Electric equipment cluster multi-target scheduling optimization method and device, terminal and medium

Abstract

The invention discloses an electric equipment cluster multi-target scheduling optimization method and device, a terminal and a medium, and the technical scheme provided by the invention comprises the steps: firstly, determining a carbon emission factor of each node in a target power grid according to the topological structure information of the target power grid and a power grid carbon emission flow transmission principle, and then, determining the carbon emission factor of each node in the target power grid; the method comprises the following steps: acquiring power utilization parameters and use environment parameters of a power utilization equipment cluster, constructing an objective function of a Pareto scheduling optimization model comprising an environment comfort deviation sub-function and a power utilization cost sub-function, and related scheduling constraint conditions, and resolving the Pareto scheduling optimization model to obtain a power utilization parameter of the power utilization equipment cluster. According to the method, the multi-target scheduling data considering the power utilization comfort level of the user and the carbon emission converted power utilization cost is obtained, so that a worker can execute scheduling optimization on the power utilization equipment cluster based on the indication of the multi-target scheduling data, the scheduling effect can better accord with the actual power utilization condition, and the accuracy of power scheduling is further improved.

Description

Technical Field

[0001] The present application relates to the technical field of electric power carbon emission scheduling, and in particular to a multi-objective scheduling optimization method, device, terminal and medium for a cluster of electric equipment. Background Art

[0002] The power industry is one of the industries with the largest carbon emissions. According to statistics, the amount of carbon produced by the power industry accounts for about 40% of the total CO2 emissions of the energy industry. For the power industry, low-carbon power generation is an urgent goal that needs reform; while reducing carbon emissions and adjusting electricity consumption plans, it is also necessary to satisfy electricity consumers to obtain the greatest benefits as possible, which poses new challenges to the resource allocation of smart grids and the consideration of economic and environmental benefits.

[0003] At present, smart grids mostly use the average carbon emission factor method to optimize the power grid under the electricity and carbon trading market. In terms of optimization and coordination between the power generation side and the demand side, the method reduces power usage and reduces carbon emissions from the aspects of scheduling power consumption plans and controlling power consumption. This method can be used as one of the important reference indicators to measure the carbon content per kilowatt-hour of electricity used by power consumers to a large extent. However, this method uses the prescribed carbon emission index as the main scheduling target and ignores the consideration of the power consumers' comfort level. In actual situations, power consumers may adopt power consumption behaviors that are contrary to the goal of reducing carbon emissions due to their own comfort needs, resulting in the existing power scheduling method failing to match the actual power consumption situation and having technical problems of low accuracy. Summary of the invention

[0004] The present application provides a multi-objective scheduling optimization method, device, terminal and medium for a cluster of power-consuming equipment, which are used to solve the technical problem that the existing power scheduling method fails to match the actual power consumption situation and has low accuracy.

[0005] In order to solve the above technical problems, the first aspect of the present application provides a multi-objective scheduling optimization method for a power equipment cluster, comprising:

[0006] Determine the carbon emission factor of each node in the target power grid according to the topological structure information of the target power grid and the transmission principle of the carbon emission flow of the power grid;

[0007] Obtaining power consumption parameters and usage environment parameters of a preset power consumption equipment cluster in the target power grid, and constructing an objective function and scheduling constraint conditions of a Pareto scheduling optimization model corresponding to the power consumption equipment cluster, wherein the objective function includes: an environmental comfort deviation sub-function constructed based on the usage environment parameters, and an power consumption cost sub-function constructed based on the power consumption parameters and the carbon emission factor;

[0008] The Pareto scheduling optimization model is solved by a preset heuristic optimization algorithm, so as to perform scheduling optimization on the power-consuming equipment cluster according to the solution result.

[0009] Optionally, the electrical equipment cluster includes: an air conditioning cluster.

[0010] Optionally, when the electrical equipment cluster is an air conditioning cluster, the objective function is specifically:

[0011]

[0012] In the formula, in the formula, is the environmental comfort deviation subfunction; is the electricity cost sub-function; N is the number of air-conditioning clusters; is the indoor temperature demand weight of the room where the nth air conditioner is located in the tth period; and are the average indoor temperature and the preset indoor temperature of the room where the nth air conditioner is located in the tth period respectively; is the switch status of the nth air conditioner; It represents the duration of the nth air conditioner being turned on in the tth time period; is the rated power of the nth air conditioner load; s represents the number of time periods; is the electricity carbon emission factor of the nth user in the tth period; are the carbon emission prices in the tth time period respectively.

[0013] Optionally, when the electric equipment cluster is an air conditioning cluster, the scheduling constraint condition is specifically:

[0014] .

[0015] Optionally, the heuristic optimization algorithm is specifically a raccoon optimization algorithm.

[0016] Optionally, solving the Pareto scheduling optimization model by a preset heuristic optimization algorithm to perform scheduling optimization on the power-consuming equipment cluster according to the solving result specifically includes:

[0017] Iterate and optimize the Pareto scheduling optimization model through the raccoon optimization algorithm, record the optimal solution obtained in each round of iteration, and when the number of iterations reaches a preset maximum number of iterations, output an optimal solution set containing each of the optimal solutions;

[0018] According to the optimal solution set, an optimal compromise solution is determined through a grey target strategy, so as to perform scheduling optimization on the power-consuming device cluster according to optimization parameters corresponding to the optimal compromise solution.

[0019] Optionally, the calculation formula of the carbon emission factor is:

[0020]

[0021] In the formula, and denote the carbon emission factors of the i-th and j-th load nodes respectively; represents the active power output of the power plant connected to the i-th load node; is the carbon emission factor of electricity from the connected power plant; represents the active power injected from the jth node to the ith node branch; Represents the set of branch connected nodes of the i-th node.

[0022] At the same time, the second aspect of the present application provides a multi-objective scheduling optimization device for a power consumption equipment cluster, comprising:

[0023] A node carbon emission factor determination unit, used to determine the carbon emission factor of each node in the target power grid according to the topological structure information of the target power grid and in combination with the transmission principle of the power grid carbon emission flow;

[0024] A multi-objective optimization model construction unit, used to obtain the power consumption parameters and usage environment parameters of the power consumption equipment cluster preset in the target power grid, and construct the objective function and scheduling constraint conditions of the Pareto scheduling optimization model corresponding to the power consumption equipment cluster, wherein the objective function includes: an environmental comfort deviation sub-function constructed based on the usage environment parameters, and an electricity cost sub-function constructed based on the power consumption parameters and the carbon emission factor;

[0025] The multi-objective optimization model solving unit is used to solve the Pareto scheduling optimization model through a preset heuristic optimization algorithm, so as to perform scheduling optimization for the power-consuming equipment cluster according to the solving result.

[0026] A third aspect of the present application provides a multi-objective scheduling optimization terminal for a power-consuming device cluster, comprising: a memory and a processor;

[0027] The memory is used to store program codes corresponding to a multi-objective scheduling optimization method for a cluster of electric equipment provided in the first aspect of the present application;

[0028] The processor is configured to execute the program code.

[0029] The fourth aspect of the present application provides a computer-readable storage medium, in which a program code corresponding to a multi-objective scheduling optimization method for a cluster of power consumption equipment as provided in the first aspect of the present application is stored.

[0030] It can be seen from the above technical solutions that this application has the following advantages:

[0031] The technical solution provided in the present application first determines the carbon emission factor of each node in the target power grid according to the topological structure information of the target power grid and the transmission principle of the power grid carbon emission flow, and then obtains the power consumption parameters and usage environment parameters of the power equipment cluster, and constructs the objective function of the Pareto scheduling optimization model including the environmental comfort deviation sub-function and the power cost sub-function, as well as the relevant scheduling constraints, and then solves the Pareto scheduling optimization model to obtain multi-objective scheduling data that takes into account the user's power comfort and carbon emission converted power cost, so that the staff can perform scheduling optimization of the power equipment cluster based on the instructions of the multi-objective scheduling data, so that the scheduling effect can be more in line with the actual power consumption situation, further improving the accuracy of power scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0033] Figure 1 A flowchart of an embodiment of a multi-objective scheduling optimization method for a cluster of power-consuming equipment provided in this application.

[0034] Figure 2 Schematic diagram of carbon emission flow transmission in power grid.

[0035] Figure 3 Schematic diagram of the flow of the optimization algorithm for Raccoon.

[0036] Figure 4 A structural schematic diagram of an embodiment of a multi-objective scheduling optimization device for a cluster of power-consuming equipment provided in this application.

[0037] Figure 5 A schematic diagram of the structure of an embodiment of a multi-objective scheduling optimization terminal for a cluster of power-consuming equipment provided in this application. DETAILED DESCRIPTION

[0038] The embodiments of the present application provide a multi-objective scheduling optimization method, device, terminal and medium for a cluster of power-consuming equipment, which are used to solve the technical problem that the existing power scheduling method fails to match the actual power consumption situation and has low accuracy.

[0039] In order to make the purpose, features, and advantages of the invention of this application more obvious and easy to understand, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the embodiments described below are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0040] First, a detailed description of an embodiment of a multi-objective scheduling optimization method for a power consumption equipment cluster provided by the present application is as follows:

[0041] See also Figure 1 The present application provides an embodiment of a multi-objective scheduling optimization method for a power consumption equipment cluster, including:

[0042] Step 101: Determine the carbon emission factor of each node in the target power grid according to the topological structure information of the target power grid and the transmission principle of the power grid carbon emission flow.

[0043] It should be noted that according to the transmission principle of carbon emission flow in power grid, the schematic diagram is as follows: Figure 2 As shown, for each node, according to the proportional sharing principle, considering the active power flowing into and out of each node, the carbon emission factor of the power injected into the node is defined as the power carbon emission factor of the branch power flowing out of the node and the load connected to the node. The specific calculation formula can be found in the following example:

[0044] (1)

[0045] In the formula, and denote the carbon emission factors of the i-th and j-th load nodes respectively; represents the active power output of the power plant connected to the i-th load node; is the carbon emission factor of electricity from the connected power plant; represents the active power injected from the jth node to the ith node branch; Represents the set of branch connected nodes of the i-th node.

[0046] Based on the above-mentioned transmission principle of carbon emission flow in the power grid, combined with the topological structure information of the target power grid that needs to be dispatched, the carbon emission factors of each node in the target power grid are calculated respectively, so that the carbon emission factors of the power grid nodes are dynamically adjusted according to the input and output of each node, so that the adjustment of the carbon emission factor data of the target power grid can better fit the actual power grid production changes, and help to improve the optimization effect of subsequent steps. At the same time, according to the dynamic carbon emission factor, it is convenient for power users to perceive the differences in time and space of carbon emission factors in real time and space, so as to change their own electricity consumption behavior and reduce carbon emissions; users can also realize real-time calculation of carbon emission factors based on carbon emission data of previous time, fully consider the carbon emission sharing responsibility in the process of power production, transportation and distribution, and fairly consider the carbon emission sharing responsibility. This can help users clarify the more accurate carbon emission sharing responsibility they bear in the process of electricity use, and can serve as the basis for users to achieve the day-ahead optimization scheduling of electricity use.

[0047] Step 102: Obtain power consumption parameters and usage environment parameters of a preset power consumption equipment cluster in the target power grid, and construct an objective function and scheduling constraint conditions of a Pareto scheduling optimization model corresponding to the power consumption equipment cluster.

[0048] Among them, the objective function includes: an environmental comfort deviation sub-function constructed based on the use of environmental parameters, and an electricity cost sub-function constructed based on electricity parameters and carbon emission factors.

[0049] It should be noted that based on the carbon emission factor determined in step 101, the power consumption parameters and use environment parameters of the preset power equipment cluster in the target power grid are obtained, and the economic cost of the user is fully considered on the basis of being able to meet the switch state, switch duration, and number constraints of the power equipment cluster. At the same time, the user's comfort is maximized and the carbon emissions are reduced. In step 102 of this embodiment, the power consumption parameters and use environment parameters of the power equipment cluster are obtained, wherein the power consumption parameters include but are not limited to the electrical quantity, state quantity, power parameters, etc. of each power equipment in the cluster during operation, which are used to characterize the operating state and operation consumption of the power equipment, and the use environment parameters include but are not limited to the measured values, standard values, etc. of the operating environment of the power equipment, such as temperature values, humidity values, etc., which are used to characterize the comfort deviation of the user when using the power equipment.

[0050] More specifically, the power consumption cluster mentioned in this embodiment includes: an air conditioning cluster;

[0051] Taking the air conditioning cluster as an example, the Pareto scheduling optimization model constructed based on step 102 in this embodiment is specifically as follows:

[0052] Objective function:

[0053] (2)

[0054] In the formula, is the environmental comfort deviation subfunction; is the electricity cost sub-function; N is the number of air-conditioning clusters; is the indoor temperature demand weight of the room where the nth air conditioner is located in the tth period; and are the average indoor temperature and the preset indoor temperature of the room where the nth air conditioner is located in the tth period respectively; is the switch status of the nth air conditioner; It represents the duration of the nth air conditioner being turned on in the tth time period; is the rated power of the nth air conditioner load; s represents the number of time periods; is the electricity carbon emission factor of the nth user in the tth period; are the carbon emission prices in the tth time period respectively.

[0055] Scheduling constraints:

[0056] (3)

[0057] In the formula, is the switch status of the nth air conditioner. Generally, It represents that the air conditioner is on, otherwise it is off. s is the time period, which specifically represents the number of hours in a day.

[0058] Step 103: Solve the Pareto scheduling optimization model using a preset heuristic optimization algorithm to perform scheduling optimization on the power-consuming equipment cluster according to the solution result.

[0059] It should be noted that, based on the Pareto scheduling optimization model constructed in step 102, the Pareto scheduling optimization model is implemented with a preset heuristic optimization algorithm to perform population initialization, population optimization and other solution steps, and output is made according to the solution results, so that the staff can perform scheduling optimization of the power equipment cluster based on the instructions of the multi-objective scheduling data. Among them, for the optimal scheduling with user comfort and reduction of carbon emission costs as the two main objectives, the raccoon optimization algorithm is preferably used in this embodiment.

[0060] It can be understood that the Raccoon Optimization Algorithm can be divided into two phases, namely the exploration phase and the exploitation phase.

[0061] like Figure 3 As shown, the first step is initialization, randomly initializing the population in the optimization space:

[0062] (4)

[0063] in, The individual represents the j-dimensional variable value of the i-th individual under t iterations, representing a solution of a certain group. To find the optimal lower bound, To find the upper bound, is a random number between [0,1]. t is the current iteration number, and j is the dimension.

[0064] Then comes the exploration phase, which simulates the process of a raccoon preying on an iguana:

[0065] At this stage, the raccoon population update is modeled according to their strategy of attacking iguanas. The general process is: some raccoons climb trees to drive away iguanas, while the remaining raccoons wait under the trees to be killed. Therefore, in this process, it is shown that the raccoon optimization algorithm can move within the search space, which means that the algorithm has a strong global search capability for the problem.

[0066] In the process of algorithm design, the best individual position in the current population Assuming the position of the iguana, assuming that the raccoon climbs the tree to drive away the raccoon and waits under the tree for half of the time during the hunting process, we can simulate the position of the raccoon on the tree through the following formula:

[0067] (5)

[0068] When the iguana is driven to the ground, it means that it can move randomly in the search space. Formula (6) can mathematically represent this behavior; therefore, the raccoons in the other half of the number will also be able to move randomly in the search space. To update the position, we use formula (6) and formula (7)

[0069] (6)

[0070] (7)

[0071] Where N is the size of the raccoon population, represents the fitness function, r represents a random value between 0 and 1, and I represents a random integer value. represents the new random position where the iguana falls to the ground at iteration t.

[0072] If the raccoon changes the objective function value at the new position, it means that the raccoon’s change to the new position is more accurate and closer to the random position when the iguana fell to the ground. If the raccoon’s objective function value does not change at the new position, it means that the raccoon’s change to the new position is biased. At this time, formula (8) needs to be used to perform a greedy selection.

[0073] (8)

[0074] Next comes the development phase, which is the simulation of a raccoon escaping from a predator:

[0075] This stage is mathematically modeled based on the raccoon's behavior of escaping from predators and updating the raccoon's position in the search space. When raccoons are avoiding their natural enemies, they will search for a safe location near their own location according to their strategy. This strategy can demonstrate its local search ability in the raccoon optimization algorithm. In this algorithm, it can be expressed as each individual in the raccoon population generates a random location near its location. The relevant mathematical expressions are formulas (9) and (10).

[0076] (9)

[0077] (10)

[0078] Where r represents a random value between 0 and 1, and T is the maximum number of iterations. are the upper and lower bounds that change with the number of iterations. As in the exploration phase, if the raccoon changes the objective function value at the new position, it means that the raccoon is more accurate in changing to the new position. If the objective function value does not change at the new position, it means that there is a deviation in the raccoon's change to the new position. At this time, formula (8) needs to be used to perform a greedy selection.

[0079] The above-mentioned optimization algorithm is used to find an optimal solution that can balance the dual indicators of user comfort index and carbon emission cost index in the air-conditioning cluster. Through continuous iteration, an optimal solution set is finally found under the premise of setting the population size and number of iterations. In addition to the raccoon optimization algorithm preferred in this embodiment, in some embodiments, other heuristic optimization algorithms can also be selected, such as fish school optimization algorithm, whale optimization algorithm, etc., which can also achieve a solution optimization effect close to that of using the raccoon optimization algorithm.

[0080] In some embodiments, after the optimal solution set is output by the raccoon optimization algorithm, the gray target decision is used to select the best compromise solution again. The obtained compromise solution can be used as the best decision for the best balanced dual index found in this embodiment, so that the staff can perform scheduling optimization for the power equipment cluster based on the instructions of the multi-objective scheduling data, so that the scheduling effect can be more in line with the actual power consumption situation, further improving the accuracy of power scheduling.

[0081] This embodiment uses the raccoon optimization algorithm to apply to the Pareto day-ahead optimal scheduling method of the air conditioning cluster with multi-objective electricity demand of users. The algorithm makes the optimization process more efficient by imitating the two stages of the raccoon preying on iguanas and the raccoon escaping from predators. Its exploration ability and balancing ability are well presented in multi-objective optimization scheduling. It can play a strong role in the optimization process, and the objective function can obtain the optimal solution with good performance faster. By using the algorithm to output the optimal solution set, and then combining the gray target decision to select the best compromise solution from the optimal solution set, it has good decision-making and balancing capabilities in the dual-objective optimization scenario of reducing carbon emissions and improving user comfort generated by users using the air conditioning cluster in this embodiment.

[0082] The above is a detailed description of an embodiment of a multi-objective scheduling optimization method for a power equipment cluster provided in the present application. The following is a detailed description of an embodiment of a multi-objective scheduling optimization device for a power equipment cluster provided in the present application.

[0083] See also Figure 4 The present application provides an embodiment of a multi-objective scheduling optimization device for a power consumption equipment cluster, comprising:

[0084] The node carbon emission factor determination unit 201 is used to determine the carbon emission factor of each node in the target power grid according to the topological structure information of the target power grid and the transmission principle of the power grid carbon emission flow;

[0085] The multi-objective optimization model building unit 202 is used to obtain the power consumption parameters and the use environment parameters of the power consumption equipment cluster preset in the target power grid, and build the objective function and dispatch constraint conditions of the Pareto dispatch optimization model corresponding to the power consumption equipment cluster, wherein the objective function includes: an environmental comfort deviation sub-function built based on the use environment parameters, and an electricity cost sub-function built based on the power consumption parameters and the carbon emission factor;

[0086] The multi-objective optimization model solving unit 203 is used to solve the Pareto scheduling optimization model by using a preset heuristic optimization algorithm, so as to perform scheduling optimization on the power equipment cluster according to the solving result.

[0087] More specifically, the electrical equipment cluster includes: an air conditioning cluster.

[0088] More specifically, when the power consumption equipment cluster is an air conditioning cluster, the objective function is:

[0089]

[0090] In the formula, in the formula, is the environmental comfort deviation subfunction; is the electricity cost sub-function; N is the number of air-conditioning clusters; is the indoor temperature demand weight of the room where the nth air conditioner is located in the tth period; and are the average indoor temperature and the preset indoor temperature of the room where the nth air conditioner is located in the tth period respectively; is the switch status of the nth air conditioner; It represents the duration of the nth air conditioner being turned on in the tth time period; is the rated power of the nth air conditioner load; s represents the number of time periods; is the electricity carbon emission factor of the nth user in the tth period; are the carbon emission prices in the tth time period respectively.

[0091] More specifically, when the power consumption equipment cluster is an air conditioning cluster, the scheduling constraint conditions are as follows:

[0092] .

[0093] More specifically, the heuristic optimization algorithm is a raccoon optimization algorithm.

[0094] More specifically, the Pareto scheduling optimization model is solved by a preset heuristic optimization algorithm, and the scheduling optimization of the power equipment cluster is performed according to the solution result, which specifically includes:

[0095] The Pareto scheduling optimization model is iterated and optimized through the Raccoon optimization algorithm, and the optimal solution obtained in each round of iteration is recorded. When the number of iterations reaches the preset maximum number of iterations, the optimal solution set containing each optimal solution is output;

[0096] According to the optimal solution set, the best compromise solution is determined through the grey target strategy, so as to perform scheduling optimization of the power equipment cluster according to the optimization parameters corresponding to the best compromise solution.

[0097] More specifically, the carbon emission factor is calculated as:

[0098]

[0099] In the formula, and denote the carbon emission factors of the i-th and j-th load nodes respectively; represents the active power output of the power plant connected to the i-th load node; is the carbon emission factor of electricity from the connected power plant; represents the active power injected from the jth node to the ith node branch; Represents the set of branch connected nodes of the i-th node.

[0100] In addition to the above-mentioned embodiment of the multi-objective scheduling optimization device for a cluster of electric equipment, the present application also provides a detailed description of embodiments of a multi-objective scheduling optimization terminal for a cluster of electric equipment and a computer-readable storage medium, wherein:

[0101] like Figure 5 As shown, the present application provides an embodiment of a multi-objective scheduling optimization terminal for a power-consuming device cluster, the terminal types include but are not limited to: personal computers, industrial computers, servers and embedded intelligent devices, the main components of the terminal include: a memory 33 and a processor 31, the memory 33 and the processor 31 can be connected via a communication bus 34;

[0102] The memory 33 is used to store the program code corresponding to the multi-objective scheduling optimization method of the power equipment cluster provided in the above embodiment;

[0103] The processor 31 is used to read and execute the program code in the memory to implement the multi-objective scheduling optimization method for the power-consuming equipment cluster.

[0104] A fourth aspect of the present application provides a computer-readable storage medium, in which a program code corresponding to a multi-objective scheduling optimization method for a cluster of electric equipment as provided in the above-mentioned embodiment is stored.

[0105] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the terminals, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0106] In the several embodiments provided in the present application, it should be understood that the disclosed terminals, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0107] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described herein can, for example, be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0108] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0109] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0110] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0111] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0112] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application 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. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A multi-objective scheduling optimization method for a power equipment cluster, characterized in that: include: Determine the carbon emission factor of each node in the target power grid according to the topological structure information of the target power grid and the transmission principle of the carbon emission flow of the power grid; Obtaining power consumption parameters and usage environment parameters of a preset power consumption equipment cluster in the target power grid, and constructing an objective function and scheduling constraint conditions of a Pareto scheduling optimization model corresponding to the power consumption equipment cluster, wherein the objective function includes: an environmental comfort deviation sub-function constructed based on the usage environment parameters, and an power consumption cost sub-function constructed based on the power consumption parameters and the carbon emission factor; The Pareto scheduling optimization model is solved by a preset heuristic optimization algorithm, so as to perform scheduling optimization on the power-consuming equipment cluster according to the solution result.

2. The multi-objective scheduling optimization method for a power equipment cluster according to claim 1 is characterized in that: The electrical equipment cluster includes: an air conditioning cluster.

3. The multi-objective scheduling optimization method for a power equipment cluster according to claim 2 is characterized in that: When the electrical equipment cluster is the air conditioning cluster, the objective function is: In the formula, is the environmental comfort deviation sub-function; is the electricity cost sub-function; N is the number of air-conditioning clusters; is the indoor temperature demand weight of the room where the nth air conditioner is located in the tth period; and are the average indoor temperature and the preset indoor temperature of the room where the nth air conditioner is located in the tth period respectively; is the switch status of the nth air conditioner; It represents the duration of the nth air conditioner being turned on in the tth time period; is the rated power of the nth air conditioner load; s represents the number of time periods; is the electricity carbon emission factor of the nth user in the tth period; are the carbon emission prices in the tth time period respectively.

4. The multi-objective scheduling optimization method for a power equipment cluster according to claim 3 is characterized in that: When the power consumption equipment cluster is the air conditioning cluster, the scheduling constraint condition is: 。 5. The multi-objective scheduling optimization method for a power equipment cluster according to claim 1 is characterized in that: The heuristic optimization algorithm is the raccoon optimization algorithm.

6. The multi-objective scheduling optimization method for a power equipment cluster according to claim 5 is characterized in that: The method of solving the Pareto scheduling optimization model by a preset heuristic optimization algorithm to perform scheduling optimization on the power-consuming equipment cluster according to the solution result includes: Iterate and optimize the Pareto scheduling optimization model through the raccoon optimization algorithm, record the optimal solution obtained in each round of iteration, and when the number of iterations reaches a preset maximum number of iterations, output an optimal solution set containing each of the optimal solutions; According to the optimal solution set, an optimal compromise solution is determined through a grey target strategy, so as to perform scheduling optimization on the power-consuming device cluster according to optimization parameters corresponding to the optimal compromise solution.

7. A multi-objective scheduling optimization method for a power equipment cluster according to any one of claims 1 to 6, characterized in that: The calculation formula of the carbon emission factor is: In the formula, and denote the carbon emission factors of the i-th and j-th load nodes respectively; represents the active power output of the power plant connected to the i-th load node; is the carbon emission factor of electricity from the connected power plant; represents the active power injected from the jth node to the ith node branch; Represents the set of branch connected nodes of the i-th node.

8. A multi-objective scheduling optimization device for a power equipment cluster, characterized in that: include: A node carbon emission factor determination unit, used to determine the carbon emission factor of each node in the target power grid according to the topological structure information of the target power grid and in combination with the transmission principle of the power grid carbon emission flow; A multi-objective optimization model construction unit, used to obtain the power consumption parameters and usage environment parameters of the power consumption equipment cluster preset in the target power grid, and construct the objective function and scheduling constraint conditions of the Pareto scheduling optimization model corresponding to the power consumption equipment cluster, wherein the objective function includes: an environmental comfort deviation sub-function constructed based on the usage environment parameters, and an electricity cost sub-function constructed based on the power consumption parameters and the carbon emission factor; The multi-objective optimization model solving unit is used to solve the Pareto scheduling optimization model through a preset heuristic optimization algorithm, so as to perform scheduling optimization for the power-consuming equipment cluster according to the solving result.

9. A multi-objective scheduling optimization terminal for a power equipment cluster, characterized in that: include: Memory and processor; The memory is used to store a program code corresponding to a multi-objective scheduling optimization method for a cluster of electric equipment as claimed in any one of claims 1 to 7; The processor is configured to execute the program code.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program code corresponding to the multi-objective scheduling optimization method for a cluster of electric equipment as described in any one of claims 1 to 7.

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