Comprehensive energy system collaborative planning method and device considering uncertain factors

Abstract

The present invention relates to the field of energy management technology, and provides a method and device for collaborative planning of an integrated energy system taking into account uncertain factors applicable to a park. Mathematical models are constructed for wind turbines, cooling loads, heating loads, and electrical loads respectively, and an operation scenario set is constructed; the characteristic data of each scenario in the operation scenario set are clustered respectively to obtain multiple data clusters of each scenario characteristic data; the scenarios are classified according to the correspondence between the scene characteristic data and the scenarios in the data clusters to obtain multiple scenario clusters corresponding to each scenario characteristic data; the multiple scenario clusters to which the current park plan belongs are determined; the intersection of the multiple scenario clusters to which the current park plan belongs is determined; and the distributed energy supply equipment of the integrated energy system and the construction site selection of the energy supply equipment are planned according to the cost data of each scenario in the intersection. The present invention helps to achieve more refined multi-energy planning and design, and improves the efficiency of multi-energy planning.

Description

Technical Field

[0001] The present invention relates to the field of energy management technology, and in particular to a method and device for collaborative planning of an integrated energy system taking uncertainty factors into account and applicable to a park. Background Art

[0002] Traditional multi-energy system planning methods rely solely on deterministic forecast scenarios, rarely considering the impact of uncertain factors. With the widespread integration of renewable energy into energy systems, multi-energy planning needs to consider the impact of uncertain factors such as renewable energy generation on planning results, exacerbating the uncertainty and complexity faced by the operation of integrated energy systems. Therefore, new multi-energy system planning methods that are suitable for uncertain factors such as renewable energy generation are needed. Summary of the Invention

[0003] In response to the above problems, the present invention provides a method and device for collaborative planning of an integrated energy system taking into account uncertain factors, particularly a method and device for collaborative planning of an integrated energy system taking into account uncertain factors suitable for a park.

[0004] In a first aspect, the present invention provides a method for collaborative planning of an integrated energy system taking uncertainty factors into account, the method comprising:

[0005] Mathematical models are constructed for wind turbines, cooling loads, heating loads, and electrical loads, respectively, and an operating scenario set is constructed based on the mathematical models. When constructing the operating scenario set, the uncertainties of wind turbines, cooling loads, heating loads, and electrical loads are taken into account to generate data for the operating scenario set that is closer to the actual environment. Constructing the operating scenario set based on the mathematical model includes: randomly sampling the mathematical models of wind turbines, cooling loads, heating loads, and electrical loads, and clustering the obtained data to generate scenarios reflecting wind power and loads. Each scenario includes wind power scenario characteristic data, characteristic data of various load scenarios, and corresponding cost data.

[0006] Clustering the characteristic data of each scenario in the running scenario set to obtain multiple data clusters of the characteristic data of each scenario;

[0007] Classify the scenes according to the correspondence between the scene feature data and the scenes in the data cluster to obtain multiple scene clusters corresponding to each scene feature data;

[0008] Based on the objective function of the park planning, the upper-level planning model and the lower-level planning model are constructed respectively, and the power balance constraints are established; the objective function of the upper-level planning model is to minimize the annual comprehensive cost; the objective function of the lower-level planning model is to minimize the operating cost and maximize the full-time operation efficiency of primary energy;

[0009] Based on the data from the operational scenario set, the upper-level and lower-level planning models are solved, and based on the solution results, the wind turbines, cooling load, heating load, and electrical load data for the current park plan are determined;

[0010] Comparing the wind turbine, cooling load, heating load, and electric load data of the current park plan with each scenario group of the same scenario feature data in the operating scenario set to determine the multiple scenario groups to which the current park plan belongs;

[0011] Determine the intersection of multiple scenario groups to which the current park plan belongs;

[0012] Based on the cost data of each scenario in the intersection, the distributed energy supply equipment of the integrated energy system and the construction site selection of the energy supply equipment are planned.

[0013] In a second aspect, the present invention provides a collaborative planning device for an integrated energy system taking uncertainty factors into account, comprising: a modeling unit, a target unit, and a solution unit connected in sequence;

[0014] a modeling unit for constructing mathematical models for wind turbines, cooling loads, heating loads, and electric loads, respectively, and constructing an operating scenario set based on the mathematical models; constructing the operating scenario set based on the mathematical models includes: randomly sampling the mathematical models of wind turbines, cooling loads, heating loads, and electric loads, and clustering the acquired data to generate scenarios reflecting wind power and loads; each scenario includes wind power scenario characteristic data, various load scenario characteristic data, and corresponding cost data; clustering the characteristic data of each scenario in the operating scenario set to obtain multiple data clusters for each scenario characteristic data; and classifying the scenarios based on the correspondence between the scenario characteristic data and the scenarios in the data clusters to obtain multiple scenario clusters corresponding to each scenario characteristic data;

[0015] The target unit is used to construct the upper-level planning model and the lower-level planning model according to the objective function of the park planning, and to establish power balance constraints. The objective function of the upper-level planning model is to minimize the annual comprehensive cost; the objective function of the lower-level planning model is to minimize the operating cost and maximize the full-time operation efficiency of primary energy.

[0016] The planning unit is used to solve the upper-level planning model and the lower-level planning model based on the data of the operating scenario set, and determine the wind turbine, cooling load, heating load and electric load data of the current park plan according to the solution results; compare the wind turbine, cooling load, heating load and electric load data of the current park plan with each scenario group of the same scenario feature data in the operating scenario set to determine the multiple scenario groups to which the current park plan belongs; determine the intersection of the multiple scenario groups to which the current park plan belongs; and plan the distributed energy supply equipment and construction site selection of the energy supply equipment of the integrated energy system according to the cost data of each scenario in the intersection.

[0017] In a third aspect, the present invention provides an electronic device comprising a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0018] Memory for storing computer programs;

[0019] The processor is used to implement the above-mentioned integrated energy system collaborative planning method taking uncertainty factors into account when executing the program stored in the memory.

[0020] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for collaborative planning of an integrated energy system taking uncertainty factors into account.

[0021] The present invention has at least the following beneficial effects:

[0022] The present invention adopts a simplification algorithm to obtain a suitable number of typical scenarios, and based on the scenario feature data, determines the scenarios that are closer to the current park planning, thereby realizing further screening of the model solution data, helping to achieve more refined planning and design, and helping to plan the construction site selection of distributed energy supply equipment and energy supply equipment in the integrated energy system, thereby improving the efficiency of multi-energy planning. By considering the operating scenarios with source-load uncertainty, it helps to improve the penetration rate of wind power in the integrated energy system, and helps to ensure the economy and power supply reliability of wind power in the long-term planning process.

[0023] The present invention proposes a collaborative planning model for an integrated energy system that takes uncertainty factors into account, which can not only achieve the goal of higher economic benefits for the integrated energy system, but also effectively improve the economy and flexibility of the integrated energy system.

[0024] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are 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.

[0026] Figure 1 This is a flow chart of the collaborative planning method of the present invention;

[0027] Figure 2 This is a schematic diagram of the structure of the collaborative planning device of the present invention;

[0028] Figure 3 Solve the flow chart for the collaborative planning model;

[0029] Figure 4 Schematic diagram of collaborative planning of integrated energy systems considering uncertainty factors;

[0030] Figure 5 This is a schematic diagram of time-of-use electricity prices;

[0031] Figure 6 is the wind power curve;

[0032] Figure 7 This is a typical daily load curve in spring and autumn;

[0033] Figure 8 This is a typical daily load curve in summer;

[0034] Figure 9 This is a typical daily load curve in winter;

[0035] Figure 10 The target change curve diagram when only operation and maintenance costs are considered for the lower-level targets;

[0036] Figure 11 This is the target change curve when only energy efficiency is considered for the lower-level target;

[0037] Figure 12 Iterate the scatter plot for the target. DETAILED DESCRIPTION

[0038] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0039] First, as Figure 1 As shown, the present invention provides a method for collaborative planning of an integrated energy system taking uncertainty factors into account, the method comprising:

[0040] Build mathematical models for wind turbines, cooling loads, heating loads, and electrical loads, and construct a set of operating scenarios based on the mathematical models;

[0041] Based on the objective function of the park planning, the upper-level planning model and the lower-level planning model are constructed respectively, and the power balance constraint conditions are established;

[0042] Based on the data of the operation scenario set, the upper-level planning model and the lower-level planning model are solved, and the distributed energy supply equipment and the construction site of the energy supply equipment of the integrated energy system are planned according to the solution results.

[0043] In specific implementation, due to the non-sustainability and volatility of wind power, there are uncertain factors that affect the stability of wind power generation; there are also uncertain factors that affect the stability of cooling load and heating load.

[0044] In the upper-level model, a genetic algorithm is used to solve a multi-objective planning model that includes indicators such as annual construction investment and operating costs, which are inherently subject to uncertainty. In the lower-level model, a multi-scenario verification model is used, targeting maximum primary energy efficiency throughout the entire operating period. This model verifies the Pareto-optimal planning solution's ability to withstand wind power and load uncertainties, fully accounting for the impact of these uncertainties.

[0045] Furthermore, in the lower-level model, the constraints of the upper-level model are modified based on the expected full-time primary energy efficiency of the lower-level model, thereby incorporating the impact of wind power and load uncertainties into the upper-level model. Finally, a quantum evolutionary algorithm is used to solve the model, resulting in a collaborative planning scheme that accounts for uncertainty and achieves higher economic benefits.

[0046] Based on the solution results, the distributed energy supply equipment of the integrated energy system and the construction site selection of the energy supply equipment are planned. First, the heat load, cooling load and electricity load data of the park are obtained, and a model is proposed. Then, based on the proposed model, the distributed energy supply equipment of the integrated energy system is planned, including the selection and configuration capacity of the combined heat, heat and power units, electric chillers, and gas boilers. Finally, the construction site of the energy supply equipment is selected based on the collaborative planning results.

[0047] In this embodiment, the mathematical models for the wind turbine generator set, cooling load, heating load and electrical load are constructed separately, including:

[0048] The historical wind speed and load data are used to construct the probability distribution model of wind turbines, cooling load, heating load and electrical load through parameter estimation.

[0049] In this embodiment, the method of constructing a probability distribution model of wind turbines, cooling load, heating load, and electric load by parameter estimation using historical wind speed and load data includes:

[0050] For wind turbines, the wind speed in a certain period of time is a two-parameter Weibull distribution, and its probability density function is:

[0051]

[0052] The output power of the fan and the fan satisfy the following relationship:

[0053]

[0054] in,

[0055]

[0056] Where v represents the actual wind speed; c and k represent the scale parameter and shape parameter respectively; r is the fan radius; ρ is the air density; P w (v) is the output power of the fan; P r is the rated power of the fan; g(x) is the calculation expression; v r Indicates rated wind speed; v in Indicates the cut-in wind speed; v out Indicates the cut-out wind speed;

[0057] The parameters of the wind turbine model are estimated using historical wind speed data.

[0058] In this embodiment, the method of constructing a probability distribution model of wind turbines, cooling load, heating load, and electric load by parameter estimation using historical wind speed and load data includes:

[0059] The cooling, heating and electricity loads follow a normal distribution, and the probability distribution of the loads is expressed as:

[0060]

[0061] in, is the probability density function; is the load power, i=e / h / c, which represents the power of electric load, heat load and cooling load respectively; μ is the mean of the random variable; σ is the variance of the random variable,

[0062] The probability distribution model of cooling load, heating load and electric load is estimated based on historical load data to obtain the parameter values.

[0063] In this embodiment, the operation scenario set is constructed based on the mathematical model, including:

[0064] Monte Carlo sampling is performed on the mathematical models of wind turbines, cooling loads, heating loads and electrical loads to generate random scenarios that reflect the uncertainty of wind power and loads.

[0065] In this embodiment, the operation scenario set is constructed based on the mathematical model, including:

[0066] Monte Carlo sampling was performed on the mathematical models of wind turbines, cooling loads, heating loads, and electrical loads. The acquired data was clustered using the k-means algorithm to generate random scenarios reflecting the uncertainty of wind power and loads. Each scenario included characteristic data for wind power scenarios, characteristic data for various load types (cooling load, heating load, and electrical load), and corresponding cost data.

[0067] In this embodiment, Monte Carlo sampling is performed on the mathematical models of wind turbines, cooling loads, heating loads, and electrical loads, and the acquired data is clustered using a k-means clustering algorithm to obtain a plurality of wind power scenario characteristic data and various load scenario characteristic data; based on the plurality of wind power scenario characteristic data and various load scenario characteristic data, a random scenario reflecting the uncertainty of wind power and load is generated, including:

[0068] The wind power scenario characteristic data is obtained in the following way: the probability density and probability distribution function of wind power output are obtained based on the historical wind farm output, an N-dimensional Copula function is constructed and the correlation coefficient of the Copula function is solved, Monte Carlo sampling is performed to generate random values with correlation, and the random values are inverted to obtain the wind power simulation scenario data; the wind power simulation scenario data is clustered and analyzed using the k-means clustering algorithm to obtain multiple wind power scenario characteristic data.

[0069] The scene characteristic data of various loads (cooling load, heating load and electric load) are obtained in the following way: the probability density and probability distribution function of each load are obtained according to the historical data of each load, Monte Carlo sampling is performed to generate random values with correlation, and the random values are inverted to obtain the simulated scene data of the load; the simulated scene data of the load are clustered using the k-means clustering algorithm to obtain multiple scene characteristic data of each load.

[0070] Based on the above-mentioned multiple wind power scenario characteristic data and various load scenario characteristic data, a scenario reflecting wind power and load is generated.

[0071] Cluster the characteristic data of each scenario in the operating scenario set to obtain multiple data clusters of each scenario characteristic data. For example, cluster the wind power scenario characteristic data in the operating scenario set to obtain multiple data clusters of wind power scenario characteristic data. Therefore, multiple scenarios in the same data cluster of wind power scenario characteristic data have strong data commonality in the wind power scenario characteristic data. Cluster the cooling load scenario characteristic data in the operating scenario set to obtain multiple data clusters of cooling load scenario characteristic data. Cluster the heating load scenario characteristic data in the operating scenario set to obtain multiple data clusters of heating load scenario characteristic data. Cluster the electric load scenario characteristic data in the operating scenario set to obtain multiple data clusters of electric load scenario characteristic data.

[0072] The aforementioned data clusters of different scenario characteristic data are equivalent to categorizing the multiple scenarios in the operational scenario set based on their different scenario characteristic data. This facilitates the selection of scenarios that are closest to the current park plan in subsequent steps. Specifically, based on the correspondence between the scenario characteristic data and scenarios in the data clusters, the scenarios are categorized to obtain multiple scenario clusters corresponding to each type of scenario characteristic data.

[0073] In this embodiment, the upper-level planning model and the lower-level planning model are constructed according to the objective function of the park planning, including:

[0074] The objective function of the upper-level planning model is to minimize the annual comprehensive cost. inv and operating costs C op , the upper objective function value is as follows:

[0075] minC=C inv +C op (5)

[0076] Among them, C inv is the equivalent annual investment cost of all equipment; C op is the operating cost of the system on a typical day;

[0077]

[0078] Among them, C ω is the unit investment cost of equipment ω; R ω The construction capacity of the equipment ω includes CCHP, electric refrigerator EC, and gas boiler GB.

[0079] In this embodiment, the upper-level planning model and the lower-level planning model are constructed according to the objective function of the park planning, and further include:

[0080] The lower level planning model is based on the running cost C op Minimization and maximization of the primary energy utilization efficiency F during the entire period are the objective functions;

[0081] The lower-level operating cost indicators include operating and maintenance costs C OM , electricity transaction cost C Trade , natural gas fuel cost C Gas and carbon taxes

[0082]

[0083] in:

[0084]

[0085]

[0086] in, is the unit maintenance cost of type i energy conversion equipment; P ik ,t is the input power of the kth type i energy conversion device in time period t; is the unit maintenance cost of type k renewable energy equipment; P lk,t is the output power of the kth type l renewable energy device in period t; C t is the electricity price in time period t; C g is the natural gas price; C c is the carbon tax price; δ e is the carbon emission factor of electricity; δ g is the carbon emission factor of natural gas; P tg Input electric power to the regional power grid; P g The gas purchasing power of the park; v LHV It is the lower calorific value of natural gas combustion.

[0087] In this embodiment, the upper-level planning model and the lower-level planning model are constructed according to the objective function of the park planning, and further include:

[0088] The lower-level efficiency index takes the full-time operation efficiency of primary energy as the objective function. Based on the electric load, cooling load, heating load and input transformer power in period t, the objective function for calculating the efficiency index is as follows:

[0089]

[0090] S t =P tg / η e η g +P w +P g +P gas (14)

[0091] Among them, F is energy utilization rate; T is simulation period; L t is the total load in time period t; is the electrical load; is the heat load; is the cooling load; S t is the total power input to the integrated energy system; P w The power of RIES input to the wind turbine; P Gas is the calorific value of the input natural gas; η e is the average power generation efficiency of the power plant; η g is the average transmission efficiency of the power plant.

[0092] In this embodiment, establishing the power balance constraint condition includes:

[0093] Establish electric load power balance constraints:

[0094]

[0095] Among them, P j,t is the total electrical load; P g,t The power output from the grid to the park; are the output powers of the cth CCHP and the wth wind turbine at time t, respectively; and are the power consumption of the e-th electric refrigerator, the g-th gas boiler and the b-th electric boiler respectively; J is the set of all nodes in the power system; Cj, Wj, Ej, Gj and Bj are the sets of CCHP, fans, electric refrigerators, gas boilers and electric boilers connected to the grid node j respectively.

[0096] In this embodiment, establishing the power balance constraint condition includes:

[0097] Establish heat load power balance constraints:

[0098] Total heat load Expressed as:

[0099]

[0100] Among them, T c,t Provide heat for CCHP c; T b,t Provide heat for electric boiler b; T g,t g is the heat supply of gas boiler; C means the number of CCHP heating equipment; B means the number of electric boilers; H means the number of gas boilers.

[0101] In this embodiment, establishing the power balance constraint condition includes:

[0102] Establish cooling load power balance constraints:

[0103] Total cooling load demand Expressed as:

[0104]

[0105] Among them, F c,t Cooling capacity of CCHP c; F e,t e is the cooling capacity of the electric chiller; H means the number of CCHP cooling equipment; I means the number of electric chillers.

[0106] In this embodiment, establishing the power balance constraint condition includes:

[0107] Establish upper and lower operating constraints for each device:

[0108]

[0109] in, is the running state variable of device w; is the total power of the integrated energy system input to the device w; Enter the total power of the integrated energy system for minimum and maximum.

[0110] In this embodiment, solving the upper-level planning model and the lower-level planning model includes:

[0111] The upper model is solved using a genetic algorithm and the Pareto optimal planning scheme set is passed to the lower model. The lower model is used to verify the ability of the Pareto optimal planning scheme set to withstand wind power and load uncertainties. The constraints of the upper model are modified according to the expected value of the primary energy utilization efficiency of the lower model throughout the entire period, thereby passing the impact of wind power and load uncertainties to the upper model and solving it using a quantum evolutionary algorithm.

[0112] In this embodiment, the distributed energy supply equipment of the integrated energy system and the construction site selection of the energy supply equipment are planned based on the solution results, including:

[0113] Based on the solution results, plan the distributed energy supply equipment of the integrated energy system, including the selection and configuration capacity of the combined heating, cooling and power units, electric chillers, and gas boilers;

[0114] The construction site of the energy supply equipment is selected based on the solution results.

[0115] Specifically, based on the solution results, determine the wind turbine, cooling load, heating load and electric load data of the current park plan; compare the wind turbine, cooling load, heating load and electric load data of the current park plan with each scenario group of the same scenario characteristic data in the operating scenario set to determine the multiple scenario groups to which the current park plan belongs; determine the intersection of the multiple scenario groups to which the current park plan belongs; and plan the distributed energy supply equipment and construction site selection of the energy supply equipment of the integrated energy system based on the cost data of each scenario in the intersection.

[0116] Specifically, the wind turbine, cooling load, heating load and electric load data of the current park plan are compared with each scenario group of the same scenario feature data in the operating scenario set to determine the multiple scenario groups to which the current park plan belongs, including: determining the central data of each scenario group of each scenario feature data in the operating scenario set; based on the same scenario feature data, comparing the scenario feature data of the current park plan with the central data of each scenario group of the same scenario feature data in the operating scenario set, and determining the scenario group to which the central data with the highest similarity belongs as the scenario group to which the current park plan belongs; and determining the multiple scenario groups to which the current park plan belongs based on each scenario feature data.

[0117] For example, the wind power scenario characteristic data of the current park plan is compared with each scenario group of the wind power scenario characteristic data in the operating scenario set to determine the scenario group that is closest to the wind power scenario characteristic data of the current park plan; the cooling load scenario characteristic data of the current park plan is compared with each scenario group of the cooling load scenario characteristic data in the operating scenario set to determine the scenario group that is closest to the cooling load scenario characteristic data of the current park plan; the heat load scenario characteristic data of the current park plan is compared with each scenario group of the heat load scenario characteristic data in the operating scenario set to determine the scenario group that is closest to the heat load scenario characteristic data of the current park plan; and so on, each scenario characteristic data of the current park plan is compared with each scenario group of the same scenario characteristic data in the operating scenario set to determine the multiple scenario groups to which the current park plan belongs. To determine the scene cluster closest to the same scene feature data as the current park plan, a distance calculation method can be used. Under the same scene feature data, the distance between the current park plan data and the corresponding data of each scene is calculated. The distance between the corresponding data of all scenes in each scene cluster and the current park plan data is summed. The scene cluster with the smallest distance sum is determined as the scene cluster closest to the same scene feature data as the current park plan. When calculating the distance of two-dimensional or multi-dimensional scene feature data, the Euclidean distance calculation method can be used. According to the above method, the multiple scene clusters to which the current park plan belongs are determined.

[0118] Determine the central data of each scene cluster for each scene feature data in the running scene set, and the process is described as follows. Under the first scene feature data, the running scene set is divided into M1 scene clusters, each scene cluster includes at least one scene, and select a data representing the scene cluster from the first scene feature data of each scene in the same scene cluster as the central data of the scene cluster, or select the average or median of the first scene feature data of each scene in the scene cluster as the central data of the scene cluster. According to the above method, determine the central data of each scene cluster for each scene feature data. When judging the similarity of data, methods such as Minkowski distance or Euclidean distance can be used.

[0119] The present invention uses a simplification algorithm to obtain a suitable number of typical scenarios, and based on the scenario feature data, determines the scenarios that are closer to the current park planning, thereby achieving further screening of the model solution data, helping to achieve more refined planning and design, and helping to plan the construction site selection of distributed energy supply equipment and energy supply equipment in the integrated energy system, thereby improving the efficiency of multi-energy planning. By considering typical operating scenarios with source-load uncertainty, it can not only increase the penetration rate of wind power in the integrated energy system, but also ensure the economy and power supply reliability of wind power in the long-term planning process. The present invention can not only achieve the goal of higher economic benefits for the integrated energy system, but also effectively improve the economy and flexibility of the integrated energy system. The energy-related coupling and interconnection of the park integrated energy system, and the planning, selection and configuration results of the system can meet the safety and economy requirements of each complex scenario involved in its operation, and the overall planning result of the system is more economical.

[0120] Second, as Figure 2 As shown, the present invention provides a comprehensive energy system collaborative planning device taking uncertainty factors into account, comprising: a modeling unit, a target unit and a solution unit connected in sequence;

[0121] a modeling unit for constructing mathematical models for wind turbines, cooling loads, heating loads, and electric loads, respectively, and constructing an operating scenario set based on the mathematical models; constructing the operating scenario set based on the mathematical models includes: randomly sampling the mathematical models of wind turbines, cooling loads, heating loads, and electric loads, and clustering the acquired data to generate scenarios reflecting wind power and loads; each scenario includes wind power scenario characteristic data, various load scenario characteristic data, and corresponding cost data; clustering the characteristic data of each scenario in the operating scenario set to obtain multiple data clusters for each scenario characteristic data; and classifying the scenarios based on the correspondence between the scenario characteristic data and the scenarios in the data clusters to obtain multiple scenario clusters corresponding to each scenario characteristic data;

[0122] The target unit is used to construct the upper-level planning model and the lower-level planning model according to the objective function of the park planning, and to establish power balance constraints. The objective function of the upper-level planning model is to minimize the annual comprehensive cost; the objective function of the lower-level planning model is to minimize the operating cost and maximize the full-time operation efficiency of primary energy.

[0123] The planning unit is used to solve the upper-level planning model and the lower-level planning model based on the data of the operating scenario set, and determine the wind turbine, cooling load, heating load and electric load data of the current park plan according to the solution results; compare the wind turbine, cooling load, heating load and electric load data of the current park plan with each scenario group of the same scenario feature data in the operating scenario set to determine the multiple scenario groups to which the current park plan belongs; determine the intersection of the multiple scenario groups to which the current park plan belongs; and plan the distributed energy supply equipment and construction site selection of the energy supply equipment of the integrated energy system according to the cost data of each scenario in the intersection.

[0124] In specific implementation, the implementation process of the integrated energy system collaborative planning device taking into account uncertain factors and the integrated energy system collaborative planning method taking into account uncertain factors of the present invention correspond to each other, and the specific way in which each unit performs the operation has been described in detail in the embodiment of the method, which will not be repeated here.

[0125] In a third aspect, the present invention provides an electronic device comprising a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other via the communication bus;

[0126] Memory for storing computer programs;

[0127] The processor is used to implement the above-mentioned integrated energy system collaborative planning method taking uncertainty factors into account when executing the program stored in the memory.

[0128] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for collaborative planning of an integrated energy system taking uncertainty factors into account.

[0129] In order to enable those skilled in the art to better understand the present invention, the present invention is described as follows with reference to the accompanying drawings:

[0130] In order to improve the energy allocation efficiency of an integrated energy system with a high proportion of renewable energy access, the present invention proposes a collaborative two-level planning model for an integrated energy system that takes into account the uncertainty of wind power and load. The upper model is a multi-objective planning model with the annual value of construction investment and operating costs as the target. It is solved by a genetic algorithm and the Pareto optimal planning scheme set is passed to the lower model. The lower model is a multi-scenario verification model with the maximum efficiency of primary energy utilization throughout the entire period as the target. It is used to verify the ability of the Pareto optimal planning scheme set to withstand the uncertainty of wind power and load. The constraints of the upper model are modified according to the expected value of the primary energy utilization efficiency throughout the entire period of the lower model, thereby passing the influence of wind power and load uncertainty to the upper model, and the quantum evolutionary algorithm is used for solution. The simulation results show that the method can obtain a more economically efficient collaborative planning scheme based on the consideration of uncertainty factors, effectively improving the economy and flexibility of the integrated energy system.

[0131] The present invention proposes a collaborative two-level planning model for an integrated energy system that takes into account the uncertainties of wind power and load. The upper-level model is a multi-objective planning model with the goal of minimizing the annual value of construction investment and operating costs. It is solved using a genetic algorithm and the optimal planning scheme set is passed to the lower-level model. The lower-level model is a multi-scenario verification model with the goal of maximizing the full-time operation efficiency of primary energy. It is used to verify the ability of the optimal planning scheme set to withstand wind power and load uncertainties. The constraints of the upper-level model are modified according to the expected value of the full-time operation efficiency of primary energy of the lower-level model, thereby passing the impact of wind power and load uncertainties to the upper-level model, and solving it using a quantum evolutionary algorithm. The effectiveness of this method was verified using an actual microgrid in a certain park as an example.

[0132] The wind power and load uncertainties studied in this paper primarily arise from the influence of wind speed on wind farm output and the random fluctuations in load demand. Historical wind speed and load data can be used to construct a probability distribution model for wind power output and load through parameter estimation. Monte Carlo sampling is then performed on this probability distribution model to generate random scenarios that reflect wind power and load uncertainties. Excessive random scenarios can complicate model solving, so a k-means clustering algorithm is used to cluster these random scenarios, generating representative scenarios that reflect wind power and load uncertainties. This is used to verify the uncertainty of planning schemes.

[0133] The wind turbine model is as follows:

[0134] The wind turbine generator system of the present invention has a wind speed of two-parameter Weibull distribution within a certain period of time, and its probability density function is:

[0135]

[0136] For wind turbines, the output power of the wind turbine and the wind turbine satisfy the following relationship:

[0137]

[0138] Where v represents the actual wind speed; c and k represent the scale parameter and shape parameter respectively; r is the fan radius; ρ is the air density; P w (v) is the output power of the fan; P r is the rated power of the fan; g(x) is the calculation expression; v r Indicates rated wind speed; v in Indicates the cut-in wind speed; v out Indicates the cut-out wind speed.

[0139] The probability distribution models of cooling, heating and electricity loads are as follows:

[0140] The cooling, heating and electricity loads follow a normal distribution, so the probability distribution of the load can be expressed as:

[0141]

[0142] in, is the probability density function; is the load power, i = e / h / c, which represents the electric load, heating load and cooling load power respectively; μ is the mean of the random variable; σ is the variance of the random variable, and its value can be obtained by parameter estimation based on historical load data.

[0143] A typical set of operating scenarios is calculated as follows:

[0144] System state sampling is a common method for quantitatively representing system uncertainty factors. Monte Carlo simulation has been widely used in system state sampling due to its good flexibility and reliability. Its method for wind power uncertainty simulation is as follows:

[0145] The first step is to obtain the probability density and probability distribution function of wind power output based on the historical wind farm output.

[0146] The second step is to construct an N-dimensional Copula function and solve the correlation coefficient of the Copula function.

[0147] The third step is to perform Monte Carlo sampling to generate a large number of correlated random values. The inverse of the random values can be used to obtain the wind power simulation scenario data.

[0148] In the fourth step, the load scenario simulation method is similar, except that joint probability distribution modeling is not required.

[0149] The use of Monte Carlo sampling to sample system states generates a large number of random scenarios, making the planning model solution difficult and computationally intensive. Therefore, a cluster analysis of the acquired data is performed using a k-means clustering algorithm to obtain typical scenarios reflecting the uncertainty of wind power and load. This not only fully reflects the uncertainty of wind power and load but also significantly improves the solution efficiency.

[0150] The objective function is as follows:

[0151] Upper-level planning model

[0152] The objective function of the upper-level planning model is to minimize the annual comprehensive cost. The decision variables are the installation type and quantity of energy equipment, considering the investment cost C inv and operating costs C op The upper objective function value is shown as follows:

[0153] minC=C inv +C op (5)

[0154] Among them, C inv is the equivalent annual investment cost of all equipment; C op is the operating cost of the system on a typical day.

[0155]

[0156] Among them, C ω is the unit investment cost of equipment ω; R ω The construction capacity of the equipment ω includes CCHP, electric refrigerator EC, and gas boiler GB.

[0157] Lower-level planning model

[0158] The lower-level planning model aims to optimize the coordinated operation of energy equipment within the energy internet in a typical daily park, with an operating cost of C op The goal is to minimize and maximize the primary energy utilization efficiency F throughout the entire period.

[0159] The lower-level operating cost indicator is composed of the operating and maintenance cost C OM , electricity transaction cost C Trade , natural gas fuel cost C Gas , carbon emissions tax composition.

[0160]

[0161] in, is the unit maintenance cost of type i energy conversion equipment; P ik,t is the input power of the k-th type i energy conversion device in period t; is the unit maintenance cost of type k renewable energy equipment; P lk,t is the output power of the kth type l renewable energy device in period t; C t is the electricity price in time period t; C g is the natural gas price; C c is the carbon tax price; δ e is the carbon emission factor of electricity; δ g is the carbon emission factor of natural gas; P tg Input electric power to the regional power grid; P g The gas purchasing power of the park; v LHV It is the lower calorific value of natural gas combustion.

[0162] The lower-level efficiency index uses the full-time operation efficiency of primary energy as the objective function, taking into account the electrical load, cooling load, heating load, and input transformer power during period t. The objective function for calculating the efficiency index is as follows:

[0163]

[0164] Among them, F is energy utilization rate; T is simulation period; L t is the total load in time period t; is the electrical load; is the heat load; is the cooling load; S t is the total power input to the integrated energy system; P w The power of RIES input to the wind turbine; P Gas is the calorific value of the input natural gas; η e is the average power generation efficiency of the power plant; η g is the average transmission efficiency of the power plant.

[0165] The constraints are as follows:

[0166] Power balance constraints

[0167] 1. Electrical load

[0168]

[0169] Among them, P j,t is the total electrical load; P g,t The power output from the grid to the park; are the output powers of the cth CCHP and the wth wind turbine at time t, respectively; and are the power consumption of the e-th electric refrigerator, the g-th gas boiler and the b-th electric boiler respectively; J is the set of all nodes in the power system; Cj, Wj, Ej, Gj and Bj are the sets of CCHP, fans, electric refrigerators, gas boilers and electric boilers connected to the grid node j respectively.

[0170] 2. Total heat load It can be expressed as:

[0171]

[0172] Among them, T c,t Provide heat for CCHP c; T b,t Provide heat for electric boiler b; T g,t g is the heat supply of gas boiler; C means the number of CCHP heating equipment; B means the number of electric boilers; H means the number of gas boilers.

[0173] 3. Total cooling load demand It can be expressed as:

[0174]

[0175] Among them, F c,t Cooling capacity of CCHP c; F e,t e is the cooling capacity of the electric chiller; H means the number of CCHP cooling equipment; I means the number of electric chillers.

[0176] The upper and lower operating limits of each device are as follows:

[0177]

[0178] in, is the running state variable of device w; is the total power of the integrated energy system input to the device w; Enter the total power of the integrated energy system for minimum and maximum.

[0179] The multi-objective programming model is solved as follows:

[0180] The construction part of the multi-objective planning model proposed in the present invention includes the selection and configuration capacity of distributed energy supply equipment, the operation part includes the typical electric heating and cooling load balance in three seasons, and the system power supply reliability will be reflected by converting it into energy utilization rate. Therefore, the solution idea of the present invention is that the upper model is solved by genetic algorithm, and the Pareto optimal planning scheme set is passed to the lower layer; the lower model is used to verify the tolerance of the Pareto optimal planning scheme set for wind power and load uncertainty, and the constraints of the upper model are modified according to the expected value of the primary energy utilization efficiency of the lower model during all periods, so as to pass the influence of wind power and load uncertainty to the upper model, and use quantum evolution algorithm (qunantum-inspired evolutionary algorithm) to solve it. The solution process is as follows Figure 3 shown.

[0181] The multi-objective collaborative planning of the integrated energy system is as follows:

[0182] The present invention proposes a distributed energy supply collaborative planning method for an integrated energy system that takes into account the uncertainty of wind power output. Figure 4 As shown in the figure, the park's heating, cooling, and electricity load data were first obtained and a model was proposed. Based on the proposed model, the distributed energy supply equipment for the integrated energy system was planned, including the selection and configuration capacity of combined heating, cooling, and power (CHP) units, electric chillers, and gas boilers. Finally, the construction site for the energy supply equipment was selected based on the collaborative planning results.

[0183] This invention takes a real project in a certain park as an example. Figure 5 As shown, the natural gas price is 3.3 yuan / m 3 , v LHV The low calorific value of natural gas combustion is 9.73 (kW·h) / m 3 , δ e 0.7921 kgCO 2 / kWh, natural gas 2.162kgCO 2 / m 3 The carbon tax price is 60 yuan / ton, the average power generation efficiency of the power plant is 0.65, the average transmission efficiency of the power plant is 0.94, the probability of a typical day in spring and autumn is 0.5, and the probability of a typical day in summer and winter is 0.25. The relevant parameters of the energy conversion equipment are shown in Table 1, the relevant parameters of the renewable energy equipment are shown in Table 2, and the wind power curve is shown in Figure 6 As shown, the typical daily load curve in spring and autumn is as follows Figure 7 The typical daily load curve in summer is shown as Figure 8 The typical daily load curve in winter is shown as Figure 9 shown.

[0184] Table 1 Related parameters of the energy conversion equipment

[0185]

[0186]

[0187] Table 2 Related parameters of the renewable energy equipment

[0188] Renewable energy equipment Capacity (kW) Investment cost (yuan / kW) Operation and maintenance cost (yuan / kW) Lifespan (years) Failure probability fan 1500 7000 0.02 20 0.01

[0189] Simulation results:

[0190] Integrated energy system planning for uncertain load scenarios

[0191] QEA is a type of random optimization algorithm, and the results of each run of the algorithm are not necessarily consistent. Therefore, to avoid the occurrence of random factors, the present invention runs the algorithm 10 times. The experimental results are as follows:

[0192] When the lower-level goal only considers the operation and maintenance costs, the operating results are shown in Table 3:

[0193] Tab.3 Consider only operational maintenance costs

[0194]

[0195] At this time, the change curves of each target are as follows Figure 10 shown.

[0196] When the lower-level goal only considers energy efficiency, the operating results are shown in Table 4:

[0197] Tab.4When considering energy efficiency only

[0198]

[0199] At this time, the change curves of each target are as follows Figure 11 As shown:

[0200] When the lower-level goal considers both operating costs and energy efficiency, the operating result is a solution set, so it cannot be quantitatively analyzed using a table. The target iteration scatter plot is as follows: Figure 12 shown.

[0201] At this time, the upper-level objectives in the solution set are shown in Table 5:

[0202] Tab.5 When considering both operating costs and energy efficiency

[0203] Operation and maintenance costs 84173684 Electricity transaction costs 307537463 Natural gas fuel costs 226765776 Carbon tax 307537463 Total investment cost 1166507888

[0204] 4.2.2 Parameter Comparison Analysis

[0205] The quantum rotation angle in QEA has a great influence on the convergence speed and convergence accuracy of the algorithm. Therefore, in order to analyze the impact of the rotation angle on the algorithm, four rotation angles of 0.02*pi, 0.06*pi, 0.1*pi, and 0.2*pi are set. The comparative experimental results are shown in Table 6:

[0206] Table 6 Contrast the results

[0207]

[0208] It can be seen from Table 6 that as the quantum rotation angle in QEA increases, the optimal values of investment cost, operating cost and energy utilization become larger, the algorithm converges faster and the convergence accuracy becomes more accurate.

[0209] 4.2.3 Comparative Analysis of Collaborative Planning

[0210] Solution 1: Integrated energy system collaborative planning scheme for deterministic load scenarios.

[0211] Option 2: A coordinated planning scheme for an integrated energy system that considers source-load uncertainty factors.

[0212] The collaborative planning results are shown in Table 7.

[0213] Table 7 Collaborative planning and comparison results

[0214] Comparison Item Solution 1 Option 2 Investment costs 8945406256 5083003883 Running costs 4378677795 1795505556 Energy utilization 0.2788 0.39925

[0215] The collaborative planning results yield the following conclusions: Table 7 shows that Option 1 has higher total planning, construction, and operation costs, resulting in a more conservative planning result. Option 2, on the other hand, offers greater economic advantages due to its consideration of the uncertainty of wind power output. Each complex scenario involved in system operation meets both economic and safety requirements. The system's power supply reliability can be measured through energy utilization. Compared to Option 1, Option 2's collaborative planning results show a higher energy utilization rate, indicating that Option 2 offers greater power supply reliability.

[0216] To address the problem of low energy allocation efficiency in an integrated energy system with a high proportion of wind power, this paper proposes a collaborative two-level planning model for an integrated energy system that considers wind power and load uncertainties. The following conclusions are drawn from simulation examples:

[0217] The highly random nature of distributed power generation (DGs) in their timing characteristics makes collaborative planning for integrated energy systems difficult due to the enormous computational complexity. Using the k-means clustering algorithm to generate an appropriate number of typical scenarios can improve the efficiency of solving planning problems. By establishing typical operating scenarios that account for wind power and load uncertainties, this not only increases wind power penetration in integrated energy systems but also ensures the economic viability and reliability of wind power during long-term planning.

[0218] The present invention proposes a collaborative two-level planning model for an integrated energy system that takes into account wind power and load uncertainties, which can not only achieve higher economic benefits for the integrated energy system, but also effectively improve the economy and flexibility of the integrated energy system.

[0219] The timing characteristics of distributed power sources are highly random, and in the collaborative planning of integrated energy systems, the huge amount of calculations will lead to difficulties in solving the problem. The present invention adopts a simplification algorithm to obtain an appropriate number of typical scenarios, which can improve the efficiency of solving planning problems. By considering typical operating scenarios with source-load uncertainty, not only can the penetration rate of wind power in the integrated energy system be improved, but also the economy and power supply reliability of wind power in the long-term planning process are guaranteed. The present invention can not only achieve the goal of higher economic benefits for the integrated energy system, but also effectively improve the economy and flexibility of the integrated energy system. The energy-related coupling and interconnection of the park integrated energy system, the planning, selection and configuration results of the system can meet the safety and economy requirements of each complex scenario involved in its operation, and the overall planning results of the system are more economical.

[0220] In order to improve the energy allocation efficiency of an integrated energy system with a high proportion of renewable energy access, the present invention proposes a collaborative two-level planning model for an integrated energy system that takes into account the uncertainty of wind power and load. The upper model is a multi-objective planning model with the annual value of construction investment and operating costs as the target. It is solved by a genetic algorithm and the Pareto optimal planning scheme set is passed to the lower model. The lower model is a multi-scenario verification model with the maximum efficiency of primary energy utilization throughout the entire period as the target. It is used to verify the ability of the Pareto optimal planning scheme set to withstand the uncertainty of wind power and load. The constraints of the upper model are modified according to the expected value of the primary energy utilization efficiency throughout the entire period of the lower model, thereby passing the influence of wind power and load uncertainty to the upper model, and the quantum evolutionary algorithm is used for solution. The simulation results show that the method can obtain a more economically efficient collaborative planning scheme based on the consideration of uncertainty factors, effectively improving the economy and flexibility of the integrated energy system.

[0221] Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A collaborative planning method for an integrated energy system taking uncertainty factors into account, characterized by: The method comprises: Mathematical models are constructed for wind turbines, cooling loads, heating loads, and electrical loads, respectively, and an operation scenario set is constructed based on the mathematical models. The operation scenario set is constructed based on the mathematical models, including: randomly sampling the mathematical models of wind turbines, cooling loads, heating loads, and electrical loads, and clustering the acquired data to generate scenarios reflecting wind power and loads. Each scenario includes wind power scenario characteristic data, various load scenario characteristic data, and corresponding cost data. There is a corresponding relationship between the wind power scenario characteristic data, cooling load scenario characteristic data, heating load scenario characteristic data, and electrical load scenario characteristic data in each scenario. Clustering each scenario characteristic data in the operating scenario set to obtain multiple data clusters of each scenario characteristic data, including: clustering the wind power scenario characteristic data in the operating scenario set to obtain multiple data clusters of wind power scenario characteristic data; clustering the cooling load scenario characteristic data in the operating scenario set to obtain multiple data clusters of cooling load scenario characteristic data; clustering the heat load scenario characteristic data in the operating scenario set to obtain multiple data clusters of heat load scenario characteristic data; clustering the electric load scenario characteristic data in the operating scenario set to obtain multiple data clusters of electric load scenario characteristic data; Classify the scenes according to the correspondence between the scene feature data and the scenes in the data cluster to obtain multiple scene clusters corresponding to each scene feature data; Based on the objective function of the park planning, the upper-level planning model and the lower-level planning model are constructed respectively, and the power balance constraints are established; the objective function of the upper-level planning model is to minimize the annual comprehensive cost; the objective function of the lower-level planning model is to minimize the operating cost and maximize the full-time operation efficiency of primary energy; Based on the data from the operational scenario set, the upper-level and lower-level planning models are solved, and based on the solution results, the wind turbines, cooling load, heating load, and electrical load data for the current park plan are determined; Comparing the wind turbine, cooling load, heating load, and electric load data of the current park plan with each scenario group of the same scenario feature data in the operating scenario set to determine the multiple scenario groups to which the current park plan belongs; Determine the intersection of multiple scenario groups to which the current park plan belongs; Based on the cost data of each scenario in the intersection, the distributed energy supply equipment of the integrated energy system and the construction site selection of the energy supply equipment are planned.

2. The method for collaborative planning of an integrated energy system taking uncertainty factors into account according to claim 1, characterized in that: The mathematical models for the wind turbine generator set, cooling load, heating load and electrical load are constructed separately, including: The historical wind speed and load data are used to construct the probability distribution model of wind turbines, cooling load, heating load and electrical load through parameter estimation.

3. The method for collaborative planning of an integrated energy system taking uncertainty factors into account according to claim 2, characterized in that: The method of constructing a probability distribution model of a wind turbine generator system by parameter estimation using historical wind speed and load data includes: The wind speed adopts a two-parameter Weibull distribution, and its probability density function is: The probability distribution model of the wind turbine is constructed by parameter estimation based on historical wind speed and load data. The output power of the wind turbine and the wind turbine satisfy the following relationship: Where v represents the actual wind speed; c and k represent the scale parameter and shape parameter respectively; r is the radius of the wind turbine; P w (v) is the output power of the fan; P r is the rated power of the fan; v r Indicates rated wind speed; v in Indicates the cut-in wind speed; v out Indicates the cut-out wind speed; The parameters of the wind turbine model are estimated using historical wind speed data.

4. The method for collaborative planning of an integrated energy system taking uncertainty factors into account according to claim 2, characterized in that: The probability distribution model for estimating cooling load, heating load and electric load through parameters using historical wind speed and load data also includes: The cooling, heating and electricity loads follow a normal distribution, and the probability distribution of the loads is expressed as: in, is the probability density function; is the load power, i=e / h / c, which represents the power of electric load, heat load and cooling load respectively; μ is the mean of the random variable; σ is the variance of the random variable, The probability distribution model of cooling load, heating load and electric load is estimated based on historical load data to obtain the parameter values.

5. The method for collaborative planning of an integrated energy system taking uncertainty factors into account according to claim 1, characterized in that: The random sampling of mathematical models of wind turbines, cooling loads, heating loads and electrical loads includes: Monte Carlo sampling is performed on the mathematical models of wind turbines, cooling loads, heating loads and electrical loads to generate scenarios reflecting wind power and loads.

6. The method for collaborative planning of an integrated energy system taking uncertainty factors into account according to claim 1, characterized in that: The mathematical models of wind turbines, cooling loads, heating loads, and electrical loads are randomly sampled, and the acquired data is clustered to generate scenarios reflecting wind power and loads, including: Monte Carlo sampling is performed on the mathematical models of wind turbines, cooling loads, heating loads and electrical loads, and the acquired data is clustered to obtain multiple wind power scenario characteristic data and various load scenario characteristic data; based on the multiple wind power scenario characteristic data and various load scenario characteristic data, a scenario reflecting wind power and load is generated.

7. The method for collaborative planning of an integrated energy system taking uncertainty factors into account according to claim 6, characterized in that: Monte Carlo sampling is performed on the mathematical model of the wind turbine, and the acquired data is clustered to obtain multiple wind power scenario feature data, including: The wind turbine scenario is obtained by: The probability density and probability distribution function of wind power output are obtained based on the historical wind farm output. Construct an N-dimensional Copula function and solve the Copula function correlation coefficient. Perform Monte Carlo sampling to generate random values with correlation; Invert the random value to obtain the simulated scenario data of wind power; The wind power simulation scene data is clustered using a k-means clustering algorithm to obtain a plurality of wind power scene feature data.

8. The method for collaborative planning of an integrated energy system taking uncertainty factors into account according to claim 6, characterized in that: Monte Carlo sampling is performed on the mathematical models of cooling load, heating load, and electrical load, and the acquired data is clustered to obtain characteristic data of multiple load scenarios, including: The cooling load, heating load, and electrical load scenarios are obtained by: According to the historical data of various loads, the probability density and probability distribution function of each load are obtained, and Monte Carlo sampling is performed to generate random values with correlation; Invert the random value to obtain the simulated scenario data of the load; The load simulation scenario data is clustered using a k-means clustering algorithm to obtain a plurality of load scenario feature data of various types.

9. The method for collaborative planning of an integrated energy system taking uncertainty factors into account according to claim 1, characterized in that: According to the objective function of the park planning, the upper-level planning model and the lower-level planning model are constructed respectively, including: The objective function of the upper-level planning model is to minimize the annual comprehensive cost C. The objective function value of the upper-level planning model is shown as follows: minC=C inv +C op (5) Among them, C inv is the equivalent annual investment cost of all equipment; C op is the operating cost of the system on a typical day; Among them, C ω is the unit investment cost of equipment ω; R ω The construction capacity of the equipment ω includes CCHP, electric refrigerator EC, and gas boiler GB.

10. The method for collaborative planning of an integrated energy system taking uncertainty factors into account according to claim 1, characterized in that: The establishing of the power balance constraint condition includes: Establish upper and lower operating constraints for each device: in, is the running state variable of device w; is the total power of the integrated energy system input to the device w; Enter the total power of the integrated energy system for minimum and maximum.

11. The method for collaborative planning of an integrated energy system taking uncertainty factors into account according to claim 1, characterized in that: Solving the upper-level planning model and the lower-level planning model includes: The upper model is solved using a genetic algorithm and the set of Pareto optimal planning solutions is passed to the lower layer; The lower model is solved using a quantum evolutionary algorithm. The lower model is used to verify the Pareto optimal planning scheme set's ability to withstand wind power and load. The constraints of the upper model are modified according to the expected value of the primary energy utilization efficiency of the lower model throughout the entire period, and the impact of wind power and load is passed to the upper model.

12. The method for collaborative planning of an integrated energy system taking uncertainty factors into account according to any one of claims 1 to 11, characterized in that: Compare the wind turbine, cooling load, heating load, and electric load data of the current park plan with each scenario group of the same scenario feature data in the operating scenario set to determine multiple scenario groups to which the current park plan belongs, including: Determine the central data of each scenario cluster of each scenario feature data in the running scenario set; Based on the same scene feature data, the scene feature data of the current park plan is compared with the central data of each scene cluster of the same scene feature data in the running scene set, and the scene cluster to which the central data with the highest similarity belongs is determined as the scene cluster to which the current park plan belongs; Based on the characteristic data of each scenario, multiple scenario groups to which the current park planning belongs are determined.

13. An integrated energy system collaborative planning device taking uncertainty factors into account, characterized in that: include: Modeling unit, target unit and solution unit connected in sequence; A modeling unit is used to construct mathematical models for wind turbines, cooling loads, heating loads, and electrical loads, and to construct an operation scenario set based on the mathematical models; The operation scenario set is constructed based on the mathematical model, including: randomly sampling the mathematical models of wind turbines, cooling loads, heating loads and electric loads, and clustering the obtained data to generate scenarios reflecting wind power and loads; each scenario includes wind power scenario characteristic data, various load scenario characteristic data, and corresponding cost data, and there is a corresponding relationship between the wind power scenario characteristic data, cooling load scenario characteristic data, heating load scenario characteristic data, and electric load scenario characteristic data in each scenario; clustering the characteristic data of each scenario in the operation scenario set to obtain multiple data groups of the characteristic data of each scenario, including: clustering the operation scenario Clustering the concentrated wind power scene feature data to obtain multiple data clusters of wind power scene feature data; clustering the cooling load scene feature data in the operation scene set to obtain multiple data clusters of cooling load scene feature data; clustering the heating load scene feature data in the operation scene set to obtain multiple data clusters of heating load scene feature data; clustering the electric load scene feature data in the operation scene set to obtain multiple data clusters of electric load scene feature data; classifying the scenes according to the correspondence between the scene feature data and the scenes in the data clusters to obtain multiple scene clusters corresponding to each scene feature data; The target unit is used to construct the upper-level planning model and the lower-level planning model according to the objective function of the park planning, and to establish power balance constraints. The objective function of the upper-level planning model is to minimize the annual comprehensive cost; the objective function of the lower-level planning model is to minimize the operating cost and maximize the full-time operation efficiency of primary energy. The planning unit is used to solve the upper-level planning model and the lower-level planning model based on the data of the operating scenario set, and determine the wind turbine, cooling load, heating load and electric load data of the current park plan according to the solution results; compare the wind turbine, cooling load, heating load and electric load data of the current park plan with each scenario group of the same scenario feature data in the operating scenario set to determine the multiple scenario groups to which the current park plan belongs; determine the intersection of the multiple scenario groups to which the current park plan belongs; and plan the distributed energy supply equipment and construction site selection of the energy supply equipment of the integrated energy system according to the cost data of each scenario in the intersection.

14. An electronic device, characterized in that: The processor, the communication interface, the memory and the communication bus are connected to each other via the communication bus. Memory for storing computer programs; The processor is configured to implement the integrated energy system collaborative planning method taking uncertainty factors into account as described in any one of claims 1 to 12 when executing the program stored in the memory.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for collaborative planning of an integrated energy system taking uncertainty factors into account according to any one of claims 1 to 12 is implemented.

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