Power distribution system operation optimization method and related device

Abstract

The embodiment of the invention discloses a power distribution system operation optimization method and a related device. The method comprises the following steps: determining meteorological data in a prediction time period and an electrical load of target equipment in a power distribution system; determining a dynamic current threshold value of the target equipment according to the meteorological data and the electrical load; constructing a multi-objective planning model according to the plurality of optimization objectives; determining a new energy consumption strategy according to the dynamic current threshold; according to the multi-target planning model and the new energy consumption strategy, determining an operation strategy of the power distribution system; importing an operation strategy of the power distribution system into the electromagnetic transient simulation model, and performing field-circuit coupling verification to obtain a verification result; and adjusting the multi-target planning model according to the verification result until the verification result indicates that the power distribution system is in a safe operation state. In this way, the problem that static threshold constraint conservative and dynamic threshold technology collaborative optimization capability is insufficient is solved, and the absorption capability and dynamic safety margin of a power distribution system in a new energy high-permeability scene are improved.

Description

Technical Field

[0001] The present application relates to the field of power distribution network operation management, and in particular to a power distribution system operation optimization method and related devices. Background Art

[0002] The current distribution system generally relies on static thermal stability threshold settings based on extreme operating conditions thermal balance calculations. Its conservative design leads to a serious underestimation of the actual carrying capacity of the equipment. With the development of renewable energy power generation, static thresholds are difficult to adapt to dynamic operating requirements, which not only causes idle equipment capacity and limited new energy consumption space, but also forces distribution network planning to adopt a redundant expansion strategy, pushing up investment costs.

[0003] The existing dynamic threshold technology is still limited to the dynamic adjustment of the threshold of a single device. It fails to form a coordinated optimization mechanism with energy storage scheduling, load flexible regulation and new energy output characteristics, and cannot achieve the absorption capacity in new energy high penetration scenarios and improve the dynamic safety margin. Summary of the invention

[0004] In view of this, the embodiments of the present application provide a distribution system operation optimization method and related devices, in order to solve the problems of conservative static threshold constraints and insufficient collaborative optimization capabilities of dynamic threshold technology, and to improve the distribution system's absorption capacity and dynamic safety margin in scenarios with high penetration of new energy.

[0005] In a first aspect, an embodiment of the present application provides a method for optimizing operation of a power distribution system, comprising:

[0006] Determine the meteorological data and the power load of the target equipment in the power distribution system within the forecast period;

[0007] Determining a dynamic current threshold of the target device according to the meteorological data and the power load;

[0008] Constructing a multi-objective programming model according to a plurality of optimization objectives, wherein the optimization objectives are indicators for optimizing the operating state of the power distribution system;

[0009] Determine a new energy consumption strategy according to the dynamic current threshold, wherein the new energy consumption strategy is used to characterize the type of electric energy that is preferentially used by the power distribution system, and the new energy consumption strategy also includes a constraint condition, which is a condition for ensuring the safe operation of the power distribution system;

[0010] Determining an operation strategy of the power distribution system according to the multi-objective planning model and the new energy consumption strategy;

[0011] The operation strategy of the power distribution system is introduced into the electromagnetic transient simulation model to perform field-circuit coupling verification and obtain verification results;

[0012] The multi-objective programming model is adjusted according to the verification result until the verification result indicates that the power distribution system is in a safe operating state.

[0013] In a possible embodiment, the multiple optimization objectives include: a first optimization objective, a second optimization objective and a third optimization objective; the multi-objective planning model is constructed based on the multiple optimization objectives, including: determining the first optimization objective based on the dynamic current threshold, the rated current of the target device and the weight coefficient of the prediction period, the first optimization objective is used to characterize the limit threshold of the target device in the distribution system; determining the second optimization objective based on the actual power generation of new energy and the maximum power generation of new energy, the second optimization objective is used to characterize the maximization of the new energy consumption rate of the distribution system; determining the third optimization objective based on the charging and discharging cost coefficient, the energy storage cycle degradation coefficient, the energy storage charging power and the energy storage discharging power, the third optimization objective is used to characterize the minimization of the energy storage regulation cost of the distribution system; normalizing the first optimization objective, the second optimization objective and the third optimization objective to obtain the multi-objective planning model.

[0014] In a possible embodiment, the new energy consumption strategy is determined according to the dynamic current threshold, including: when the dynamic current threshold is in a high threshold period, the new energy consumption strategy is determined to be a first consumption strategy, and the first consumption strategy refers to a strategy of giving priority to using new energy loads for power generation; and, according to the maximum power generation of the new energy and the baseline current when no new energy is connected, the actual power generation of the new energy is determined, and the distribution system generates power according to the actual power generation of the new energy to implement the first consumption strategy; when the dynamic current threshold is in a low threshold period, the new energy consumption strategy is determined to be a second efficiency strategy, and the second consumption strategy refers to a strategy of giving priority to triggering energy storage charging; and, according to the maximum power generation of the new energy and the baseline current, the energy storage charging power is determined, and the distribution system gives priority to charging according to the energy storage charging power to implement the second consumption strategy.

[0015] In a possible embodiment, determining the new energy consumption strategy according to the dynamic current threshold also includes: determining the distribution network flow balance constraint according to the actual power generation of the new energy, the power grid purchased power, the energy storage discharge power, the energy storage charging power, the target line resistance and the target line current; determining the energy storage constraint in the constraint condition according to the charging and discharging efficiency and the time interval; determining the load flexible regulation constraint in the constraint condition according to the load regulation amount and the maximum regulation proportional coefficient; determining the constraint condition according to the distribution network flow balance constraint, the energy storage constraint and the load flexible regulation constraint.

[0016] In a possible embodiment, the operation strategy of the distribution system is determined according to the multi-objective planning model and the new energy consumption strategy, including: determining the initial parameters of the multi-objective particle swarm algorithm, the initial parameters including: particle swarm size, maximum number of iterations, initial weight, acceleration factor and the dynamic current threshold; generating multiple initial populations according to the actual power generation of the new energy, the energy storage charging power, the energy storage discharging power and the load regulation amount, any particle in the multiple initial populations represents a candidate solution, and the candidate solution corresponds to an operation strategy of the distribution system; calculating the multiple optimization objectives of each particle in the current population function value, obtain an objective function value after aggregation processing, process the objective function value through penalty function method, and obtain a modified fitness value function; judge whether the fitness value function meets the preset condition, the preset condition means that the number of iterations is less than or equal to the maximum number of iterations, and the change rate of the archive set is greater than the preset change rate; if the fitness value function does not meet the preset condition, execute the iterative optimization strategy; if the fitness value function meets the preset condition, select the target fitness value function from the archive set as the operation strategy of the distribution system, and the target fitness value function is the minimum value of multiple fitness function values ​​in the archive set.

[0017] In a possible embodiment, the execution of the iterative optimization strategy includes: updating the acceleration factor; sorting the multiple initial populations according to the dominance relationship and selecting particles with priority in the sorting; for particles in the same non-dominated layer, calculating their crowding distances in the target space, sorting them from small to large according to the crowding distances, and excluding particles with the smallest crowding distances to update the archive set; updating particle positions according to individual historical optimality, random data in the archive set, and the initial population; and recalculating the fitness value function according to the updated acceleration factor, the particle position, and the archive set.

[0018] In a possible embodiment, the operation strategy of the power distribution system is imported into the electromagnetic transient simulation model to perform field-circuit coupling verification to obtain a verification result, including: constructing a reference power distribution system model through the electromagnetic transient simulation model; inputting the operation strategy of the power distribution system into the reference power distribution system model, and calculating and determining the real-time currents of multiple devices in the reference power distribution model; determining the internal temperatures of the multiple devices according to the real-time current; and determining the verification result according to the real-time current and the internal temperature of the devices, the verification result including: a first verification result for indicating a safe operating state of the power distribution system, and a second verification result for indicating an unsafe operating state of the power distribution system, the first verification result referring to a result that the real-time current is less than a preset current and the internal temperature of the device is less than a preset temperature, and the second verification result referring to a result that the real-time current is greater than a preset current, and / or the internal temperature of the device is greater than a preset temperature.

[0019] In a second aspect, an embodiment of the present application provides a distribution system operation optimization device, comprising: a first determination unit, a second determination unit, a model building unit, a third determination unit, a fourth determination unit, a verification unit and an adjustment unit; wherein the first determination unit is used to determine the meteorological data within the prediction period and the power load of the target device in the distribution system; the second determination unit is used to determine the dynamic current threshold of the target device according to the meteorological data and the power load; the model building unit is used to build a multi-objective planning model according to multiple optimization objectives, wherein the optimization objective is an indicator for optimizing the operating state of the distribution system; the third determination unit is used to determine the dynamic current threshold of the target device according to the meteorological data and the power load; The current threshold is used to determine the new energy consumption strategy, the new energy consumption strategy is used to characterize the type of electric energy that the distribution system preferentially uses, and the new energy consumption strategy also includes constraints, which are conditions for ensuring the safe operation of the distribution system; the fourth determination unit is used to determine the operation strategy of the distribution system according to the multi-objective planning model and the new energy consumption strategy; the verification unit is used to import the operation strategy of the distribution system into the electromagnetic transient simulation model, perform field-circuit coupling verification, and obtain a verification result; the adjustment unit is used to adjust the multi-objective planning model according to the verification result until the verification result indicates that the distribution system is in a safe operation state.

[0020] In a third aspect, an embodiment of the present application provides an electronic device, comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for executing the steps of any method in the first aspect of the embodiment of the present application.

[0021] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps described in any method of the first aspect of the embodiment of the present application.

[0022] In a fifth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in any method of the first aspect of the embodiment of the present application. The computer program product may be a software installation package.

[0023] It can be seen that through the above-mentioned distribution system operation optimization method and related devices, the meteorological data and the power load of the target equipment in the distribution system within the forecast period are first determined; secondly, the dynamic current threshold of the target equipment is determined according to the meteorological data and the power load; then, a multi-objective planning model is constructed according to multiple optimization objectives; then, the new energy consumption strategy is determined according to the dynamic current threshold; the operation strategy of the distribution system is determined according to the multi-objective planning model and the new energy consumption strategy; then the operation strategy of the distribution system is imported into the electromagnetic transient simulation model, and the field-circuit coupling verification is performed to obtain the verification result; finally, the multi-objective planning model is adjusted according to the verification result until the verification result indicates that the distribution system is in a safe operation state. In this way, the problems of equipment capacity waste and limited new energy consumption space caused by traditional static thresholds, as well as the problem of limited power generation capacity improvement caused by the lack of existing dynamic threshold technology and coordinated optimization of system operation are solved, which can improve the power generation and consumption capacity and dynamic safety margin of the distribution system in the scenario of high penetration of new energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 It is a schematic diagram of an application scenario of a power distribution system operation optimization method provided in an embodiment of the present application;

[0026] Figure 2 It is a flow chart of a method for optimizing the operation of a power distribution system provided in an embodiment of the present application;

[0027] Figure 3 It is a schematic diagram of a flow chart for determining an operation strategy of a power distribution system provided in an embodiment of the present application;

[0028] Figure 4 It is a flowchart of an iterative optimization strategy provided in an embodiment of the present application;

[0029] Figure 5 It is a schematic diagram of a flow chart of field-circuit coupling simulation verification provided by an embodiment of the present application;

[0030] Figure 6 It is a flow chart of another method for optimizing the operation of a power distribution system provided in an embodiment of the present application;

[0031] Figure 7 It is a block diagram of the functional units of a power distribution system operation optimization device provided in an embodiment of the present application;

[0032] Figure 8 It is a structural block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0033] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0034] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0035] It should be understood that the term "and / or" in this article is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article indicates that the associated objects before and after are in an "or" relationship.

[0036] The "multiple" in the embodiments of the present application refers to two or more. The "connection" in the embodiments of the present application refers to various connection modes such as direct connection or indirect connection to achieve communication between devices, and the embodiments of the present application do not make any limitation on this.

[0037] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0038] The following is an explanation of the relevant contents, concepts, meanings, technical issues, technical solutions, beneficial effects, etc. involved in the embodiments of the present application.

[0039] I DTR :(Dynamic Current Threshold (Rating), Dynamic Thermal Rating) Dynamically adjusts the current rating of the device so that it can maximize the current carrying capacity within a certain time range while avoiding thermal stress exceeding the limit.

[0040] Multi-Objective Particle Swarm Optimization (MOPSO): is an optimization algorithm based on swarm intelligence. In multi-objective optimization problems, there are multiple conflicting objective functions. The purpose of MOPSO is to find a set of non-inferior solutions (also called Pareto optimal solutions) that trade off between multiple objectives.

[0041] Field-circuit coupling simulation: It is a simulation technology that combines electromagnetic transient simulation and finite element simulation. It can reflect the topological structure of the distribution network and the transient characteristics of power equipment through electromagnetic transient simulation, and reflect the magnetic-thermal-fluid characteristics inside the power equipment through finite element simulation.

[0042] The current distribution system generally relies on static thermal stability threshold settings based on extreme operating conditions thermal balance calculations. Its conservative design leads to a serious underestimation of the actual carrying capacity of the equipment. With the development of renewable energy power generation, static thresholds are difficult to adapt to dynamic operating needs, which not only causes idle equipment capacity and limited new energy consumption space, but also forces distribution network planning to adopt a redundant expansion strategy, pushing up investment costs. The existing dynamic threshold technology is still limited to the dynamic adjustment of the threshold of a single device, and has failed to form a coordinated optimization mechanism with energy storage scheduling, load flexibility adjustment and new energy output characteristics, and cannot achieve the absorption capacity in new energy high penetration scenarios and improve the dynamic safety margin.

[0043] To solve the above problems, the embodiments of the present application provide a distribution system operation optimization method and related devices, in order to solve the problems of conservative static threshold constraints and insufficient collaborative optimization capabilities of dynamic threshold technology, and to improve the distribution system's absorption capacity and dynamic safety margin in scenarios with high penetration of new energy.

[0044] First, combine Figure 1 A method for optimizing the operation of a power distribution system in an embodiment of the present application is described. Figure 1 is a schematic diagram of an application scenario of a power distribution system operation optimization method provided in an embodiment of the present application, such as Figure 1 As shown, the power distribution system 100 includes: a transformer 110, a cable 120 and other key equipment 130; wherein, in the distribution network, after the electric energy is emitted from the power station, it is generally transmitted through a high-voltage transmission line to reduce the energy loss during the transmission process. When the electric energy is transmitted to the distribution network, the voltage needs to be reduced to a level suitable for the user. For example, in the urban distribution network, the voltage of the high-voltage transmission line may reach 110kV or higher, while the electrical equipment of residential users generally uses a voltage of 220V or 380V. The transformer 110 can reduce a higher input voltage (such as 10kV) to a low voltage of 380V / 220V to meet the power needs of different users. The cable 120 is a key carrier for the transmission of electric energy in the distribution network. It can transmit electric energy from the substation to each power consumption area, such as transmitting high voltage electricity from the urban substation to the distribution room of the community, or transmitting electric energy from the total step-down substation in the industrial plant to the distribution box of each workshop. Compared with overhead lines, cables are less affected by environmental factors (such as bad weather, tree growth, etc.) and can stably transmit electrical energy in complex urban environments and underground passages. Other key equipment 130 may be switch cabinets, ring network cabinets, distribution boxes and other equipment, all of which are used to control the distribution network and can realize circuit connection and disconnection operations. When a circuit fault occurs (such as short circuit, overload), the equipment can play a protective role to prevent the scope of the fault from expanding.

[0045] Combine the following Figure 2 A method for optimizing the operation of a power distribution system provided in an embodiment of the present application is described. Figure 2 : is a flow chart of a method for optimizing the operation of a power distribution system provided in an embodiment of the present application, which specifically includes the following steps:

[0046] Step S210, determining meteorological data within the forecast period and power load of target equipment in the power distribution system.

[0047] Among them, the target device is Figure 1 As shown in the figure, the transformer 110, the cable 120 and other key equipment 130 are shown.

[0048] Step S220, determining a dynamic current threshold of the target device according to meteorological data and power load.

[0049] Among them, through the dynamic threshold module, the meteorological data and power load curve in the future prediction period (such as the next 24 hours) are input to calculate the dynamic current threshold I of the target device in the future prediction period DTR,t , will I DTR,tThe power flow equation of the distribution network is introduced as a dynamic constraint, allowing the line power to adjust with environmental conditions.

[0050] Step S230, constructing a multi-objective programming model according to multiple optimization objectives.

[0051] Among them, the optimization target is an indicator for optimizing the operating state of the distribution system. In one possible embodiment, multiple optimization targets include: a first optimization target, a second optimization target and a third optimization target; based on multiple optimization targets, a multi-objective planning model is constructed, including: determining the first optimization target based on the dynamic current threshold, the rated current of the target device and the weight coefficient of the forecast period, the first optimization target is used to characterize the limit threshold of the target device in the distribution system; determining the second optimization target based on the actual power generation of new energy and the maximum power generation of new energy, the second optimization target is used to characterize the maximization of the new energy consumption rate of the distribution system; determining the third optimization target based on the charging and discharging cost coefficient, the energy storage cycle degradation coefficient, the energy storage charging power and the energy storage discharging power, the third optimization target is used to characterize the minimization of the energy storage regulation cost of the distribution system; normalizing the first optimization target, the second optimization target and the third optimization target to obtain a multi-objective planning model.

[0052] Specifically, the first optimization goal is used to measure the safety margin of the equipment under the dynamic threshold. The larger the safety margin, the safer the equipment operation. It is to maximize the operating efficiency of the power distribution system under the premise of ensuring the safety of the equipment. Specifically, the dynamic threshold margin maximization, that is, the first optimization goal F1, is determined according to the following formula:

[0053]

[0054] Among them, I DTR,t is the dynamic current threshold of the target device; I rated is the rated current of the target device, λ t is the weight coefficient of the forecast period, reflecting the difference between load peak and valley, such as λ=1.2 in peak period and λ=0.8 in valley period. This formula can be used to quantify the safety margin: Margin=I DTR,t / I rated -1, when the margin = 0.2, it means that the actual current is 83% of the threshold (1 / (1+0.2) = 0.83), and a 20% safety margin is reserved; by maximizing the total margin, the algorithm is guided to maximize equipment utilization within a safe range. For example, at night when the temperature is low, the cable is allowed to run close to 120A (small margin but high efficiency); at high temperatures, it is limited to 80A (large margin but safer); load period differentiation: during peak periods (λ is large), the margin is increased first to avoid overload; during valley periods (λ is small), the margin can be appropriately reduced to absorb more new energy.

[0055] Specifically, the second optimization goal is the distributor of the distribution system dispatching, ensuring that new energy generation, such as wind power generation, photovoltaic power generation, etc., is absorbed as much as possible to reduce waste and abandoned power. Specifically, the maximum absorption rate of new energy is determined according to the following formula, that is, the second optimization goal F2:

[0056]

[0057] Among them, P RES,t is the actual power generation of renewable energy, P RES,max,t The maximum power generation of new energy; this formula can be used to improve the quantitative consumption efficiency: the actual clean power generated and the theoretical maximum possible power generation (0 when there is no wind / no light) determine the hourly consumption rate (0%-100%). The larger the sum, the less waste of new energy power. It can also drive the algorithm to prioritize the generation of new energy and reduce power abandonment; in coordination with energy storage targets and load regulation, it can realize the distribution system operation plan of "how much is generated and how much is used".

[0058] Specifically, the role of the third optimization goal is to balance the economic efficiency of new energy consumption and energy storage use, and avoid cost surges due to over-reliance on energy storage. The energy storage regulation cost minimization, i.e. the third optimization goal F3, is determined specifically according to the following formula:

[0059]

[0060] Among them, P ch,t , P dis,t is the energy storage charging and discharging power, c ch is the charging cost coefficient, including electricity price and equipment loss; c dis is the discharge cost coefficient, including equipment loss and opportunity cost; c deg is the energy storage cycle degradation coefficient, which reflects the loss of battery life caused by charging and discharging. Minimizing the energy storage cost can prevent excessive use of energy storage in pursuit of new energy consumption rate, and avoid the contradiction of "reduced power abandonment but sharply increased costs"; through the cycle degradation coefficient, the algorithm can be guided to reduce high-frequency charging and discharging (such as minute-level adjustment) to extend the battery life; finally, a game can be formed with the new energy consumption rate in the multi-objective function to find the best balance point of "consumption volume-cost". For example, when the new energy consumption income (such as government subsidies) is higher than the energy storage cost, the algorithm will tend to consume more; when the energy storage cost is too high (such as serious battery aging), the algorithm will automatically adjust to load regulation or grid purchase, such as in the later stage of battery use (cycle degradation coefficient c deg increases), the system will reduce energy storage charging and discharging and adopt a more economical load management strategy.

[0061] Specifically, the above multi-objectives are normalized to obtain the aggregation function (multi-objective programming model):

[0062]

[0063] Among them, ω1, ω2, and ω3 are dynamic weight coefficients, which can be adjusted in real time through the entropy weight method. Specifically, the "information amount" of each target can be calculated based on the entropy value in information theory, and the weight can be automatically assigned: if a certain target differs greatly between different schemes (such as the sharp fluctuation of the new energy consumption rate), it means that it has a great impact on the decision-making, and the weight increases. On the contrary, if a certain target differs little (such as the stable energy storage cost), the weight decreases.

[0064] It can be seen that in this embodiment, the first optimization target is determined according to the dynamic current threshold, the rated current of the target equipment and the weight coefficient of the predicted period; secondly, the second optimization target is determined according to the actual power generation of new energy and the maximum power generation of new energy; then the third optimization target is determined according to the charging and discharging cost coefficient, the energy storage cycle degradation coefficient, the energy storage charging power and the energy storage discharging power; finally, the first optimization target, the second optimization target and the third optimization target are normalized to obtain a multi-objective planning model. In this way, while ensuring the safe operation of the target equipment, as much new energy as possible is absorbed, while reducing the cost of energy storage and improving the overall efficiency of the distribution system.

[0065] Step S240, determining a new energy consumption strategy according to the dynamic current threshold.

[0066] Among them, the new energy consumption strategy is used to characterize the type of electric energy that the distribution system prioritizes. The new energy consumption strategy also includes constraints, which are conditions to ensure the safe operation of the distribution system.

[0067] In one possible embodiment, a new energy consumption strategy is determined according to a dynamic current threshold, including: when the dynamic current threshold is in a high threshold period, the new energy consumption strategy is determined to be a first consumption strategy, and the first consumption strategy refers to a strategy of giving priority to using new energy loads for power generation; and, according to the maximum power generation of new energy and the baseline current when no new energy is connected, the actual power generation of new energy is determined, and the distribution system generates power according to the actual power generation of new energy to implement the first consumption strategy; when the dynamic current threshold is in a low threshold period, the new energy consumption strategy is determined to be a second efficiency strategy, and the second consumption strategy refers to a strategy of giving priority to triggering energy storage charging; and, according to the maximum power generation of new energy and the baseline current, the energy storage charging power is determined, and the distribution system gives priority to charging according to the energy storage charging power to implement the second consumption strategy.

[0068] Among them, the dynamic current threshold I is determined according to the meteorological data DTR When the I / O ratio is high, such as at night when the temperature is low and / or when the wind is strong, the heat dissipation efficiency of the equipment is improved in low temperature environments or strong winds, allowing a larger current to pass through. Therefore, the distribution system gives priority to absorbing new energy power and increasing the power transmission capacity. DTR It means that IDTR,t Find the mean, and the time period greater than the mean is high I DTR,t Similarly, the time period less than the mean is low I DTR,t Time period; Specifically, the first consumption strategy can be calculated and determined by the following formula:

[0069]

[0070] Among them, I base It is the baseline current when no new energy is connected. The first consumption strategy instructs the distribution system to give priority to renewable energy to generate electricity at full load, which achieves the effect of directly utilizing the line capacity expansion capacity to generate more electricity and avoid waste of resources. For example, in an environment with high wind speed at night, 15%-30% more electricity can be generated.

[0071] Among them, the dynamic current threshold I is determined according to the meteorological data DTR When the temperature is low, such as high temperature at noon and / or windless weather, the distribution system will give priority to storing excess power to avoid wind and solar power abandonment; the specific second consumption strategy can be calculated and determined by the following formula:

[0072]

[0073] Among them, the second consumption strategy instructs the distribution system to trigger energy storage charging, store excess electricity, and adjust flexible loads (such as postponing electric vehicle charging), avoiding wind and solar power abandonment. For example, during the peak photovoltaic power generation at noon, energy storage absorbs 10% of excess electricity.

[0074] It can be seen that in this embodiment, when the dynamic current threshold is in the high threshold period, the new energy consumption strategy is determined to be the first consumption strategy; and, according to the maximum power generation of new energy and the baseline current when there is no new energy access, the actual power generation of new energy is determined, and the distribution system generates electricity according to the actual power generation of new energy to achieve the first consumption strategy; when the dynamic current threshold is in the low threshold period, the new energy consumption strategy is determined to be the second efficiency strategy; and, according to the maximum power generation of new energy and the baseline current, the energy storage charging power is determined, and the distribution system is charged according to the energy storage charging power to achieve the second consumption strategy. In this way, through energy storage and load regulation, the excess power in the high dynamic current threshold period is transferred to the low dynamic current threshold period for use, maximizing the operating efficiency of the target equipment in the distribution system while ensuring safety constraints.

[0075] Step S250, determining the operation strategy of the power distribution system according to the multi-objective planning model and the new energy consumption strategy.

[0076] Among them, the operation strategy refers to solving the multi-objective planning model under the constraints of the new energy consumption strategy to obtain the optimal actual power generation of new energy, energy storage charging and discharging power, and load regulation.

[0077] Step S260: import the operation strategy of the power distribution system into the electromagnetic transient simulation model, perform field-circuit coupling verification, and obtain verification results.

[0078] Specifically, the electromagnetic transient simulation model can be simulation software, such as PSCAD / EMTDC, MATLAB / Simulink+SimPowerSystems, etc.

[0079] Step S270, adjusting the multi-objective planning model according to the verification result until the verification result indicates that the power distribution system is in a safe operating state.

[0080] It can be seen that through the embodiment of the present application, the meteorological data and the power load of the target equipment in the distribution system within the forecast period are first determined; secondly, the dynamic current threshold of the target equipment is determined according to the meteorological data and the power load; then a multi-objective planning model is constructed according to multiple optimization objectives; then the new energy consumption strategy is determined according to the dynamic current threshold; the operation strategy of the distribution system is determined according to the multi-objective planning model and the new energy consumption strategy; then the operation strategy of the distribution system is imported into the electromagnetic transient simulation model, and the field-circuit coupling verification is performed to obtain the verification result; finally, the multi-objective planning model is adjusted according to the verification result until the verification result indicates that the distribution system is in a safe operating state. In this way, the problems of equipment capacity waste and limited new energy consumption space caused by traditional static thresholds, as well as the problem of limited power generation capacity improvement caused by the lack of coordinated optimization of existing dynamic threshold technology and system operation are solved, which can improve the power generation and consumption capacity and dynamic safety margin of the distribution system in the scenario of high penetration of new energy.

[0081] In a possible embodiment, the new energy consumption strategy is determined according to the dynamic current threshold, and also includes: determining the distribution network flow balance constraint according to the actual power generation of new energy, the power grid purchased power, the energy storage discharge power, the energy storage charging power, the target line resistance and the target line current; determining the energy storage constraint in the constraint condition according to the charging and discharging efficiency and the time interval; determining the load flexible regulation constraint in the constraint condition according to the load regulation amount and the maximum regulation ratio coefficient; determining the constraint condition according to the distribution network flow balance constraint, the energy storage constraint and the load flexible regulation constraint.

[0082] Specifically, the formula for calculating the distribution network power flow balance constraint is as follows:

[0083]

[0084] Among them, P grid,t The power purchased from the grid, R i is the target line resistance, I i,t is the target line current. The power flow balance constraint formula of the distribution network is used to ensure the real-time balance between power generation and power consumption and avoid voltage fluctuations.

[0085] Specifically, the formula for calculating the energy storage constraint is as follows:

[0086]

[0087] Among them, η ch , η dis is the charge and discharge efficiency, Δt is the time interval. Through the energy storage constraint formula, the energy storage system power must be maintained between 20% and 80% to prevent over-charge and over-discharge of the battery and extend its service life.

[0088] Specifically, the formula for calculating the load flexibility adjustment constraint is as follows:

[0089] 0≤ΔP load,t ≤γ·P load,t ;

[0090] Among them, ΔP load,t is the load reduction amount in period t, and γ is the maximum adjustment ratio. Through the load flexible adjustment constraint formula, the non-critical load can be reduced by 15% at most in each period to avoid affecting the normal electricity consumption of residents.

[0091] It can be seen that in this embodiment, the distribution network flow balance constraint is determined according to the actual power generation of new energy, the power purchased by the power grid, the energy storage discharge power, the energy storage charging power, the target line resistance and the target line current; the energy storage constraint in the constraint condition is determined according to the charging and discharging efficiency and the time interval; the load flexibility adjustment constraint in the constraint condition is determined according to the load adjustment amount and the maximum adjustment ratio coefficient; the constraint condition is determined according to the distribution network flow balance constraint, the energy storage constraint and the load flexibility adjustment constraint. In this way, the flow balance of the distribution system is ensured, the service life of the energy storage is extended, and the ability of the distribution system to cope with fluctuations can be improved.

[0092] See also Figure 3 , Figure 3 is a flow chart of determining an operation strategy of a power distribution system provided in an embodiment of the present application, such as Figure 3 The steps shown include:

[0093] S310, determining initial parameters of the multi-objective particle swarm algorithm.

[0094] Among them, the initial parameters include: particle swarm size, maximum number of iterations, initial weight, acceleration factor and dynamic current threshold.

[0095] S320, generating a plurality of initial populations according to the actual power generation of new energy, energy storage charging power, energy storage discharging power and load regulation amount.

[0096] Among them, any particle in the multiple initial populations represents a candidate solution, and a candidate solution corresponds to an operation strategy of a distribution system. Specifically, the initial parameters are set, including the particle swarm size N, the maximum number of iterations iter max , initial weights ω1, ω2, ω3 and acceleration factors c1, c2, dynamic thermal stability threshold I DTR，t ; Generate the initial population X k , subscript k represents the kth particle, each particle represents a candidate solution, encoded as a vector X = [P RES,1 ,P ch,1 ,P dis,1 ,ΔP load,1 ,…,P RES,T ], the dimension is 3T (T is the number of time periods)

[0097] S330, calculating the function values ​​of multiple optimization objectives of each particle in the current population, obtaining an objective function value after aggregation processing, processing the objective function value by a penalty function method, and obtaining a modified fitness value function.

[0098] Specifically, the objective function values ​​F1, F2, and F3 in the current population are calculated, and F is obtained after aggregation. The penalty function method is used to handle the out-of-bounds constraints, and the corrected fitness value F is obtained through the following formula: penalty :

[0099]

[0100] Where α=0.1·0.95 iter is the attenuation coefficient.

[0101] S340, determining whether the fitness value function meets a preset condition.

[0102] The preset condition refers to that the number of iterations is less than or equal to the maximum number of iterations, and the change rate of the archive set is greater than the preset change rate.

[0103] S350: If the fitness value function does not meet the preset conditions, an iterative optimization strategy is executed.

[0104] In one possible embodiment, an iterative optimization strategy is performed, see Figure 4 , Figure 4 is a flow chart of an iterative optimization strategy provided by an embodiment of the present application, such as Figure 4 The steps shown include:

[0105] S410, updating the acceleration factor.

[0106] Specifically, please refer to the following formula for updating the acceleration factors c1 and c2:

[0107]

[0108] S420, sorting the multiple initial populations according to the dominance relationship, and selecting particles with priority in the sorting.

[0109] Among them, the population is stratified according to the Pareto dominance relationship, and particles with high rankings are selected first.

[0110] S430, for particles in the same non-dominated layer, calculate their crowding distances in the target space, sort them from small to large crowding distances, exclude particles with the smallest crowding distances, and update the archive set.

[0111] Among them, for particles in the same non-dominated layer, the crowding distance D in the target space is calculated by the following formula: k Keep non-dominated solutions. If the number exceeds N, press D k Sort from small to large, and prioritize eliminating the solution with the least congestion.

[0112]

[0113] S440, updating the particle position according to the individual historical optimum, the random data in the archive set and the initial population.

[0114] Specifically, it includes: updating the particle position according to the velocity-displacement formula; the velocity update formula is as follows:

[0115]

[0116] Among them, pbest k For the individual historical best, gbest is randomly selected from the archive set; the position update formula is as follows:

[0117]

[0118] Among them, for variables outside the range (such as P ch,t >P ch,max ) is truncated at the boundary, that is, P ch,t =P ch,max .

[0119] S450, recalculating the fitness value function according to the updated acceleration factor, particle position and archive set.

[0120] This means returning to step S330.

[0121] S360: If the fitness value function meets the preset conditions, a target fitness value function is selected from the archive set as an operation strategy of the distribution system.

[0122] The target fitness value function is the minimum value among multiple fitness function values ​​in the archive set; let iter = iter + 1, and judge iter>iter max or Δ archive <∈, if the condition is met, select the solution X closest to the ideal point (with the smallest F) from the archive set k ; Otherwise return to S350.

[0123] It can be seen that in this embodiment, the improved MOPSO algorithm improves the solution efficiency, makes the adaptability to dynamic scenarios stronger, and improves the robustness of the algorithm, providing reliable technical support for the safe and efficient operation of new energy high-penetration power grids.

[0124] See also Figure 5 , Figure 5 is a flow chart of a field-circuit coupling simulation verification provided by an embodiment of the present application, such as Figure 5 As shown, the following steps are included:

[0125] S510, constructing a reference power distribution system model through an electromagnetic transient simulation model.

[0126] Among them, the distribution network model is constructed in the electromagnetic transient simulation software, including new energy inverters, energy storage converters, lines and loads, etc. The new energy output curve, energy storage charging and discharging power, and load adjustment at the ideal point are solved for parameter input. The finite element model of key equipment (transformers, cables) is constructed in the finite element simulation software.

[0127] S520, inputting the operation strategy of the power distribution system into the reference power distribution system model, and calculating and determining the real-time currents of multiple devices in the reference power distribution model.

[0128] Among them, field-circuit coupling simulation and electromagnetic transient simulation are performed to calculate the real-time current I of each device. i,t

[0129] S530, determining the internal temperature of the multiple devices according to the real-time current.

[0130] Among them, the finite element simulation receives the transformer and the real-time current of the cable, and calculates the internal magnetic field distribution, eddy current loss and maximum temperature of the equipment.

[0131] S540, determining a verification result according to the real-time current and the internal temperature of the device.

[0132] Among them, the verification results include: a first verification result used to indicate the safe operating status of the distribution system, and a second verification result used to indicate the unsafe operating status of the distribution system. The first verification result refers to the result that the real-time current is less than the preset current and the internal temperature of the equipment is less than the preset temperature. The second verification result refers to the result that the real-time current is greater than the preset current, and / or the internal temperature of the equipment is greater than the preset temperature.

[0133] Specifically, a visualization solution can be used to determine whether the current of the device is out of bounds (I i,t >I DLR,i,t ), the device that exceeds the limit is marked in red; determine whether the device temperature exceeds the limit (T max,i,t >T rated,i ), the equipment that crosses the boundary is marked in red; the equipment that does not cross the boundary is marked in green. Plotting the curve of new energy output changing with time P RES,t , mark the output limit. Draw the current curve of the power distribution equipment and superimpose I i,t with I DLR,i,t , the red area marks the out-of-bounds period. Display the magnetic-thermal simulation results and show the temperature field distribution cloud map of the equipment.

[0134] S550, modify the weight coefficient.

[0135] Specifically, if there is a current out-of-bounds situation, it is determined that the optimized scenario has a safety risk, the weight coefficients ω1, ω2, and ω3 are modified, and the optimization model is solved again to intuitively evaluate the impact of the optimization strategy on the thermal stability and electrical safety of the equipment, and provide a quantitative basis for dynamic capacity expansion and risk warning of the distribution network.

[0136] In another possible embodiment, see Figure 6 , Figure 6 is a flow chart of another method for optimizing the operation of a power distribution system provided in an embodiment of the present application, such as Figure 6 As shown, the method comprises the following steps: S61: importing meteorological data and power load, and determining the dynamic current threshold I of the target device through the dynamic threshold module DTR,t ; S62: Construct a multi-objective optimization model based on dynamic thresholds; S63: Use a multi-objective particle swarm algorithm to solve the multi-objective optimization model to determine the actual power generation of new energy, the energy storage charging and discharging power, and the load regulation; S64: Import the distribution system operation strategy into the simulation model to verify the field-circuit coupling; S65: Output the target device current curve, the actual power generation curve of new energy, and the finite element magnetic-thermal joint simulation output to display the device temperature field distribution cloud map; S66: Determine whether there is current or temperature out of bounds; if so, jump to S62; if not, jump to S67; S67: End.

[0137] and Figure 2 For details on the implementation of Figure 7 , Figure 7 7 is a functional unit composition block diagram of a power distribution system operation optimization device provided in an embodiment of the present application, wherein the power distribution system operation optimization device 700 includes: a first determination unit 710, a second determination unit 720, a model construction unit 730, a third determination unit 740, a fourth determination unit 750, a verification unit 760, and an adjustment unit 770; wherein the first determination unit 710 is used to determine the meteorological data within the forecast period and the power load of the target device in the power distribution system; the second determination unit 720 is used to determine the dynamic current threshold of the target device according to the meteorological data and the power load; the model construction unit 730 is used to construct a multi-objective planning model according to multiple optimization objectives, and the optimization objective is to optimize the distribution An indicator of the operating status of the power system; a third determination unit 740, used to determine the new energy consumption strategy according to the dynamic current threshold, the new energy consumption strategy is used to characterize the type of electric energy that the distribution system preferentially uses, and the new energy consumption strategy also includes constraints, which are conditions for ensuring the safe operation of the distribution system; a fourth determination unit 750, used to determine the operating strategy of the distribution system according to the multi-objective planning model and the new energy consumption strategy; a verification unit 760, used to import the operating strategy of the distribution system into the electromagnetic transient simulation model, perform field-circuit coupling verification, and obtain a verification result; an adjustment unit 770, used to adjust the multi-objective planning model according to the verification result until the verification result indicates that the distribution system is in a safe operating state.

[0138] In a possible embodiment, the multiple optimization objectives include: a first optimization objective, a second optimization objective and a third optimization objective; based on the multiple optimization objectives, a multi-objective planning model is constructed, and the model construction unit 730 is specifically used to: determine the first optimization objective based on the dynamic current threshold, the rated current of the target device and the weight coefficient of the prediction period, and the first optimization objective is used to characterize the limit threshold of the target device in the distribution system; determine the second optimization objective based on the actual power generation of new energy and the maximum power generation of new energy, and the second optimization objective is used to characterize the maximization of the new energy consumption rate of the distribution system; determine the third optimization objective based on the charging and discharging cost coefficient, the energy storage cycle degradation coefficient, the energy storage charging power and the energy storage discharging power, and the third optimization objective is used to characterize the minimization of the energy storage regulation cost of the distribution system; normalize the first optimization objective, the second optimization objective and the third optimization objective to obtain a multi-objective planning model.

[0139] In one possible embodiment, a new energy consumption strategy is determined according to a dynamic current threshold, and the third determination unit 740 is specifically used to: when the dynamic current threshold is in a high threshold period, the new energy consumption strategy is determined to be a first consumption strategy, and the first consumption strategy refers to a strategy of giving priority to using new energy loads for power generation; and, according to the maximum power generation of new energy and the baseline current when no new energy is connected, the actual power generation of new energy is determined, and the distribution system generates power according to the actual power generation of new energy to implement the first consumption strategy; when the dynamic current threshold is in a low threshold period, the new energy consumption strategy is determined to be a second efficiency strategy, and the second consumption strategy refers to a strategy of giving priority to triggering energy storage charging; and, according to the maximum power generation of new energy and the baseline current, the energy storage charging power is determined, and the distribution system gives priority to charging according to the energy storage charging power to implement the second consumption strategy.

[0140] In a possible embodiment, the new energy consumption strategy is determined according to the dynamic current threshold, and the third determination unit 740 is specifically used to: determine the distribution network flow balance constraint according to the actual power generation of new energy, the power grid purchased power, the energy storage discharge power, the energy storage charging power, the target line resistance and the target line current; determine the energy storage constraint in the constraint condition according to the charging and discharging efficiency and the time interval; determine the load flexibility regulation constraint in the constraint condition according to the load regulation amount and the maximum regulation ratio coefficient; determine the constraint condition according to the distribution network flow balance constraint, the energy storage constraint and the load flexibility regulation constraint.

[0141] In a possible embodiment, the operation strategy of the distribution system is determined according to the multi-objective planning model and the new energy consumption strategy, and the fourth determination unit 750 is specifically used to: determine the initial parameters of the multi-objective particle swarm algorithm, the initial parameters include: particle swarm size, maximum number of iterations, initial weight, acceleration factor and dynamic current threshold; generate multiple initial populations according to the actual power generation of new energy, energy storage charging power, energy storage discharging power and load regulation, any particle in the multiple initial populations represents a candidate solution, and a candidate solution corresponds to an operation strategy of the distribution system; calculate the function values ​​of multiple optimization objectives of each particle in the current population, obtain an objective function value after aggregation processing, process the objective function value by the penalty function method, and obtain a corrected fitness value function; determine whether the fitness value function meets the preset conditions, the preset conditions refer to that the number of iterations is less than or equal to the maximum number of iterations, and the change rate of the archive set is greater than the preset change rate; if the fitness value function does not meet the preset conditions, the iterative optimization strategy is executed; if the fitness value function meets the preset conditions, the target fitness value function is selected from the archive set as the operation strategy of the distribution system, and the target fitness value function is the minimum value of multiple fitness function values ​​in the archive set.

[0142] In a possible embodiment, an iterative optimization strategy is executed, and the fourth determination unit 750 is specifically used to: update the acceleration factor; sort multiple initial populations according to the dominance relationship and select particles with priority in the sorting; for particles in the same non-dominated layer, calculate their crowding distance in the target space, sort them from small to large according to the crowding distance, and exclude particles with the smallest crowding distance to update the archive set; update the particle position according to the individual historical optimum, random data in the archive set, and the initial population; recalculate the fitness value function according to the updated acceleration factor, particle position, and archive set.

[0143] In one possible embodiment, the operation strategy of the distribution system is imported into the electromagnetic transient simulation model, and the field-circuit coupling verification is performed to obtain the verification result. The verification unit 760 is specifically used to: construct a reference distribution system model through the electromagnetic transient simulation model; input the operation strategy of the distribution system into the reference distribution system model, and calculate and determine the real-time current of multiple devices in the reference distribution model; determine the internal temperature of multiple devices according to the real-time current; determine the verification result according to the real-time current and the internal temperature of the device, and the verification result includes: a first verification result for indicating the safe operation state of the distribution system, and a second verification result for indicating the unsafe operation state of the distribution system, the first verification result refers to the result that the real-time current is less than the preset current and the internal temperature of the device is less than the preset temperature, and the second verification result refers to the result that the real-time current is greater than the preset current, and / or the internal temperature of the device is greater than the preset temperature.

[0144] Figure 8 is a structural block diagram of an electronic device provided in an embodiment of the present application. Figure 8 As shown, the electronic device 800 may include one or more of the following components: a processor 801, and a memory 802 coupled to the processor 801, wherein the memory 802 may store one or more computer programs, and the one or more computer programs may be configured to implement the methods described in the above examples when executed by one or more processors 801.

[0145] The processor 801 may include one or more processing cores. The processor 801 uses various interfaces and lines to connect the various parts of the entire electronic device 800, and executes various functions and processes data of the electronic device 800 by running or executing instructions, programs, code sets or instruction sets stored in the memory 802, and calling data stored in the memory 802. Optionally, the processor 801 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 801 can integrate one or more combinations of a central processing unit (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU) and a modem. It is understandable that the above-mentioned modem may not be integrated into the processor 801, and may be implemented separately through a communication chip.

[0146] The memory 802 may include a random access memory (RAM) or a read-only memory (ROM). The memory 802 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 802 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method examples, etc. The data storage area may also store data created by the electronic device 800 during use, etc.

[0147] It is understandable that the electronic device 800 may include more or fewer structural elements than those in the above structural block diagram, for example, a power module, physical buttons, a WiFi (Wireless Fidelity) module, a speaker, a Bluetooth module, a sensor, etc., which are not limited here.

[0148] An embodiment of the present application also provides a computer storage medium, on which a computer program / instruction is stored. When the computer program / instruction is executed by a processor, part or all of the steps of any method recorded in the above method embodiments are implemented.

[0149] An embodiment of the present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute part or all of the steps of any method recorded in the above method embodiments.

[0150] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0151] In the several embodiments provided in the present application, it should be understood that the disclosed methods, devices and systems can be implemented in other ways. For example, the device embodiments described above are only schematic; for example, the division of units is only a logical function division, and there may be other division methods in actual implementation; for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0152] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0153] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may be physically included separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0154] The above-mentioned integrated unit implemented in the form of a software functional unit can be stored in a computer-readable storage medium. The above-mentioned software functional unit is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform some steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a volatile memory or a non-volatile memory. Among them, the non-volatile memory can be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM) or a flash memory. The volatile memory can be a random access memory (RAM), which is used as an external cache. By way of example but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (double data rate SDRAM, DDR SDRAM), enhanced synchronous dynamic random access memory (enhanced SDRAM, ESDRAM), synchronous link dynamic random access memory (synchlink DRAM, SLDRAM) and direct memory bus random access memory (directrambus RAM, DR RAM) and other media that can store program code.

[0155] Although the present invention is disclosed as above, the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions without departing from the spirit and scope of the present invention, and can make various changes and modifications, including the combination of the above-mentioned different functions and implementation steps, including software and hardware implementation methods, all of which are within the scope of protection of the present invention.

Claims

1. A method for optimizing the operation of a power distribution system, characterized in that: include: Determine the meteorological data and the power load of the target equipment in the power distribution system within the forecast period; Determining a dynamic current threshold of the target device according to the meteorological data and the power load; Constructing a multi-objective programming model according to a plurality of optimization objectives, wherein the optimization objectives are indicators for optimizing the operating state of the power distribution system; Determine a new energy consumption strategy according to the dynamic current threshold, wherein the new energy consumption strategy is used to characterize the type of electric energy that is preferentially used by the power distribution system, and the new energy consumption strategy also includes a constraint condition, which is a condition for ensuring the safe operation of the power distribution system; Determining an operation strategy of the power distribution system according to the multi-objective planning model and the new energy consumption strategy; The operation strategy of the power distribution system is introduced into the electromagnetic transient simulation model to perform field-circuit coupling verification and obtain verification results; The multi-objective programming model is adjusted according to the verification result until the verification result indicates that the power distribution system is in a safe operating state.

2. The method according to claim 1, characterized in that The multiple optimization objectives include: a first optimization objective, a second optimization objective, and a third optimization objective; and constructing a multi-objective programming model according to the multiple optimization objectives includes: Determining the first optimization target according to the dynamic current threshold, the rated current of the target device, and the weight coefficient of the prediction period, wherein the first optimization target is used to characterize the limit threshold of the target device in the power distribution system; Determining the second optimization target according to the actual power generation of the new energy and the maximum power generation of the new energy, wherein the second optimization target is used to represent the maximization of the new energy consumption rate of the power distribution system; Determine the third optimization target according to the charge and discharge cost coefficient, the energy storage cycle degradation coefficient, the energy storage charging power and the energy storage discharging power, wherein the third optimization target is used to characterize the minimization of the energy storage regulation cost of the power distribution system; The first optimization objective, the second optimization objective, and the third optimization objective are normalized to obtain the multi-objective programming model.

3. The method according to claim 2, characterized in that Determining the new energy consumption strategy according to the dynamic current threshold includes: When the dynamic current threshold is in the high threshold period, the new energy consumption strategy is determined to be the first consumption strategy, and the first consumption strategy refers to a strategy of giving priority to using new energy loads for power generation; and According to the maximum power generation of the new energy and the baseline current when no new energy is connected, the actual power generation of the new energy is determined, and the power distribution system generates power according to the actual power generation of the new energy to implement the first consumption strategy; When the dynamic current threshold is in the low threshold period, the new energy consumption strategy is determined to be a second efficiency strategy, and the second consumption strategy refers to a strategy of preferentially triggering energy storage charging; and, The energy storage charging power is determined according to the maximum power generation of the new energy and the baseline current, and the power distribution system is charged preferentially according to the energy storage charging power to implement the second consumption strategy.

4. The method according to claim 3, characterized in that The determining of the new energy consumption strategy according to the dynamic current threshold further includes: Determine the power flow balance constraint of the distribution network according to the actual power generation of the new energy, the power purchased by the power grid, the energy storage discharge power, the energy storage charging power, the target line resistance and the target line current; Determining the energy storage constraint in the constraint condition according to the charge and discharge efficiency and the time interval; Determining the load flexibility adjustment constraint in the constraint condition according to the load adjustment amount and the maximum adjustment proportional coefficient; The constraint condition is determined according to the distribution network flow balance constraint, the energy storage constraint, and the load flexible regulation constraint.

5. The method according to claim 4, characterized in that Determining the operation strategy of the power distribution system according to the multi-objective planning model and the new energy consumption strategy includes: Determine the initial parameters of the multi-objective particle swarm algorithm, the initial parameters including: particle swarm size, maximum number of iterations, initial weight, acceleration factor and the dynamic current threshold; Generate multiple initial populations according to the actual power generation of the new energy, the energy storage charging power, the energy storage discharging power and the load adjustment amount, wherein any particle in the multiple initial populations represents a candidate solution, and the candidate solution corresponds to an operation strategy of the power distribution system; Calculate the function values ​​of multiple optimization objectives of each particle in the current population, obtain an objective function value after aggregation processing, process the objective function value by a penalty function method, and obtain a modified fitness value function; Determine whether the fitness value function meets a preset condition, wherein the preset condition is that the number of iterations is less than or equal to the maximum number of iterations, and the change rate of the archive set is greater than a preset change rate; If the fitness value function does not meet the preset conditions, an iterative optimization strategy is executed; if the fitness value function meets the preset conditions, a target fitness value function is selected from the archive set as the operation strategy of the power distribution system, and the target fitness value function is the minimum value among the multiple fitness function values ​​in the archive set.

6. The method according to claim 5, characterized in that The execution iterative optimization strategy comprises: updating the acceleration factor; sorting the multiple initial populations according to the dominance relationship, and selecting particles with priority in the sorting; For particles in the same non-dominated layer, calculate their crowding distances in the target space, sort them from small to large according to the crowding distances, and exclude particles with the smallest crowding distances to update the archive set; Update the particle position according to the individual historical optimum, the random data in the archive set and the initial population; The fitness value function is recalculated according to the updated acceleration factor, the particle position and the archive set.

7. The method according to any one of claims 1 to 6, characterized in that: The step of importing the operation strategy of the power distribution system into the electromagnetic transient simulation model to perform field-circuit coupling verification and obtain verification results includes: Constructing a reference power distribution system model through the electromagnetic transient simulation model; Inputting the operation strategy of the power distribution system into the reference power distribution system model, and calculating and determining the real-time current of multiple devices in the reference power distribution model; Determining internal temperatures of the devices of the plurality of devices according to the real-time current; The verification result is determined based on the real-time current and the internal temperature of the device, and the verification result includes: a first verification result for indicating the safe operating state of the power distribution system, and a second verification result for indicating the unsafe operating state of the power distribution system, the first verification result means that the real-time current is less than a preset current, and the internal temperature of the device is less than a preset temperature, and the second verification result means that the real-time current is greater than a preset current, and / or the internal temperature of the device is greater than a preset temperature.

8. A distribution system operation optimization device, characterized in that: include: A first determining unit, a second determining unit, a model building unit, a third determining unit, a fourth determining unit, a verification unit, and an adjustment unit; wherein, The first determination unit is used to determine the meteorological data within the forecast period and the power load of the target equipment in the power distribution system; The second determination unit is used to determine the dynamic current threshold of the target device according to the meteorological data and the power load; The model building unit is used to build a multi-objective programming model according to multiple optimization objectives, wherein the optimization objectives are indicators for optimizing the operating state of the power distribution system; The third determination unit is used to determine a new energy consumption strategy according to the dynamic current threshold, wherein the new energy consumption strategy is used to characterize the type of electric energy preferentially used by the power distribution system, and the new energy consumption strategy also includes a constraint condition, which is a condition for ensuring the safe operation of the power distribution system; The fourth determination unit is used to determine the operation strategy of the power distribution system according to the multi-objective planning model and the new energy consumption strategy; The verification unit is used to import the operation strategy of the power distribution system into the electromagnetic transient simulation model, perform field-circuit coupling verification, and obtain a verification result; The adjustment unit is used to adjust the multi-objective programming model according to the verification result until the verification result indicates that the power distribution system is in a safe operating state.

9. An electronic device, characterized in that: The method comprises a processor and a memory storing execution instructions, wherein the memory stores one or more programs; when the processor executes the execution instructions stored in the memory, the processor executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: An energy data management program is stored, including execution instructions. When a processor of an electronic device executes the execution instructions, the processor executes the method according to any one of claims 1-7.

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