Target allocation optimization method and system

Abstract

The invention provides a target distribution optimization method and system, and belongs to the technical field of power systems, and the method comprises the steps: constructing initial mathematical models of various types of equipment in a power distribution network; constructing a multi-objective optimization function by taking the total economic cost and the energy utilization rate as optimization objectives; setting a plurality of constraint conditions based on the physical characteristics of all the devices and all the initial mathematical models; optimizing the initial mathematical models of all the uncontrollable devices to obtain optimized mathematical models; determining a target allocation scheme based on the initial mathematical models of all the controllable devices, the optimization mathematical models of all the uncontrollable devices and a multi-target optimization function; and obtaining an initial population based on all the target allocation schemes, and performing iterative screening on the initial population to determine an optimal target allocation scheme. According to the method, the total economic cost and the energy utilization rate of the optimal target distribution scheme are realized by establishing the multi-target optimization model of various types of equipment in the power distribution network and performing iterative screening by using the fireworks algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a target allocation optimization method and system. Background Art

[0002] With the transformation of the global energy structure and the rapid development of renewable energy, a large number of renewable energy sources such as wind power and photovoltaics have been connected to the power system. These renewable energy sources have the advantages of being clean and low-carbon, but they also bring challenges such as intermittency, volatility and uncontrollability. These characteristics cause the distribution network to face problems such as difficulty in maintaining the balance between power supply and demand and increased voltage and frequency fluctuations during operation.

[0003] Traditional distribution network target allocation optimization methods are mainly based on controllable conventional energy generation, such as thermal power, hydropower and natural gas power generation. These methods assume that the power output is stable and controllable, and the load demand changes are predictable, so they can be optimized through deterministic models and algorithms. However, with the access of a high proportion of renewable energy, traditional methods find it difficult to fully consider the uncertainty and volatility of renewable energy, and thus fail to optimize the total economic cost and energy utilization of the target allocation scheme.

[0004] Therefore, under the current technological background, how to achieve the optimal total economic cost and energy utilization of the target allocation plan has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] The present invention provides a target allocation optimization method, system, electronic device and storage medium, which are used to solve the defects in the prior art and achieve the total economic cost and energy utilization rate of the optimized target allocation solution.

[0006] The present invention provides a target allocation optimization method, comprising the following steps: constructing initial mathematical models of various types of equipment in the distribution network of each area respectively; the equipment includes controllable equipment and uncontrollable equipment; Taking the total economic cost and energy utilization rate as optimization targets, and constructing a multi-objective optimization function based on the optimization targets and all the initial mathematical models; Based on the physical characteristics of all the equipment and all the initial mathematical models, a plurality of constraints are set; Based on the multi-objective optimization function and all the constraints, the initial mathematical models of all the uncontrollable devices are optimized to obtain the optimized mathematical models of all the uncontrollable devices; Based on the initial mathematical models of all the controllable devices, the optimized mathematical models of all the uncontrollable devices and the multi-objective optimization function, a Pareto solution set is determined; the Pareto solution set includes multiple groups of feasible solutions, each group of feasible solutions corresponds to a target allocation scheme; Based on all the target allocation schemes, an initial population is obtained, and the initial population is iteratively screened to determine the optimal target allocation scheme.

[0007] According to a target allocation optimization method provided by the present invention, the initial population is obtained based on all the target allocation schemes, and the initial population is iteratively screened to determine the optimal target allocation scheme, which specifically includes: Encoding each of the target allocation schemes in binary form to obtain a plurality of initial binary codes, wherein a binary number is used to represent the allocation of each target, each bit represents an available resource, a value of 1 on the bit indicates that the resource is allocated to the target, and a value of 0 indicates that the resource is not allocated; Taking all the initial binary codes as an initial population; In each round of iteration, based on any two of the initial binary codes, a new binary code is generated, and the fitness of the new binary code is compared with that of all the initial binary codes, and individuals with higher fitness are selected as members of the next round of iteration population, until a population of offspring allocation schemes is obtained after a preset number of iterations; The offspring allocation scheme population is merged with the initial population to form a final population, and the final population is iterated based on the Pareto algorithm to determine the optimal individual from the Pareto frontier solution set, and the resource allocation scheme corresponding to the binary code of the optimal individual is used as the optimal target allocation scheme.

[0008] According to a target allocation optimization method provided by the present invention, generating a new binary code based on any two of the initial binary codes specifically includes: Performing an XOR operation on any two of the initial binary codes to obtain a binary difference; Convert one of the initial binary code and the binary difference value into decimal to obtain a decimal code and a decimal difference value; The decimal difference is multiplied by a preset weight factor and then summed with the decimal code to obtain a new decimal code; The new decimal code is converted into binary to obtain the new binary code.

[0009] According to a target allocation optimization method provided by the present invention, the types include source type, network type, load type and storage type; The initial mathematical model of the device of the source type is expressed by the following formula: ; in, is the output power of the device of the ith source type at time t, is the maximum output power of the device of the ith source type, is the operating status of the device of the ith source type at time t, with a value of 1 or 0. A value of 1 indicates operation, and a value of 0 indicates non-operation. is the power loss of the equipment of the i-th source type at time t; The initial mathematical model of the mesh type device is expressed by the following formula: ; in, is the power from node i to node j, is the voltage at node i, is the voltage at node j, is the conductance in the admittance matrix, is the susceptance in the admittance matrix; is the angle between node i and node j; The initial mathematical model of the device of the described load type is expressed by the following formula: ; in, is the power of the k-th source type device at time t, is the base load of the equipment of the kth source type at time t, is the adjustable load of the equipment of the kth source type at time t; The initial mathematical model of the storage type device is expressed by the following formula: ; in, is the power of the lth storage type device at time t+1, is the power of the lth storage type device at time t, is the charging power of the lth storage type device at time t, is the discharge power of the lth storage type device at time t, For charging efficiency, is the discharge efficiency.

[0010] According to a target allocation optimization method provided by the present invention, the total economic cost and energy utilization rate are taken as optimization targets, and a multi-objective optimization function is constructed based on the optimization targets and all the initial mathematical models, which specifically includes: Based on the optimization target and all the initial mathematical models, constructing a total economic cost optimization function and an energy utilization optimization function; Based on the total economic cost optimization function and the energy utilization rate optimization function, a multi-objective optimization function is constructed, wherein the total economic cost optimization function and the energy utilization rate optimization function are negatively correlated; The total economic cost optimization function is expressed by the following formula: ; ; ; ; ; ; The energy utilization optimization function is expressed by the following formula: ; in, is the total economic cost, is the sum of the investment costs of all types of equipment, It is the sum of the operation and maintenance costs of source type equipment and storage type equipment. The cost of purchasing electricity for all types of equipment, Cost of sales for all types of equipment, is the sum of the power of all types of equipment, is the energy utilization rate, The output power of the uncontrollable device.

[0011] According to a target allocation optimization method provided by the present invention, the constraint condition is expressed by the following formula: ; ; ; ; ; in, is the sum of the power of all types of equipment, is the sum of the power losses of all types of equipment, is the maximum charging power of the device of the lth storage type, is the maximum discharge power of the device of the lth storage type, is the minimum power of the device of the lth storage type, is the maximum power of the device of the lth storage type, is the voltage of node j at time t, is the frequency of node j at time t.

[0012] According to a target allocation optimization method provided by the present invention, the Pareto solution set is determined based on the initial mathematical models of all the controllable devices, the optimized mathematical models of all the uncontrollable devices and the multi-objective optimization function, specifically including: Integrate the initial mathematical models of all the controllable devices, the optimized mathematical models of all the uncontrollable devices and the multi-objective optimization function to construct a multi-objective optimization model; Based on the multi-objective optimization model, generating an initial solution set that satisfies all the constraint conditions, wherein the initial solution set includes multiple groups of feasible solutions; Compare all feasible solutions in the initial solution set pairwise to determine the dominance relationship; Identify all feasible solutions that are not dominated by other solutions to form the first non-dominated layer; The stratified feasible solutions are removed from the initial solution set, and the above process is repeated for the remaining feasible solutions to form a second non-dominated layer until all feasible solutions are stratified; For each feasible solution of the non-dominated layer, sort them according to the numerical value on each objective function, and for the sorted feasible solutions, calculate the sum of the differences between the adjacent feasible solutions in the objective function value as the crowding distance, and set the crowding distance of the feasible solution on the boundary of each non-dominated layer to infinity; All the feasible solutions are sorted from high to low according to the non-dominated level and the crowding distance, and the feasible solutions that meet the predetermined scale requirements are selected from all the sorted feasible solutions to form a Pareto solution set.

[0013] The present invention also provides a target allocation optimization system, comprising the following modules: A model building module, used to build initial mathematical models of various types of equipment in the distribution network of each area; the equipment includes controllable equipment and uncontrollable equipment; A multi-objective optimization function construction module, used to take the total economic cost and energy utilization rate as optimization targets, and to construct a multi-objective optimization function based on the optimization targets and all the initial mathematical models; A constraint condition setting module, used for setting a plurality of constraint conditions based on the physical characteristics of all devices and all the initial mathematical models; An optimization module, configured to optimize the initial mathematical models of all the uncontrollable devices based on the multi-objective optimization function and all the constraints to obtain optimized mathematical models of all the uncontrollable devices; A processing module, configured to determine a Pareto solution set based on the initial mathematical models of all the controllable devices, the optimized mathematical models of all the uncontrollable devices, and the multi-objective optimization function; the Pareto solution set includes multiple groups of feasible solutions, each group of feasible solutions corresponds to a target allocation scheme; The processing module is also used to obtain an initial population based on all the target allocation schemes, and iteratively screen the initial population to determine the optimal target allocation scheme.

[0014] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, any one of the target allocation optimization methods described above is implemented.

[0015] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the target allocation optimization method as described above is implemented.

[0016] The present invention also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the target allocation optimization method described above is implemented.

[0017] In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: By constructing the initial mathematical models of controllable and uncontrollable devices in the distribution network of each region, the dynamic behavior, operating characteristics and power state of various types of equipment can be accurately described. This modeling method based on the physical characteristics of the equipment refines the performance of the equipment in different environments and operating conditions, provides a reliable data basis for the subsequent optimization process, and makes the model more adaptable and robust in practical applications. By taking the total economic cost and energy utilization rate as the optimization objectives, and constructing a multi-objective optimization function based on these objectives and all the initial mathematical models, it is possible to comprehensively consider economic benefits and energy efficiency in the resource allocation plan to achieve the optimal allocation of resources. This multi-objective optimization function provides quantitative indicators for the cost and energy utilization of various types of equipment, which helps to maximize the utilization rate of renewable energy while ensuring cost control, thereby effectively improving the economy and environmental protection of the system. By setting multiple constraints based on the physical characteristics of the equipment and all the initial mathematical models, it is ensured that the model meets the actual physical operation requirements during the optimization process. The set power, charge and discharge, voltage and frequency constraints ensure the feasibility of the optimization plan, so that each device does not exceed its safety boundary during the allocation process, thereby preventing the occurrence of system overload and instability, and providing a guarantee for the stable and safe operation of the system. By optimizing the initial mathematical model of uncontrollable devices based on multi-objective optimization functions and all constraints, the stability of the system can be maintained when dealing with output fluctuations of uncontrollable devices. This step makes the model of uncontrollable devices more robust and can maintain a stable operating state under different scenarios, significantly reducing the system imbalance caused by fluctuating power generation such as wind power and photovoltaic power generation, and helping the distribution network to achieve economical and efficient operation when a high proportion of renewable energy is connected. By determining the Pareto solution set based on the initial mathematical model of all controllable devices, the optimized mathematical model of uncontrollable devices and the multi-objective optimization function, a set of allocation schemes that achieve an ideal balance between total economic cost and energy utilization are generated. The construction of the Pareto solution set provides different preferred combinations for resource allocation schemes in multi-objective optimization, provides a wide range of alternatives for the final resource allocation selection, and ensures adaptability and flexibility under different scenarios. By obtaining the initial population based on all objective allocation schemes and iteratively screening the initial population, extensive exploration is achieved in different solution spaces and convergence to the optimal solution. The diversification of the initial population and its iterative screening process enable the model to maintain a balance between global search and local optimization, avoiding falling into local optimality, and ultimately forming an optimal target allocation plan with excellent performance in economic and energy utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 It is one of the flow charts of the target allocation optimization method provided by the present invention.

[0020] Figure 2 This is the second flow chart of the target allocation optimization method provided by the present invention.

[0021] Figure 3 It is a coding schematic diagram of the source-grid-load-storage resource node target allocation scheme provided by the present invention.

[0022] Figure 4 This is the third flow chart of the target allocation optimization method provided by the present invention.

[0023] Figure 5 It is a Pareto solution diagram of the source-grid-load-storage resource node target allocation scheme provided by the present invention.

[0024] Figure 6 It is a structural schematic diagram of the target allocation optimization system provided by the present invention.

[0025] Figure 7 It is a structural schematic diagram of the electronic device provided by the present invention. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] It should be noted that, in the description of the present invention, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "include one..." do not exclude the existence of other identical elements in the process, method, article or device including the elements. The orientation or positional relationship indicated by the terms "upper", "lower", etc. is based on the orientation or positional relationship shown in the accompanying drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the system or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to the specific circumstances.

[0028] The terms "first", "second", etc. in the present invention are used to distinguish similar objects, and are not used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present invention can be implemented in an order other than those illustrated or described herein, and the objects distinguished by "first", "second", etc. are generally of the same type, and the number of objects is not limited. For example, the first object can be one or more. In addition, "and / or" means at least one of the connected objects, and the character " / " generally indicates that the objects associated with each other are in an "or" relationship.

[0029] Combine the following Figure 1-Figure 7 The target allocation optimization method, system, electronic device and storage medium provided by the present invention are described.

[0030] Figure 1 It is one of the flow charts of the target allocation optimization method provided by the present invention, such as Figure 1 As shown, including but not limited to the following steps: Step 1: Construct initial mathematical models of various types of equipment in the distribution network of each area respectively; the equipment includes controllable equipment and uncontrollable equipment.

[0031] In the embodiment of step 1, it is first necessary to construct an initial mathematical model of various types of equipment in the distribution network of each region. The construction of this model is to accurately reflect the physical characteristics, operating status and mutual influence of the four types of equipment in the distribution network, namely, source, network, load and storage, and provide reliable basic data support for subsequent optimization. Specifically, the construction of the initial mathematical model of the equipment can comprehensively describe the dynamic behavior and operating constraints of each device, so that the optimization scheme can formulate an effective allocation strategy based on the actual operating characteristics of the equipment. This process is not only crucial to the stable operation of the distribution network, but also lays a solid foundation for achieving economical and efficient energy allocation and improving the utilization rate of renewable energy. All equipment can also be divided into controllable equipment and uncontrollable equipment. Controllable equipment (such as natural gas power generation, hydropower generation and thermal power generation) has a relatively stable output and can adjust its power output according to demand. In contrast, uncontrollable equipment (such as wind power generation and solar photovoltaic power generation) has volatility and uncertainty in output due to its dependence on environmental factors. When the mathematical model is initially constructed, the mathematical models of controllable and uncontrollable equipment are consistent.

[0032] In this embodiment, the source type device generally includes distributed power generation equipment, such as solar photovoltaic and wind power generation. In order to ensure the stable output of the source device under different light intensity and wind speed conditions, it is necessary to consider the dynamic change characteristics of its output power. Specifically, the initial mathematical model of the source type device is expressed by the following formula: ; in, is the output power of the device of the ith source type at time t, is the maximum output power of the device of the ith source type, is the operating status of the device of the ith source type at time t, with a value of 1 or 0. A value of 1 indicates operation, and a value of 0 indicates non-operation. is the power loss of the device of the i-th source type at time t; by constructing this model, the operating status of each source device at different time nodes can be intuitively described, ensuring that the loss is controlled while maximizing the output power, thereby effectively improving the power generation efficiency and stability of the source device.

[0033] For network type devices, since they play a key role in connection and transmission in the distribution network, it is necessary to build their power transmission characteristics so that the network can remain stable even when the voltage fluctuates. The initial mathematical model of the network device can describe the voltage and power relationship between nodes through the power flow equation. The initial mathematical model of the type of device is expressed by the following formula: ; in, is the power from node i to node j, is the voltage at node i, is the voltage at node j, is the conductance in the admittance matrix, is the susceptance in the admittance matrix; is the angle between node i and node j; through this model, the voltage, current and phase angle changes during power transmission in the distribution network can be accurately described, thereby effectively supporting the optimized control of network equipment in practical applications and ensuring the stable transmission of power in the distribution network.

[0034] For load-type devices, the focus of model construction is to accurately describe their load impact on the distribution network. In home, commercial and industrial scenarios, the power demand of load devices such as electric vehicle charging piles and lighting equipment has significant volatility. To cope with this feature, the mathematical model of the load device is represented by the superposition of the basic load and the adjustable load. The initial mathematical model of the load-type device is expressed by the following formula: ; in, is the power of the k-th source type device at time t, is the base load of the equipment of the kth source type at time t, is the adjustable load of the k-th source type of equipment at time t; this model can better reflect the power consumption patterns and changing trends of different types of loads, and is helpful to optimize the power demand of load equipment through reasonable regulation and reduce the peak load pressure of the power grid.

[0035] In terms of energy storage equipment, considering its important role in balancing supply and demand and shaving peaks and filling valleys, the model should be able to accurately describe the charging and discharging behavior and power state changes of energy storage equipment. The initial mathematical model of energy storage type equipment is expressed by the following formula: ; in, is the power of the lth storage type device at time t+1, is the power of the lth storage type device at time t, is the charging power of the lth storage type device at time t, is the discharge power of the lth storage type device at time t, For charging efficiency, The model can simulate the dynamic changes of the power of the energy storage device in detail, so as to ensure that the energy storage device can be charged and discharged efficiently while meeting the power demand, thus improving the utilization rate of energy storage resources.

[0036] Step 2: Take the total economic cost and energy utilization rate as optimization objectives, and construct a multi-objective optimization function based on the optimization objectives and all initial mathematical models.

[0037] In the embodiment of step 2, it is first necessary to construct a multi-objective optimization function with the total economic cost and energy utilization as the optimization targets. The purpose of this process is to improve the utilization rate of renewable energy, minimize energy waste and alleviate energy cost pressure while ensuring the economy of the system. By optimizing the cost and energy utilization rate at the same time, various resources (source, network, load, storage) in the distribution network can be more effectively utilized to achieve overall economic and efficient operation. Since various types of equipment in the distribution network have multiple operating characteristics and complex relationships, constructing a multi-objective optimization model that can cover these factors has become the key to achieving efficient management.

[0038] Firstly, based on the optimization objectives and all initial mathematical models, the total economic cost optimization function and energy utilization optimization function are constructed.

[0039] To achieve this goal, the total economic cost is first considered as an important consideration for optimization. The total economic cost includes various expenditures such as the initial investment of equipment, operation and maintenance costs, and electricity purchase and sales costs, which can comprehensively measure the economic efficiency of the system. Specifically, the sum of the investment costs of all types of equipment Includes the infrastructure costs of source, network, load and storage equipment. The sum of the operating and maintenance costs of source type equipment and storage type equipment The calculation is based on the real-time operating status and power demand of source-type equipment and storage-type equipment, which reflects both the physical characteristics of the equipment and the economic burden of its daily operation. In addition, the distribution network also faces the power purchase cost of all types of equipment. and sales costs of all types of equipment This cost is based on the total power of the equipment. The calculation can reflect the power supply and demand relationship between the devices at each end of the system. This part of the optimization can be expressed by the following formula: ; ; ; ; .

[0040] At the same time, energy utilization As another optimization goal, the focus is on increasing the proportion of renewable energy in the system and reducing dependence on non-renewable energy. High energy utilization not only helps reduce the consumption of traditional energy in the distribution network, but also effectively controls carbon emissions and other environmental impacts. When calculating energy utilization, the output power of uncontrollable equipment (such as wind power, photovoltaic and other renewable energy equipment) is selected. As the numerator, the denominator is the total power output of all power sources in the distribution network, including controllable and uncontrollable source devices and energy storage devices. The energy utilization optimization target is expressed by the following formula: ; This formula clarifies the proportion of renewable energy in all power generation resources, thus providing a basis for quantifying energy utilization efficiency.

[0041] Secondly, based on the economic total cost optimization function and the energy utilization rate optimization function, a multi-objective optimization function is constructed. The economic total cost optimization function and the energy utilization rate optimization function are negatively correlated. When constructing a multi-objective optimization model, the above two objectives can be combined to better balance economic efficiency and energy efficiency and achieve efficient operation of the distribution network. By incorporating the total economic cost and energy utilization rate into the optimization objectives at the same time, a multi-objective optimization function is formed, and the two are logically negatively correlated, that is, when the economic cost of the system decreases, the energy utilization rate is likely to decrease. Therefore, by adjusting the utilization rate of renewable energy to balance the total economic cost in the optimization model, the energy utilization effect can be improved while ensuring cost control.

[0042] Step 3: Based on the physical characteristics of all devices and all initial mathematical models, set multiple constraints.

[0043] In the embodiment of step 3, in order to achieve safe and stable operation of the distribution network, it is necessary to set multiple constraints based on the physical characteristics of the equipment and all initial mathematical models. The setting of these constraints is intended to ensure the normal operation of various types of equipment under different load demands and power supply conditions, avoid problems such as overload or voltage-frequency fluctuations in the system, and thus ensure the practical feasibility and effectiveness of the optimization scheme. By setting reasonable power, charge-discharge and voltage-frequency constraints, it is possible not only to ensure that the solution of the optimization model meets physical reality, but also to prevent the system from having unreasonable configuration schemes during the solution process, which is crucial to improving the robustness of the model and the applicability of the optimization results.

[0044] First, in order to ensure the power balance of the distribution system under the load changes of various types of equipment, the overall power balance constraint conditions are set. Power balance means that at any time, the total power output of each type of equipment matches the total load demand of the system. Specifically, the power balance conditions of the system are: ; in, is the sum of the power of all types of equipment, It is the sum of the power losses of all types of equipment. Through this balance condition, the model can dynamically adjust the power output of various types of equipment to ensure that the system can achieve supply and demand matching at each time point, thereby avoiding operational risks caused by insufficient power or overload.

[0045] In the operation of the "source-grid-load-storage" system, there are differences in the flexibility resource adjustment capabilities of different devices, which leads to different response characteristics of various types of equipment. In particular, uncontrollable distributed power generation equipment (such as wind power, photovoltaic power generation) and load demand are often affected by the environment and user behavior, showing large volatility. In order to effectively deal with this volatility, it is necessary to introduce uncertainty factors into the model to ensure that the system can achieve robust operation under different scenarios, thereby improving the stability of the system and the utilization rate of renewable energy.

[0046] Specifically, for uncontrollable distributed generation equipment, its power generation fluctuates with external conditions (such as wind speed and light intensity), which is difficult to accurately predict. Therefore, the actual power generation of uncontrollable generation source i at time t is defined as , assuming that it conforms to a certain probability distribution P or is included in the scene set Similarly, the load demand is also uncertain. The actual power demand of load k at time t is It is also assumed that the probability distribution P or contained in the scene set By incorporating these uncertainties into the scenario set U, the fluctuations of uncontrollable power generation and load demand under different scenarios can be comprehensively described.

[0047] On this basis, the objective function constructed aims to maintain the robustness of the system for all possible uncertainty scenarios, that is, to effectively cope with volatility in any scenario. To this end, the optimization goal is set to minimize the expected total economic cost and maximize the utilization rate of renewable energy of uncontrollable equipment (such as wind power, photovoltaics, etc.). Specifically, the optimization goal can be expressed as: or ;in, is an uncertainty vector containing and . Minimize the expected total cost under all uncertainty scenarios and ensure the adaptability of the optimization scheme under different environmental conditions, thereby improving the economy and flexibility of the system.

[0048] After introducing uncertainty conditions, the system constraints need to be valid for all uncertainty scenarios. In order to meet the power balance constraints and ensure that the power supply and demand of the system are balanced at every moment, the following constraints are defined: ; This constraint indicates that the total power supply capacity and total demand load of each type of equipment must be balanced in different uncertainty scenarios. The left side is the effective output power of all source devices and energy storage devices, and the right side is the sum of the actual demand power of load devices and renewable energy power generation equipment. This constraint ensures that even when there are fluctuations in power generation and load demand, the system can still maintain a stable power supply and demand balance and avoid operational failures caused by insufficient or excessive power.

[0049] On the basis of ensuring power balance, it is also necessary to constrain the charging and discharging behavior of energy storage equipment to ensure that the charging and discharging power and energy state of energy storage equipment are within a reasonable range. Energy storage equipment plays an important role in balancing the supply and demand of the power grid and shaving peaks and filling valleys. Its charging and discharging behavior must operate within the rated power and power range. Specific constraints include: ; in, is the maximum charging power of the device of the lth storage type, is the maximum discharge power of the device of the lth storage type, is the minimum power of the device of the lth storage type, is the maximum power of the device of the lth storage type. By limiting the charging and discharging power and power state of the energy storage device, the energy storage device can be prevented from being damaged due to abnormal operations such as overcharging and over-discharging, thereby improving the safety of the system and the service life of the equipment.

[0050] In addition, to ensure the stability of the voltage and frequency of the distribution network, the voltage and frequency constraints of the network equipment are set. The fluctuation of voltage and frequency in the distribution network will directly affect the stable operation of the equipment, especially when the power fluctuation of uncontrollable equipment is large, the control of voltage and frequency is particularly important. The voltage and frequency constraints of the network equipment are: ; in, is the voltage of node j at time t, is the frequency of node j at time t. Vmin and Vmax are the minimum and maximum voltage values ​​of the node, respectively, and fmin and fmax are the minimum and maximum values ​​of the node frequency. By constraining voltage and frequency, the model can ensure that the power transmission process remains within a reasonable electrical parameter range, prevent power supply quality problems caused by unstable voltage or frequency, and ensure the stable operation of the distribution network.

[0051] Step 4: Based on the multi-objective optimization function and all constraints, the initial mathematical models of all uncontrollable devices are optimized to obtain the optimized mathematical models of all uncontrollable devices.

[0052] In the embodiment of step 4, the initial mathematical model of the uncontrollable equipment is optimized based on the multi-objective optimization function and all set constraints to cope with the uncertainties in its power output and load demand. The purpose of this optimization process is to improve the operational robustness of the uncontrollable equipment under volatility scenarios, ensure that the system can remain stable under a variety of uncertainties, and minimize operating costs and energy waste.

[0053] The power output of uncontrollable distributed generation equipment depends on external environmental factors, such as wind speed and light intensity, so its power generation output needs to be modeled as an uncertainty distribution in model optimization. Specifically, the actual power generation power of uncontrollable distributed generation equipment i at time t and the actual load demand of load device k at time t All are assumed to conform to a certain probability distribution or be included in the scene set U. By defining the scene set and , the output and demand of uncontrollable equipment can be simulated in multiple scenarios, so that the model can better reflect the volatility in actual operation. The core of optimization is to solve every possible scenario, so as to minimize the expected total economic cost under all scenarios. and maximize energy efficiency , ensuring the stability and economy of the system.

[0054] To achieve this goal, the optimization model needs to ensure that the power balance and constraints under all uncertain scenarios are valid. and load demand As a key input in the optimization process, in each uncertainty scenario, the model will dynamically adjust the power output and demand satisfaction of each device to meet the power balance conditions: ; This condition ensures that the power supply and demand of the system can be consistent in different scenarios, avoiding the problem of power supply imbalance caused by uncontrollable power generation and load demand fluctuations. The power balance constraint also takes into account the charging and discharging status of the energy storage device, so that the energy storage device can be balanced when power generation is insufficient or load demand peaks.

[0055] Through this optimization process, the mathematical model of uncontrollable equipment has been improved, which can ensure the reliability of power supply under different uncertainty scenarios. The effect of optimization is reflected in many aspects: on the one hand, through multi-scenario optimization and robustness improvement, the system can effectively cope with the volatility of uncontrollable power generation and load demand, avoid power shortages or oversupply, and thus improve the operational stability of the system. On the other hand, the optimized model can minimize the total economic cost, reduce unnecessary energy loss and economic expenditure on the basis of ensuring the smooth operation of the system. In addition, the optimization process also increases the utilization rate of renewable energy. Through the adjustment of energy storage equipment and the reasonable distribution of loads, the phenomenon of wind and solar power abandonment is minimized, thereby realizing flexible regulation under the high proportion of renewable energy grid connection.

[0056] Step 5: Based on the initial mathematical models of all controllable devices, the optimized mathematical models of all uncontrollable devices and the multi-objective optimization function, determine the Pareto solution set; the Pareto solution set includes multiple groups of feasible solutions, each group of feasible solutions corresponds to a target allocation scheme.

[0057] In the embodiment of step 5, in order to effectively select the optimal target allocation scheme among multiple devices, the Pareto solution set is determined based on the initial mathematical model of all controllable devices, the optimized mathematical model of uncontrollable devices, and the multi-objective optimization function. The goal of determining the Pareto solution set is to generate a set of optimal solutions that balance the total economic cost and energy utilization of the system, so that no solution can improve one goal without sacrificing another goal. Through Pareto optimization, a set of potential allocation schemes can be provided for subsequent optimization steps, laying the foundation for further screening out the global optimal resource allocation scheme.

[0058] In a possible implementation, step 5 specifically includes the following steps: Integrate the initial mathematical models of all controllable devices, the optimized mathematical models of all uncontrollable devices, and the multi-objective optimization function to construct a multi-objective optimization model; Based on the multi-objective optimization model, an initial solution set that satisfies all constraints is generated, and the initial solution set includes multiple groups of feasible solutions; Compare all feasible solutions in the initial solution set pairwise to determine the dominance relationship; Identify all feasible solutions that are not dominated by other solutions to form the first non-dominated layer; Remove the stratified feasible solutions from the initial solution set, and repeat the above process for the remaining feasible solutions to form the second non-dominated layer until all feasible solutions are stratified; For each feasible solution of the non-dominated layer, sort them according to the numerical value on each objective function, and for the sorted feasible solutions, calculate the sum of the differences between the adjacent feasible solutions in the objective function value as the crowding distance, and set the crowding distance of the feasible solution on the boundary of each non-dominated layer to infinity; All feasible solutions are sorted from high to low according to the non-dominated level and crowding distance, and feasible solutions that meet the predetermined scale requirements are selected from all the sorted feasible solutions to form a Pareto solution set.

[0059] Specifically, first of all, the purpose of integrating the mathematical models of all equipment with the multi-objective optimization function is to build a complete multi-objective optimization model to more systematically reflect the real-time status and operating characteristics of the equipment. This step incorporates the behaviors and properties of all sources, networks, loads, and storage devices into a unified optimization framework, so that the model can operate effectively under uncertain scenarios based on the dynamic response and power output of the equipment. Based on this model, the model generates an initial solution set based on constraints such as power balance, voltage frequency, and charging and discharging, ensuring that each set of feasible solutions provides different allocation strategies while meeting various operating constraints.

[0060] Furthermore, the multi-objective optimization model is used to generate an initial solution set that satisfies all constraints. The solution set contains multiple feasible solutions that meet the actual operating conditions. Each solution set represents a target allocation scheme, that is, an allocation strategy under different combinations of total economic cost and energy utilization. The generation of the initial solution set ensures that the model has sufficient solution space coverage and provides a wealth of alternative solutions for the subsequent screening process. The effect of this step is to construct a diverse solution set covering different scenarios, thus laying the foundation for the subsequent screening of the Pareto optimal solution set.

[0061] Furthermore, the model compares all feasible solutions in the initial solution set pairwise to determine the dominance relationship. The dominance relationship is used to identify solutions that show absolute dominance between total economic cost and energy utilization: when one solution is superior to another solution in all objectives and has a strict advantage in at least one objective, the former dominates the latter. Through this process, the model is able to identify a set of solutions that achieve an effective trade-off between total economic cost and energy utilization, providing support for further determination of the non-dominated layer.

[0062] Furthermore, the first non-dominated layer is formed by identifying and extracting the solutions in the initial solution set that are not dominated by other solutions. This layer represents the set of feasible solutions that achieve the best balance in the optimization objectives, and it is impossible to improve one objective without sacrificing another objective. The extraction of the first non-dominated layer enables the model to lock in a batch of candidate optimal solutions, providing a basis for subsequent multi-level screening. After eliminating the first non-dominated layer, the model repeats the judgment of the dominance relationship of the remaining solution set, and forms the second and third non-dominated layers in turn until all feasible solutions are processed in layers. The multi-level division of the non-dominated layer ensures that the superiority of the solution set decreases from top to bottom, so that each layer of solutions represents the local optimal solution under a certain trade-off to a certain extent.

[0063] Furthermore, in order to further measure the distribution state of each layer of solutions, the model sorts the solutions of each non-dominated layer according to the numerical value of the objective function, and calculates the crowding distance of adjacent feasible solutions. The crowding distance reflects the density between solutions, which can help the model screen out solutions with uniform distribution and wider coverage. In particular, setting an infinite crowding distance at the boundary solution of the Pareto frontier helps to maintain the diversity of the solution set and avoid over-concentration in a certain target range. The calculation of the crowding distance ensures that the solution set is reasonably distributed on each optimization target, thereby improving the applicability and flexibility of the model.

[0064] Finally, the model sorts all feasible solutions according to the non-dominated level and crowding distance, and selects feasible solutions that meet the predetermined scale requirements to form a Pareto solution set. Each solution in this solution set represents an optimally balanced resource allocation scheme that is both competitive in terms of total economic cost and excellent in terms of energy utilization. Through this screening and optimization method, the model can obtain a diversified solution set while laying the foundation for the subsequent selection of the final optimal target allocation scheme.

[0065] Step 6: Based on all target allocation schemes, obtain the initial population, and iteratively screen the initial population to determine the optimal target allocation scheme.

[0066] In the embodiment of step 6, the goal is to generate an initial population based on all Pareto solution sets, and finally determine the optimal resource allocation plan through iterative screening. This step uses the heuristic global search capability of the fireworks algorithm to binary encode the allocation plans in the Pareto solution set to form an initial population, and generate better solutions through iteration to achieve the optimal configuration of total economic cost and energy utilization. The fireworks algorithm simulates the process of fireworks explosion, and uses different "sparks" to perform a global search of the solution space, effectively avoiding the model from falling into a local optimal solution, while improving the convergence speed. In this way, it can be ensured that the final selected allocation plan achieves an ideal balance between various objectives and is suitable for practical applications.

[0067] In one possible implementation, refer to Figure 2 , 2 is a second flow chart of the target allocation optimization method provided by the present invention, such as Figure 2 As shown, step 6 specifically includes steps 61-64: Step 61: Encode each target allocation scheme in binary form to obtain multiple initial binary codes, where a binary number is used to represent the allocation status of each target, each bit represents an available resource, and a value of 1 on the bit indicates that the resource is allocated to the target, and a value of 0 indicates that it is not allocated.

[0068] Specifically, the allocation status of each resource is represented by a binary "0" or "1" in the code, where "1" indicates that the resource is allocated to the corresponding target and "0" indicates that it is not allocated. Assuming that the target allocation scheme involves multiple resources, such as wind power, photovoltaics, power grid, energy storage, etc., the status of each target resource combination is represented by a binary string, so that the combination of multiple resources can be reflected in one section of code. For example, for a scenario with 5 targets and 4 resources (the resources are wind power, photovoltaics, power grid, and energy storage), a 4-bit binary code can be assigned to each target, such as "0101" indicating that wind power and energy storage resources are allocated to the target. Finally, the encoding length of the entire allocation scheme is n×m, where n represents the number of targets and m represents the number of resources.

[0069] Through this encoding method, the model can structurally represent the resource allocation of each target, providing a standardized representation method for subsequent optimization. Figure 3 , Figure 3 This is a schematic diagram of the coding of the target allocation scheme for source-grid-load-storage resource nodes provided by the present invention. For example, a code string "01010000111010110011" can represent a specific resource allocation scheme: target 1 allocates photovoltaic and energy storage resources, target 2 does not allocate any resources, target 3 allocates wind power, photovoltaic and grid resources, target 4 allocates wind power and grid resources, and target 5 allocates grid and energy storage resources. Each set of binary codes not only reflects the allocation details of the scheme, but also enables the model to quickly compare, cross and mutate different solutions during the optimization iteration process.

[0070] The effect of this encoding scheme is that it converts complex resource allocation problems into digital encoding, allowing the model to operate solutions more efficiently in the solution space. Each binary string represents an independent resource allocation strategy, which can easily perform fitness evaluation and optimization operations, helping the model to continuously generate better solutions during the search process. At the same time, binary encoding makes the "spark explosion" operation and fitness evaluation of the fireworks algorithm more convenient, effectively enhancing the algorithm's global search capability and convergence speed, and laying a solid foundation for the subsequent screening of the optimal resource allocation solution.

[0071] Step 62: All initial binary codes are used as the initial population.

[0072] Specifically, the multiple binary codes generated in step 61 are put into the initial population as individuals one by one, and each binary code represents an independent resource allocation strategy. In this way, each individual in the initial population not only reflects the matching relationship between the target and the resource, but also serves as the "spark" seed in the fireworks algorithm, providing a basis for subsequent iterative explosion and mutation operations.

[0073] Step 63: In each round of iteration, based on any two initial binary codes, a new binary code is generated, and the fitness of the new binary code is compared with that of all the initial binary codes, and individuals with higher fitness are selected as members of the next round of iteration population, until a preset number of iterations is passed to obtain a population of offspring allocation schemes.

[0074] In the embodiment of step 63, in order to optimize the resource allocation scheme in the initial population and make it gradually approach the optimal solution, the initial population is iterated multiple times using the spark explosion operation of the fireworks algorithm. In each round of iteration, by mutating any two initial binary codes to generate new solutions, the model can explore new areas of the solution space, improve the diversity of solutions, and select better individuals through fitness comparison, thereby ensuring that the population has global search capabilities while converging. The fireworks algorithm is used because its unique "spark explosion" process can not only simulate global searches, but also deeply explore local areas, avoiding the risk of falling into local optimal solutions, so that the model can converge to the global optimal solution in a shorter iteration cycle.

[0075] In one possible implementation, refer to Figure 4 , Figure 4 This is the third flow chart of the target allocation optimization method provided by the present invention, such as Figure 4 As shown, in step 63, based on any two initial binary codes, the method for generating a new binary code specifically includes steps 631-634: Step 631: Perform an XOR operation on any two initial binary codes to obtain a binary difference.

[0076] Step 632: Convert one of the initial binary codes and the binary difference value into decimal to obtain a decimal code and a decimal difference value.

[0077] Step 633: multiply the decimal difference by a preset weight factor and then sum the result with the decimal code to obtain a new decimal code.

[0078] Step 634: Convert the new decimal code into binary to obtain a new binary code.

[0079] In the embodiment of steps 631 to 634, the purpose is to generate new individual codes through the spark explosion operation of the fireworks algorithm to increase the diversity of the population and optimize the resource allocation scheme. By performing an XOR operation on the binary codes of two individuals and introducing random mutations, the model can explore new solution spaces on a global scale, thereby enhancing the optimization effect on resource allocation. Introducing the spark explosion operation into the optimization of resource allocation can prevent the model from falling into a local optimum and ensure that the optimal solution that balances the total economic cost and energy utilization is found.

[0080] In step 631, two binary coded individuals are first randomly selected from the population, such as codes A and B. These individuals represent different resource allocation schemes, and each bit of 0 or 1 in the code corresponds to the allocation state of a specific resource. By performing a bitwise XOR operation on the two codes, a binary difference Δ is generated, which represents the difference between the two individuals. Through the XOR operation, the model can generate a new representation of the allocation state, so that the new solution is expanded on the difference between the two original solutions, providing a preliminary data basis for generating new individuals.

[0081] In step 632, the binary difference Δ is converted into a decimal code to facilitate subsequent calculations. At the same time, one of the original codes (such as code A) is selected and converted into a decimal code. The decimal difference and the decimal original code obtained in this way can be mathematically operated to provide numerical support for the subsequent spark explosion generation process. The conversion of the decimal code makes the numerical operation of the explosion weight factor more direct, ensuring that the generated new code can produce a practical mutation effect.

[0082] In step 633, the decimal difference Δ is multiplied by the preset explosion weight factor, and the result is added to the decimal code of the original code A to generate a new individual position. The core of the spark explosion is to control the influence of the difference through the weight factor, so that the generated new individuals have a certain degree of randomness while maintaining similarity with the original code. By controlling the explosion weight, the distance between the new solution generated by the explosion and the original solution can be adjusted, thereby achieving local and global balanced search in the solution space. This newly generated decimal code reflects the potential changes in the total economic cost and energy utilization of the resource allocation scheme, enabling the model to find a better solution in the new allocation combination.

[0083] In step 634, the newly generated decimal code is converted back to binary code to re-represent the new resource allocation scheme. The new binary code represents the resource allocation status in each bit, forming a new resource combination. This code conversion enables the model to directly introduce the new solution into the population and evaluate the fitness as a new individual. The new code generated by this mutation not only enhances the diversity of the population, but also provides more potential high-quality solutions for subsequent fitness comparisons.

[0084] Step 64: Merge the offspring allocation scheme population with the initial population to form a final population, and iterate the final population based on the Pareto algorithm to determine the optimal individual from the Pareto frontier solution set, and use the binary code corresponding to the resource allocation scheme of the optimal individual as the optimal target allocation scheme.

[0085] In the embodiment of step 64, the final population is formed by merging the offspring allocation scheme population with the initial population, and the Pareto sorting and crowding distance are used for screening to ensure that the selected allocation scheme achieves the best balance between the total economic cost and energy utilization. The purpose of this screening process is to comprehensively compare the initial and offspring individuals and retain those allocation schemes that are competitive in terms of optimization objectives. According to the execution results of the fireworks algorithm, this process will eventually produce multiple non-inferior strata (Pareto front strata), providing a basis for the final screening of superior individuals.

[0086] First, the generated population of offspring allocation solutions is merged with the initial population to retain the diverse individuals generated in the population during the optimization process. This merged population is used as input, and the merged population is subjected to non-inferiority stratification (grading) through Pareto sorting. During the sorting process, the model will determine the superiority and inferiority relationship of each individual based on its performance in terms of total economic cost and energy utilization, and screen out individuals that are not dominated by other solutions to form the first non-inferiority layer. Specifically, refer to Figure 5 , Figure 5 It is the Pareto solution set diagram of the target allocation scheme of source, network, load and storage resource nodes provided by the present invention. Figure 5As shown in Figure 1, the first non-inferior layer (Pareto front) includes multiple individuals that have reached a balance in the optimization objectives, and these individuals have shown optimal solutions in both economic and energy utilization. This layer consists of five allocation scheme individuals, namely individuals A1, A2, A3, A4 and A5, which have ideal performance in both objectives and their ranks are all 1.

[0087] After the first non-inferior layer individuals are extracted and removed from the population, the model further screens the remaining individuals to determine the second non-inferior layer. This layer consists of individuals that have not achieved the superiority of the first non-inferior layer but still have good performance on both objectives. The second non-inferior layer contains six individuals (B1, B2, B3, B4, B5, and B6), which may be slightly deficient in one objective but have good results on another objective, so they are given a rank value of 2. Next, individuals outside the non-inferior layer continue to be screened to determine the third non-inferior layer, which contains individuals that are more balanced in terms of objectives, but are slightly inferior to the first two levels. The third non-inferior layer consists of four individuals (C1, C2, C3, and C4) with a rank value of 3.

[0088] On the basis of Pareto sorting and non-inferiority stratification, in order to further maintain the diversity of the solution set and the uniformity of distribution, the crowding distance of individuals in each non-inferiority layer is calculated. The crowding distance measures the distribution density by comparing the difference between adjacent individuals in the target value, and gives priority to those individuals with farther distribution to maintain the diversity of the solution set. In particular, an infinite crowding distance is set on the boundary individuals of each layer to ensure the priority retention of boundary solutions.

[0089] Through Pareto sorting and crowding distance screening, the model will select a spark population with the same allocation scheme size as the initial population from the merged population. The individuals in this spark population are based on Pareto non-inferiority stratification and combined with crowding distance sorting to ensure that the selected individuals can maintain balanced economy and energy utilization in different resource allocation scenarios, and can be widely distributed and adapt to different operating scenarios.

[0090] Finally, this optimization screening process in step 64 effectively forms multiple non-inferior strata in the population, and selects the final Pareto solution set from the first non-inferior stratum step by step according to the crowding distance, and determines the optimal individual from the final Pareto solution set, and uses the binary code corresponding to the resource allocation scheme of the optimal individual as the optimal target allocation scheme. The optimal individual (such as A1) shows the best balance between the optimization objectives, is suitable for different resource allocation requirements, and provides a highly adaptable and optimized allocation scheme for the resource management of the "source-grid-load-storage" system.

[0091] Reference Figure 6 , Figure 6: is a schematic diagram of the structure of the target allocation optimization system provided by the present invention, the system comprises: A model building module, used to build initial mathematical models of various types of equipment in the distribution network of each area; the equipment includes controllable equipment and uncontrollable equipment; A multi-objective optimization function building module is used to take the total economic cost and energy utilization rate as optimization targets, and to build a multi-objective optimization function based on the optimization targets and all initial mathematical models; A constraint setting module is used to set multiple constraint conditions based on the physical characteristics of all equipment and all initial mathematical models; An optimization module, used to optimize the initial mathematical models of all uncontrollable devices based on a multi-objective optimization function and all constraints to obtain optimized mathematical models of all uncontrollable devices; A processing module, used to determine a Pareto solution set based on an initial mathematical model of all controllable devices, an optimized mathematical model of all uncontrollable devices, and a multi-objective optimization function; the Pareto solution set includes multiple groups of feasible solutions, each group of feasible solutions corresponds to a target allocation scheme; The processing module is also used to obtain an initial population based on all target allocation schemes, and iteratively screen the initial population to determine the optimal target allocation scheme.

[0092] In a possible implementation manner, the processing module is further configured to: Encode each target allocation scheme in binary form to obtain multiple initial binary codes, where a binary number is used to represent the allocation of each target, each bit represents an available resource, and a value of 1 on the bit indicates that the resource is allocated to the target, and a value of 0 indicates that the resource is not allocated; All initial binary codes are used as the initial population; In each round of iteration, a new binary code is generated based on any two initial binary codes, and the fitness of the new binary code is compared with that of all the initial binary codes, and individuals with higher fitness are selected as members of the next round of iteration population, until a population of offspring allocation schemes is obtained after a preset number of iterations; The offspring allocation scheme population is merged with the initial population to form the final population, and the final population is iterated based on the Pareto algorithm to determine the optimal individual from the Pareto frontier solution set, and the resource allocation scheme corresponding to the binary code of the optimal individual is used as the optimal target allocation scheme.

[0093] In a possible implementation manner, the processing module is further configured to: Perform an XOR operation on any two initial binary codes to obtain a binary difference; Convert one of the initial binary codes and the binary difference value into decimal to obtain a decimal code and a decimal difference value; The decimal difference is multiplied by a preset weight factor and then summed with the decimal code to obtain a new decimal code; Convert the new decimal code to binary to obtain the new binary code.

[0094] In a possible implementation, the multi-objective optimization function building module is further used to: Based on the optimization objectives and all initial mathematical models, the total economic cost optimization function and energy utilization optimization function are constructed; Based on the total economic cost optimization function and the energy utilization rate optimization function, a multi-objective optimization function is constructed. The total economic cost optimization function and the energy utilization rate optimization function are negatively correlated.

[0095] In a possible implementation manner, the processing module is further configured to: Integrate the initial mathematical models of all controllable devices, the optimized mathematical models of all uncontrollable devices, and the multi-objective optimization function to construct a multi-objective optimization model; Based on the multi-objective optimization model, an initial solution set that satisfies all constraints is generated, and the initial solution set includes multiple groups of feasible solutions; Compare all feasible solutions in the initial solution set pairwise to determine the dominance relationship; Identify all feasible solutions that are not dominated by other solutions to form the first non-dominated layer; Remove the stratified feasible solutions from the initial solution set, and repeat the above process for the remaining feasible solutions to form the second non-dominated layer until all feasible solutions are stratified; For each feasible solution of the non-dominated layer, sort them according to the numerical value on each objective function, and for the sorted feasible solutions, calculate the sum of the differences between the adjacent feasible solutions in the objective function value as the crowding distance, and set the crowding distance of the feasible solution on the boundary of each non-dominated layer to infinity; All feasible solutions are sorted from high to low according to the non-dominated level and crowding distance, and feasible solutions that meet the predetermined scale requirements are selected from all the sorted feasible solutions to form a Pareto solution set.

[0096] It should be noted that the target allocation optimization system provided by the present invention can execute the target allocation optimization method of any of the above embodiments during specific operation, which will not be elaborated in this embodiment.

[0097] Figure 7 is a schematic diagram of the structure of the electronic device provided by the present invention, such as Figure 7As shown, the electronic device may include: a processor 710 (processor), a communication interface 720 (CommunicationsInterface), a memory 730 (memory) and a communication bus 740 , wherein the processor 710 , the communication interface 720 , and the memory 730 communicate with each other through the communication bus 740 . The processor 710 can call the logic instructions in the memory 730 to execute the target allocation optimization method, which includes: constructing initial mathematical models of various types of equipment in the distribution network of each area respectively; the equipment includes controllable equipment and uncontrollable equipment; taking the total economic cost and energy utilization rate as optimization targets, and constructing a multi-objective optimization function based on the optimization targets and all the initial mathematical models; setting multiple constraints based on the physical characteristics of all the equipment and all the initial mathematical models; optimizing the initial mathematical models of all uncontrollable equipment based on the multi-objective optimization function and all the constraints to obtain the optimized mathematical models of all the uncontrollable equipment; determining the Pareto solution set based on the initial mathematical models of all controllable equipment, the optimized mathematical models of all uncontrollable equipment and the multi-objective optimization function; the Pareto solution set includes multiple groups of feasible solutions, each group of feasible solutions corresponds to a target allocation plan; based on all the target allocation plans, obtaining an initial population, and iteratively screening the initial population to determine the optimal target allocation plan.

[0098] In addition, the logic instructions in the above-mentioned memory 730 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0099] On the other hand, the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the target allocation optimization method provided by the above-mentioned embodiments, the method including: constructing initial mathematical models of various types of equipment in the distribution network of each area respectively; the equipment includes controllable equipment and uncontrollable equipment; taking the total economic cost and energy utilization rate as optimization objectives, and constructing a multi-objective optimization function based on the optimization objectives and all initial mathematical models; setting multiple constraints based on the physical characteristics of all equipment and all initial mathematical models; optimizing the initial mathematical models of all uncontrollable equipment based on the multi-objective optimization function and all constraints to obtain the optimized mathematical models of all uncontrollable equipment; determining the Pareto solution set based on the initial mathematical models of all controllable equipment, the optimized mathematical models of all uncontrollable equipment and the multi-objective optimization function; the Pareto solution set includes multiple groups of feasible solutions, each group of feasible solutions corresponds to a target allocation scheme; obtaining an initial population based on all target allocation schemes, and iteratively screening the initial population to determine the optimal target allocation scheme.

[0100] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which is implemented by the processor 710 to execute the target allocation optimization method provided in the above-mentioned embodiments when executed, the method comprising: constructing initial mathematical models of various types of equipment in the distribution network of each area respectively; the equipment comprises controllable equipment and uncontrollable equipment; taking the total economic cost and energy utilization rate as optimization objectives, and constructing a multi-objective optimization function based on the optimization objectives and all the initial mathematical models; setting multiple constraints based on the physical characteristics of all the equipment and all the initial mathematical models; optimizing the initial mathematical models of all the uncontrollable equipment based on the multi-objective optimization function and all the constraints to obtain the optimized mathematical models of all the uncontrollable equipment; determining the Pareto solution set based on the initial mathematical models of all the controllable equipment, the optimized mathematical models of all the uncontrollable equipment and the multi-objective optimization function; the Pareto solution set comprises multiple groups of feasible solutions, each group of feasible solutions corresponds to a target allocation scheme; obtaining an initial population based on all the target allocation schemes, and iteratively screening the initial population to determine the optimal target allocation scheme.

[0101] The system embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, i.e., they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative effort.

[0102] Through the description of the above implementation modes, those skilled in the art can clearly understand that each implementation mode can be implemented by means of software plus a necessary general hardware platform, or of course by hardware. Based on such an understanding, the above technical solution can essentially or in other words be embodied in the form of a software product that contributes to the prior art. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiment.

[0103] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A target allocation optimization method, characterized in that: include: Constructing initial mathematical models of various types of equipment in the distribution network of each region respectively; the equipment includes controllable equipment and uncontrollable equipment; Taking the total economic cost and energy utilization rate as optimization targets, and constructing a multi-objective optimization function based on the optimization targets and all the initial mathematical models; Based on the physical characteristics of all the equipment and all the initial mathematical models, a plurality of constraints are set; Based on the multi-objective optimization function and all the constraints, the initial mathematical models of all the uncontrollable devices are optimized to obtain the optimized mathematical models of all the uncontrollable devices; Based on the initial mathematical models of all the controllable devices, the optimized mathematical models of all the uncontrollable devices and the multi-objective optimization function, a Pareto solution set is determined; the Pareto solution set includes multiple groups of feasible solutions, each group of feasible solutions corresponds to a target allocation scheme; Based on all the target allocation schemes, an initial population is obtained, and the initial population is iteratively screened to determine the optimal target allocation scheme.

2. The target allocation optimization method according to claim 1, characterized in that: The step of obtaining an initial population based on all the target allocation schemes and iteratively screening the initial population to determine an optimal target allocation scheme specifically includes: Encoding each of the target allocation schemes in binary form to obtain a plurality of initial binary codes, wherein a binary number is used to represent the allocation of each target, each bit represents an available resource, a value of 1 on the bit indicates that the resource is allocated to the target, and a value of 0 indicates that the resource is not allocated; Taking all the initial binary codes as an initial population; In each round of iteration, based on any two of the initial binary codes, a new binary code is generated, and the fitness of the new binary code is compared with that of all the initial binary codes, and individuals with higher fitness are selected as members of the next round of iteration population, until a population of offspring allocation schemes is obtained after a preset number of iterations; The offspring allocation scheme population is merged with the initial population to form a final population, and the final population is iterated based on the Pareto algorithm to determine the optimal individual from the Pareto frontier solution set, and the resource allocation scheme corresponding to the binary code of the optimal individual is used as the optimal target allocation scheme.

3. The target allocation optimization method according to claim 2, characterized in that: The generating a new binary code based on any two of the initial binary codes specifically includes: Performing an XOR operation on any two of the initial binary codes to obtain a binary difference; Convert one of the initial binary code and the binary difference value into decimal to obtain a decimal code and a decimal difference value; The decimal difference is multiplied by a preset weight factor and then summed with the decimal code to obtain a new decimal code; The new decimal code is converted into binary to obtain the new binary code.

4. The target allocation optimization method according to claim 1, characterized in that: The types include source type, network type, load type and storage type; The initial mathematical model of the device of the source type is expressed by the following formula: ； in, is the output power of the device of the ith source type at time t, is the maximum output power of the device of the ith source type, is the operating status of the device of the ith source type at time t, with a value of 1 or 0. A value of 1 indicates operation, and a value of 0 indicates non-operation. is the power loss of the equipment of the i-th source type at time t; The initial mathematical model of the mesh type device is expressed by the following formula: ； in, is the power from node i to node j, is the voltage at node i, is the voltage at node j, is the conductance in the admittance matrix, is the susceptance in the admittance matrix; is the angle between node i and node j; The initial mathematical model of the device of the described load type is expressed by the following formula: ； in, is the power of the k-th source type device at time t, is the base load of the equipment of the kth source type at time t, is the adjustable load of the equipment of the kth source type at time t; The initial mathematical model of the storage type device is expressed by the following formula: ； in, is the power of the lth storage type device at time t+1, is the power of the lth storage type device at time t, is the charging power of the lth storage type device at time t, is the discharge power of the lth storage type device at time t, For charging efficiency, is the discharge efficiency.

5. The target allocation optimization method according to claim 4, characterized in that: The total economic cost and energy utilization rate are taken as optimization targets, and a multi-objective optimization function is constructed based on the optimization targets and all the initial mathematical models, specifically including: Based on the optimization target and all the initial mathematical models, constructing a total economic cost optimization function and an energy utilization optimization function; Based on the total economic cost optimization function and the energy utilization rate optimization function, a multi-objective optimization function is constructed, wherein the total economic cost optimization function and the energy utilization rate optimization function are negatively correlated; The total economic cost optimization function is expressed by the following formula: ； ； ； ; ； ； The energy utilization optimization function is expressed by the following formula: ； in, is the total economic cost, is the sum of the investment costs of all types of equipment, It is the sum of the operation and maintenance costs of source type equipment and storage type equipment. The cost of purchasing electricity for all types of equipment, Cost of sales for all types of equipment, is the sum of the power of all types of equipment, is the energy utilization rate, The output power of the uncontrollable device.

6. The target allocation optimization method according to claim 4, characterized in that: The constraint condition is expressed by the following formula: ； ； ； ； ； in, is the sum of the power of all types of equipment, is the sum of the power losses of all types of equipment, is the maximum charging power of the device of the lth storage type, is the maximum discharge power of the device of the lth storage type, is the minimum power of the device of the lth storage type, is the maximum power of the device of the lth storage type, is the voltage of node j at time t, is the frequency of node j at time t.

7. The target allocation optimization method according to claim 1, characterized in that: Determining the Pareto solution set based on the initial mathematical models of all the controllable devices, the optimized mathematical models of all the uncontrollable devices and the multi-objective optimization function specifically includes: Integrate the initial mathematical models of all the controllable devices, the optimized mathematical models of all the uncontrollable devices and the multi-objective optimization function to construct a multi-objective optimization model; Based on the multi-objective optimization model, generating an initial solution set that satisfies all the constraint conditions, wherein the initial solution set includes multiple groups of feasible solutions; Compare all feasible solutions in the initial solution set pairwise to determine the dominance relationship; Identify all feasible solutions that are not dominated by other solutions to form the first non-dominated layer; The stratified feasible solutions are removed from the initial solution set, and the above process is repeated for the remaining feasible solutions to form a second non-dominated layer until all feasible solutions are stratified; For each feasible solution of the non-dominated layer, sort them according to the numerical value on each objective function, and for the sorted feasible solutions, calculate the sum of the differences between the adjacent feasible solutions in the objective function value as the crowding distance, and set the crowding distance of the feasible solution on the boundary of each non-dominated layer to infinity; All the feasible solutions are sorted from high to low according to the non-dominated level and the crowding distance, and the feasible solutions that meet the predetermined scale requirements are selected from all the sorted feasible solutions to form a Pareto solution set.

8. A target allocation optimization system, characterized in that: include: A model building module, used to build initial mathematical models of various types of equipment in the distribution network of each area; the equipment includes controllable equipment and uncontrollable equipment; A multi-objective optimization function construction module, used to take the total economic cost and energy utilization rate as optimization targets, and to construct a multi-objective optimization function based on the optimization targets and all the initial mathematical models; A constraint condition setting module, used for setting a plurality of constraint conditions based on the physical characteristics of all devices and all the initial mathematical models; An optimization module, configured to optimize the initial mathematical models of all the uncontrollable devices based on the multi-objective optimization function and all the constraints to obtain optimized mathematical models of all the uncontrollable devices; A processing module, configured to determine a Pareto solution set based on the initial mathematical models of all the controllable devices, the optimized mathematical models of all the uncontrollable devices, and the multi-objective optimization function; the Pareto solution set includes multiple groups of feasible solutions, each group of feasible solutions corresponds to a target allocation scheme; The processing module is also used to obtain an initial population based on all the target allocation schemes, and iteratively screen the initial population to determine the optimal target allocation scheme.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the computer program, the target allocation optimization method according to any one of claims 1 to 7 is implemented.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the target allocation optimization method according to any one of claims 1 to 7 is implemented.

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