Load resource scheduling method and related devices

Abstract

This application provides a load resource scheduling method and related devices. The method includes: obtaining load resource parameters; constructing an objective function based on the total operating cost, comprehensive voltage deviation, and local consumption rate; the constraint conditions of the objective function include: peak-valley fluctuation constraint, linear power flow constraint, and load response quantity balance constraint; based on the load resource parameters, combining with the Chebyshev chaotic mapping algorithm, using the improved ant lion algorithm to solve the objective function to obtain a scheduling plan; performing scheduling processing on the load resources based on the scheduling plan. In the embodiments of this application, an effective scheduling strategy is formed by constructing an objective function. The objective function is restricted by multiple constraint conditions, which ensures the feasibility and rationality of the scheduling plan. By optimizing and solving the objective function, the scheduling cost of the system can be effectively reduced, and at the same time, the utilization efficiency of electric energy can be improved. Further, resource scheduling processing can be better carried out, and the stability and economy of the power grid can be enhanced.

Description

Technical Field

[0001] This application relates to the technical field of load resource scheduling, and in particular, to a load resource scheduling method and related devices. Background Art

[0002] The regional energy system can aggregate various different consumer loads, benefit from economies of scale, and improve energy use efficiency. However, with the grid connection of distributed energy, the volatility and uncertainty of its power generation bring new challenges to the stable operation of the power grid; traditional scheduling methods are difficult to meet the needs of power grid operation. In the existing scheduling algorithms, the price fluctuations and policy changes in the power market may affect the implementation of the scheduling plan. If the market price fluctuates greatly or the policy changes, it may cause the data center to be unable to purchase or sell electricity according to the original plan, increasing the operating cost and risk. Summary of the Invention

[0003] In view of this, the purpose of this application is to propose a load resource scheduling method, including:

[0004] Obtain load resource parameters;

[0005] Construct an objective function based on the total operating cost, comprehensive voltage deviation, and local consumption rate; the constraint conditions of the objective function include: peak-valley fluctuation constraint, linear power flow constraint, and load response quantity balance constraint;

[0006] Based on the load resource parameters, combined with the Chebyshev chaos mapping algorithm, use the improved ant lion algorithm to solve the objective function to obtain a scheduling plan;

[0007] Schedule the load resources based on the scheduling plan.

[0008] In a possible implementation manner, the objective function is represented by the following formula:

[0009]

[0010]

[0011]

[0012] Wherein, represents the total operating cost, represents the number of units in the region, represents the operating cost in this region, represents the carbon emission cost, represents the energy loss cost, represents the region, represents the comprehensive voltage deviation, represents the total number of nodes, represents the voltage of node , represents the reference voltage of node . represents the in - situ consumption rate represents the total time period represents the total output of renewable energy represents the actual load response amount represents the time interval represents the moment represents the actual output power of photovoltaic represents the actual output power of wind power

[0013] In a possible implementation, the peak - valley fluctuation constraint includes:

[0014]

[0015] Among them, represents the transmission power of the tie - line before the action of the regional integrated energy system represents the transmission power of the tie - line after the action of the regional integrated energy system represents the daily load rate of the regional integrated energy system represents the daily average load represents the daily maximum load represents the load rate of the tie - line before the action of the regional integrated energy system represents the load rate of the tie - line after the action of the regional integrated energy system;

[0016] The linear power flow constraint includes:

[0017]

[0018] Among them, represents the lower limit of the power flow in branch . represents the power flow in branch . represents the upper limit of the power flow in branch . represents the number of nodes in the region represents the power transfer distribution factor represents the total transmission power respectively represent the total transmission power and the electrical load;

[0019] The load response amount balance constraint includes:

[0020]

[0021] Among them, 5% represents the absolute upper limit value of the change rate of power consumption. represents the total time period. represents the AC load. represents the response amount of the AC load. represents the moment. represents the DC load. represents the response amount of the DC load.

[0022] In a possible implementation manner, based on the load resource parameters, combined with the Chebyshev chaos mapping algorithm, the improved ant lion algorithm is used to solve the objective function to obtain a scheduling scheme, including:

[0023] Initialize the ant population using the Chebyshev chaos mapping to obtain the first ant population.

[0024] Introduce a preset proportional parameter to adjust the first ant population to obtain the second ant population.

[0025] Based on the second ant population, based on the random movement mechanism of ants, adopt the tournament selection mechanism, and randomly select at least one best parameter candidate solution from the population containing the objective function.

[0026] According to the magnitude relationship between the fitness value of each best parameter candidate solution and the fitness value of the local optimal solution, iteratively update the position corresponding to the best parameter candidate solution.

[0027] In response to reaching the preset number of iterations and satisfying the solution result, end the iteration to obtain the optimal solution.

[0028] In a possible implementation manner, the proportional parameter is represented by the following formula:

[0029]

[0030] Among them, represents the proportional parameter. represents the number of iterations. represents the maximum number of iterations. represents the dynamic adjustment parameter.

[0031] In a possible implementation manner, the method further includes:

[0032] In response to a preset number of ants concentrating in a local area of the search space, perform random guidance processing on the ants concentrating in the local area of the search space.

[0033] Based on the same inventive concept, an embodiment of the present application further provides a load resource scheduling device, including:

[0034] An acquisition module, configured to acquire load resource parameters;

[0035] A construction module, configured to construct an objective function based on the total operating cost, the comprehensive voltage deviation, and the in-situ consumption rate; the constraint conditions of the objective function include: peak-valley fluctuation constraint, linear power flow constraint, and load response quantity balance constraint;

[0036] A solution module, configured to solve the objective function based on the load resource parameters, in combination with the Chebyshev chaotic mapping algorithm, using the improved ant lion algorithm to obtain a scheduling plan;

[0037] A scheduling module, configured to perform scheduling processing on the load resources based on the scheduling plan.

[0038] Based on the same inventive concept, an embodiment of the present application further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, it implements the load resource scheduling method described in any one of the above.

[0039] Based on the same inventive concept, an embodiment of the present application further provides a non-transitory computer-readable storage medium, which stores computer instructions for causing the computer to execute the load resource scheduling method described in any one of the above.

[0040] Based on the same inventive concept, an embodiment of the present application further provides a computer program product, which includes computer program instructions for causing the computer program product to execute the load resource scheduling method described in any one of the above.

[0041] As can be seen from the above, the load resource scheduling method and related devices provided by this application obtain load resource parameters; construct an objective function based on the total operating cost, comprehensive voltage deviation, and local consumption rate; the constraint conditions of the objective function include: peak-valley fluctuation constraint, linear power flow constraint, and load response quantity balance constraint; based on the load resource parameters, combined with the Chebyshev chaotic mapping algorithm, use the improved ant lion algorithm to solve the objective function to obtain a scheduling plan; perform scheduling processing on the load resources based on the scheduling plan. In the construction of the objective function in the embodiments of this application, key indicators such as the total operating cost, comprehensive voltage deviation, and local consumption rate are considered. By comprehensively considering these factors, the economy and rationality of the scheduling plan are ensured. Among them, the total operating cost covers the operating cost, carbon emission cost, and energy loss cost, fully reflecting the comprehensive benefits of the operation of the power system. In addition, as an important indicator of the power system, the comprehensive voltage deviation is directly related to the safe operation of the power grid. By controlling it, the risk of equipment damage can be effectively reduced, and the power supply reliability can be improved. The local consumption rate reflects the utilization efficiency of renewable energy and promotes the goal of green development. Secondly, for the setting of constraint conditions, including peak-valley fluctuation constraint, linear power flow constraint, and load response quantity balance constraint, etc., further ensures the feasibility of the scheduling plan. By controlling the peak-valley fluctuation, the stable operation of the power load is ensured, and the instability of the power grid operation is reduced. The linear power flow constraint enables the reasonable distribution of power flow in the network, avoiding the occurrence of overload phenomena, and helping to improve the overall carrying capacity of the power system. The load response quantity balance constraint ensures the flexibility and timeliness of power response, enabling the power grid to dynamically adjust according to the actual load demand. Thirdly, in the solution process, the improved ant lion algorithm is applied, combined with the Chebyshev chaotic mapping algorithm for the initialization of the ant population, greatly improving the diversity and efficiency of the search. The Chebyshev chaotic mapping can effectively expand the search space of the population, make the distribution of solutions more uniform, and at the same time reduce the convergence time of the algorithm. The improved ant lion algorithm maintains the excellent characteristics of ant colony intelligent optimization. Through the random movement mechanism and tournament selection mechanism, it ensures to quickly find the optimal solution among numerous solutions. This innovative combined algorithm enables the scheduling plan to obtain high-quality solutions in a short time and improves the adaptability of the power system to complex environments. In addition, random guidance processing is also introduced, and this design effectively avoids the problem of the algorithm falling into local optimal solutions. By adjusting the local concentrated area, the flexibility of the entire search process is enhanced, and the exploration ability of the algorithm in the global range is improved. Description of the Drawings

[0042] To more clearly illustrate the technical solutions in the present application or related technologies, the following will briefly introduce the drawings required for use in the embodiments or related technology descriptions. Obviously, the drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0043] Figure 1 Schematic diagram of the load resource scheduling method process for the embodiments of the present application;

[0044] Figure 2 Schematic diagram of the regional source-load system structure for the embodiments of the present application;

[0045] Figure 3 Schematic diagram of the regional integrated electric and thermal energy system for the embodiments of the present application;

[0046] Figure 4 Schematic diagram of the voltage condition of the nodes for the embodiments of the present application;

[0047] Figure 5 Schematic diagram of the regional system energy supply and demand response for the embodiments of the present application;

[0048] Figure 6 Schematic diagram of the structure of the load resource scheduling device for the embodiments of the present application;

[0049] Figure 7 Schematic diagram of the structure of the electronic device for the embodiments of the present application. Detailed implementation manners

[0050] To make the objectives, technical solutions, and advantages of the present application clearer and more understandable, the following further elaborates on the present application in detail in conjunction with specific embodiments and with reference to the drawings.

[0051] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should have the ordinary meaning understood by those of ordinary skill in the field to which the present application belongs. The "first", "second", and similar terms used in the embodiments of the present application do not denote any order, quantity, or importance, but are only used to distinguish different components. Words such as "including" or "comprising" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. Words such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right", etc. are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0052] It can be understood that before using the technical solutions of the various embodiments of the present disclosure, the types, usage scopes, usage scenarios, etc. of the personal information involved will be informed to the user in an appropriate manner, and the authorization of the user will be obtained.

[0053] For example, when responding to receiving an active request from a user, a prompt message is sent to the user to clearly prompt the user that the operation requested by the user will require obtaining and using the user's personal information. Thus, the user can autonomously choose whether to provide personal information to software or hardware such as an electronic device, an application program, a server, or a storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0054] As an optional but non-limiting implementation manner, the manner of sending a prompt message to the user in response to receiving an active request from the user may be, for example, in the form of a pop-up window, and the prompt message may be presented in text in the pop-up window. In addition, the pop-up window may also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device.

[0055] It can be understood that the above process of notifying and obtaining the user's authorization is only illustrative and does not constitute a limitation on the implementation manner of the present disclosure. Other manners that meet the relevant laws and regulations can also be applied to the implementation manner of the present disclosure.

[0056] As described in the background art section, the district energy system can aggregate a variety of different consumer loads, benefit from economies of scale, and improve energy use efficiency. However, with the grid connection of distributed energy, the volatility and uncertainty of its power generation bring new challenges to the stable operation of the power grid; traditional scheduling methods are no longer able to meet the requirements of power grid operation. In the scheduling algorithms in the prior art, the price fluctuations and policy changes in the power market may affect the execution of the scheduling plan. If the market price fluctuates greatly or the policy changes, it may cause the data center to be unable to purchase or sell electricity according to the original plan, increasing the operating cost and risk.

[0057] In view of the above considerations, an embodiment of the present application proposes a load resource scheduling method, which includes obtaining load resource parameters; constructing an objective function based on the total operating cost, the comprehensive voltage deviation, and the local consumption rate; the constraint conditions of the objective function include: peak-valley fluctuation constraint, linear power flow constraint, and load response quantity balance constraint; based on the load resource parameters, combined with the Chebyshev chaotic mapping algorithm, using the improved ant lion algorithm to solve the objective function to obtain a scheduling plan; and performing scheduling processing on the load resources based on the scheduling plan. In the construction of the objective function in the embodiment of the present application, key indicators such as the total operating cost, the comprehensive voltage deviation, and the local consumption rate are considered. By comprehensively considering these factors, the economy and rationality of the scheduling plan are ensured. Among them, the total operating cost covers the operating cost, the carbon emission cost, and the energy loss cost, which fully reflects the comprehensive benefits of the operation of the power system. In addition, as an important indicator of the power system, the comprehensive voltage deviation is directly related to the safe operation of the power grid. By controlling it, the risk of equipment damage can be effectively reduced, and the power supply reliability can be improved. The local consumption rate reflects the utilization efficiency of renewable energy and promotes the goal of green development. Secondly, for the setting of constraint conditions, including peak-valley fluctuation constraint, linear power flow constraint, and load response quantity balance constraint, etc., the feasibility of the scheduling plan is further ensured. By controlling the peak-valley fluctuation, the stable operation of the power load is ensured, and the instability of the power grid operation is reduced. The linear power flow constraint enables the reasonable distribution of power flow in the network, avoids the occurrence of overload phenomena, and helps to improve the overall carrying capacity of the power system. The load response quantity balance constraint ensures the flexibility and timeliness of power response, enabling the power grid to dynamically adjust according to the actual load demand. Thirdly, in the solution process, the improved ant lion algorithm is applied, and the Chebyshev chaotic mapping algorithm is combined for the initialization of the ant population, which greatly improves the diversity and efficiency of the search. The Chebyshev chaotic mapping can effectively expand the search space of the population, make the distribution of solutions more uniform, and at the same time reduce the convergence time of the algorithm. The improved ant lion algorithm maintains the excellent characteristics of ant colony intelligent optimization. Through the random movement mechanism and the tournament selection mechanism, it ensures to quickly find the optimal solution among numerous solutions. This innovative combined algorithm enables the scheduling plan to obtain high-quality solutions in a short time and improves the adaptability of the power system to complex environments. In addition, random guidance processing is also introduced, and this design effectively avoids the problem of the algorithm falling into a local optimal solution. By adjusting the local concentrated area, the flexibility of the entire search process is enhanced, and the exploration ability of the algorithm in the global range is improved.

[0058] Hereinafter, the technical solutions of the embodiments of the present application will be described in detail through specific embodiments.

[0059] Referring to Figure 1 , the load resource scheduling method of the embodiment of the present application includes the following steps:

[0060] Step S101: Obtain load resource parameters;

[0061] Step S102: Construct an objective function based on the total operating cost, comprehensive voltage deviation, and in-situ consumption rate; the constraint conditions of the objective function include: peak-valley fluctuation constraint, linear power flow constraint, and load response quantity balance constraint;

[0062] Step S103: Based on the load resource parameters, combined with the Chebyshev chaos mapping algorithm, use the improved ant lion algorithm to solve the objective function to obtain a scheduling plan;

[0063] Step S104: Perform scheduling processing on the load resources based on the scheduling plan.

[0064] Regarding step S101, first, before solving the objective function, it is necessary to obtain the corresponding load resource parameters. The load resource parameters to be obtained are described below.

[0065] Furthermore, regarding step S102, construct an objective function based on the total operating cost, comprehensive voltage deviation, and in-situ consumption rate; the constraint conditions of the objective function include: peak-valley fluctuation constraint, linear power flow constraint, and load response quantity balance constraint.

[0066] In some embodiments, the regional source-load system is a complex system that integrates a multi-energy transmission network and multi-energy load users. These two parts both show significant hierarchical structures in geographical distribution and operation management; the main role of various energy stations is to provide multi-energy loads to the user side, with the core of ensuring the energy utilization efficiency of users and reducing the operating cost at the same time; multi-energy load users are connected to the regional energy station through the multi-energy transmission network to achieve energy supply and consumption, and they each have different energy-consuming devices and energy management strategies.

[0067] Reference Figure 2 , which is a schematic diagram of the structure of the regional source-load system according to the embodiment of the present application.

[0068] In this system structure, the distribution network and the regional heat network work together and serve as the power supply main body at the same time. Through precise energy supply equipment, various types of energy are stably transported to the user side. When the power supply is insufficient, the system can intelligently dispatch the electric energy resources of the superior power grid to quickly fill the power gap and ensure the continuity and stability of the overall energy supply.

[0069] Based on the above analysis of the structure of the regional source-load system, it can be seen that this system combines multiple energies as the power supply main body. Therefore, in order to ensure the effective scheduling of the source and load, a source-load collaborative scheduling model is constructed. This model is constructed by comprehensively considering the scheduling cost, energy consumption, and operation stability of the regional source-load system.

[0070] The objective function of the model includes minimizing the total operating cost , minimizing the comprehensive voltage deviation , and maximizing the energy consumption .

[0071] In some embodiments, the objective function is represented by the following formula:

[0072]

[0073]

[0074]

[0075] Wherein, represents the total operating cost, represents the number of units in the area, represents the operating cost in this area, represents the carbon emission cost, represents the energy loss cost, represents the area, represents the comprehensive voltage deviation, represents the total number of nodes, represents the node voltage, represents the node base voltage, represents the in-situ consumption rate, represents the total time period, represents the total output of renewable energy, represents the actual load response amount, represents the time interval, represents the moment, represents the actual output power of photovoltaic, represents the actual output power of wind power.

[0076] Wherein, and The calculation formula of is:

[0077]

[0078] Wherein, represents the total output of renewable energy, represents the response amount of AC load, represents the response amount of DC load, represents the actual load response amount, represents the actual output power vector of photovoltaic, represents the actual output power vector of wind power.

[0079] After determining the objective function for regional source-load coordinated scheduling, corresponding constraint conditions are set in combination with the requirements of regional source-load coordinated scheduling and the situation of the objective function to ensure that the coordinated objective function is within a reasonable range.

[0080] In some embodiments, the constraint conditions of the objective function include: peak-valley fluctuation constraint, linear power flow constraint, and load response quantity balance constraint.

[0081] The core objective of source-load collaborative optimization is to reduce the peak-valley fluctuations of the transmission power of the tie line between the power grid and users. Under the action of the regional integrated energy system, it promotes the collaborative work between the power grid and the user side, thereby significantly reducing the peak-valley difference of the transmission power of the tie line and effectively improving the stability and balance of the daily load rate to ensure the smooth and efficient energy supply.

[0082] The peak-valley fluctuation constraint includes:

[0083]

[0084] Among them, represents the transmission power of the tie line before the action of the regional integrated energy system, represents the transmission power of the tie line after the action of the regional integrated energy system, represents the daily load rate of the regional energy system, represents the daily average load, represents the daily maximum load, represents the load rate of the tie line before the action of the regional integrated energy system, represents the load rate of the tie line after the action of the regional integrated energy system.

[0085] When conducting regional source-load coordinated scheduling, in order to more flexibly adjust the power flow direction of the power grid, reduce the abandonment of renewable energy, and improve the utilization rate of renewable energy; and to limit the direction and magnitude of the power flow and keep the voltage of each node in the power grid within a reasonable range, linear power flow constraint conditions are set.

[0086] The linear power flow constraint includes:

[0087]

[0088] Among them, represents the lower limit of the power flow in branch in, represents the power flow in branch in, represents the upper limit of the power flow in branch in, represents the number of nodes in the region, represents the power transfer distribution factor, represents the total transmission power, respectively represent the total transmission power and the electrical load;

[0089] To improve the flexibility and response speed of regional source-load coordinated scheduling, quickly adjust the power generation output to meet the load demand, design the load response quantity balance constraint conditions, so that the dispatching center can perform dispatching according to the actual changes in the load.

[0090] The load response quantity balance constraint includes:

[0091]

[0092] Among them, 5% represents the absolute upper limit value of the change rate of electricity consumption, represents the total time period, represents the AC load, represents the response quantity of the AC load, represents the moment, represents the DC load, represents the response quantity of the DC load.

[0093] Furthermore, for steps S103 and S104, based on the load resource parameters, combined with the Chebyshev chaotic mapping algorithm, use the improved ant lion algorithm to solve the objective function, obtain the dispatching plan, and perform dispatching processing on the load resources based on the dispatching plan.

[0094] In this embodiment, after completing the construction of the regional source-load coordinated scheduling model, to solve the scheduling model of this multi-objective function, this application combines the Chebyshev chaotic mapping algorithm and uses the improved ant lion optimization algorithm for solution. Under the background of multi-objective optimization, this algorithm consists of three regional source-load dispatching objective functions: the lowest total operating cost, the minimum comprehensive voltage deviation, and the maximum energy consumption, which form an optimization search space. The ants are a kind of trial solution or candidate solution in this space, and each ant corresponds to a different dispatching plan or configuration. These plans are designed to meet the multi-objective requirements of regional source-load coordinated scheduling. The ants move randomly in the search space to find potential optimal solutions.

[0095] In some embodiments, solving the preset objective function by using the improved antlion algorithm includes: initializing the ant population by using the Chebyshev chaotic map to obtain the first ant population; introducing a preset proportional parameter to adjust the first ant population to obtain the second ant population; based on the second ant population, taking the random movement mechanism of ants as the basis, adopting the tournament selection mechanism, and randomly selecting at least one best parameter candidate solution from the population containing the objective function; iteratively updating the position corresponding to the best parameter candidate solution according to the magnitude relationship between the fitness value of each best parameter candidate solution and the fitness value of the local optimal solution; and ending the iteration to obtain the optimal solution in response to reaching the preset number of iterations and satisfying the solution result.

[0096] At the beginning of the algorithm, the ants representing each scheduling candidate solution are randomly placed in the search space to form an initial ant population representing the objective function of the source-load scheduling of all regions. The ants representing each scheduling candidate solution move randomly in the search space. To prevent them from crossing the boundary of the solution search space, the positions of the ants need to be adjusted, and these positions represent a series of decision points or states in the actual scheduling problem. Specifically, these positions can be mapped to the matching relationship among the three objective functions of the lowest total operating cost, the smallest comprehensive voltage deviation, and the largest energy consumption in the scheduling problem. The formula is:

[0097]

[0098] Where represents the ant at the adjusted position corresponding to the th iteration, represents the position of the ant (candidate solution) at the th iteration, represents the lower limit of the position range of the ant (candidate solution) at the th iteration, represents the upper limit of the position range of the ant (candidate solution) at the th iteration, represents the maximum value of the objective function mapped by the ant at the th iteration, represents the minimum value of the objective function mapped by the ant at the

[0099] In some embodiments, to ensure the diversity of solutions of each scheduling objective function in the regional source-load scheduling model and the quality of the initial solution, the present application introduces the Chebyshev chaotic mapping to initialize the initial ant population representing all the regional source-load scheduling objective functions. The generated initial population after mapping can cover the entire solution space, ensuring the uniform distribution of the initial solution in the solution space. The mapping formula is:

[0100]

[0101] where represents the mapping position of the solution after the -th iteration, represents the mapping coefficient, represents the mapping position of the solution after the -th iteration.

[0102] In some embodiments, the proportional parameter is represented by the following formula:

[0103]

[0104] where represents the proportional parameter, represents the number of iterations, represents the maximum number of iterations, represents the dynamic adjustment parameter.

[0105] In this embodiment, to ensure that the ants representing each scheduling candidate solution search in the effective solution space, an adaptive method is adopted to reduce the walking range of the ants, simulate the process of the ants entering the ant colony, and introduce the proportional parameter to achieve the dynamic adjustment of the ants.

[0106] Furthermore, based on the random movement mechanism of the ants, the tournament selection mechanism is adopted. By random means, several best parameter candidate solutions of the regional source-load scheduling model are selected from the current population containing the regional source-load scheduling objective functions, that is, the individuals compete to obtain the individual with the best performance and the path of the elite ant colony (the set of candidate solutions with high fitness values). This path is a series of decision sequences or scheduling schemes representing the actual scheduling problem when the algorithm is solving. These paths are gradually constructed by the ants according to the pheromone concentration and heuristic information (such as distance, cost, efficiency, etc.) during the simulated foraging behavior, and they map a series of ordered decisions from the initial state to the target state. In each round of iteration, the positions of the ants representing each scheduling candidate solution will be dynamically adjusted and updated according to their performance and interaction with the set of candidate solutions with high fitness values, and the update formula is:

[0107]

[0108] Among them, represents the position of the ant after dynamic adjustment and update at the th iteration, represents the position of the ant selected by the mechanism around the ant colony,

[0109] Furthermore, during the solution process, there is a screening process for individuals within the population. This process is to screen the individuals representing each candidate solution in the solution space of the regional source-load scheduling model. The strategy adopted is to compare the fitness values between the candidate solutions and the local optimal solutions (ant lions) one by one, rather than mixing and sorting or merging them. The core of this mechanism is that once it is found that the fitness performance of a certain candidate solution exceeds the corresponding local optimal solution, an update operation is triggered immediately, that is, the current position of this local optimal solution will be replaced by the position of this more superior candidate solution. At this time, position update is performed, and its formula is:

[0110]

[0111] Among them, represents at the th iteration, the position of the selected ant (candidate solution), represents at the th iteration, the current position of the local optimal solution, represents the fitness function.

[0112] In some embodiments, the method further includes: in response to a preset number of ants concentrating in a local area of the search space, randomly guiding the ants concentrating in the local area of the search space.

[0113] In this embodiment, during the optimization process, if a large number of ants representing all the regional source-load scheduling objective functions concentrate in a certain local area of the search space, then the algorithm is likely to fall into a local optimal solution and cannot find the global optimal solution. By randomly guiding the ants representing all the regional source-load scheduling objective functions into a new area, the exploration ability of the algorithm can be increased, enabling it to discover more potential solution spaces. The random guiding formula is:

[0114]

[0115] In the formula: represents at the th iteration, the position of the ant after random guidance, represents the lower limit of the entire solution search space, represents the random number generation function, Represents the upper limit of the entire solution search space.

[0116] After guiding the ants to randomly search according to the formula for the ant colony optimization algorithm representing the overall regional source-load scheduling objective function, position updates are performed, and it is judged whether the maximum number of iterations is satisfied. If satisfied, the solution result is output; if not, the population is re-initialized and a new iteration is started until the best collaborative scheduling scheme is obtained. After obtaining the best collaborative scheduling scheme, the load resources are scheduled according to this scheduling scheme.

[0117] Reference Figure 3 , is the structure of the regional integrated electric-thermal energy system according to the embodiment of the present application.

[0118] In another feasible embodiment, as Figure 3 shown, taking the regional integrated electric-thermal energy system of a certain area as the test object, the system includes photovoltaic, wind power plant, combined heat and power generation, energy storage system and heat network, and the voltage level of the system is 12.66 kV. The operating parameters of the system are shown in Table 1.

[0119] Table 1 Related parameters of the system

[0120]

[0121] When the present application performs regional energy collaborative scheduling, it mainly focuses on multi-objective scheduling. To verify the collaborative scheduling effect of this method, collaborative scheduling is carried out with the operating cost and energy consumption as the criteria, and the scheduling results under different load demands are obtained, as shown in Table 2.

[0122] Table 2 Scheduling results under different load demands

[0123]

[0124] After analyzing the test results in Table 2, it is concluded that: under different load demands of different sizes, before the regional source-load collaborative scheduling is carried out by the present application, the operating cost is more than 150,000 yuan, and the maximum energy consumption is ; after the regional source-load collaborative scheduling is carried out by the present application, the operating cost is significantly reduced, with a maximum value of 122,100 yuan, and the energy consumption is significantly improved, all above . Therefore, the present application has good application effects, can improve the energy consumption capacity, and reduce the operating cost.

[0125] Reference Figure 4 , is the schematic diagram of the voltage situation of the nodes according to the embodiment of the present application.

[0126] The original voltage fluctuation of the energy system node was between 0.92 and 1.1 p.u., and the system stability was poor. Therefore, to further verify the collaborative scheduling effect of this application, the voltage conditions of different nodes at different operating times were tested. The test results are as Figure 4 shown.

[0127] After Figure 4 analyzing the test results, it is concluded that after the regional source-load collaborative scheduling is carried out through this application, the voltage stability of each node in the system can be guaranteed, and the voltage fluctuates within the allowable range at different operating times, that is, between 0.92 and 1.05 p.u., so as to ensure the stability of the system.

[0128] Refer to Figure 5 , which is a schematic diagram of the energy supply and demand response of the regional system in the embodiment of this application.

[0129] To verify the applicability of this application, after the source-load collaborative scheduling is carried out through this application, the supply and demand response conditions of the regional energy system are obtained. The test results are as Figure 5 shown.

[0130] After Figure 5 analyzing the test results, it is concluded that after the source-load collaborative scheduling is carried out through this application, the response degree between the daily output result and the load demand result is good, and the regional system can meet the balance of output and demand, and realize the balance of the multi-energy transmission network and multi-energy load users in the system.

[0131] When this application is in scheduling, the real-time performance and load response ability of scheduling are particularly important. To verify the scheduling performance of this application, the scheduling response time deviation rate and the average deviation rate of response quantity are used as test indicators. The values of the two indicators are between 0 and 100%. The smaller the result, the better the scheduling performance. The calculation formula of the indicator is:

[0132]

[0133]

[0134] Among them, represents the scheduling response time deviation rate, represents the actual duration of load response, represents the actual set time of load response, represents the average deviation rate of response quantity, represents the load response quantity at the first time, represents the load response quantity at the second time, represents the total time period.

[0135] According to the above formula, after the collaborative scheduling of the method of the present invention under different load ratios, the deviation rate of the scheduling response time and the average deviation rate of the response volume are tested, and the results are shown in Table 3.

[0136] Table 3 Test results of the deviation rate of the response time and the average deviation rate of the response volume

[0137]

[0138] After analyzing the test results in Table 3, it can be concluded that under different load ratios, after the regional source-load collaborative scheduling by the method of the present invention, the deviation rate of the response time and the average deviation rate of the response volume are small, and the maximum deviations are only 0.24% and 5.2% respectively, and the scheduling response performance is good, meeting the scheduling requirements.

[0139] As can be seen from the above embodiments, for the load resource scheduling method described in the embodiments of the present application, load resource parameters are obtained; an objective function is constructed based on the total operating cost, the comprehensive voltage deviation, and the in-situ consumption rate; the constraint conditions of the objective function include: peak-valley fluctuation constraint, linear power flow constraint, and load response quantity balance constraint; based on the load resource parameters, combined with the Chebyshev chaotic mapping algorithm, the improved ant lion algorithm is used to solve the objective function to obtain a scheduling plan; and the load resources are scheduled based on the scheduling plan. In the construction of the objective function in the embodiments of the present application, key indicators such as the total operating cost, the comprehensive voltage deviation, and the in-situ consumption rate are considered. By comprehensively considering these factors, the economy and rationality of the scheduling plan are ensured. Among them, the total operating cost covers the operating cost, the carbon emission cost, and the energy loss cost, fully reflecting the comprehensive benefits of the operation of the power system. In addition, as an important indicator of the power system, the comprehensive voltage deviation is directly related to the safe operation of the power grid. By controlling it, the risk of equipment damage can be effectively reduced, and the reliability of power supply can be improved. The in-situ consumption rate reflects the utilization efficiency of renewable energy and promotes the goal of green development. Secondly, for the setting of the constraint conditions, including peak-valley fluctuation constraint, linear power flow constraint, and load response quantity balance constraint, etc., the feasibility of the scheduling plan is further ensured. By controlling the peak-valley fluctuation, the stable operation of the power load is ensured, and the instability of the power grid operation is reduced. The linear power flow constraint enables the reasonable distribution of power flow in the network, avoiding the occurrence of overload phenomena, and helping to improve the overall carrying capacity of the power system. The load response quantity balance constraint ensures the flexibility and timeliness of the power response, enabling the power grid to dynamically adjust according to the actual load demand. Thirdly, in the solution process, the improved ant lion algorithm is applied, and the Chebyshev chaotic mapping algorithm is combined for the initialization of the ant population, greatly improving the diversity and efficiency of the search. The Chebyshev chaotic mapping can effectively expand the search space of the population, make the distribution of solutions more uniform, and at the same time reduce the convergence time of the algorithm. The improved ant lion algorithm maintains the excellent characteristics of ant colony intelligent optimization. Through the random movement mechanism and the tournament selection mechanism, it ensures that the optimal solution can be quickly found among numerous solutions. This innovative combined algorithm enables the scheduling plan to obtain high-quality solutions in a short time and improves the adaptability of the power system to complex environments. In addition, random guidance processing is also introduced, and this design effectively avoids the problem of the algorithm falling into local optimal solutions. By adjusting the local concentrated area, the flexibility of the entire search process is enhanced, and the exploration ability of the algorithm in the global scope is improved.

[0140] It should be noted that the method of the embodiment of the present application can be executed by a single device, such as a computer or a server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In such a distributed scenario, one of the multiple devices can only execute one or more steps of the method of the embodiment of the present application, and these multiple devices will interact with each other to complete the described method.

[0141] It should be noted that some embodiments of the present application have been described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the above embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0142] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present application also provides a load resource scheduling device.

[0143] Reference Figure 6 , the load resource scheduling device includes:

[0144] An acquisition module 61, configured to acquire load resource parameters;

[0145] A construction module 62, configured to construct an objective function based on the total operating cost, the comprehensive voltage deviation, and the in-situ consumption rate; the constraint conditions of the objective function include: peak-valley fluctuation constraint, linear power flow constraint, and load response quantity balance constraint;

[0146] A solution module 63, configured to solve the objective function based on the load resource parameters, in combination with the Chebyshev chaotic mapping algorithm, using the improved ant lion algorithm to obtain a scheduling plan;

[0147] A scheduling module 64, configured to perform scheduling processing on the load resources based on the scheduling plan.

[0148] For the sake of description convenience, when describing the above device, it is divided into various modules according to functions for separate description. Of course, when implementing the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0149] The device of the above embodiment is used to implement the corresponding load resource scheduling method in any of the foregoing embodiments and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0150] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present application further provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where when the processor executes the program, the load resource scheduling method described in any of the above embodiments is implemented.

[0151] Figure 7 FIG. shows a more specific schematic diagram of the hardware structure of the electronic device provided in this embodiment. The device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. Among them, the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are communicatively connected to each other inside the device through the bus 1050.

[0152] The processor 1010 may be implemented in a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0153] The memory 1020 may be implemented in the form of a ROM (Read Only Memory), a RAM (Random Access Memory), a static storage device, a dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0154] The input / output interface 1030 is used to connect to an input / output module to implement information input and output. The input / output module may be configured as a component in the device (not shown in the figure) or externally connected to the device to provide corresponding functions. Among them, the input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

[0155] The communication interface 1040 is used to connect to a communication module (not shown in the figure) to implement communication interaction between this device and other devices. Among them, the communication module may implement communication in a wired manner (such as USB, network cable, etc.) or in a wireless manner (such as mobile network, WIFI, Bluetooth, etc.).

[0156] The bus 1050 includes a path for transmitting information between various components of the device, such as the processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040.

[0157] It should be noted that although the above device only shows the processor 1010, the memory 1020, the input / output interface 1030, the communication interface 1040, and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, those skilled in the art can understand that the above device may also only include the components necessary to implement the solution of the embodiments of this specification, and does not necessarily include all the components shown in the figure.

[0158] The electronic device of the above embodiment is used to implement the corresponding load resource scheduling method in any of the foregoing embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0159] Based on the same inventive concept, corresponding to the method of any of the above embodiments, the present application also provides a non-transitory computer-readable storage medium storing computer instructions for causing the computer to execute the load resource scheduling method as described in any of the foregoing embodiments.

[0160] The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette tapes, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible by a computing device.

[0161] The computer instructions stored in the storage medium of the above embodiment are used to cause the computer to execute the load resource scheduling method as described in any of the foregoing embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0162] Based on the same inventive concept, corresponding to the load resource scheduling method described in any of the above embodiments, the present disclosure also provides a computer program product, which includes computer program instructions. In some embodiments, the computer program instructions can be executed by one or more processors of a computer to cause the computer and / or the processor to execute the load resource scheduling method. Corresponding to the execution subjects corresponding to the steps in the various embodiments of the load resource scheduling method, the processors executing the corresponding steps can belong to the corresponding execution subjects.

[0163] The computer program product of the above embodiment is used to cause the computer and / or the processor to execute the load resource scheduling method described in any of the above embodiments, and has the beneficial effects of the corresponding method embodiments, which will not be elaborated here.

[0164] Those of ordinary skill in the art should understand that: The discussion of any of the above embodiments is only exemplary, and is not intended to imply that the scope of the present application (including the claims) is limited to these examples; Under the idea of the present application, the technical features in the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations in different aspects of the embodiments of the present application as described above, and they are not provided in detail for the sake of brevity.

[0165] In addition, for the sake of simplicity of description and discussion, and in order not to make the embodiments of the present application difficult to understand, the well-known power / ground connections to integrated circuit (IC) chips and other components may or may not be shown in the provided drawings. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform on which the embodiments of the present application are to be implemented (that is, these details should be completely within the understanding of those skilled in the art). In the case where specific details (such as circuits) are set forth to describe the exemplary embodiments of the present application, it will be apparent to those skilled in the art that the embodiments of the present application can be implemented without these specific details or with variations of these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0166] Although the present application has been described in conjunction with specific embodiments of the present application, many substitutions, modifications, and variations of these embodiments will be apparent to those of ordinary skill in the art based on the foregoing description. For example, other memory architectures (such as dynamic RAM (DRAM)) can be used with the embodiments discussed.

[0167] Embodiments of the present application are intended to cover all such substitutions, modifications, and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of the present application shall be included within the protection scope of the present application.

Claims

1. A load resource scheduling method, characterized in that: include: Get load resource parameters; The objective function is constructed based on the total operating cost, comprehensive voltage deviation and local consumption rate; The constraint conditions of the objective function include: peak-valley fluctuation constraint, linear power flow constraint and load response balance constraint; Based on the load resource parameters, in combination with the Chebyshev chaotic mapping algorithm, the improved ant lion algorithm is used to solve the objective function to obtain a scheduling scheme, including: using the Chebyshev chaotic mapping to initialize the ant population to obtain a first ant population; introducing a preset proportion parameter to adjust the first ant population to obtain a second ant population; based on the second ant population, based on the random movement mechanism of ants, using a tournament selection mechanism, in a random manner, selecting at least one optimal parameter candidate solution from the population containing the objective function; according to the size relationship between the fitness value of each of the optimal parameter candidate solutions and the fitness value of the local optimal solution, iteratively updating the position corresponding to the optimal parameter candidate solution; in response to reaching a preset number of iterations and satisfying the solution result, ending the iteration to obtain the optimal solution; Performing scheduling processing on load resources based on the scheduling scheme; Wherein, the ratio parameter is expressed by the following formula: in, represents the scale parameter, represents the number of iterations, represents the maximum number of iterations, Indicates dynamic adjustment parameters; The method further comprises: In response to a preset number of ants concentrating in a local area of ​​the search space, performing a random guiding process on the ants concentrating in the local area of ​​the search space; The random bootstrap formula is: in: Indicated in The ant After random bootstrapping, represents the lower limit of the entire solution search space, represents the random number generation function, Represents the upper limit of the entire solution search space.

2. The method according to claim 1, characterized in that: The objective function is expressed by the following formula: in, represents the total operating cost, Indicates the number of units in the area, represents the operating cost in this area, represents the carbon emission cost, represents the energy loss cost, Indicates the area, Represents the comprehensive voltage deviation, Represents the total number of nodes, Representation Node The voltage, Representation Node The reference voltage, represents the local consumption rate, Indicates the total time period, is the total output of renewable energy, Indicates the actual response of the load. Indicates the time interval, Indicates the time, Indicates the actual output power of photovoltaic, Indicates the actual output power of wind power.

3. The method according to claim 1, characterized in that The peak-to-valley fluctuation constraints include: in, represents the transmission power of the interconnection line before the action of the regional integrated energy system, represents the transmission power of the interconnection line after the action of the regional integrated energy system, represents the daily load rate of the regional integrated energy system, represents the average daily load, Indicates the maximum daily load, represents the load rate of the interconnection line before the action of the regional integrated energy system, It represents the load rate of the interconnection line after the action of the regional integrated energy system; The linear power flow constraints include: in, Indicates branch The lower limit of the tidal current in Indicates branch The trend in Indicates branch The upper limit of the current in Indicates the number of nodes in the region, represents the power transfer distribution factor, represents the total delivered power, Respectively represent the total transmitted power and electrical load; The load response balance constraint includes: Among them, 5% represents the absolute upper limit of the change rate of electricity consumption. Indicates the total time period, represents the AC load, Indicates the response of the AC load, Indicates the time, Indicates DC load, Indicates the response amount of the DC load.

4. A load resource scheduling device, characterized in that: include: An acquisition module, configured to acquire load resource parameters; A building module, configured to build an objective function based on a total operating cost, a comprehensive voltage deviation, and a local consumption rate; The constraint conditions of the objective function include: peak-valley fluctuation constraint, linear power flow constraint and load response balance constraint; The solution module is configured to solve the objective function based on the load resource parameters and in combination with the Chebyshev chaos mapping algorithm using the improved ant lion algorithm to obtain a scheduling scheme, including: initializing the ant population using the Chebyshev chaos mapping to obtain a first ant population; introducing a preset proportion parameter to adjust the first ant population to obtain a second ant population; based on the second ant population, based on the random movement mechanism of ants, using a tournament selection mechanism, randomly selecting at least one optimal parameter candidate solution from the population containing the objective function; iteratively updating the position corresponding to the optimal parameter candidate solution according to the size relationship between the fitness value of each optimal parameter candidate solution and the fitness value of the local optimal solution; in response to reaching a preset number of iterations and satisfying the solution result, ending the iteration to obtain the optimal solution; A scheduling module, configured to schedule load resources based on the scheduling scheme; Wherein, the ratio parameter is expressed by the following formula: in, represents the scale parameter, represents the number of iterations, represents the maximum number of iterations, Indicates dynamic adjustment parameters; The device also includes: A random guiding module, configured to perform random guiding processing on the ants concentrated in the local area of ​​the search space in response to a preset number of ants concentrated in the local area of ​​the search space; The random bootstrap formula is: in: Indicated in The ant After random bootstrapping, represents the lower limit of the entire solution search space, represents the random number generation function, Represents the upper limit of the entire solution search space.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the method according to any one of claims 1 to 3 is implemented.

6. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to enable a computer to execute the method according to any one of claims 1 to 3.

7. A computer program product, comprising computer program instructions, which, when executed on a computer, cause the computer to execute the method according to any one of claims 1 to 3.

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