Energy scheduling method, system, computer device and storage medium

Abstract

The present application relates to an energy scheduling method, device, computer equipment and storage medium. In the above energy scheduling method, by obtaining power data in multiple park microgrids, a scheduling plan is obtained according to the power data and the microgrid group scheduling model, and phase change cooling air conditioners are used for energy scheduling among the multiple park microgrids according to the scheduling plan. Since the microgrid group scheduling model is constructed based on the parameter information of the vehicles in the park microgrids and the parameter information of the phase change cooling air conditioners, therefore, the scheduling plan can be solved by using the microgrid group scheduling model, and energy scheduling is realized among the respective park microgrids through the scheduling plan, which is equivalent to connecting the respective park microgrids to each other, realizing the energy flow between the park microgrids, and overcoming the problems of energy waste and excessive cost caused by independent scheduling of the park microgrids. The new energy consumption level is improved, and the operating economy of the power system is improved.

Description

Technical Field

[0001] The present application relates to the technical field of electric power energy, and particularly relates to an energy scheduling method, system, computer device, and storage medium. Background Art

[0002] In the field of electric power energy, in order to relieve the energy consumption pressure and reduce the electricity cost, many industrial parks make use of new energy power generation such as wind turbines and photovoltaics according to local conditions. However, while the electricity generated by new energy power generation is capable of supporting the electricity load of the industrial park, when the park load cannot consume the electricity generated by new energy power generation, the surplus electricity will be sold to the large power grid at a low price, causing economic losses to the owner.

[0003] Currently, phase change materials with good cold storage characteristics are generally used to solve the problem of new energy consumption in industrial park microgrids. However, since urban industrial park microgrids are generally independently scheduled and cannot give full play to the complementary advantages and synergy benefits among them, it is still difficult to solve the problem of new energy consumption in industrial park microgrids even with the use of phase change materials. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide an energy scheduling method, device, computer device, and storage medium.

[0005] In a first aspect, an energy scheduling method, the method includes:

[0006] Obtain power data in multiple industrial park microgrids;

[0007] According to the power data and the microgrid group scheduling model, obtain a scheduling plan; the microgrid group scheduling model is constructed according to the parameter information of the vehicles in the industrial park microgrid and the parameter information of the phase change cold storage air conditioner;

[0008] Perform energy scheduling among multiple industrial park microgrids by using the phase change cold storage air conditioner according to the scheduling plan.

[0009] In one embodiment, the microgrid group scheduling model includes the spatial transfer model of the vehicle, the operation model of the phase change cold storage air conditioner, and the objective function; the obtaining of the scheduling plan according to the power data and the microgrid group scheduling model includes:

[0010] Obtain the scheduling plan according to the power data, the spatial transfer model, the operation model, and the objective function; the spatial transfer model is used to constrain the position of the vehicle in the microgrid park, the operation model is to constrain the operation power of the phase change cold storage air conditioner, and the objective function is constructed according to the parameters related to the cost when energy scheduling is performed in the industrial park microgrid by using the phase change cold storage air conditioner.

[0011] In one embodiment, the microgrid group scheduling model also includes a first constraint condition between the vehicle and the phase change cold storage air conditioner and a second constraint condition of minimum cost, and obtaining the scheduling scheme according to the power data, the spatial transfer model, the operation model and the objective function includes:

[0012] The spatial transfer model, the operation model and the objective function are solved according to the power data, the first constraint condition and the second constraint condition to obtain the scheduling plan.

[0013] In one embodiment, the spatial transfer model includes:

[0014]

[0015]

[0016] B ij,t+1 +B jj,t+1 ≥B ij,t

[0017] B ii,T =B ii,0

[0018] Among them, B ij,t Indicates whether the vehicle is on the road between park i and park j, B ii,t Indicates whether the vehicle is in park i, B jj,t Indicates whether the vehicle is in park j, T ij Represents the driving time of a vehicle between park i and park j.

[0019] In one embodiment, the method further comprises:

[0020] The driving time of the vehicle between the parks is calculated based on the congestion data, the length of the roads between the parks and the driving speed of the vehicle.

[0021] In one embodiment, the operating model includes:

[0022]

[0023]

[0024]

[0025]

[0026]

[0027]

[0028]

[0029]

[0030] Among them, is the operating power of the phase change cool storage air conditioner at location i in the park for t hours; is the operating power of chiller 1 of the phase change cool storage air conditioner, is the rated power of chiller 1 of the phase change cool storage air conditioner; is the operating power of chiller 2 of the phase change cool storage air conditioner, is the rated power of chiller 2 of the phase change cool storage air conditioner; is the operating power of the cold release machine of the phase change cool storage air conditioner, is the rated power of the cold release machine of the phase change cool storage air conditioner; η c1 is the refrigeration efficiency of chiller 1, η d is the cold release efficiency of the cold release machine; η c2 is the refrigeration efficiency of chiller 2; is the energy storage of the phase change cool storage air conditioner at time t; is the maximum energy storage capacity of the phase change cool storage air conditioner; ξ air represents the energy storage retention rate, represents the energy storage corresponding to the phase change material loaded or unloaded by the vehicle in park i at the t-th hour; is the actual power of the cold load at time t in park i, represents the energy storage of the phase change material at the end of scheduling; represents the energy storage of the phase change material at the start of scheduling.

[0031] In one of the embodiments, the objective function includes:

[0032]

[0033]

[0034]

[0035]

[0036] Among them, s represents the scenarios of renewable energy power generation, electricity / cold load, and S represents the set of scenarios of renewable energy power generation, electricity / cold load; is the total cost of power exchange between the park microgrid i and the external power grid at time t; represents the operating cost of the vehicle transferring or loading and unloading the phase change material in park i; represents the electricity purchase price of the park microgrid; represents the electricity selling price of the park microgrid; represents the power exchanged between the park microgrid i and the external power grid; C in represents the cost of loading and unloading phase change materials once, C out represents the cost of transporting phase change materials, A ii,t Indicates whether the vehicle loads or unloads phase change materials in park i at hour t, B ii,t Indicates whether the vehicle is in park i, represents the actual power of wind power in park i at time t, represents the actual power of photovoltaic power in park i at time t, represents the actual power of the electrical load in park i at time t, It represents the operating power of the phase change cold storage air conditioner in park i for t hours.

[0037] In one embodiment, the first constraint condition includes:

[0038] A ii,t ≤B ii,t

[0039]

[0040]

[0041] 0≤E t ≤E max

[0042] E T =E0

[0043] Among them, A ii,t Indicates that the vehicle loads and unloads phase change materials in park i at hour t; B ii,t Indicates whether the vehicle is in park i; H represents the energy storage corresponding to the phase change material loaded or unloaded by the vehicle in park i at hour t; M E represents the maximum energy storage of phase change material that can be loaded or unloaded per hour by the vehicle; t Represents the energy storage of phase change materials on vehicles; E max Represents the phase change material energy storage of the vehicle's rated load; air Represents the energy storage retention rate.

[0044] In a second aspect, a computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the method described in the first aspect when executing the computer program.

[0045] In a third aspect, a computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0046] The above energy scheduling method obtains power data in multiple park microgrids, obtains a scheduling plan according to the power data and the microgrid group scheduling model, and performs energy scheduling between multiple park microgrids using phase change cooling air conditioners according to the scheduling plan. Since the microgrid group scheduling model is constructed based on the parameter information of vehicles in the park microgrids and the parameter information of phase change cooling air conditioners, a scheduling plan can be solved using the microgrid group scheduling model. Energy scheduling is achieved between each park microgrid through this scheduling plan, which is equivalent to connecting each park microgrid to each other, realizing the energy flow between park microgrids, and overcoming the problems of energy waste and excessive costs caused by independent scheduling of park microgrids. It improves the new energy consumption level and improves the operating economy of the power system. Brief Description of the Drawings

[0047] Figure 1 It is an application environment diagram of the energy scheduling method in an embodiment;

[0048] Figure 2 It is a flowchart of the energy scheduling method in an embodiment;

[0049] Figure 3 It is a correlation diagram of three parks;

[0050] Figure 4 It is a prediction data diagram of Park 1;

[0051] Figure 5 It is a line graph of the transfer / loading and unloading of phase change cooling materials by vehicles between different parks;

[0052] Figure 6 It is a line graph of the operating power of the phase change cooling air conditioner in the park microgrid at different times;

[0053] Figure 7 It is a line graph of the interaction power between the park microgrid and the external power grid at different times;

[0054] Figure 8 It is a flowchart of the energy scheduling method in an embodiment;

[0055] Figure 9 It is a structural block diagram of the energy scheduling device in an embodiment;

[0056] Figure 10 It is a structural block diagram of the energy scheduling device in an embodiment;

[0057] Figure 11 It is an internal structure diagram of a computer device in an embodiment. Detailed Embodiments

[0058] In order to make the objectives, technical solutions and advantages of the present application more clear and understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0059] In the field of electric power energy, in order to relieve the energy consumption pressure and reduce the electricity cost, many industrial parks make use of new energy power generation such as wind turbines and photovoltaics according to local conditions. However, while the electricity generated by new energy power generation is capable of supporting the electricity load of the industrial park, when the park load cannot consume the electricity generated by new energy power generation, the remaining electricity will be sold to the large power grid at a relatively low price, causing economic losses to the owner. At present, phase change materials with good cold storage characteristics are generally used to solve the problem of new energy consumption in industrial park microgrids. However, since urban industrial park microgrids are generally independently dispatched and cannot give full play to the complementary advantages and synergy benefits among them, it is still difficult to solve the problem of new energy consumption in industrial park microgrids even with the use of phase change materials. Based on this, the present application provides an energy scheduling method to solve the above problems, and the following embodiments will elaborate on the above method in detail.

[0060] The energy scheduling method provided by the present application can be applied to an application environment as Figure 1 shown, including Industrial Park 1, Industrial Park 2, Industrial Park 3, and Vehicle 106. Industrial Park 1, Industrial Park 2, and Industrial Park 3 all include phase change cold storage air conditioners 104. The phase change cold storage air conditioner 104 includes a refrigerator 1041, a refrigerator 1042, a cold release machine 1043, and a phase change cold storage material 102. Among them, Industrial Park 1, Industrial Park 2, and Industrial Park 3 are different regions participating in microgrid energy scheduling, and there can also be four or more, which is not limited here. Each of Industrial Park 1, Industrial Park 2, and Industrial Park 3 is equipped with a phase change cold storage air conditioner 104, and the operation mode of the phase change cold storage air conditioner 104 in each park can be adjusted according to the amount of the phase change cold storage material 102 loaded and unloaded by the vehicle 106. The phase change cold storage air conditioner 104 includes a refrigerator 1041, a refrigerator 1042, a cold release machine 1043, and a phase change cold storage material 102. The refrigerator 11041 can directly supply cold like a conventional air conditioner. The cold energy generated by the refrigerator 1042 is used to be stored in the phase change cold storage material 102. The cold release machine 1043 is used to release the energy in the phase change cold storage material 102 for use in the park where it is located. The phase change cold storage material 102 is an energy storage device that absorbs or releases energy by changing the state of the phase change cold storage material. The vehicle 106 is used to transport the phase change cold storage material 102 among Industrial Park 1, Industrial Park 2, and Industrial Park 3 to realize the energy flow among the parks. Optionally, Figure 1 the application environment shown further includes a server. The server can obtain the power data of each park, and thus obtain a scheduling plan according to the power data and the microgrid group scheduling model. This server can be implemented by an independent server or a server cluster composed of multiple servers.

[0061] Those skilled in the art can understand that Figure 1 The structure shown in Figure 1 is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0062] In one embodiment, as Figure 2 shown, an energy scheduling method is provided. Taking the case where this method is applied to a server as an example, the method includes the following steps:

[0063] S101, obtain power data in multiple park microgrids.

[0064] Among them, the power data is the power generation data and power consumption data in each park. For example, the power data may include the predicted wind power and the maximum predicted wind power error, the predicted photovoltaic power and the maximum predicted photovoltaic power error, the predicted electric load power and the maximum predicted electric load power error, the predicted cooling load power and the maximum predicted cooling load power error in each park. The power data may also include the relationships between the predicted wind power among the parks, the relationships between the predicted photovoltaic power, the relationships between the predicted electric load power, the relationships between the predicted cooling load power, and so on. In the embodiments of this application, no limitations are imposed.

[0065] In this embodiment, first, the total number of parks willing to participate in the energy scheduling among the park microgrids is obtained through various means, and at the same time, power data such as the predicted wind power, predicted photovoltaic power, predicted electric load power, and predicted cooling load power of each park willing to participate in the energy scheduling among the park microgrids is collected. By analyzing and comparing the historical predicted wind power and the actual wind power, the historical predicted photovoltaic power and the actual photovoltaic power, the historical predicted electric load power and the actual electric load power, and the historical predicted cooling load power and the actual cooling load power of each park, the maximum predicted wind power error, the maximum predicted photovoltaic power error, the maximum predicted electric load power error, the maximum predicted cooling load power error, etc. of each park are obtained. In practical applications, the total number of parks willing to participate in the energy scheduling among the park microgrids can be obtained through research and publicity. Taking three parks as an example. The relevant relationships of the three parks are as Figure 3 shown. Collect the predicted wind power, predicted photovoltaic power, predicted electric load power, and predicted cooling load power predicted by the dispatch centers of these three park microgrids. Among them, the predicted data of Park 1 is as Figure 4 shown.

[0066] Optionally, in the embodiments of the present application, the relationship between the powers of each park can also be determined according to the power data of each park microgrid. For example, the predicted wind power of Park 2 is 1 / 4 of that of Park 1, the predicted photovoltaic power is 1 / 4 of that of Park 1, the predicted electric load power is 1.0 times that of Park 1, and the predicted cooling load power is 1.0 times that of Park 1. The predicted wind power of Park 3 is 1 / 6 of that of Park 1, the predicted photovoltaic power is 1 / 6 of that of Park 1, the predicted electric load power is 1.0 times that of Park 1, and the predicted cooling load power is 1.0 times that of Park 1. The above data are uniformly summarized into the matrix P i It can be expressed as:

[0067]

[0068] Among them, represents the predicted wind power of the i-th park at the t-th hour of the day before, represents the predicted photovoltaic power of the i-th park at the t-th hour of the day before, represents the predicted electric load power of the i-th park at the t-th hour of the day before, represents the predicted cooling load power of the i-th park at the t-th hour of the day before; T is the scheduling period of the park microgrid, and the value is 24 hours.

[0069] On this basis, by analyzing and comparing the historical predicted power and actual power data of each park microgrid dispatching center, the maximum wind power prediction error e WT of each park, the maximum photovoltaic power prediction error e PV , the maximum electric load power prediction error e EL and the maximum cooling load power prediction error e CL are obtained. For example, e WT = 0.2, e PV = 0.2, e EL = 0.1, e CL = 0.1. According to various power errors, the relationship between various predicted powers and actual powers can be obtained, and thus the actual power can be constrained according to the relationship between the above predicted power and actual power. For example:

[0070] The relationship between the predicted wind power and the actual wind power can be expressed as:

[0071]

[0072] The relationship between the predicted photovoltaic power and the actual photovoltaic power can be expressed as:

[0073]

[0074] The relationship between the predicted electric load power and the actual electric load power can be expressed as:

[0075]

[0076] The relationship between the predicted cooling load power and the actual cooling load power can be expressed as:

[0077]

[0078] Wherein, represents the actual wind power in Park i, represents the actual photovoltaic power in Park i, represents the actual electrical load power in Park i, represents the actual cooling load power in Park i.

[0079] S102. Obtain a scheduling plan according to the power data and the microgrid group scheduling model.

[0080] Among them, the microgrid group scheduling model is constructed according to the parameter information of the vehicles in the park microgrid and the parameter information of the phase change cool storage air conditioner.

[0081] In this embodiment, the parameter information of the vehicle may include congestion data between parks, road lengths between parks, vehicle driving speeds, energy storage corresponding to the phase change cool storage material carried by the vehicle, energy storage retention rate, whether the vehicle is on the road between parks at time t, whether the vehicle is in the park at time t, the initial position of the vehicle, and the vehicle position at the end of the scheduling, etc., which are not limited in the embodiments of the present application. The parameter information of the phase change cool storage air conditioner may include the rated power of the refrigerating machine and the cold release machine in the phase change cool storage air conditioner, the operating power at time t, the energy storage of the phase change cool storage material at time t, the maximum energy storage capacity of the phase change cool storage air conditioner, the energy storage of the phase change cool storage material at the start of the scheduling, and the energy storage of the phase change cool storage material at the end of the scheduling, etc., which are not limited in the embodiments of the present application.

[0082] The scheduling plan is a park microgrid energy flow plan, which is used to guide the operating power of the phase change cool storage air conditioner in each park when the daily operating cost is minimized under the worst scenarios of new energy generation and load demand, guide the vehicle to transfer the phase change cool storage material, and guide the interactive power between each park microgrid and the external power grid.

[0083] In this embodiment, the congestion data, the road lengths between the parks, and the vehicle driving speeds between the parks can be obtained from the local transportation department, or the congestion data, the road lengths between the parks, and the vehicle driving speeds between the parks can be collected by oneself. According to the actual situation and requirements, the energy storage, energy storage retention rate, etc. corresponding to the phase change cold storage material with the rated load of the vehicle are obtained, and according to the actual situation and requirements, the rated powers of the two refrigerating machines and the cold release machine in the phase change cold storage air conditioner, the maximum energy storage capacity of the phase change cold storage air conditioner, etc. are set. A microgrid group scheduling model is constructed by obtaining the vehicle parameter information and the parameter information of the phase change cold storage air conditioner, and a scheduling plan is obtained by solving the microgrid group scheduling model. For example, the scheduling plan may include whether the vehicle is on the road between the parks at time t, whether the vehicle is in the park at time t, the initial position of the vehicle and the position of the vehicle at the end of the scheduling; the operating powers of the two refrigerating machines and the cold release machine in the phase change cold storage air conditioner at time t, the energy storage of the phase change cold storage material at time t, the energy storage of the phase change cold storage material at the start of the scheduling and the energy storage of the phase change cold storage material at the end of the scheduling, etc. All these information can be obtained by solving the microgrid group scheduling model.

[0084] S103, according to the scheduling plan, use the phase change cold storage air conditioner to perform energy scheduling among multiple park microgrids.

[0085] In this embodiment, after obtaining the scheduling plan, according to the scheduling result of the scheduling plan, the vehicle loads and unloads the phase change cold storage material between the parks at different times, and at the same time, the phase change cold storage air conditioners in each park operate according to the scheduling result of the scheduling plan, releasing the energy storage or storing the produced cold energy in the phase change cold storage material to complete the energy scheduling.

[0086] For the above energy scheduling method, by obtaining the power data in multiple park microgrids, according to the power data and the microgrid group scheduling model, a scheduling plan is obtained, and according to the scheduling plan, the phase change cold storage air conditioner is used to perform energy scheduling among multiple park microgrids. Since the microgrid group scheduling model is constructed according to the parameter information of the vehicles in the park microgrids and the parameter information of the phase change cold storage air conditioner, therefore, the scheduling plan can be obtained by solving the microgrid group scheduling model, and through this scheduling plan, energy scheduling is realized among the park microgrids, which is equivalent to connecting the park microgrids to each other and realizing the energy flow between the park microgrids, overcoming the problems of energy waste and high cost caused by the independent scheduling of the park microgrids. The new energy consumption level is improved, and the operating economy of the power system is improved.

[0087] In one embodiment, the microgrid group scheduling model may include a spatial transfer model of the vehicle, an operating model of the phase change cold storage air conditioner, and an objective function. The embodiment of the present application also provides a specific implementation manner of the above "obtaining a scheduling plan according to the power data and the microgrid group scheduling model", and the following introduces this specific implementation manner:

[0088] Obtain a scheduling plan according to the power data, the spatial transfer model, the operating model, and the objective function;

[0089] Among them, the space transfer model is used to constrain the position of the vehicle in the microgrid park, the operation model is used to constrain the operation power of the phase change cold storage air conditioner, and the objective function is constructed based on the parameters related to cost when the phase change cold storage air conditioner is used for energy scheduling in the park microgrid.

[0090] In this embodiment, the space transfer model is constructed from vehicle parameter information. This space transfer model can constrain the position and behavior of the vehicle in the microgrid park. For example, this space transfer model can include constraints such as the uniqueness of the vehicle's spatial position, the time that must be spent for the vehicle to transfer between parks, the vehicle being on the road or having arrived at the park at the next moment during the vehicle's travel between parks, and the vehicle returning to the initial position after the scheduling is completed. The operation model is constructed from the parameters of the phase change cold storage air conditioner. The operation model can include constraints such as the operation power of the refrigerating machine of the phase change cold storage air conditioner being less than or equal to the rated power, the operation power of the cold release machine being less than or equal to the rated power, the balance of cold energy supply and demand, the energy storage of the phase change cold storage air conditioner at time t being related to the energy storage of the phase change cold storage air conditioner at the previous moment, the energy storage of the phase change material loaded and unloaded by the vehicle at time t, and the energy storage of the phase change material stored by the refrigerating machine 2 and released by the cold release machine, and the energy storage of the phase change cold storage material returning to the initial value at the end of the scheduling. The objective function can be the daily operation cost minimum function under the most adverse scenarios of new energy power generation and load demand. The objective function can include the total cost of power exchange between the park microgrid and the external power grid at time t, the operation cost of the vehicle transferring or loading and unloading phase change materials in the park, etc. In the embodiments of the present application, the parameters, functions, forms, etc. of the space transfer model, operation model, and objective function are not limited thereto.

[0091] In this embodiment, by obtaining power data, using the space transfer model to constrain the spatial position and behavior of the vehicle can make the spatial position of the vehicle more accurate. Using the operation model to constrain the operation power of the phase change cold storage air conditioner can make the operation of the phase change cold storage air conditioner safer. Using the microgrid group scheduling model solved by the objective function can minimize the cost of the parks participating in energy scheduling, so that the obtained scheduling plan is more perfect and more economical.

[0092] In one embodiment, a specific implementation manner of the above "obtaining a scheduling plan according to power data, a space transfer model, an operation model, and an objective function" is provided. The following introduces this specific implementation manner:

[0093] According to the power data, the first constraint condition, and the second constraint condition, the space transfer model, the operation model, and the objective function are solved to obtain a scheduling plan.

[0094] Among them, the first constraint condition is the correlation constraint condition between the vehicle and the on-vehicle phase change energy storage material. The first constraint condition may include that the vehicle can only load and unload the phase change material at the location of the park, the energy storage of the on-vehicle phase change energy storage material at time t is related to the energy storage of the on-vehicle phase change energy storage material at time t-1 and the energy storage of the phase change energy storage material at time t. The energy storage of the on-vehicle phase change energy storage material cannot be greater than the energy storage corresponding to the maximum on-vehicle capacity. At the end of the scheduling, the energy storage of the on-vehicle phase change material should return to the initial value, etc. The second constraint condition is the minimum cost constraint condition. The second constraint condition may include the park electricity cost constraint, the park microgrid electricity power balance constraint, the cost of the vehicle transferring and loading and unloading the phase change material, etc.

[0095] Optionally, a specific implementation manner of the "first constraint condition" is provided. A manifestation form of the "first constraint condition" may include relational expressions (1)-(5): The following introduces this specific implementation manner:

[0096] A ii,t ≤B ii,t (1)

[0097]

[0098]

[0099] 0≤E t ≤E max (4)

[0100] E T =E0 (5)

[0101] Among them, A ii,t represents the vehicle loading and unloading the phase change material in park i at the t-th hour; B ii,t represents whether the vehicle is in park i; represents the energy storage corresponding to the phase change material loaded or unloaded by the vehicle in park i at the t-th hour; H M represents the maximum energy storage of the phase change material that the vehicle can load or unload per hour; E t represents the energy storage of the phase change material on the vehicle; E max represents the energy storage of the phase change material rated to be carried by the vehicle; ζ air represents the energy storage retention rate; among them, ζ air =0.99.

[0102] Relations (1)-(2) indicate that the vehicle can only load and unload phase change materials at the location of the park; relation (3) indicates that the energy storage of the vehicle-mounted phase change cold storage material at time t is related to the energy storage of the vehicle-mounted phase change cold storage material at time t-1 and the energy storage of the phase change cold storage material loaded and unloaded at time t; relation (4) indicates that the energy storage of the vehicle-mounted phase change cold storage material cannot be greater than the energy storage corresponding to the maximum vehicle capacity; relation (5) indicates that the energy storage of the vehicle-mounted phase change cold storage material should return to the initial value at the end of the scheduling.

[0103] In this embodiment, by obtaining the power data, the spatial transfer model, the operation model, and the objective function are constrained using the first constraint condition and the second constraint condition, the microgrid group scheduling model is solved, and a scheduling plan is obtained. At the same time, using the first constraint condition to constrain the vehicle and the vehicle-mounted phase change material can make the transportation process of the vehicle and the change process of the vehicle-mounted phase change material more reasonable, and the solved microgrid group scheduling model is more accurate. Using the second constraint condition can minimize the cost and minimize the economic loss of the park under different scenarios of wind power prediction power, different photovoltaic prediction power, different electric load prediction power, and different cooling load prediction power in a cycle.

[0104] In one embodiment, a specific implementation manner of the above "spatial transfer model" is provided. A manifestation form of the "spatial transfer model" may include relations (6)-(9):

[0105]

[0106]

[0107] B ij,t+1 +B jj,t+1 ≥B ij,t (8)

[0108] B ii,T =B ii,0 (9)

[0109] Among them, B ij,t indicates whether the vehicle is on the road between park i and park j, B ii,t indicates whether the vehicle is in park i, B jj,t indicates whether the vehicle is in park j, and T ij indicates the driving time of the vehicle between park i and park j.

[0110] In this embodiment, relation (6) indicates the uniqueness of the vehicle's spatial position. Relation (7) indicates that the vehicle must spend T ijTime. Equation (8) indicates that a vehicle is traveling from park i to park j. It is on the road at the current moment and will either still be on the road or have arrived at park j at the next moment. Equation (9) indicates that the vehicle will return to its initial position at the end of the scheduling.

[0111] It should be noted that the above-mentioned "spatial transfer model" is only an example. The "spatial transfer model" can also be other forms of expression or a variation of the above-mentioned relationship, or include other constraints, and is not limited to the embodiments of the present application.

[0112] Furthermore, the travel time of vehicles between parks can be calculated in the following manner: the travel time of vehicles between parks is calculated based on congestion data, the length of roads between parks and the travel speed of vehicles.

[0113] For example, the congestion data indicates the congestion time of a vehicle traveling from park i to park j, the inter-park road length indicates the distance between parks i and j, and the vehicle driving speed indicates the speed at which the vehicle transports the phase change cold storage material between parks. In this embodiment, the driving time of the vehicle between parks can be calculated by equation (10):

[0114]

[0115] In the above formula, L ij represents the length of the road between parks, which is 15 km, v represents the vehicle speed, which is 20 km / h, ΔT ij represents the congestion time of vehicles from park i to park j, which is 15 minutes, T ij It represents the driving time of vehicles between parks. Through calculation, it is found that the driving time of vehicles between parks is 1 hour.

[0116] In one embodiment, a specific implementation of the “operation model” is provided. A representation of the “operation model” may include equations (11)-(18):

[0117]

[0118]

[0119]

[0120]

[0121]

[0122]

[0123]

[0124]

[0125] Among them, is the operating power of the phase change cool storage air conditioner at site i in the park for t hours; is the operating power of chiller 1 of the phase change cool storage air conditioner, is the rated power of chiller 1 of the phase change cool storage air conditioner; is the operating power of chiller 2 of the phase change cool storage air conditioner, is the rated power of chiller 2 of the phase change cool storage air conditioner; is the operating power of the cold release machine of the phase change cool storage air conditioner, is the rated power of the cold release machine of the phase change cool storage air conditioner; η c1 is the refrigeration efficiency of chiller 1, η d is the cold release efficiency of the cold release machine; η c2 is the refrigeration efficiency of chiller 2; is the energy storage of the phase change cool storage air conditioner at time t; is the maximum energy storage capacity of the phase change cool storage air conditioner; ξ air represents the energy storage retention rate, represents the energy storage corresponding to the phase change material loaded or unloaded by the vehicle at site i in the t-th hour; is the actual power of the cooling load at time t in park i, represents the energy storage of the phase change material at the end of the scheduling; represents the energy storage of the phase change material at the start of the scheduling.

[0126] In this example, the relational expression (11) indicates that the operating power of the phase change cool storage air conditioner at time t is the sum of the operating power of chiller 1, the operating power of chiller 2, and the operating power of the cold release machine. The relational expression (12) indicates that the operating power of chiller 1 should be greater than or equal to 0 and less than or equal to the rated power of chiller 1. For example, the rated power of chiller 1 is 300 kw. The relational expression (13) indicates that the operating power of chiller 2 should be greater than or equal to 0 and less than or equal to the rated power of chiller 2. For example, the rated power of chiller 2 is 600 kw. The relational expression (14) indicates that the operating power of the cold release machine should be greater than or equal to 0 and less than or equal to the rated power of the cold release machine. For example, the rated power of the cold release machine is 100 kw. The relational expression (15) indicates that the supply and demand of cold energy should be balanced. For example, η c1 = 4.2, η d = 42.5. The relational expression (16) indicates that the energy storage of the phase change cool storage air conditioner at time t is related to the energy storage of the phase change cool storage air conditioner at time t - 1, the energy storage of the phase change material loaded and unloaded by the vehicle at time t, and the energy storage of the phase change material stored by chiller 2 and released by the cold release machine. For example, ξ air = 0.99, η c2= 2.8. The relationship (17) indicates that the energy storage of the phase change cool storage air conditioner at time t should be greater than or equal to 0 and less than or equal to the maximum energy storage capacity of the phase change cool storage air conditioner. For example, the maximum energy storage capacity of the phase change cool storage air conditioner is 2000 kWh. The relationship (18) indicates that the energy storage of the phase change material should return to the initial value at the end of the scheduling.

[0127] In one embodiment, a specific implementation of the "objective function" is provided. One form of the "objective function" may include relationships (19)-(22):

[0128]

[0129]

[0130]

[0131]

[0132] Among them, s represents the scenarios of renewable energy power generation, electricity / cooling load, and S represents the set of scenarios of renewable energy power generation, electricity / cooling load; is the total cost of power exchange between the microgrid i in the park and the external power grid at time t; represents the operating cost of the vehicle transferring or loading and unloading the phase change material in the park i; represents the electricity purchase price of the microgrid in the park; represents the electricity selling price of the microgrid in the park; represents the power exchange between the microgrid i in the park and the external power grid; C in represents the cost of single loading and unloading of the phase change material, C out represents the cost of transporting the phase change material, A ii,t represents whether the vehicle is loading and unloading the phase change material in the park i at the t-th hour, B ii,t represents whether the vehicle is in the park i, represents the actual power of the wind power in the park i at time t, represents the actual power of the photovoltaic in the park i at time t, represents the actual power of the electricity load in the park i at time t, represents the operating power of the phase change cool storage air conditioner in the park i for t hours.

[0133] In this example, the relationship (19) represents the objective function, that is, the daily operating cost minimum function under the worst scenarios of new energy power generation and load demand. The relationship (20) represents the electricity cost constraint of the park i. For example, is 0.6 yuan / kWh from 0:00 to 7:00 and from 22:00 to 24:00, is 1.5 yuan / kWh from 7:00 to 10:00 and from 13:00 to 18:00, and the rest of the time It is 1.8 yuan / kWh. It can be obtained from the distribution network dispatching center. For example, It can be 0.2 yuan / kWh. The relation (21) represents the power balance constraint of the park microgrid. The relation (22) represents the cost of vehicle transferring phase change material and loading and unloading phase change material. For example, C in = 50 yuan / time, C out = 50 yuan / time.

[0134] Furthermore, the relation (19), i.e., the objective function, can be transformed into linearized relations (23)-(25) based on the dual transformation theory.

[0135]

[0136]

[0137]

[0138] Among them, They are continuous variables respectively; It is the total cost of power exchange between the park microgrid i and the external power grid at time t; It represents the operating cost of the vehicle transferring or loading and unloading phase change material in the park i; It represents the predicted wind power of the park i at hour t of the day before, It represents the predicted photovoltaic power of the park i at hour t of the day before, It represents the predicted electrical load power of the park i at hour t of the day before, It represents the predicted cooling load power of the park i at hour t of the day before; e WT It represents the maximum wind power prediction error, e PV It represents the maximum photovoltaic power prediction error, e EL It represents the maximum electrical load prediction error, e CL It represents the maximum cooling load prediction error. For example, e WT = 0.2, e PV = 0.2, e EL = 0.1, e CL = 0.1; It is the operating power of the phase change cooling storage air conditioner at hour t in the park i; It represents the power exchange between the park microgrid and the external power grid; η c1 It is the refrigeration efficiency of the refrigerator 1, η d It is the cooling release efficiency of the cooling release machine.

[0139] Transform the relation (20) into linearized relations (26)-(29)

[0140]

[0141]

[0142]

[0143]

[0144] Among them, represents the exchanged power between the park microgrid and the external power grid; represents the purchased power of the park microgrid; The sold power of the park microgrid; represents the power purchase behavior of the park microgrid; represents the power selling behavior of the park microgrid; represents the capacity of the connection line between Park i and the external power grid, with a value of 1900 kw.

[0145] Thus, the scheduling plan with the minimum daily operating cost under the most adverse scenarios of new energy generation and load demand is obtained. For example, the daily operating cost of three parks is 21,698 yuan, including 21,098 yuan for electricity consumption from the external power grid and 600 yuan for the cost of transporting / loading and unloading phase change cold storage materials. As Figure 5 shown, it represents a line graph of the vehicle transporting / loading and unloading phase change cold storage materials between different parks. As Figure 6 shown, it represents a line graph of the operating power of the phase change cold storage air conditioner in the park microgrid at different times. As Figure 7 shown, it represents the interaction power between the park microgrid and the external power grid at different times.

[0146] Combining all the above embodiments, the present application also provides an energy scheduling method. As Figure 8 shown, this method includes:

[0147] S201, obtaining power data in multiple park microgrids.

[0148] S202, solving the space transfer model, operation model, and objective function according to the power data, the first constraint condition, and the second constraint condition to obtain a scheduling plan.

[0149] S203, performing energy scheduling between multiple park microgrids using phase change cold storage air conditioners according to the scheduling plan.

[0150] The above steps are described in the foregoing description. For detailed content, please refer to the foregoing description, which will not be repeated here.

[0151] It should be understood that although Figure 2 - 8The steps in the flowchart are shown in sequence according to the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 2 - 8 At least a part of the steps may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0152] In one embodiment, as Figure 9 shown, an energy scheduling device is provided, including:

[0153] A first acquisition module 11, configured to acquire power data in multiple park microgrids.

[0154] A second acquisition module 12, configured to acquire a scheduling plan according to the power data and a microgrid group scheduling model; the microgrid group scheduling model is constructed according to the parameter information of vehicles in the park microgrid and the parameter information of phase change cool storage air conditioners.

[0155] A scheduling module 13, configured to perform energy scheduling among multiple park microgrids by using phase change cool storage air conditioners according to the scheduling plan.

[0156] In one embodiment, the above-mentioned second acquisition module 12 includes:

[0157] An acquisition unit 121, configured to acquire a scheduling plan according to the power data, a space transfer model, an operation model, and an objective function; the space transfer model is used to constrain the position of the vehicle in the microgrid park, the operation model is used to constrain the operation power of the phase change cool storage air conditioner, and the objective function is constructed according to the parameters related to the cost when performing energy scheduling by using the phase change cool storage air conditioner in the park microgrid.

[0158] In one embodiment, the above-mentioned second acquisition unit 121 includes:

[0159] An acquisition subunit 1211, configured to solve the space transfer model, the operation model, and the objective function according to the power data, a first constraint condition, and a second constraint condition to obtain a scheduling plan.

[0160] In one embodiment, the space transfer model includes:

[0161]

[0162]

[0163] Bij,t+1 +B jj,t+1 ≥B ij,t

[0164] B ii,T =B ii,0

[0165] Among them, B ij,t Indicates whether the vehicle is on the road between park i and park j, B ii,t Indicates whether the vehicle is in park i, B jj,t Indicates whether the vehicle is in park j, T ij Represents the driving time of a vehicle between park i and park j.

[0166] In one embodiment, Figure 10 As shown, the energy scheduling device also includes:

[0167] The third acquisition module 14 is used to calculate the driving time of the vehicle between the parks according to the congestion data, the length of the road between the parks and the driving speed of the vehicle.

[0168] In one embodiment, running the model includes:

[0169]

[0170]

[0171]

[0172]

[0173]

[0174]

[0175]

[0176]

[0177] in, is the operating power of the phase-change cold storage air conditioner at location i in the park for t hours; is the operating power of the refrigerator 1 of the phase change cold storage air conditioner, is the rated power of the refrigerator 1 of the phase change cold storage air conditioner; is the operating power of refrigerator 2 of the phase change cold storage air conditioner, is the rated power of the refrigerator 2 of the phase change cold storage air conditioner; is the operating power of the cooling machine of the phase change cold storage air conditioner, is the rated power of the cooling machine of the phase change cold storage air conditioner; η c1is the refrigeration efficiency of the refrigerator 1, η d is the heat release efficiency of the heat release machine; η c2 is the refrigeration efficiency of the refrigerator 2; is the energy storage of the phase change thermal energy storage air conditioner at time t; is the maximum energy storage capacity of the phase change thermal energy storage air conditioner; ξ air represents the energy storage retention rate, represents the energy storage corresponding to the phase change material loaded or unloaded by the vehicle in the park i at the t-th hour; is the actual power of the cooling load at time t in the park i, represents the energy storage of the phase change material at the end of the scheduling; represents the energy storage of the phase change material at the start of the scheduling.

[0178] In one embodiment, the objective function includes:

[0179]

[0180]

[0181]

[0182]

[0183] where s represents the scenarios of renewable energy power generation, electricity / cooling load, and S represents the set of scenarios of renewable energy power generation, electricity / cooling load; is the total cost of power exchange between the park microgrid i and the external power grid at time t; represents the operating cost of the vehicle transferring or loading and unloading phase change materials in the park i; represents the electricity purchase price of the park microgrid; represents the electricity selling price of the park microgrid; represents the power exchange between the park microgrid i and the external power grid; C in represents the cost of single loading and unloading of phase change materials, C out represents the cost of transporting phase change materials, A ii,t represents whether the vehicle loads and unloads phase change materials in the park i at the t-th hour, B ii,t represents whether the vehicle is in the park i, represents the actual power of wind power in the park i at time t, represents the actual power of photovoltaic in the park i at time t, represents the actual power of the electricity load in the park i at time t, represents the operating power of the phase change thermal energy storage air conditioner in the park i for t hours.

[0184] In one embodiment, the first constraint condition includes:

[0185] A ii,t ≤Bii,t

[0186]

[0187]

[0188] 0 ≤ E t ≤ E max

[0189] E T = E0

[0190] Wherein, A ii,t represents the vehicle loading and unloading phase change materials in park i at the t-th hour; B ii,t represents whether the vehicle is in park i; represents the energy storage corresponding to the phase change materials loaded or unloaded by the vehicle in park i at the t-th hour; H M represents the maximum energy storage corresponding to the phase change materials that the vehicle can load or unload per hour; E t represents the energy storage of the phase change materials on the vehicle; E max represents the energy storage of the phase change materials rated to be carried by the vehicle; ζ air represents the energy storage retention rate.

[0191] For the specific limitations of the energy scheduling device, reference can be made to the limitations on the energy scheduling method in the above text, which will not be elaborated here. Each module in the above energy scheduling device can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in the processor of the computer device in hardware form or be independent of it, or can be stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules.

[0192] In one embodiment, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 11As shown. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected via a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The communication interface of the computer device is used to communicate with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, carrier network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it realizes a method for quickly locating faults in the converter valve control system. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or buttons, trackballs, or touchpads provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0193] Those skilled in the art can understand that Figure 11 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0194] In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the following steps are realized:

[0195] Obtain power data in multiple park microgrids;

[0196] According to the power data and the microgrid cluster scheduling model, obtain a scheduling plan; the microgrid cluster scheduling model is constructed according to the parameter information of vehicles in the park microgrid and the parameter information of phase change cold storage air conditioners;

[0197] Perform energy scheduling among multiple park microgrids using phase change cold storage air conditioners according to the scheduling plan.

[0198] In one embodiment, when the processor executes the computer program, the following steps are also realized:

[0199] Obtain a scheduling plan according to the power data, the space transfer model, the operation model, and the objective function; the space transfer model is used to constrain the position of vehicles in the microgrid park, the operation model is to constrain the operating power of the phase change cold storage air conditioner, and the objective function is constructed according to the parameters related to cost when performing energy scheduling using phase change cold storage air conditioners in the park microgrid.

[0200] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0201] According to the power data, the first constraint and the second constraint, the spatial transfer model, the operation model and the objective function are solved to obtain the scheduling plan.

[0202] In one embodiment, when the processor executes the computer program, the following method is also implemented: the spatial transfer model includes:

[0203]

[0204]

[0205] B ij,t+1 +B jj,t+1 ≥B ij,t

[0206] B ii,T =B ii,0

[0207] Among them, B ij,t Indicates whether the vehicle is on the road between park i and park j, B ii,t Indicates whether the vehicle is in park i, B jj,t Indicates whether the vehicle is in park j, T ij Represents the driving time of a vehicle between park i and park j.

[0208] In one embodiment, when the processor executes the computer program, the processor further implements the following steps:

[0209] The travel time between parks is calculated based on congestion data, the length of roads between parks, and the vehicle travel speed.

[0210] In one embodiment, when the processor executes the computer program, the following method is also implemented: running the model includes:

[0211]

[0212]

[0213]

[0214]

[0215]

[0216]

[0217]

[0218]

[0219] Among them, is the operating power of the phase change cool storage air conditioner at the park i for t hours; is the operating power of the chiller 1 of the phase change cool storage air conditioner, is the rated power of the chiller 1 of the phase change cool storage air conditioner; is the operating power of the chiller 2 of the phase change cool storage air conditioner, is the rated power of the chiller 2 of the phase change cool storage air conditioner; is the operating power of the cold release machine of the phase change cool storage air conditioner, is the rated power of the cold release machine of the phase change cool storage air conditioner; η c1 is the refrigeration efficiency of the chiller 1, η d is the cold release efficiency of the cold release machine; η c2 is the refrigeration efficiency of the chiller 2; is the energy storage of the phase change cool storage air conditioner at time t; is the maximum energy storage capacity of the phase change cool storage air conditioner; ξ air represents the energy storage retention rate, represents the energy storage corresponding to the phase change material loaded or unloaded by the vehicle in the park i in the t-th hour; is the actual power of the cooling load at time t in the park i, represents the energy storage of the phase change material at the end of the scheduling; represents the energy storage of the phase change material at the start of the scheduling.

[0220] In one embodiment, when the processor executes the computer program, the following method is also implemented: The objective function includes:

[0221]

[0222]

[0223]

[0224]

[0225] Among them, s represents the scenarios of renewable energy power generation, electricity / cooling load, and S represents the set of scenarios of renewable energy power generation, electricity / cooling load; is the total cost of power exchange between the park microgrid i and the external power grid at time t; represents the operating cost of the vehicle transferring or loading and unloading the phase change material in the park i; represents the electricity purchase price of the park microgrid; represents the electricity selling price of the park microgrid; represents the power exchange between the park microgrid i and the external power grid; C in represents the cost of single loading and unloading of the phase change material, C outrepresents the cost of transporting phase change materials, A ii,t Indicates whether the vehicle loads or unloads phase change materials in park i at hour t, B ii,t Indicates whether the vehicle is in park i, represents the actual power of wind power in park i at time t, represents the actual power of photovoltaic power in park i at time t, represents the actual power of the electrical load in park i at time t, It represents the operating power of the phase change cold storage air conditioner in park i for t hours.

[0226] In one embodiment, when the processor executes the computer program, the following method is further implemented: the first constraint condition includes:

[0227] A ii,t ≤B ii,t

[0228]

[0229]

[0230] 0≤E t ≤E max

[0231] E T =E0

[0232] Among them, A ii,t Indicates that the vehicle loads and unloads phase change materials in park i at hour t; B ii,t Indicates whether the vehicle is in park i; H represents the energy storage corresponding to the phase change material loaded or unloaded by the vehicle in park i at hour t; M E represents the maximum energy storage of phase change material that can be loaded or unloaded per hour by the vehicle; t Represents the energy storage of phase change materials on vehicles; E max Represents the phase change material energy storage of the vehicle's rated load; air Represents the energy storage retention rate.

[0233] In one embodiment, a computer readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0234] Obtain power data from multiple campus microgrids;

[0235] Obtain a dispatching plan based on power data and a microgrid group dispatching model; the microgrid group dispatching model is constructed based on the parameter information of the vehicles in the park microgrid and the parameter information of the phase change cold storage air conditioner;

[0236] According to the dispatching plan, phase change cold storage air conditioners are used for energy dispatching between multiple park microgrids.

[0237] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0238] Obtain a scheduling plan according to power data, a space transfer model, an operation model, and an objective function; the space transfer model is used to constrain the position of the vehicle in the microgrid park, the operation model is to constrain the operating power of the phase change cooling air conditioner, and the objective function is constructed based on the cost-related parameters when the phase change cooling air conditioner is used for energy scheduling in the park microgrid.

[0239] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0240] Solve the space transfer model, the operation model, and the objective function according to the power data, the first constraint condition, and the second constraint condition to obtain a scheduling plan.

[0241] In one embodiment, when the processor executes the computer program, the following method is further implemented: The space transfer model includes:

[0242]

[0243]

[0244] B ij,t+1 +B jj,t+1 ≥B ij,t

[0245] B ii,T =B ii,0

[0246] Wherein, B ij,t represents whether the vehicle is on the road between park i and park j, B ii,t represents whether the vehicle is in park i, B jj,t represents whether the vehicle is in park j, and T ij represents the driving time of the vehicle between park i and park j.

[0247] In one embodiment, when the processor executes the computer program, the following steps are further implemented:

[0248] Calculate the driving time of the vehicle between parks according to the congestion data, the length of the road between parks, and the driving speed of the vehicle.

[0249] In one embodiment, when the processor executes the computer program, the following method is further implemented: The operation model includes:

[0250]

[0251]

[0252]

[0253]

[0254]

[0255]

[0256]

[0257]

[0258] Among them, is the operating power of the phase change cool storage air conditioner at site i of the park for t hours; is the operating power of chiller 1 of the phase change cool storage air conditioner, is the rated power of chiller 1 of the phase change cool storage air conditioner; is the operating power of chiller 2 of the phase change cool storage air conditioner, is the rated power of chiller 2 of the phase change cool storage air conditioner; is the operating power of the cold release machine of the phase change cool storage air conditioner, is the rated power of the cold release machine of the phase change cool storage air conditioner; η c1 is the refrigeration efficiency of chiller 1, η d is the cold release efficiency of the cold release machine; η c2 is the refrigeration efficiency of chiller 2; is the energy storage of the phase change cool storage air conditioner at time t; is the maximum energy storage capacity of the phase change cool storage air conditioner; ξ air represents the energy storage retention rate, represents the energy storage corresponding to the phase change material loaded or unloaded by the vehicle at site i in the t-th hour; is the actual power of the cold load at time t in site i of the park, represents the energy storage of the phase change material at the end of the scheduling; represents the energy storage of the phase change material at the start of the scheduling.

[0259] In one embodiment, when the processor executes the computer program, the following method is also implemented. The objective function includes:

[0260]

[0261]

[0262]

[0263]

[0264] Among them, s represents the scenario of renewable energy power generation and electricity / cooling load, and S represents the set of scenarios of renewable energy power generation and electricity / cooling load; is the total cost of power exchange between the park microgrid i and the external grid at time t; It represents the operating cost of vehicles transferring or loading and unloading phase change materials in park i; It indicates the electricity price purchased by the park microgrid; Indicates the electricity price of the park microgrid; represents the power exchanged between the park microgrid i and the external power grid; C in represents the cost of loading and unloading phase change materials once, C out represents the cost of transporting phase change materials, A ii,t Indicates whether the vehicle loads or unloads phase change materials in park i at hour t, B ii,t Indicates whether the vehicle is in park i, represents the actual power of wind power in park i at time t, represents the actual power of photovoltaic power in park i at time t, represents the actual power of the electrical load in park i at time t, It represents the operating power of the phase change cold storage air conditioner in park i for t hours.

[0265] In one embodiment, when the processor executes the computer program, the following method is further implemented: the first constraint condition includes:

[0266] A ii,t ≤B ii,t

[0267]

[0268]

[0269] 0≤E t ≤E max

[0270] E T =E0

[0271] Among them, A ii,t Indicates that the vehicle loads and unloads phase change materials in park i at hour t; B ii,t Indicates whether the vehicle is in park i; H represents the energy storage corresponding to the phase change material loaded or unloaded by the vehicle in park i at hour t; M E represents the maximum energy storage of phase change material that can be loaded or unloaded per hour by the vehicle; t Represents the energy storage of phase change materials on vehicles; E max Represents the phase change material energy storage of the vehicle's rated load; air Represents the energy storage retention rate.

[0272] A computer-readable storage medium provided by the above embodiments has a similar implementation principle and technical effect to the above method embodiments, and will not be elaborated here.

[0273] Those of ordinary skill in the art can understand that all or part of the processes in the above method embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above method embodiments. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0274] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0275] The above-described embodiments merely represent several implementation manners of the present application. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. An energy scheduling method, characterized in that, The method includes: Obtaining power data in multiple park microgrids; Obtaining a scheduling plan according to the power data and a microgrid group scheduling model; the microgrid group scheduling model is constructed according to parameter information of vehicles and parameter information of phase change cooling storage air conditioners in the park microgrid; the microgrid group scheduling model includes a spatial transfer model of the vehicle, an operation model of the phase change cooling storage air conditioner, an objective function, a first constraint condition between the vehicle and the phase change cooling storage air conditioner, and a second constraint condition of the lowest cost; Performing energy scheduling among multiple park microgrids according to the scheduling plan by using the phase change cooling storage air conditioner.

2. The method according to claim 1, wherein The obtaining of the scheduling plan according to the power data and the microgrid group scheduling model includes: Obtaining the scheduling plan according to the power data, the spatial transfer model, the operation model, and the objective function; the spatial transfer model is used to constrain the position of the vehicle in the microgrid park, the operation model is to constrain the operation power of the phase change cooling storage air conditioner, and the objective function is constructed according to parameters related to cost when performing energy scheduling by using the phase change cooling storage air conditioner in the park microgrid.

3. The method according to claim 2, characterized in that, The obtaining of the scheduling plan according to the power data, the spatial transfer model, the operation model, and the objective function includes: Performing solution calculation on the spatial transfer model, the operation model, and the objective function according to the power data, the first constraint condition, and the second constraint condition to obtain the scheduling plan.

4. The method according to claim 2 or 3, characterized in that, The spatial transfer model includes: B ij,t+1 +B jj,t+1 ≥B ij,t B ii,T = B ii,0 Among them, B ij,t Indicates whether the vehicle is on the road between park i and park j, B ii,t Indicates whether the vehicle is in park i, B jj,t Indicates whether the vehicle is in park j, T ij Represents the driving time of a vehicle between park i and park j.

5. The method according to claim 4, wherein The method further includes: Calculating the driving time of the vehicle between parks according to congestion data, the road length between parks, and the vehicle driving speed.

6. The method according to claim 2 or 3, characterized in that, The operation model includes: Among them, is the operating power of the phase change cool storage air conditioner at site i in the park for t hours; is the operating power of chiller 1 of the phase change cool storage air conditioner, is the rated power of chiller 1 of the phase change cool storage air conditioner; is the operating power of chiller 2 of the phase change cool storage air conditioner, is the rated power of chiller 2 of the phase change cool storage air conditioner; is the operating power of the cool release machine of the phase change cool storage air conditioner, is the rated power of the cool release machine of the phase change cool storage air conditioner; η c1 is the refrigeration efficiency of chiller 1, η d is the cool release efficiency of the cool release machine; η c2 is the refrigeration efficiency of chiller 2; is the energy storage of the phase change cool storage air conditioner at time t; is the maximum energy storage capacity of the phase change cool storage air conditioner; ξ air represents the energy storage retention rate, represents the energy storage corresponding to the phase change material loaded or unloaded by the vehicle at site i in the park in the t-th hour; is the actual power of the cooling load at time t in site i of the park, represents the energy storage of the phase change material at the end of the scheduling; represents the energy storage of the phase change material at the start of the scheduling.

7. The method according to claim 2 or 3, characterized in that, The objective function includes: Among them, s represents the scenarios of renewable energy power generation, electricity / cooling load, and S represents the set of scenarios of renewable energy power generation, electricity / cooling load; is the total cost of power exchange between the microgrid i in the park and the external power grid at time t; represents the operating cost of the vehicle transferring or loading and unloading phase change materials in the park i; represents the electricity purchase price of the microgrid in the park; represents the electricity selling price of the microgrid in the park; represents the power exchange between the microgrid i in the park and the external power grid; C in represents the cost of single loading and unloading of phase change materials, C out represents the cost of transporting phase change materials, A ii,t represents whether the vehicle loads and unloads phase change materials in the park i at the t-th hour, B ii,t represents whether the vehicle is in the park i, represents the actual power of wind power in the park i at time t, represents the actual power of photovoltaic in the park i at time t, represents the actual power of the electricity load in the park i at time t, represents the operating power of the phase change cool storage air conditioner in the park i for t hours.

8. The method according to claim 3, wherein The first constraint condition includes: A ii,t ≤B ii,t 0 ≤ E t ≤ E max E T = E0 Among them, A ii,t represents the phase change material loaded and unloaded by the vehicle in park i at the t-th hour; B ii,t represents whether the vehicle is in park i; represents the energy storage corresponding to the phase change material loaded or unloaded by the vehicle in park i at the t-th hour; H M represents the energy storage corresponding to the maximum amount of phase change material that the vehicle can load or unload per hour; E t represents the energy storage of the phase change material on the vehicle; E max represents the energy storage of the phase change material rated to be carried by the vehicle; ζ air represents the energy storage retention rate.

9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 8.

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