Load resource scheduling method and related equipment

Abstract

The invention provides a load resource scheduling method and related equipment. The method comprises the following steps: acquiring a load resource parameter; constructing a target function based on the total operation cost, the comprehensive voltage deviation and the local consumption rate; the constraint conditions of the target function comprise peak-valley fluctuation constraint, linear power flow constraint and load response quantity balance constraint; based on load resource parameters, combining a Chebyshev chaotic mapping algorithm, and utilizing an improved ant lion algorithm to solve the target function to obtain a scheduling scheme; and carrying out scheduling processing on load resources based on the scheduling scheme. According to the embodiment of the invention, an effective scheduling strategy is formed by constructing the objective function. The objective function is limited by a plurality of constraint conditions, which ensures the feasibility and rationality of the scheduling scheme. By optimizing and solving the objective function, the scheduling cost of the system can be effectively reduced, and meanwhile, the utilization efficiency of electric energy is improved. Furthermore, resource scheduling processing can be better carried out, and the stability and economical efficiency of a power grid are improved.

Description

Technical Field

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

[0002] Regional energy systems can aggregate a variety of different consumer loads, benefit from economies of scale, and improve energy efficiency. However, with the grid connection of distributed energy, the volatility and uncertainty of its power generation have brought new challenges to the stable operation of the power grid; traditional scheduling methods can no longer meet the needs of power grid operation. In the scheduling algorithms of existing technologies, price fluctuations and policy changes in the electricity market may have an impact on the execution of the scheduling plan. If the market price fluctuates greatly or the policy changes, the data center may not be able to purchase or sell electricity as originally planned, increasing operating costs and risks. Summary of the invention

[0003] In view of this, the purpose of this application is to propose a load resource scheduling method, including: Get load resource parameters; An objective function is constructed 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 balance constraint; 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 plan; The load resources are scheduled based on the scheduling scheme.

[0004] In a possible implementation, the objective function is expressed by the following formula:

[0005]

[0006]

[0007] 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.

[0008] In a possible implementation, the peak-to-valley fluctuation constraint includes:

[0009] 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, Indicates the daily load rate of the regional 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:

[0010] 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, They represent the total transmitted power and electrical load respectively; The load response balance constraint includes:

[0011] 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.

[0012] In a possible implementation, 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 solution, including: The ant population is initialized using Chebyshev chaotic mapping to obtain the first ant population; Introducing a preset ratio parameter, adjusting the first ant population to obtain a second ant population; Based on the second ant population, based on the random movement mechanism of ants, a tournament selection mechanism is adopted to randomly select 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, the iteration is terminated to obtain the optimal solution.

[0013] In a possible implementation, the ratio parameter is expressed by the following formula:

[0014] in, represents the scale parameter, represents the number of iterations, represents the maximum number of iterations, Indicates dynamically adjusted parameters.

[0015] In a possible implementation, the method further includes: In response to a preset number of ants concentrating in a local area of ​​the search space, a random guiding process is performed on the ants concentrating in the local area of ​​the search space.

[0016] Based on the same inventive concept, the embodiment of the present application further provides a load resource scheduling device, including: An acquisition module, configured to acquire load resource parameters; A construction module is configured to construct an objective function based on 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; A 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 an improved ant lion algorithm to obtain a scheduling solution; The scheduling module is configured to schedule the load resources based on the scheduling scheme.

[0017] Based on the same inventive concept, an embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, a load resource scheduling method as described in any one of the above is implemented.

[0018] Based on the same inventive concept, an embodiment of the present application also provides a non-transitory computer-readable storage medium, which stores computer instructions, and the computer instructions are used to enable the computer to execute any of the above-mentioned load resource scheduling methods.

[0019] Based on the same inventive concept, an embodiment of the present application further provides a computer program product, which includes computer program instructions, and the computer instructions are used to enable the computer program product to execute any of the load resource scheduling methods described above.

[0020] As can be seen from the above, the load resource scheduling method and related equipment provided by the present 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 balance constraint; 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; and the load resources are scheduled based on the scheduling scheme. In terms of the construction of the objective function, the embodiment of the present application considers key indicators such as total operating cost, comprehensive voltage deviation and local consumption rate. Through comprehensive consideration of these factors, the economy and rationality of the scheduling scheme are ensured. Among them, the total operating cost covers the operating cost, carbon emission cost and energy loss cost, which fully reflects the comprehensive benefits of the power system operation. In addition, the comprehensive voltage deviation, as an important indicator of the power system, is directly related to the safe operation of the power grid. Through its control, the risk of equipment damage can be effectively reduced and the reliability of power supply can be improved. The local consumption rate reflects the utilization efficiency of renewable energy and promotes the goal of green development. Secondly, the setting of constraint conditions, including peak-valley fluctuation constraint, linear power flow constraint and load response balance constraint, further ensures the feasibility of the dispatching scheme. By controlling the peak-valley fluctuation, the smooth 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 the flow of power in the network, avoids the occurrence of overload, and helps to improve the overall carrying capacity of the power system. The load response balance constraint ensures the flexibility and timeliness of the power response, so that the power grid can be dynamically adjusted according to the actual load demand. Thirdly, in the solution process, the improved ant lion algorithm is applied, combined with the Chebyshev chaos mapping algorithm to initialize the ant population, which greatly improves the diversity and efficiency of the search. The Chebyshev chaos mapping can effectively expand the search space of the population, make the distribution of solutions more uniform, and reduce the convergence time of the algorithm. The improved ant lion algorithm maintains the excellent characteristics of ant colony intelligent optimization, and ensures that the optimal solution is quickly found among many solutions through the random movement mechanism and tournament selection mechanism. This innovative combined algorithm enables the dispatching scheme 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 introduced, which effectively avoids the problem of the algorithm falling into the local optimal solution. By adjusting the local concentrated area, the flexibility of the entire search process is enhanced, and the algorithm's exploration ability in the global range is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the present application or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0022] Figure 1 A schematic diagram of a load resource scheduling method flow in an embodiment of the present application; Figure 2 This is a schematic diagram of the regional source-load system structure of an embodiment of the present application; Figure 3 A schematic diagram of a regional electric and thermal integrated energy system according to an embodiment of the present application; Figure 4 A schematic diagram of voltage conditions of nodes in an embodiment of the present application; Figure 5 A schematic diagram of the regional system energy supply and demand response of an embodiment of the present application; Figure 6 This is a schematic diagram of the structure of a load resource scheduling device according to an embodiment of the present application; Figure 7 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

[0024] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be the usual meanings understood by people with ordinary skills in the field to which the present application belongs. The "first", "second" and similar words used in the embodiments of the present application do not represent any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0025] It is understandable that before using the technical solutions of each embodiment of the present disclosure, the type, scope of use, usage scenarios, etc. of the personal information involved will be informed to the user in an appropriate manner, and the user's authorization will be obtained.

[0026] For example, in response to receiving an active request from a user, a prompt message is sent to the user to clearly remind the user that the operation requested to be performed will require obtaining and using the user's personal information. Thus, the user can independently choose whether to provide personal information to software or hardware such as an electronic device, application, server, or storage medium that performs the operation of the technical solution of the present disclosure according to the prompt message.

[0027] As an optional but non-limiting implementation, in response to receiving the user's active request, the prompt information may be sent to the user in the form of a pop-up window, in which the prompt information may be presented in text form. 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.

[0028] It is understandable that the above notification and the process of obtaining user authorization are merely illustrative and do not constitute a limitation on the implementation of the present disclosure. Other methods that meet relevant laws and regulations may also be applied to the implementation of the present disclosure.

[0029] As described in the background technology section, regional energy systems can aggregate a variety of different consumer loads, benefit from economies of scale, and improve energy efficiency. However, with the grid connection of distributed energy sources, the volatility and uncertainty of their power generation have brought new challenges to the stable operation of the power grid; traditional scheduling methods have been difficult to meet the needs of power grid operation. In the scheduling algorithms in the prior art, price fluctuations and policy changes in the electricity market may have an impact on the execution of the scheduling plan. If the market price fluctuates greatly or the policy changes, the data center may not be able to purchase or sell electricity as originally planned, increasing operating costs and risks.

[0030] Based on the above considerations, the embodiment of the present application proposes a load resource scheduling method, by 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 balance constraint; 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; and the load resources are scheduled based on the scheduling scheme. In terms of the construction of the objective function, the embodiment of the present application considers key indicators such as the total operating cost, the comprehensive voltage deviation and the local consumption rate. Through the comprehensive consideration of these factors, the economy and rationality of the scheduling scheme 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 power system operation. In addition, the comprehensive voltage deviation, as an important indicator of the power system, is directly related to the safe operation of the power grid. Through its control, the risk of equipment damage can be effectively reduced and the reliability of power supply can be improved. The local consumption rate reflects the utilization efficiency of renewable energy and promotes the goal of green development. Secondly, the setting of constraint conditions, including peak-valley fluctuation constraint, linear power flow constraint and load response balance constraint, further ensures the feasibility of the dispatching scheme. By controlling the peak-valley fluctuation, the smooth 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 the flow of power in the network, avoids the occurrence of overload, and helps to improve the overall carrying capacity of the power system. The load response balance constraint ensures the flexibility and timeliness of the power response, so that the power grid can be dynamically adjusted according to the actual load demand. Thirdly, in the solution process, the improved ant lion algorithm is applied, combined with the Chebyshev chaos mapping algorithm to initialize the ant population, which greatly improves the diversity and efficiency of the search. The Chebyshev chaos mapping can effectively expand the search space of the population, make the distribution of solutions more uniform, and reduce the convergence time of the algorithm. The improved ant lion algorithm maintains the excellent characteristics of ant colony intelligent optimization, and ensures that the optimal solution is quickly found among many solutions through the random movement mechanism and tournament selection mechanism. This innovative combined algorithm enables the dispatching scheme 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 introduced, which effectively avoids the problem of the algorithm falling into the local optimal solution. By adjusting the local concentrated area, the flexibility of the entire search process is enhanced, and the algorithm's exploration ability in the global range is improved.

[0031] The technical solutions of the embodiments of the present application are described in detail below through specific examples.

[0032] refer to Figure 1 The load resource scheduling method of the embodiment of the present application includes the following steps: Step S101, obtaining load resource parameters; Step S102, 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 balance constraint; Step S103, based on the load resource parameters and in combination with the Chebyshev chaos mapping algorithm, the objective function is solved using an improved ant lion algorithm to obtain a scheduling solution; Step S104: scheduling the load resources based on the scheduling scheme.

[0033] Regarding step S101, first, before solving the objective function, the corresponding load resource parameters need to be obtained. The load resource parameters that need to be obtained are described below.

[0034] Further with respect to step S102, an objective function is constructed based on the total operating cost, the comprehensive voltage deviation and the local consumption rate; the constraints of the objective function include: peak-valley fluctuation constraint, linear power flow constraint and load response balance constraint.

[0035] 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 show a significant hierarchical structure in terms of geographical distribution and operation management. The main function of various energy stations is to provide multi-energy loads to the user side, with the core of ensuring the user's energy efficiency while reducing operating costs. Multi-energy load users are connected to regional energy stations through the multi-energy transmission network to realize energy supply and consumption. They each have different energy-consuming equipment and energy management strategies.

[0036] refer to Figure 2 , is a schematic diagram of the regional source-load system structure of an embodiment of the present application.

[0037] In this system structure, the distribution network and the regional heating network work together as the main power supply entities, and through sophisticated energy supply equipment, they stably deliver various types of energy to the user end. When encountering insufficient power supply, the system can intelligently dispatch the power resources of the superior power grid, quickly fill the power shortage, and ensure the continuity and stability of the overall energy supply.

[0038] Based on the above analysis of the regional source-load system structure, it can be seen that the system combines multiple energy sources as the power supply theme. Therefore, in order to ensure the effective scheduling of sources and loads, a source-load coordinated scheduling model is constructed. The model takes into account the scheduling cost, energy consumption, and operation stability of the regional source-load system as a whole.

[0039] The objective function of the model includes the minimum total operating cost , the comprehensive voltage deviation is minimal , the largest energy consumption .

[0040] In some embodiments, the objective function is expressed by the following formula:

[0041]

[0042]

[0043] 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.

[0044] in, and The calculation formula is:

[0045] in, is the total output of renewable energy, Indicates the response of the AC load, Indicates the response of the DC load, Indicates the actual response of the load. represents the actual output power vector of the photovoltaic, Represents the actual output power vector of wind power.

[0046] After the objective function of regional source-load collaborative scheduling is determined, the corresponding constraints are set in combination with the needs of regional source-load collaborative scheduling and the objective function situation to ensure that the collaborative objective function is within a reasonable range.

[0047] In some embodiments, the constraints of the objective function include: peak-valley fluctuation constraints, linear power flow constraints, and load response quantity balance constraints.

[0048] The core goal of source-load collaborative optimization is to reduce the peak-to-valley fluctuations in the transmission power of the interconnection lines between the power grid and users. Under the influence of the regional integrated energy system, the grid and the user side are encouraged to work together, thereby significantly reducing the peak-to-valley difference in the transmission power of the interconnection lines, and effectively improving the stability and balance of the daily load rate, ensuring a smooth and efficient energy supply.

[0049] The peak-to-valley fluctuation constraints include:

[0050] 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, Indicates the daily load rate of the regional 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.

[0051] During regional source-load coordinated dispatching, linear power flow constraints are set to more flexibly adjust the power flow of the power grid, reduce the abandonment of renewable energy, and improve the utilization rate of renewable energy; and to limit the direction and size of the power flow, keep the voltage of each node in the power grid within a reasonable range.

[0052] The linear power flow constraints include:

[0053] 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; In order to improve the flexibility and response speed of regional source-load coordinated dispatching, quickly adjust the power generation output to meet the load demand, and design the load response quantity balance constraint conditions, the dispatching center can dispatch according to the actual changes in the load.

[0054] The load response balance constraint includes:

[0055] 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.

[0056] Furthermore, for step S103 and step S104, based on the load resource parameters and in combination with the Chebyshev chaos mapping algorithm, the improved ant lion algorithm is used to solve the objective function to obtain a scheduling scheme, and the load resources are scheduled based on the scheduling scheme.

[0057] In this embodiment, after completing the construction of the regional source-load collaborative scheduling model, in order to solve the scheduling model of the multi-objective function, this application combines the Chebyshev chaos mapping algorithm and adopts the improved ant lion optimization algorithm for solution. In the context of multi-objective optimization, the algorithm consists of three regional source-load scheduling objective functions: the lowest total operating cost, the smallest comprehensive voltage deviation, and the largest energy consumption. The search space is composed of ants, which are a trial solution or candidate solution in the space. Each ant corresponds to a different scheduling scheme or configuration, which is designed to meet the multi-objective requirements of regional source-load collaborative scheduling. Ants move randomly in the search space to find potential optimal solutions.

[0058] In some embodiments, the use of the improved ant lion algorithm to solve a preset objective function includes: using Chebyshev chaotic mapping to initialize the ant population to obtain a first ant population; introducing a preset proportional 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 based on the 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.

[0059] At the beginning of the algorithm, ants representing each candidate solution are randomly placed in the search space to form an initial ant population representing the source-load scheduling objective function of the entire region. Ants representing each candidate solution move in the search space in a random manner. To prevent them from crossing the boundary of the solution search space, the position of the ants needs to be adjusted. This position represents a series of decision points or states in the actual scheduling problem. Specifically, these positions can be mapped to the matching relationship between 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:

[0060] in, Represents ants exist The corresponding adjusted position in the iteration is Represents ants (Candidate solution) in The position at the iteration, Represents ants (Candidate solution) in The lower limit of the position range under iterations, Represents ants (Candidate solution) in The upper limit of the position range under iterations, Represents ants exist The maximum value of the objective function mapped to in the iterations, Represents ants exist The minimum value of the objective function mapped to in the iterations.

[0061] In some embodiments, in order to ensure the diversity of solutions to each scheduling objective function in the regional source-load scheduling model and the quality of the initial solution, the present application introduces Chebyshev chaos mapping to initialize the initial ant population representing all regional source-load scheduling objective functions. The initial population generated after mapping can be spread throughout the entire solution space, ensuring that the initial solution is evenly distributed in the solution space. The mapping formula is:

[0062] in, Indicates The mapping position of the solution after iterations, represents the mapping coefficient, Indicates The mapped position of the solution after iterations.

[0063] In some embodiments, the ratio parameter is represented by the following formula:

[0064] in, represents the scale parameter, represents the number of iterations, represents the maximum number of iterations, Indicates dynamically adjusted parameters.

[0065] In this embodiment, in order to ensure that the ants representing each candidate scheduling solution search in the valid solution space, an adaptive method is used to reduce the walking range of the ants, simulate the process of ants entering the ant colony, and introduce a proportional parameter Realize dynamic adjustment of ants.

[0066] Furthermore, based on the random movement mechanism of ants, a tournament selection mechanism is adopted to randomly select the best parameter candidate solutions of several regional source load scheduling models from the current population containing the regional source load scheduling objective function, that is, individuals compete to obtain the path of the best performing individuals and elite ant colonies (a set of candidate solutions with high fitness values). This path is a series of decision sequences or scheduling schemes that represent the actual scheduling problem when the algorithm is solving it. These paths are gradually constructed by ants based on pheromone concentrations and heuristic information (such as distance, cost, efficiency, etc.) when simulating foraging behavior. 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. The update formula is:

[0067] in, Indicates that after dynamic adjustment and update, the ant exist The position at the iteration, represents the position of the ants selected by the mechanism around the ant colony, Represents the position of random movement near the elite ant colony.

[0068] Furthermore, when solving the problem, there will be a screening process for individuals in the group. This process is to screen 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 solution (antlion) 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 candidate solution exceeds the corresponding local optimal solution, an update operation is triggered immediately, that is, the current position of the local optimal solution will be replaced by the position of this more superior candidate solution. At this time, the position is updated, and the formula is:

[0069] in, Indicated in At the iteration, the selected ant (Candidate solution) location, Indicated in At the iteration, the current position of the local optimal solution is Represents the fitness function.

[0070] In some embodiments, the method further includes: in response to a preset number of ants concentrating in a local area of ​​the search space, performing random guiding processing on the ants concentrated in the local area of ​​the search space.

[0071] In this embodiment, during the optimization process, if a large number of ants representing the source-load scheduling objective function of all regions are concentrated in a local area of ​​the search space, the algorithm is likely to fall into a local optimal solution and fail to find a global optimal solution. By randomly guiding the ants representing the source-load scheduling objective function of all regions into new areas, the algorithm's exploration ability can be increased, enabling it to discover more potential solution spaces. The random guidance formula is:

[0072] Where: 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.

[0073] According to this formula, the ants representing the source-load scheduling objective function of all regions are guided to search randomly, and then the position is updated to determine whether it meets the maximum number of iterations. If it meets the maximum number of iterations, the solution is output. If not, the population is initialized again and a new iteration is started until the best collaborative scheduling solution is obtained. After the best collaborative scheduling solution is obtained, the load resources are scheduled according to the scheduling solution.

[0074] refer to Figure 3 , which is the regional electric and thermal integrated energy system structure of an embodiment of the present application.

[0075] In another possible embodiment, if Figure 3 As shown in Figure 1, a regional electric and thermal integrated energy system in a certain area is used as the test object. The system includes photovoltaic, wind power plants, combined heat and power generation, energy storage systems and heat networks. The voltage level of the system is 12.66 kV. The operating parameters of the system are shown in Table 1.

[0076] Table 1 System related parameters

[0077] This application mainly uses multi-objective scheduling when conducting regional energy coordinated scheduling. In order to verify the coordinated scheduling effect of this method, coordinated scheduling is carried out based on operating costs and energy consumption, and its scheduling results under different load demands are obtained, as shown in Table 2.

[0078] Table 2 Scheduling results under different load demands

[0079] After analyzing the test results in Table 2, it is concluded that under different load demands, before the regional source-load coordinated dispatching is carried out through this application, the operating cost is more than 150,000 yuan, and the maximum energy consumption is After the regional source-load coordinated dispatching was carried out through this application, the operating cost was significantly reduced, with the maximum value being 122,100 yuan, and the energy consumption was significantly increased, both in Therefore, this application has a good application effect, can improve the energy consumption capacity, and reduce the operating cost.

[0080] refer to Figure 4 , is a schematic diagram of the voltage conditions of the nodes of an embodiment of the present application.

[0081] The original voltage fluctuation of the energy system node is between 0.92 and 1.1 pu, and the system stability is poor. Therefore, in order to further verify the collaborative scheduling effect of this application, the voltage of different nodes in different operating periods is tested. The test results are as follows: Figure 4 shown.

[0082] right Figure 4 After analyzing the test results, it was concluded that after regional source-load coordinated scheduling is carried out through this application, the voltage stability of each node in the system can be guaranteed, and the voltage can be guaranteed to fluctuate within the allowable range, that is, between 0.92 and 1.05 pu, in different operating periods, thereby ensuring the stability of the system.

[0083] refer to Figure 5 , which is a schematic diagram of the regional system energy supply and demand response situation of an embodiment of the present application.

[0084] To verify the applicability of this application, after the source-load coordinated dispatching is carried out through this application, the supply and demand response of the regional energy system is obtained. The test results are as follows: Figure 5 shown.

[0085] right Figure 5After analyzing the test results, it was concluded that after the source-load coordinated scheduling was carried out through this application, the response degree between the daily output results and the load demand results was good, the regional system was able to meet the balance between output and demand, and achieve a balance between the multi-energy transmission network and multi-energy load users in the system.

[0086] In this application, the real-time and load responsiveness of the dispatch are particularly important. In order to verify the dispatch performance of this application, the dispatch response time deviation rate is used. The average deviation rate of the response 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 indicators is:

[0087]

[0088] in, represents the scheduling response time deviation rate, Indicates the actual duration of load response, Indicates the actual set time of load response, represents the average deviation rate of the response quantity, Indicates the load response at the first time, Indicates the load response at the second time, Indicates the total time period.

[0089] According to the above formula, the scheduling response time deviation rate of the method of the present invention after coordinated scheduling under different load ratios is calculated. The average deviation rate of the response The test results are shown in Table 3.

[0090] Table 3 Test results of response time deviation rate and response volume average deviation rate

[0091] After analyzing the test results in Table 3, it is concluded that under different load ratios, after regional source-load coordinated scheduling is performed using the method of the present invention, the response time deviation rate and the average response amount deviation rate are small, with the maximum deviations being only 0.24% and 5.2%, respectively. The scheduling response performance is good and meets the scheduling requirements.

[0092] It can be seen from the above embodiments that the load resource scheduling method described in the embodiment of the present application obtains load resource parameters; constructs 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 balance constraint; 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; and the load resources are scheduled based on the scheduling scheme. In terms of the construction of the objective function, the embodiment of the present application considers key indicators such as total operating cost, comprehensive voltage deviation and local consumption rate. Through comprehensive consideration of these factors, the economy and rationality of the scheduling scheme are ensured. Among them, the total operating cost covers the operating cost, carbon emission cost and energy loss cost, which fully reflects the comprehensive benefits of the power system operation. In addition, the comprehensive voltage deviation, as an important indicator of the power system, is directly related to the safe operation of the power grid. Through its control, the risk of equipment damage can be effectively reduced and the reliability of power supply can be improved. The local consumption rate reflects the utilization efficiency of renewable energy and promotes the goal of green development. Secondly, the setting of constraint conditions, including peak-valley fluctuation constraint, linear power flow constraint and load response balance constraint, further ensures the feasibility of the dispatching scheme. By controlling the peak-valley fluctuation, the smooth 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 the flow of power in the network, avoids the occurrence of overload, and helps to improve the overall carrying capacity of the power system. The load response balance constraint ensures the flexibility and timeliness of the power response, so that the power grid can be dynamically adjusted according to the actual load demand. Thirdly, in the solution process, the improved ant lion algorithm is applied, combined with the Chebyshev chaos mapping algorithm to initialize the ant population, which greatly improves the diversity and efficiency of the search. The Chebyshev chaos mapping can effectively expand the search space of the population, make the distribution of solutions more uniform, and reduce the convergence time of the algorithm. The improved ant lion algorithm maintains the excellent characteristics of ant colony intelligent optimization, and ensures that the optimal solution is quickly found among many solutions through the random movement mechanism and tournament selection mechanism. This innovative combined algorithm enables the dispatching scheme 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 introduced, which effectively avoids the problem of the algorithm falling into the local optimal solution. By adjusting the local concentrated area, the flexibility of the entire search process is enhanced, and the algorithm's exploration ability in the global range is improved.

[0093] It should be noted that the method of the embodiment of the present application can be performed by a single device, such as a computer or server. The method of this embodiment can also be applied to a distributed scenario and completed by multiple devices cooperating with each other. In the case of such a distributed scenario, one of the multiple devices can only perform one or more steps in the method of the embodiment of the present application, and the multiple devices will interact with each other to complete the described method.

[0094] It should be noted that the above describes some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0095] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a load resource scheduling device.

[0096] refer to Figure 6 , the load resource scheduling device comprises: An acquisition module 61 is configured to acquire load resource parameters; A construction module 62 is configured to construct 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 balance constraint; A solution module 63 is configured to solve the objective function based on the load resource parameters and in combination with the Chebyshev chaos mapping algorithm using an improved ant lion algorithm to obtain a scheduling solution; The scheduling module 64 is configured to schedule the load resources based on the scheduling scheme.

[0097] For the convenience of description, the above device is described in terms of functions divided into various modules. Of course, when implementing the present application, the functions of each module can be implemented in the same or multiple software and / or hardware.

[0098] The device of the above embodiment is used to implement the corresponding load resource scheduling method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

[0099] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the load resource scheduling method described in any of the above embodiments is implemented.

[0100] Figure 7 A more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment is shown, and the device may include: a processor 1010, a memory 1020, an input / output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input / output interface 1030, and the communication interface 1040 are connected to each other through the bus 1050 in the device.

[0101] The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

[0102] The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

[0103] The input / output interface 1030 is used to connect the input / output module to realize information input and output. The input / output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. 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.

[0104] The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0105] The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010 , the memory 1020 , the input / output interface 1030 , and the communication interface 1040 ).

[0106] 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, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

[0107] The electronic device of the above embodiment is used to implement the corresponding load resource scheduling method in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be described in detail here.

[0108] Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the load resource scheduling method described in any of the above embodiments.

[0109] 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. Information can be computer-readable instructions, data structures, modules of programs, 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 technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

[0110] The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the load resource scheduling method described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

[0111] 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 so that the computer and / or the processor executes the load resource scheduling method. Corresponding to the execution subject corresponding to each step in each embodiment of the load resource scheduling method, the processor that executes the corresponding step may belong to the corresponding execution subject.

[0112] The computer program product of the above embodiment is used to enable 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 repeated here.

[0113] A person skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

[0114] In addition, to simplify the description and discussion, and in order not to make the embodiments of the present application difficult to understand, the known power / ground connections to the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device may 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 fully within the scope of understanding of those skilled in the art). Where specific details (e.g., circuits) are set forth to describe exemplary embodiments of the present application, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details or with changes in these specific details. Therefore, these descriptions should be considered illustrative rather than restrictive.

[0115] Although the present application has been described in conjunction with specific embodiments of the present application, many alternatives, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the discussed embodiments.

[0116] The 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 should be included in the scope of protection 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, combined with the Chebyshev chaos mapping algorithm, the improved ant lion algorithm is used to solve the objective function to obtain a scheduling plan; The load resources are scheduled based on the scheduling scheme.

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, Indicates the daily load rate of the regional 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, They represent the total transmitted power and electrical load respectively; 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. The method according to claim 1, characterized in that 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: The ant population is initialized using Chebyshev chaotic mapping to obtain the first ant population; Introducing a preset ratio parameter, adjusting the first ant population to obtain a second ant population; Based on the second ant population, based on the random movement mechanism of ants, a tournament selection mechanism is adopted to randomly select 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, the iteration is terminated to obtain the optimal solution.

5. The method according to claim 4, characterized in that The proportionality parameter is expressed by the following formula: in, represents the scale parameter, represents the number of iterations, represents the maximum number of iterations, Indicates dynamically adjusted parameters.

6. The method according to claim 4, characterized in that The method further comprises: In response to a preset number of ants concentrating in a local area of ​​the search space, a random guiding process is performed on the ants concentrating in the local area of ​​the search space.

7. 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; A 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 an improved ant lion algorithm to obtain a scheduling solution; The scheduling module is configured to schedule the load resources based on the scheduling scheme.

8. 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 6 is implemented.

9. 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 6.

10. 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 6.

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