power plant data to obtain multiple evaluation targets; The model building module is used to build a multi-objective optimization scheduling model according to the multiple evaluation objectives; The model processing module is used to perform fuzzy processing on the multi-objective optimization scheduling model to obtain a satisfaction model; The strategy solving module is used to input the target virtual power plant data into the satisfaction model to obtain multiple scheduling strategies; The strategy evaluation module is used to comprehensively evaluate the multiple scheduling strategies according to the multiple evaluation targets to obtain multiple evaluation results, select a target evaluation result from the multiple evaluation results, select the scheduling strategy corresponding to the target evaluation result, and obtain the target optimal scheduling strategy.

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