Resource configuration method of power distribution network and electronic device

Abstract

The application discloses a resource configuration method of a power distribution network and electronic equipment. It relates to the technical field of power supply operation and dispatching, and comprises the following steps: obtaining multiple operation scenarios of a target power distribution network in a prediction period based on historical operation data corresponding to multiple park nodes in the target power distribution network and meteorological prediction information of a region where the target power distribution network is located; determining a target function of the target power distribution network based on the multiple operation scenarios and preset characteristic parameters corresponding to the multiple park nodes; and optimizing an initial resource configuration strategy of the target power distribution network in the prediction period with the minimum function value of the target function as an optimization target to obtain a target resource configuration strategy of the target power distribution network in the prediction period. The application solves the technical problems of poor resource collaborative scheduling capability between parks in the power distribution network, high operation cost of the power distribution network and unreliable operation of the power distribution network caused by incomplete consideration of factors when the power distribution network faces complex operation scenarios.

Description

Technical Field

[0001] This invention relates to the field of power supply operation and dispatch technology, and more specifically, to a resource allocation method and electronic equipment for a power distribution network. Background Technology

[0002] With the large-scale integration of renewable energy generation, distributed energy storage systems, electric vehicles, and microgrid technologies, the distribution network architecture is rapidly evolving towards an active distribution network form with multi-directional coupling of "grid-source-load-storage." The distribution network is no longer merely a terminal network for power distribution, but is gradually evolving into a core hub supporting distributed power grid connection, microgrid islanded operation, park-level energy trading, and bidirectional demand response. This structural change makes the distribution network highly dynamic, uncertain, and complexly interactive in both time and space, posing technical challenges to its operation control, coordination scheduling, and power supply guarantee capabilities. Related technologies have significant shortcomings in distribution network resource allocation, mainly in the following aspects:

[0003] Resource scheduling based on static operating conditions or single fault modes struggles to adapt to changing network topologies and stochastic resource output, resulting in poor adaptability of resource allocation schemes in actual operation. Furthermore, the use of uniform and homogenized control strategies in resource allocation fails to effectively differentiate the response characteristics and regulation capabilities of different load units; there is a lack of quantitative evaluation mechanisms for differences in regulation timeliness and response capabilities. Although distribution network resource allocation methods in related technologies can provide basic power guarantee capabilities to a certain extent, in the face of complex operating scenarios, incomplete consideration of factors leads to poor inter-regional resource coordination and scheduling capabilities, high distribution network operating costs, and operational unreliability.

[0004] There is currently no effective solution to the above problems. Summary of the Invention

[0005] This invention provides a resource allocation method and electronic device for a power distribution network, which at least solves the technical problems of poor resource coordination and scheduling capabilities between power distribution zones, high operating costs, and unreliable operation caused by incomplete consideration of factors when the power distribution network faces complex operating scenarios.

[0006] According to one aspect of the present invention, a resource allocation method for a distribution network is provided, comprising: obtaining multiple operating scenarios of the target distribution network during a forecast period based on historical operating data corresponding to various park nodes in the target distribution network and meteorological forecast information of the area where the target distribution network is located, wherein the historical operating data includes at least load data, power generation output data and fault data of the corresponding park nodes during the historical period, the multiple operating scenarios correspond to multiple different fault levels, the multiple park nodes correspond to multiple different industry types, the historical period is a period of a first predetermined duration before the current period, and the forecast period is a period of a second predetermined duration after the current period; determining an objective function of the target distribution network based on the multiple operating scenarios and preset characteristic parameters corresponding to the various park nodes, wherein the preset characteristic parameters include the response speed coefficient, adjustable load ratio and critical load ratio of the corresponding park nodes, and the objective function represents the total operating cost of the target distribution network under multiple operating scenarios during the forecast period; optimizing the initial resource allocation strategy of the target distribution network during the forecast period with the minimum function value of the objective function as the optimization objective, to obtain the target resource allocation strategy of the target distribution network during the forecast period.

[0007] According to another aspect of the present invention, a resource allocation device for a distribution network is also provided, comprising: an operation scenario acquisition module, configured to obtain multiple operation scenarios of the target distribution network during a forecast period based on historical operation data corresponding to various park nodes in the target distribution network and meteorological forecast information of the area where the target distribution network is located, wherein the historical operation data includes at least load data, power generation output data and fault data of the corresponding park nodes during the historical period, the multiple operation scenarios correspond to various different fault levels, the various park nodes correspond to various different industry types, the historical period is a period of a first predetermined duration before the current period, and the forecast period is a period of a second predetermined duration after the current period; an objective function determination module, configured to determine an objective function of the target distribution network based on the multiple operation scenarios and preset characteristic parameters corresponding to various park nodes, wherein the preset characteristic parameters include the response speed coefficient, adjustable load ratio and critical load ratio of the corresponding park nodes, and the objective function represents the total operating cost of the target distribution network under multiple operation scenarios during the forecast period; and a target resource allocation strategy determination module, configured to optimize the initial resource allocation strategy of the target distribution network during the forecast period with the minimum function value of the objective function as the optimization objective, to obtain the target resource allocation strategy of the target distribution network during the forecast period.

[0008] According to another aspect of the present invention, a non-volatile storage medium is also provided, the non-volatile storage medium storing a plurality of instructions adapted for loading by a processor and executing any one of the resource allocation methods for the power distribution network described herein.

[0009] According to another aspect of the present invention, an electronic device is also provided, including one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement any one of the resource allocation methods for the power distribution network.

[0010] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of the resource allocation method for the power distribution network as described in any one of the present invention.

[0011] In this embodiment of the invention, multiple operating scenarios of the target distribution network during the forecast period are obtained based on historical operating data corresponding to various park nodes in the target distribution network and meteorological forecast information of the area where the target distribution network is located. The historical operating data includes at least the load data, power generation output data, and fault data of the corresponding park nodes during the historical period. Multiple operating scenarios correspond to multiple different fault levels, and multiple park nodes correspond to multiple different industry types. The historical period is a period of a first predetermined duration before the current period, and the forecast period is a period of a second predetermined duration after the current period. Based on the multiple operating scenarios and the preset characteristic parameters corresponding to each of the multiple park nodes, the objective function of the target distribution network is determined. The preset characteristic parameters include the response speed coefficient, adjustable load ratio, and critical load ratio of the corresponding park nodes. The objective function represents the target... The total operating cost of the standard distribution network under various operating scenarios during the forecast period is calculated. The initial resource allocation strategy of the target distribution network during the forecast period is optimized by minimizing the function value of the objective function. This yields the target resource allocation strategy for the target distribution network during the forecast period. By constructing an objective function that integrates preset characteristic parameters corresponding to various park nodes and multiple operating scenarios, the initial resource allocation strategy of the target distribution network during the forecast period is optimized, thereby accurately determining the target resource allocation strategy. This achieves the technical effects of improving the resource coordination and scheduling capability between parks in the distribution network, reducing the operating cost of the distribution network, and improving operational reliability. Furthermore, it solves the technical problems of poor resource coordination and scheduling capability between parks in the distribution network, high operating costs, and unreliable operation caused by incomplete consideration of factors when the distribution network faces complex operating scenarios. Attached Figure Description

[0012] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0013] Figure 1 This is a flowchart of a resource allocation method for a power distribution network according to an embodiment of the present invention;

[0014] Figure 2 This is a flowchart of an optional objective function determination method according to an embodiment of the present invention;

[0015] Figure 3 This is a flowchart of an optional resource allocation method for a power distribution network according to an embodiment of the present invention;

[0016] Figure 4 This is a schematic diagram of a resource allocation device for a power distribution network according to an embodiment of the present invention;

[0017] Figure 5 This is a schematic diagram of a resource allocation electronic device for a power distribution network according to an embodiment of the present invention. Detailed Implementation

[0018] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0019] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0020] According to an embodiment of the present invention, a method embodiment for resource allocation of a power distribution network is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0021] Figure 1 This is a flowchart of a resource allocation method for a distribution network according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0022] Step S102: Based on the historical operating data of various park nodes in the target distribution network and the meteorological forecast information of the area where the target distribution network is located, multiple operating scenarios of the target distribution network during the forecast period are obtained. The historical operating data includes at least the load data, power generation output data and fault data of the corresponding park nodes during the historical period. Multiple operating scenarios correspond to multiple different fault levels, and multiple park nodes correspond to multiple different industry types. The historical period is the period of the first predetermined duration before the current period, and the forecast period is the period of the second predetermined duration after the current period.

[0023] Optionally, historical operational data of various park nodes in the target distribution network can be collected over a historical period (e.g., the past year or three years). This historical operational data includes, but is not limited to, load data for each park, actual output data of distributed power sources (e.g., photovoltaic, wind power), and information on historical equipment failures, line trips, and other events. This historical operational data can accurately reflect the differentiated operational behavior of different industrial parks under different seasons, weekdays and holidays, and meteorological conditions. Furthermore, by combining this data with meteorological forecasts for the target distribution network's location, the changing trends of distributed energy output and the impact of weather on the distribution network can be further deduced. The meteorological forecasts include at least the ambient temperature, wind speed, probability of rainfall, and solar radiation intensity of the target distribution network during the forecast period (e.g., the next 24 or 72 hours). Various park nodes include science and technology park nodes, cultural and creative park nodes, heavy industrial park nodes, comprehensive park nodes, and high-end manufacturing park nodes. The industrial park nodes are categorized into several types: Science and Technology Park Nodes, Cultural and Creative Park Nodes, Heavy Industry Park Nodes, and High-Energy Industrial Park Nodes. Science and Technology Park Nodes are industrial parks focused on high-density computing and precision electronic equipment, including information technology, artificial intelligence, big data centers, and cloud computing platforms. Cultural and Creative Park Nodes are industrial parks primarily engaged in cultural and creative industries, film and television production, exhibitions, performing arts, and high-end commerce. Heavy Industry Park Nodes are industrial parks with high-energy-consuming, continuous production processes, such as steel, chemical, metallurgy, shipbuilding, and large-scale machinery processing. Comprehensive Park Nodes are industrial parks integrating residential, commercial, office, light industry, and public services, such as large urban complexes, new city development zones, or industry-city integration demonstration zones. High-end Manufacturing Park Nodes are industrial parks focused on high-tech, high-value-added, and low-fault-tolerance manufacturing processes, such as precision instruments, semiconductors, biomedicine, and core components for new energy vehicles. Based on this, a set of representative operating scenarios is constructed by comprehensively analyzing historical load patterns, output characteristics, and fault distribution, combined with the uncertainties of meteorological forecasts. These scenarios include normal operation scenarios, general fault operation scenarios, and extreme fault operation scenarios. In generating normal scenarios, load curves closely coupled with the characteristics of their respective industries can be constructed for various park nodes based on historical operational data. For example, the bi-peak load of a science and technology park reflects the combined effect of equipment operation and employee commuting schedules; the time curve of a cultural and creative park reflects its activity dependence; and the periodic fluctuations of a heavy industrial park stem from the operational cycles of production and energy-intensive processes. Simultaneously, photovoltaic output data integrates the statistical distribution of seasonal irradiance variations and weather types (such as sunny, cloudy, and rainy) to make new energy output more realistically representative. Grid-side parameters are conservatively set, such as rated capacity and full interconnection of tie lines. Unsupervised compression of historical data is performed using clustering algorithms to automatically identify statistically significant typical daily patterns and assign probability weights, ultimately forming a normal operation scenario covering different seasons, weekdays, and holiday combinations, possessing probability distribution characteristics.This method frees the optimization input of candidate objective functions from manually preset ideal operating conditions, instead grounding it in real operational patterns, which greatly improves the adaptability of subsequent scheduling strategies under normal conditions. In generating general fault scenarios, actual engineering failure scenarios (single component outage, i.e., N-1 fault) and simultaneous failures of two components (i.e., N-2 fault) can be used as the target, avoiding the computational explosion caused by blindly enumerating all possible fault combinations. By establishing a component importance assessment system, priority is given to high-risk nodes such as main lines, key transformers, and core switches to ensure that fault scenarios have actual threats. For example, single component outage scenarios comprehensively cover single-point faults to ensure no omissions. Simultaneous failures of two components employ an intelligent screening strategy based on electrical topology and functional correlation, such as prioritizing combinations of strongly coupled fault pairs, such as multiple main transformers in the same substation. In generating extreme fault scenarios, vulnerable areas and key hub nodes of the target distribution network can be identified, coupled with meteorological disaster models (such as typhoon paths, ice thickness, and lightning strike density) to establish a quantitative relationship between component failure probability and disaster intensity, thereby simulating regional, multi-point synchronous failure events caused by natural disasters. By leveraging Monte Carlo simulations to generate numerous combinations of multi-component faults (i.e., Nk faults), this study focuses on catastrophic modes that lead to the disconnection of the target distribution network, widespread blackouts, or complete shutdowns of critical substations, such as main substation power loss or simultaneous interruptions of multiple high-voltage tie lines. In these extreme scenarios, the target distribution network's power supply capacity is compressed to its limit, external support is nearly ineffective, and reliance on the park's own energy storage, distributed power sources, and mutual backup capabilities is crucial. Constructing these diverse operational scenarios lays a solid foundation for achieving the operational goal of ensuring power supply to the target distribution network during faults, thereby overcoming the risk of strategy failure caused by the simplification of scenarios or inaccurate assumptions in related technologies.

[0024] Step S104: Based on multiple operating scenarios and the preset characteristic parameters corresponding to each of the multiple park nodes, determine the objective function of the target distribution network. The preset characteristic parameters include the response speed coefficient, adjustable load ratio, and critical load ratio of the corresponding park nodes. The objective function represents the total operating cost of the target distribution network under multiple operating scenarios during the forecast period.

[0025] Optionally, a mathematical framework is established, driven by preset characteristic parameters corresponding to various park nodes, input by various operating scenarios, and optimized by the comprehensive operating cost of the target distribution network. Under this framework, all parks are no longer considered homogeneous load units. Instead, based on their industrial attributes and operating characteristics, each type of park node is assigned a set of preset characteristic parameters with physical meaning and engineering measurement basis. Among these, the response speed coefficient indicates the ability of the corresponding park node to complete active power regulation within a predetermined time after receiving a dispatch instruction from the target distribution network; the adjustable load ratio indicates the proportion of load that can be reduced or transferred by the target distribution network to the total load of the corresponding park node under normal operating conditions; and the critical load ratio indicates the proportion of load that the corresponding park node must retain to ensure the safety or production continuity of the target distribution network under fault operating conditions. In specific applications, the values ​​of the preset characteristic parameters can be adjusted and corrected according to actual operating scenarios and needs. Table 1 shows the results of the preset characteristic parameter values ​​corresponding to various park nodes according to an embodiment of the present invention. This step, by establishing an objective function that integrates the response speed, adjustable load ratio, and critical load ratio of various park nodes into multiple operating scenarios, can achieve a precise trade-off between cost and power supply guarantee for different types of parks under fault and normal operation conditions. This enables optimization decisions to prioritize high-critical loads while incentivizing rapid response resources to participate in regulation, thereby significantly improving the power supply resilience of the target distribution network under uncertainties in multiple operating scenarios and reducing the operating cost of the target distribution network.

[0026] Table 1

[0027]

[0028] In one alternative embodiment, Figure 2 This is a flowchart of an optional objective function determination method according to an embodiment of the present invention, such as... Figure 2 As shown, based on various operating scenarios and the preset characteristic parameters corresponding to various park nodes, the objective function of the target distribution network is determined, including:

[0029] Step S202: Based on the power supply capacity attenuation degree corresponding to each of the various operating scenarios, obtain the power purchase price of the target distribution network from the upper-level transmission network during the predicted period. The power supply capacity attenuation degree is used to indicate the proportion of the decrease in the power supply capacity of the target distribution network relative to the normal power supply capacity in the case of a fault in the upper-level transmission network in the corresponding operating scenario.

[0030] Step S204: Based on the preset lifespan cost, the response speed coefficients of various park nodes, the adjustable load ratio and the critical load ratio, the energy storage lifespan cost corresponding to various park nodes is obtained. The energy storage lifespan cost represents the lifespan cost of each unit of electricity of the energy storage device in the corresponding park node due to scheduling loss.

[0031] Step S206: Based on the preset penalty cost and the critical load ratio corresponding to each of the various park nodes, the load penalty price of the target distribution network during the forecast period is obtained, wherein the load penalty price represents the penalty price borne for implementing load interruption for each unit of electricity.

[0032] Step S208: Based on the response speed coefficients of various park nodes, the electricity sales price in the predicted period is obtained.

[0033] Step S210: Based on the response speed coefficients of various park nodes and multiple target distances, obtain the multiple park transmission prices of the target distribution network during the prediction period. Here, the multiple target distances represent the distance between any two park nodes among the various park nodes, and the multiple park transmission prices represent the reward price for transmitting a unit of electricity between any two park nodes.

[0034] Step S212: Determine the objective function based on the electricity purchase price, load penalty price, electricity sales price, transmission prices of multiple parks, and the energy storage lifespan cost corresponding to each of the various park nodes.

[0035] Optionally, in constructing the objective function, cost and price are no longer treated as externally given constants, but are determined through the dynamic feedback relationship between the operating scenario and the characteristics of the park. For example, the electricity purchase price is dynamically adjusted upwards according to the degree of decline in the power supply capacity of the upper-level power grid. That is, when a fault occurs and the external grid's support capacity decreases, the external electricity purchase price is automatically raised to guide the park to prioritize the use of local resources. The energy storage life cost is combined with the park's response speed and adjustment frequency to quantify the differentiated losses caused by frequent charging and discharging to the energy storage life. For example, the faster the response and the more frequent the adjustment, the higher the unit energy storage dispatch cost, thereby curbing unnecessary overuse. The load penalty price is dynamically adjusted according to the proportion of critical loads in the park. For example, the higher the proportion of critical loads in the park, the higher the power outage penalty, ensuring that core loads such as people's livelihood, communication, and manufacturing are prioritized when resources are scarce. The electricity sales price is positively correlated with the park's response speed. For example, the faster the response of the park (such as a comprehensive park with rapid energy storage capabilities), the higher the utility of its electricity sales to the grid, effectively incentivizing active participation in distribution network regulation. The inter-park transmission price comprehensively considers response speed and physical distance. Parks with fast response and short distances receive higher rewards for mutual assistance, promoting the formation of a local resilience network of nearby mutual assistance and rapid support.

[0036] In one optional embodiment, an objective function is determined based on the electricity purchase price, load penalty price, electricity sales price, transmission prices of multiple industrial parks, and energy storage lifetime costs corresponding to various industrial park nodes. This includes determining the self-contained unit generation cost, the node purchase volume for various operating scenarios, the self-contained unit generation, energy storage discharge power, energy storage charging power, load reduction, first transmission volume, and second transmission volume. Here, the node purchase volume represents the electricity purchased by the electrical nodes of the target distribution network from the upstream transmission network under the corresponding operating scenario; the self-contained unit generation cost represents the cost per unit of electricity consumed by the self-contained units in the target distribution network; the self-contained unit generation represents the generation of the target industrial park nodes in the target distribution network under the corresponding operating scenario; the target industrial park nodes are those containing self-contained units; and the energy storage discharge power represents the corresponding... The discharge power of energy storage devices in the park nodes under the corresponding operating scenario, the charging power of energy storage devices in the corresponding park nodes under the corresponding operating scenario, the load reduction amount represents the active power that the load of the electrical node can be reduced under the corresponding operating scenario, the first transmission power represents the power transmitted by the corresponding park node to the upper-level transmission network under the corresponding operating scenario, and the second transmission power represents the power transmitted between any two park nodes; based on the purchase price of electricity, the load penalty price, the sales price of electricity, the transmission price of multiple parks, the generation cost of self-owned units, the energy storage life cost corresponding to each of the various park nodes, and the node purchase power, self-owned unit generation, energy storage discharge power, energy storage charging power, load reduction amount, first transmission power and second transmission power corresponding to each of the various operating scenarios, the objective function is determined.

[0037] Optionally, multiple variables are introduced during the construction of the objective function, each strictly bound to its physical behavior and operating scenario. Among them, the node's purchased electricity reflects the electricity purchase cost of the target distribution network; the self-owned unit's power generation is calculated by multiplying the unit's power generation cost to reflect the fuel and maintenance costs of the self-owned unit; the charging and discharging power of energy storage devices is converted into equivalent costs based on the cycle life degradation characteristics of the energy storage devices, avoiding the neglect of long-term degradation effects by only considering charging and discharging losses in related technologies; the load reduction is weighted by penalty prices, and its value is dynamically adjusted according to the park type to ensure stronger protection for high-value loads (such as core equipment in science parks and heavy industrial production lines); the first transmission electricity is the utility obtained by the park from selling electricity to the main grid, included in the negative cost item to incentivize resource transmission; the second transmission electricity introduces differentiated mutual assistance electricity prices between parks, which not only reflects physical line losses and transmission capacity constraints, but also guides parks with rapid adjustment capabilities to actively support vulnerable nodes through a mechanism where those with faster response speeds and higher contributions receive higher returns. This objective function automatically balances the priority relationship between cost optimization and power supply reliability under different operating conditions, providing a solid and feasible foundation for building a new distribution network operation system with market incentive mechanisms, resource sharing capabilities, and the willingness of multiple stakeholders to collaborate.

[0038] In an optional embodiment, when there are multiple electrical nodes and multiple target park nodes, the objective function is determined based on the electricity purchase price, load penalty price, electricity sales price, transmission prices of multiple parks, generation cost of self-owned generating units, energy storage lifespan cost corresponding to each of the various park nodes, and the node purchase volume, self-owned generating unit generation, energy storage discharge power, energy storage charging power, load reduction, first transmission volume, and second transmission volume corresponding to each of the various operating scenarios. This objective function is determined in the following manner:

[0039] ;

[0040] in, This represents the function value of the objective function. Let represent the weight corresponding to any given runtime scenario, 'a' represent the index of any given runtime scenario, and 'A' represent the number of runtime scenarios. This represents the electricity purchase price at any given forecast time within a forecast period, where t represents the index of any forecast time, and T represents the number of forecast times. This represents the node purchase volume of any electrical node among multiple electrical nodes at any given prediction time, under any operating scenario. n represents the index of any electrical node, and N represents the number of electrical nodes. This represents the cost of generating electricity from the self-contained generating unit at any predicted time. This represents the self-owned generator output of any target park node among multiple target park nodes under any operating scenario at any given prediction time, where g represents the index of any target park node, and G represents the number of multiple target park nodes. This represents the energy storage lifetime cost of any one type of park node among various park nodes, where i represents the index of any one type of park node, and I represents the number of various park nodes. This represents the energy storage discharge power of any type of park node at any predicted time under any operating scenario. This represents the energy storage charging power of any type of park node at any predicted time under any operating scenario. This represents the load penalty price at any forecast time. This represents the load reduction of any electrical node at any predicted time under any operating scenario. This represents the response speed coefficient of any type of park node. This represents the electricity price at any predicted time. This represents the first transmitted power of any given park node at any predicted time under any operating scenario. This represents the electricity transmission price of the park at any predicted time. This represents the second power transmission from any type of park node to a specified park node at any predicted time and under any operating scenario, where j represents the index of the specified park node.

[0041] Optionally, the mathematical formula of the objective function fully demonstrates the calculation logic of the total operating cost of the target distribution network under complex operating conditions. This function uses the probability distribution of multiple operating scenarios as weights and multiple prediction times as time axes, comprehensively considering all costs and utility items from the grid side, park side, energy storage side, and market side. Among these, electricity purchase cost, self-owned unit generation cost, energy storage charging and discharging lifespan loss cost, and load reduction penalty cost constitute the main expenditures of the target distribution network. Inter-park energy mutual assistance incentives are included as positive utility, forming a two-way closed loop. The price of inter-park power transmission is affected by response speed and is related to the physical distance between parks, prompting the target distribution network to form a local power supply network with proximity, mutual assistance, and rapid response, rather than indiscriminate dispatch. Energy storage lifespan cost is directly related to park characteristics, causing high-response parks with frequent dispatch to bear higher loss costs, avoiding excessive resource use and achieving sustainable operation. All variables in this objective function are precisely mapped to a three-dimensional index of operating scenario, time, and node, ensuring that every cost and every unit of power has a clear destination. The objective function is unified under a mathematical framework, organically integrating five complex factors: uncertainty of the operating scenario, heterogeneity of park types, differentiation of response capabilities, dynamics of spatial mutual assistance, and orientation of utility incentives. This makes the optimization of resource allocation no longer blindly minimize the total cost, but intelligently identify in what scenario, by which park, in what way, and at what cost, resources are most effectively contributed.

[0042] Step S106: With the minimum function value of the objective function as the optimization objective, optimize the initial resource allocation strategy of the target distribution network during the forecast period to obtain the target resource allocation strategy of the target distribution network during the forecast period.

[0043] Optionally, minimizing the objective function value can be the optimization goal. Essentially, this involves integrating all operating costs and utilities of the target distribution network within the forecast period into a comprehensive evaluation system. Through mathematical optimization algorithms, an optimal set of resource allocation schemes is automatically searched to minimize the expected total cost of the target distribution network under all possible operating scenarios and their probability distributions. The resource allocation strategy refers to the coordinated decision-making regarding multiple controllable variables at each node of the target distribution network at each forecast time and under each operating scenario. These variables include electricity purchased from the main grid, the output of self-owned generating units in each area, the charging and discharging power of energy storage devices, the reduction of non-critical loads, electricity sold to the main grid, and the power transmitted between areas. Multiple variables in the objective function collectively determine how to allocate and utilize limited energy resources to cope with external disturbances and internal demand fluctuations. This optimization process does not involve adjusting a single resource in isolation, but rather achieves collaborative decision-making across multiple sources, points, and timeframes. For example, in extreme fault scenarios, it automatically reduces high-priced electricity purchases, prioritizes the discharge of fast-responding energy storage in industrial parks, cuts low-critical loads, incentivizes rapid mutual support between neighboring parks, and restricts electricity sales by low-response parks, thereby minimizing costs while ensuring critical loads. Ultimately, the optimized resource allocation strategy is a refined set of operational instructions encompassing time, scenarios, nodes, and equipment. Adaptive optimization of resource allocation strategies overcomes the limitations of pre-planned or static rule-based resource allocation in related technologies, enabling a shift from experience-based decision-making to data-driven, scenario-adaptive, and multi-objective collaborative optimization.

[0044] In one optional embodiment, the initial resource allocation strategy of the target distribution network during the forecast period is optimized with the minimum function value of the objective function as the optimization objective, to obtain the target resource allocation strategy of the target distribution network during the forecast period. This includes: determining common operating constraints applicable to the target distribution network; determining the unique operating constraints corresponding to various park nodes, wherein the unique operating constraints represent specific constraints applicable to the corresponding park nodes; and optimizing the initial resource allocation strategy based on the common operating constraints and their respective unique operating constraints, with the minimum function value of the objective function as the optimization objective, to obtain the target resource allocation strategy.

[0045] Optionally, by dividing the operational constraints of the target distribution network into two levels—general constraints and park-specific constraints—efficient solutions to the objective function can be achieved. General operational constraints encompass the underlying physical and topological rules for the safe operation of the target distribution network, such as power flow balance, voltage and current limits, radial network structure, energy storage charging and discharging capabilities, and tie-line power limits. These form the common foundation for ensuring the physical feasibility of the target distribution network. Meanwhile, the specific operational constraints for various park nodes are tailored to the industry attributes and energy consumption characteristics of each type of park node. For example, technology parks are prohibited from reducing core server loads, cultural and creative parks are prohibited from shedding loads and selling electricity during large-scale events, high-end manufacturing parks are not allowed to participate in electricity sales by disconnecting production line power, heavy industrial parks must meet the thermoelectric coupling relationship of combined heat and power (CHP), and integrated parks need to coordinate the coordinated charging and discharging of electric vehicles and energy storage. These specific constraints are not simple additions, but rather transform the park's operational logic, business needs, and user comfort—non-electrical attributes—into mathematical conditions that can be identified and executed by the optimization model. During the optimization process, general and specific constraints together constitute a complete feasible solution space. Within this feasible solution space, with the goal of minimizing the total cost of the target distribution network, the initial resource allocation strategy is globally optimized, automatically coordinating the electricity purchase, generation, energy storage, load, and mutual assistance behaviors of each node under different scenarios. This hierarchical modeling approach avoids resource mismatch or functional failure caused by one-size-fits-all constraints in related technologies. For example, this hierarchical constraint method will not compromise the stability of high-end manufacturing production lines by forcing all parks to participate in electricity sales, nor will it cause user complaints by ignoring the activity characteristics of cultural and creative parks.

[0046] In one optional embodiment, determining common operating constraints applicable to the target distribution network includes: determining a first common operating constraint applicable to lines in the target distribution network; determining a second common operating constraint applicable to electrical nodes in the target distribution network; determining a third common operating constraint applicable to target campus nodes in the target distribution network, wherein the target campus node is a campus node containing self-owned generating units; and determining a fourth common operating constraint applicable to multiple campus nodes in the target distribution network.

[0047] Optionally, by constructing four types of common operational constraints applicable to the target distribution network, the boundaries of the distribution network, from physical topology to operational behavior, can be comprehensively covered, ensuring that the optimization strategy has engineering feasibility and stability in any scenario. Specifically, the first constraint targets lines, clarifying power transmission limits, current thermal stability boundaries, and apparent power capacity limits to prevent line overload from causing equipment damage or thermal stability collapse, which is the physical foundation for the safe operation of the target distribution network. The second constraint focuses on electrical nodes, establishing upper and lower limits of voltage amplitude, node power conservation relationships, and power flow continuity to ensure the voltage quality and energy balance of the entire network and avoid local voltage exceedances or power islanding out of control. The third constraint targets park nodes with self-owned units, limiting their unit output range, ramp rate, and start-stop logic to ensure that the regulation behavior of distributed power sources does not impact the grid frequency and dynamic stability, achieving source-end controllability. The fourth constraint applies to all park nodes, uniformly standardizing energy storage charging and discharging capabilities, charging and discharging efficiency, energy state conservation, charging and discharging state mutual exclusion, and the directionality and capacity upper limit of power interaction between parks, achieving standardized and quantifiable management of distributed resources.

[0048] In an optional embodiment, when there are multiple lines and multiple electrical nodes in the target distribution network, a first common operating constraint applicable to the lines in the target distribution network is determined, including: determining the first common operating constraint as: the power parameters of any line in any of the multiple prediction times included in the prediction period under any operating scenario do not exceed a preset power parameter threshold, wherein the power parameters include at least: current, active power and reactive power;

[0049] ;

[0050] ;

[0051] ;

[0052] ; ;

[0053] ; ;

[0054] ;

[0055] in, This represents the active power flowing from any electrical node in the target distribution network at any given time to the first electrical node under any operating scenario. Let represent the active power flowing from the second electrical node to any electrical node at any predicted time under any operating scenario, where 'a' represents the index of any operating scenario, 't' represents the index of any predicted time, 'd' represents the index of any electrical node, and 'e' represents the index of the first electrical node. Indicates the index of the second electrical node. This represents the resistance of any circuit. This represents the reactance of any line, and de represents the index of any line including any electrical node and the first electrical node. This represents the reactive power flowing from any electrical node to the first electrical node at any predicted time under any operating scenario. It represents the square of the current of any line under any operating scenario at any predicted time. This indicates the switching state of any line at any predicted time under any operating scenario. Represents the first preset constant. It represents the square of the voltage at any electrical node at any predicted time under any operating scenario. This represents the squared value of the first electrical node at any predicted time under any operating scenario. Denotes the Euclidean norm. This represents the active power flow of any given line. This represents the reactive power flow of any given line. This represents the active energy stored by the energy storage device of any type of park node among multiple park nodes at any given predicted time, under any operating scenario. 'i' represents the index of any type of park node, and 'I' represents the number of multiple park nodes. This represents the active energy stored by energy storage devices at any given park node under any operating scenario at the next prediction time from any given prediction time. This represents the energy storage charging power of any type of park node at any predicted time under any operating scenario. This represents the energy storage discharge power of any type of park node at any predicted time under any operating scenario. This represents the time interval between any prediction time and the next prediction time. This indicates the charging efficiency of the energy storage device. This represents the discharge efficiency of the energy storage device, where N represents the number of electrical nodes. This represents the set of power nodes deployed in any given operating scenario. This represents the flow of traffic from any electrical node to the first electrical node at any predicted time under any operating scenario. This indicates whether a power supply path is formed between the second electrical node and any other electrical node at any predicted time. It is a binary variable. Indicates the formation of a power supply path. This indicates that no power supply path has been established; Indicates the number of multiple lines. It represents the load demand of any electrical node at any predicted time under any operating scenario.

[0056] Optionally, by constructing the first common operational constraints at the line level in the operation of the target distribution network, the electrical safety boundary and network structure constraints of the target distribution network are integrated into a complete constraint set that can be embedded in the optimization model. This enables accurate modeling of complex operating states under multiple lines, multiple nodes, and multiple scenarios. Specifically, firstly, by limiting the electrical parameters such as line current, active power, and reactive power within their respective preset thresholds, it is ensured that all lines do not experience overload or thermal stability exceedances under any operating scenario and at any predicted time. , , ,in, This indicates the preset upper voltage threshold. This represents the current of any line at any predicted time under any operating scenario. Indicates the preset current threshold. It represents the active power of any line at any predicted time under any operating scenario. This indicates the preset active power threshold. This represents the reactive power of any line under any operating scenario at any predicted time. First, a preset reactive power threshold is defined. Second, a nonlinear relaxation model based on the coupling of voltage squared difference with line impedance and power flow is adopted to accurately characterize the logical relationship between the line switch state and the voltage and power flow at both ends of the nodes. That is, when the switch is open, the voltage difference constraint automatically fails, allowing the voltage at both ends to float freely; when the switch is closed, the voltage difference must satisfy the approximate physical relationship of AC power flow, thereby achieving high-precision modeling of the dynamic behavior of the AC network. Furthermore, a second-order cone constraint is introduced to perform convex relaxation on the power flow equation, which can effectively solve the defect of DC power flow model ignoring reactive power and voltage coupling in related technologies, making the optimization results closer to the actual operating state of the distribution network. In terms of topology constraints, by ensuring that the total number of line switches equals the number of nodes minus the number of power sources, the network is forced to maintain a loop-free radial structure to avoid protection maloperation caused by circulating currents. By judging whether a power supply path is formed, the power flow direction of each line is ensured to be unique and reachable. In addition, the energy conservation equation of energy storage explicitly models the time-series dependency relationship between charging and discharging power and storage state, ensuring that energy storage operation does not exceed limits, thereby realizing closed-loop energy management.

[0057] In an optional embodiment, when there are multiple electrical nodes, determining a second common operating constraint applicable to the electrical nodes in the target distribution network includes: determining the second common operating constraint as follows: at any prediction time among multiple prediction times included in the prediction period, the voltage of any electrical node among the multiple electrical nodes under any operating scenario is within a preset safe voltage range; under any operating scenario, the difference in load reduction between two adjacent prediction times for any electrical node does not exceed a preset response speed coefficient; under any operating scenario, the load reduction of any electrical node at any prediction time does not exceed the product between the load power at the corresponding time and the preset load shedding ratio.

[0058] Optionally, within the target distribution network optimization framework involving multiple electrical nodes and multiple operating scenarios, the second common operational constraint constructs a constraint system to ensure the stable operation of the target distribution network from three dimensions: voltage safety, load regulation dynamism, and load reduction rationality. Specifically, firstly, it limits the voltage of any electrical node at any predicted time under any operating scenario to within a preset safe voltage range. This ensures stable voltage quality across the entire network, prevents voltage exceedances caused by power imbalance, line voltage drops, or reverse feedback from distributed power sources, and avoids equipment insulation damage or damage to user-end electrical appliances. This is the rigid bottom line for the safe operation of the target distribution network, i.e. ,in, This represents the voltage of any electrical node at any predicted time under any operating scenario. This indicates the lower limit of the preset safe voltage range. This indicates the upper limit of the preset safe voltage range. Next, a constraint on the rate of change of load reduction between adjacent time points is introduced, meaning that the change in load reduction at any node between two consecutive time points must not exceed the response speed coefficient of its respective industrial park. ,in, This represents the load reduction of any electrical node at any predicted time under any operating scenario. This represents the load reduction of any electrical node at any given forecast time in any operating scenario at the next forecast time. This represents the preset response speed coefficient for any electrical node. This constraint simulates the thermal inertia and response delay characteristics of real load equipment (such as air conditioners, temperature control systems, and industrial loads) to prevent optimization results from providing idealized but unexecutable strategies for instantaneous large-scale load shedding. It avoids equipment shocks, user discomfort, or secondary disturbances caused by drastic load fluctuations, ensuring that dispatch instructions are both engineering-operable and user-acceptable. Finally, by limiting the load reduction of any node at any given time to no more than the product of its actual load power and the preset load shedding ratio, it ensures that load shedding behavior is always within a controllable and predictable range, preventing excessive shedding from causing unnecessary power outages. ,in, This represents the preset load shedding ratio for any electrical node under any operating scenario. It represents the load power of any electrical node at any predicted time under any operating scenario.

[0059] In an optional embodiment, when there are multiple target park nodes and multiple electrical nodes, a third common operating constraint applicable to the target park nodes in the target distribution network is determined, including: determining the third common operating constraint as follows: the self-provided unit power generation of any target park node in any operating scenario at any prediction time among multiple prediction times included in the prediction period does not exceed the preset self-provided unit power generation; and in any operating scenario, the difference in self-provided unit power generation of any target park node at two adjacent prediction times among multiple prediction times does not exceed the preset power generation difference.

[0060] ;

[0061] ;

[0062] in, This represents the electricity purchase amount of any electrical node among multiple electrical nodes at any given predicted time, under any given operating scenario. This represents the power generation of distributed photovoltaic (PV) units at any given time point in any given park, under any given operating scenario. 'a' represents the index of any operating scenario, 't' represents the index of any given time point, 'n' represents the index of any electrical node, 'i' represents the index of any given park node, and 'I' represents the number of nodes across all park types. This represents the self-owned generator output of any target park node at any predicted time under any operating scenario, where g represents the index of any target park node. This represents the energy storage discharge power of any type of park node at any predicted time under any operating scenario. This represents the energy storage charging power of any type of park node at any predicted time under any operating scenario. This represents the load reduction of any electrical node at any predicted time under any operating scenario. This represents the load power of any electrical node at any predicted time under any operating scenario. The second power transmission to a designated park node at any predicted time and under any operating scenario. The second power transmission capacity is specified at any given time when a designated park node transmits power to any other park node under any given operating scenario. This indicates the index of the specified park node.

[0063] Optionally, the third common operational constraint targets target park nodes with self-owned generating units, constructing a three-pronged constraint system encompassing power output controllability, power balance consistency, and bidirectional interactive compliance. This achieves precise regulation of the operational behavior of distributed power sources and park-level energy equipment. Specifically, firstly, by limiting the power generation of any target park node's self-owned generating units to not exceed its rated capacity at any given time and under any scenario, it ensures that the output behavior of distributed power sources always remains within the physical capacity boundaries of the equipment, preventing equipment overload, protection activation, or grid frequency disturbances caused by over-generation. ,in, This represents the preset self-owned generator output of any target park node; simultaneously, the change in generator output between adjacent time points is limited to the difference between the preset generator outputs, i.e. ,in, This represents the self-provided power generation of any target park node under any operating scenario at any prediction time in the next prediction time. This represents the preset power generation difference for any target park node. By simulating the thermodynamic response characteristics of power sources such as gas turbines and diesel generator sets, it avoids the impact of sudden start-up and shutdown on the target distribution network. Secondly, through two symmetrical power balance equations, park nodes are incorporated into a unified full-chain energy flow modeling system. From the perspective of both power purchase and sale, it achieves the mutual exclusion of node-level power conservation and market interaction logic. That is, the same park can only choose to purchase or sell power at any given time, and cannot conduct bidirectional transactions simultaneously. At the same time, it also considers the differences between parks (i.e., any type of park node and a specified park node, ...). and Energy sharing within a park, treated as a resource flow on par with the main grid's power purchase and sale, is incorporated into a unified model. This allows the park to function as a triple entity—a load, a power source, and a sharing unit—overcoming the limitations of related technologies that treat parks as passive loads or isolated microgrids. Specifically, a designated park node is one that has power transmission relationships with any other park node.

[0064] In an optional embodiment, when there are multiple electrical nodes, a fourth common operating constraint applicable to multiple park nodes in the target distribution network is determined, including: determining the fourth common operating constraint as follows: at any prediction time among multiple prediction times included in the prediction period, the energy storage parameter of any type of park node in any operating scenario does not exceed a preset energy storage parameter threshold, wherein the energy storage parameter includes at least: the active energy stored by the energy storage device, the energy storage charging power, and the energy storage discharging power; in any operating scenario, the first transmission power of any type of park node at any prediction time does not exceed the product between the preset electricity sales coefficient and the preset transmission power in the corresponding operating scenario; in any operating scenario, the node purchase power of any type of park node at any prediction time does not exceed the product between the preset electricity purchase coefficient and the preset purchase power in the corresponding operating scenario; in any operating scenario, the first transmission power of any type of park node at any prediction time does not exceed the product between the preset electricity purchase coefficient and the preset purchase power in the corresponding operating scenario; in any operating scenario, the first transmission power of any type of park node at any prediction time does not exceed the product between the preset electricity purchase coefficient and the preset purchase power in the corresponding operating scenario; in any operating scenario, the first transmission power of any type of park node at any prediction time does not exceed the product between the preset electricity sales coefficient and the preset electricity purchase power in the corresponding operating scenario; in any operating scenario, the first transmission power of any type of park node at any prediction time does not exceed the product between the preset electricity purchase coefficient and the preset electricity purchase power in the corresponding operating scenario; in any operating scenario, the first transmission power of any type of park node at any prediction time does not exceed the product between the preset electricity sales ... The product of the first transmitted power at any predicted time and the power purchased by the node at the corresponding time is equal to zero; under any operating scenario, the unidirectional transmitted power between any two park nodes at any predicted time does not exceed the preset transmitted power; under any operating scenario, the power transmitted by any type of park node to a designated park node at any predicted time is equal to the power received by the designated park node from any type of park node at the corresponding time; the temperature of any type of park node at any predicted time under any operating scenario does not exceed the preset temperature; the power generation of the distributed photovoltaic unit of any type of park node at any predicted time under any operating scenario does not exceed the preset photovoltaic unit power generation; under any operating scenario, the active power allowed to be reduced from the load of any type of park node at any predicted time does not exceed the product of the preset load ratio and the preset active power reduction of any type of park node.

[0065] ;

[0066] ;

[0067] in, This indicates whether the energy storage device at any given time point in the park is in a charging state under any given operating scenario, where 'a' represents the index of any operating scenario and 't' represents the index of any given time point. It is a binary variable. This indicates that it is in a charging state. This indicates that the device is not currently charging. This indicates whether the energy storage device at any given time point in any given park node is in a discharging state under any given operating scenario. It is a binary variable. This indicates that it is in a discharge state. This indicates that the device is not in a discharge state. This indicates whether the energy storage device at any given time point in any given park node is operational under any given operating scenario. It is a binary variable. This indicates that it is in operation. This indicates that it is not yet in operation; This represents the temperature of any type of park node at any predicted time under any operating scenario. This represents the temperature of any given park node in any given operating scenario at the next prediction time from any given prediction time. This represents the preset temperature control coefficient for any type of park node under any operating scenario. This represents the active power of the temperature-controlled load at any predicted time point for any type of park node under any operating scenario. It represents the active power of the temperature-controlled load of any park node under any operating scenario at any prediction time and the next prediction time at any prediction time.

[0068] Optionally, the fourth common operational constraint lays the foundation for refined and secure coordinated regulation of resources across various types of distribution networks by constructing a comprehensive constraint system covering the entire lifecycle behavior of energy storage devices, market interactions, physical operational boundaries, and multi-physics coupling mechanisms. Specifically, firstly, by limiting the active energy storage, charging power, and discharging power of energy storage devices to not exceed their rated capacity and power limits, the physical safety and lifespan tolerance of energy storage devices can be ensured, preventing thermal runaway or capacity decay caused by overcharging or over-discharging. , , ,in, It represents the active energy stored by energy storage devices at any predicted time, under any operating scenario, for any type of park node. This represents the preset active energy storage capacity of any type of park node. This represents the energy storage charging power of any type of park node at any predicted time under any operating scenario. This indicates the preset charging power for any type of park node. This represents the energy storage discharge power of any type of park node at any predicted time under any operating scenario. This represents the preset discharge power of any type of park node; simultaneously, by introducing binary variables... , and The logical constraints ensure the implementation of engineering safety rules, such as no charging or discharging allowed before operation and simultaneous charging and discharging, eliminating control logic conflicts and making energy storage behavior interpretable and reliable. Secondly, through the dual restrictions of preset electricity purchase coefficients and preset purchase volumes for both electricity purchase and sale, namely… ,in, This represents the electricity purchases of any given park node at any given time point under any given operating scenario. This represents the preset electricity purchase coefficient for any type of park node under any operating scenario. The preset electricity purchase coefficient and preset electricity purchase amount represent the preset electricity purchase amount for any type of park node under any operating scenario. Market mechanisms are embedded into operational constraints; that is, the electricity purchase amount and electricity sales amount are jointly limited by scenario adaptive coefficients (such as reducing the electricity purchase limit and increasing electricity sales incentives during faults) and equipment capacity, respectively. This allows the target distribution network to automatically adjust the elasticity of resource acquisition and release under different operating states, improving the scenario adaptability of dispatching. Furthermore, the mutual exclusion product of electricity purchase and sales is zero, i.e. ,in, Let represent the first transmitted power of any node in any industrial park under any operating scenario at any predicted time. Mathematically, this ensures that any industrial park at any given time can only choose to buy or sell electricity to the upstream power grid, conforming to the net trading principle of the electricity market and avoiding unreasonable two-way arbitrage. Furthermore, regarding energy sharing between industrial parks, through constraints on equal two-way power and upper limits on one-way transmission, both energy conservation and fair trading can be ensured, while preventing network congestion induced by local overload of energy sharing. , ,in, This represents the second power transmission to a specified park node at any predicted time, under any operating scenario. This indicates the second power transmission capacity that a specified park node will deliver to any other park node under any operating scenario at any predicted time. This represents the preset power transmission from any type of park node to a specified park node. Temperature control constraints couple the dynamic response of temperature-controlled loads with thermodynamics, ensuring that the adjustment of flexible loads such as air conditioning and data center cooling is no longer an ideal on / off control, but a gradual adjustment conforming to physical laws. This guarantees user comfort and equipment operational safety, avoiding equipment damage or indoor environmental fluctuations caused by frequent start-stop cycles. ,in, This represents the preset temperature for any type of node in the park. The photovoltaic output ceiling constraint ensures that distributed generation does not exceed the installed capacity, while the load reduction ratio constraint transforms park characteristics (such as the proportion of critical loads) into an executable reduction ceiling, enabling resource scheduling to both meet the needs of the target distribution network and respect user habits. , , ,in, This represents the power generation of distributed photovoltaic units at any given park node under any given operating scenario at any predicted time. This represents the preset photovoltaic power generation of any type of park node at any predicted time. This represents the allowable reduction in active power of any type of load at any predicted time point under normal operating conditions. Indicates the first preset load ratio. This represents the preset reduction in active power for any type of park node under normal operating conditions. This represents the allowable reduction in active power for any type of load at any predicted time under both normal and extreme failure operating scenarios. This indicates the second preset load ratio. This indicates the preset reduction of active power for any type of park node under normal and extreme failure operating scenarios.

[0069] In one optional embodiment, specific operational constraints corresponding to various park nodes are determined, including: if any one of the park nodes is a science and technology park node, and there are multiple science and technology park nodes, the specific operational constraints of any one of the multiple science and technology park nodes are determined as follows: the active power allowed to be reduced by the first load of any science and technology park node in any operational scenario at any prediction time among multiple prediction times included in the prediction period is equal to zero, wherein the first load represents the load related to power outage; in any operational scenario, the active power allowed to be reduced by the second load of any science and technology park node at any prediction time does not exceed the difference between the load power at the corresponding time and the active power allowed to be reduced by the first load, wherein the second load represents the load not related to power outage; in any operational scenario, the sum of the power generation of the distributed photovoltaic unit, the power generation of the self-provided unit, and the energy storage discharge power of any science and technology park node at any prediction time exceeds the active power allowed to be reduced by the first load at the corresponding time.

[0070] Optionally, specific operational constraints for nodes in science and technology parks should explicitly prohibit any form of reduction of the primary load that is directly bound to critical infrastructure such as information systems, servers, and network equipment and is uninterruptible. This ensures that the computing and communication cores of the science and technology park are in a state of zero-interruption protection under any operational scenario and at any predicted time. This is the fundamental characteristic that distinguishes science and technology parks from other types of parks. ,in, This represents the active power that can be reduced from the first load of any node in the science park at any predicted time under any operating scenario, where u represents the index of any node in the science park. Based on this, the reduction of the second load, such as adjustable lighting and non-core office equipment, is strictly limited to the remaining space after subtracting the first load from the total load. In other words, it is not allowed to meet the needs of the target distribution network at the expense of core functions. ,in, This represents the amount of active power that can be reduced from the second load of any node in the science park at any predicted time under any operating scenario. This represents the load power of any node in the science park at any predicted time under any operating scenario. Next, it is required that at any given time, the sum of the total output of all dispatchable power sources within the science park, including distributed photovoltaic power, self-provided units, and energy storage discharge, must be greater than or equal to the power demand of the first load. ,in, This represents the power generation of a distributed photovoltaic (PV) unit at any node in a science park at any predicted time, under any operating scenario. This represents the self-owned generator output of any target park node at any predicted time under any operating scenario. Let represent the energy storage discharge power of any node in the science park at any predicted time under any operating scenario, and g represent the index of any target park node. This constraint indicates that even if the main grid loses power and energy storage is about to be exhausted, priority must be given to ensuring power supply to support the first load. The specific operating constraints for science park nodes in this embodiment can not only improve the power supply reliability of the science park itself, but also guide the target distribution network to prioritize serving critical infrastructure in resource allocation, energy storage configuration, and network structure optimization.

[0071] In one optional embodiment, determining the unique operational constraints corresponding to each of the multiple park nodes includes: when any one of the multiple park nodes is a cultural and creative park node, and there are multiple cultural and creative park nodes, determining the unique operational constraints of any one of the multiple cultural and creative park nodes as follows:

[0072] ; ;

[0073] in, This represents the allowable reduction in active power for any cultural and creative park node under any operating scenario at any given forecast time within the multiple forecast times included in the forecast period. 'o' represents the index of any cultural and creative park node, 't' represents the index of any forecast time, and 'a' represents the index of any operating scenario. This indicates whether any node in a cultural and creative park has activity at any predicted time under any operating scenario. It is a binary variable. This indicates that an activity exists. This indicates that no activity exists; This represents the preset load reduction ratio for any node in a cultural and creative park under any operating scenario. This represents the load power of any node in a cultural and creative park at any predicted time under any operating scenario. This represents the amount of electricity transmitted from any node in a cultural and creative park to the upper-level power grid at any predicted time under any operating scenario. This indicates the preset power supply for any node in the cultural and creative park.

[0074] Optionally, to address the unique operational constraints of cultural and creative park nodes, a dynamic response mechanism based on activity status is introduced to deeply couple the operation of the cultural and creative park with power dispatching needs. This constraint is implemented through binary variables. This system accurately identifies whether cultural and creative parks host large-scale performances, exhibitions, festivals, or other events, and dynamically adjusts their load reduction and electricity sales permissions accordingly. Specifically, when events are held, all load reduction and electricity sales are prohibited to ensure the stable operation of loads supporting the cultural experience, such as lighting, sound, stage equipment, and air conditioning systems, avoiding interruptions or safety hazards due to power regulation. When there are no events, cultural and creative parks are allowed to perform peak shaving according to a preset load reduction ratio and participate in electricity sales to the main grid. This implementation, through specific operational constraints on cultural and creative park nodes, can avoid conflicts between grid dispatch and public cultural activities, enhancing public trust and support for the target distribution network.

[0075] In an optional embodiment, when there are multiple target park nodes, and the target park nodes are park nodes that include self-provided units, the unique operational constraints corresponding to each of the multiple park nodes are determined, including: when any one of the multiple park nodes is a high-end manufacturing park node, and there are multiple high-end manufacturing park nodes, the unique operational constraints of any one of the multiple high-end manufacturing park nodes are determined as follows:

[0076] ; ; ;

[0077] in, This represents the amount of electricity transmitted from any node in a high-end manufacturing park to the upper-level power grid at any given prediction time within the multiple prediction times included in the prediction period, under any operating scenario. Here, q represents the index of any high-end manufacturing park node, t represents the index of any prediction time, and a represents the index of any operating scenario. This represents the power generation of any target park node among multiple target park nodes at any given prediction time under any operating scenario, where g represents the index of any target park node, and G represents the number of multiple target park nodes. This represents the charging power of energy storage devices in any node of a high-end manufacturing park at any predicted time under any operating scenario. This indicates whether the load of any node in a high-end manufacturing park has been cut off at any predicted time under any operating scenario. It is a binary variable. This indicates that a resection has occurred. This indicates that no resection has occurred; This represents the active power that can be reduced from the load of any node in a high-end manufacturing park at any given time under any operating scenario. Represents the first preset constant. This represents the second preset constant.

[0078] Optionally, considering the unique operational constraints of high-end manufacturing park nodes, dangerous operational paths involving disconnecting production line power for electricity sales are blocked, ensuring that the stability and safety of the industrial manufacturing process are not eroded by electricity market behavior. Specifically, firstly, the constraints explicitly limit the electricity sales power of high-end manufacturing parks to their own controllable power sources, i.e., the sum of the power generated by self-owned generating units and the discharge power of energy storage, explicitly excluding the possibility of electricity from photovoltaic power, grid backfeed, or load shedding as a source of electricity sales, fundamentally eliminating the motivation for high-end manufacturing parks to sacrifice production line power for the purpose of obtaining electricity sales benefits. Secondly, a mutual exclusion mechanism between electricity sales and load shedding is constructed: when the high-end manufacturing park has not shelved any load, the upper limit of electricity sales is a first preset constant, allowing it to sell electricity normally when there is surplus; however, once load shedding is initiated, the electricity sales capacity is immediately forced to zero; conversely, whenever load shedding occurs, power of no less than a second preset constant must be shelved to ensure the authenticity and effectiveness of dispatch instructions. The first and second preset constants can be designed collaboratively based on the engineering safety boundaries, production continuity requirements, and electricity market incentive mechanisms of the high-end manufacturing park. This embodiment, through unique operational constraints on nodes in high-end manufacturing parks, not only ensures the continuous operation of production lines, stable product quality, and equipment safety in high-end manufacturing parks, but also transforms the prevention of production stoppages due to power grid dispatch into a mathematically rigid constraint, giving the resource allocation strategy industrial-grade reliability and trustworthiness.

[0079] In one optional embodiment, specific operational constraints corresponding to each of the various park nodes are determined, including: when any one of the park nodes is a heavy industrial park node, and there are multiple heavy industrial park nodes, the specific operational constraints of any heavy industrial park node among the multiple heavy industrial park nodes are determined as follows: the electrical power of the cogeneration unit of any heavy industrial park node at any prediction time in any prediction time included in the prediction period does not exceed a preset electrical power range in any operating scenario; in any operating scenario, the electrical power of the cogeneration unit of any heavy industrial park node at any prediction time is equal to the product between the heat power at the corresponding time and the preset heat-to-power ratio; in any operating scenario, the heat load of any heavy industrial park node at any prediction time does not exceed the electrical power at the corresponding time.

[0080] Optionally, for the specific operational constraints of heavy industrial park nodes, firstly, the constraints limit the electrical power output of cogeneration units (such as gas turbines and waste heat boilers) to within the preset electrical power range of their physical design. This prevents cogeneration units from disconnecting from the grid under low load or operating beyond capacity under high load, ensuring equipment safety and lifespan. ,in, Let w represent the electrical power of a combined heat and power unit at any heavy industrial park node under any operating scenario at any predicted time, and let w represent the index of any heavy industrial park node. This represents the lower limit of the preset power range for any node in a heavy industrial park. This represents the upper limit of the preset electrical power range for any node in a heavy industrial park; secondly, by establishing a fixed proportional relationship between electrical power and thermal power, where electrical power equals thermal power multiplied by a preset thermoelectric ratio, an operational logic where heat is the dominant factor and electricity is a byproduct can be achieved. ,in, This represents the thermal power of a combined heat and power unit at any node in a heavy industrial park at any predicted time under any operating scenario. This represents the preset heat-to-power ratio for any node in a heavy industrial park. This constraint is not an approximation, but a mathematical reconstruction of the actual operating characteristics of combined heat and power (CHP) units in heavy industry. Specifically, the power generation capacity of a CHP unit is entirely driven by heat load demand and cannot be independently adjusted. Therefore, any optimization strategy attempting to treat CHP units as adjustable power sources for independent scheduling will be directly excluded by this constraint. Furthermore, the constraint stipulates that the heat load demand of the heavy industrial park must not exceed the heat power that the CHP unit can provide at that moment. This heat power is determined by the electrical power multiplied by the heat-to-power ratio, thus forming a unidirectional causal chain where electrical power determines heat power. Heat power must satisfy the heat load demand; otherwise, operation will be impossible. ,in, This represents the heat load of any node in a heavy industrial park at any predicted time under any operating scenario. This constraint can mitigate the engineering risks of excessive heat generation for the sake of generating more electricity or forced shutdown due to insufficient heat.

[0081] In one optional embodiment, specific operational constraints corresponding to each of the multiple park nodes are determined, including: when any one of the multiple park nodes is a comprehensive park node, and there are multiple comprehensive park nodes, the specific operational constraints of any comprehensive park node among the multiple comprehensive park nodes are determined as follows: the charging power of the electric vehicle reversible charging pile of any comprehensive park node at any prediction time in any operating scenario during the multiple prediction times included in the prediction period does not exceed the preset charging power of the charging equipment; the discharging power of the electric vehicle reversible charging pile of any comprehensive park node at any prediction time in any operating scenario does not exceed the preset discharging power of the discharging equipment; and the product between the discharging power and the charging power of the electric vehicle reversible charging pile of any comprehensive park node at any prediction time in any operating scenario is equal to zero.

[0082] Optionally, considering the unique operational constraints of the integrated park nodes, firstly, by setting upper limits for charging and discharging power respectively, it is ensured that the power operation of the reversible electric vehicle charging pile at any time does not exceed its hardware design capacity. This prevents overload of the charging pile, overcharging or over-discharging of the battery, or overcurrent at the grid connection point, thus ensuring equipment safety and the stable operation of the target distribution network. , ,in, This represents the charging power of a reversible electric vehicle charging pile at any integrated park node at any predicted time under any operating scenario. This represents the preset charging power of the charging equipment at any integrated park node, and z represents the index of any integrated park node. This represents the discharge power of a reversible electric vehicle charging pile at any node in the integrated park at any predicted time under any operating scenario. This represents the preset discharge power of the discharge equipment at any node in the integrated park. Secondly, by requiring the product of charging power and discharging power to be zero (i.e., neither can be non-zero simultaneously), the reversible charging station for electric vehicles can only be in one mode—charging or discharging—at any given time, preventing both absorption and release of electrical energy and thus avoiding bidirectional power cycling. This constraint system transforms reversible charging piles for electric vehicles from passive loads into dynamic energy storage resources with clearly defined operating rules. This not only improves the utilization efficiency of distributed resources within the integrated park but also enhances the buffering capacity and response resilience of the regional power grid under load fluctuations, uncertainties in distributed output, or local fault scenarios.

[0083] Through the above steps S102 to S106, the goal of optimizing the initial resource allocation strategy of the target distribution network during the prediction period can be achieved by constructing an objective function that integrates the preset characteristic parameters corresponding to various park nodes and various operating scenarios. This will accurately determine the target resource allocation strategy, thereby improving the resource coordination and scheduling capability between parks in the distribution network, reducing the operating cost of the distribution network, and improving the operational reliability. In turn, this will solve the technical problems of poor resource coordination and scheduling capability between parks in the distribution network, high operating cost of the distribution network, and unreliable operation caused by incomplete consideration of factors when the distribution network faces complex operating scenarios.

[0084] Based on the above embodiments and optional embodiments, the present invention proposes an optional implementation method. Figure 3 This is a flowchart of an optional resource allocation method for a distribution network according to an embodiment of the present invention, such as... Figure 3 As shown, the method includes:

[0085] S1: A multi-scenario generation method, specifically including: constructing multiple hierarchical operating scenarios based on historical operating data and risk evolution patterns of the target distribution network, namely normal scenarios, general fault scenarios, and extreme fault scenarios. The normal scenario uses cluster analysis of historical load curves, meteorological data, and distributed energy output sequences to extract representative typical daily operating patterns, accurately depicting the differences in load characteristics between seasonal changes, weekdays, and holidays. The general fault scenario employs a selection strategy prioritizing electrical distance and functional association, enumerating N-1 and N-2 faults. The extreme fault scenario integrates disaster dynamics models and Monte Carlo simulations to generate Nk faults, realistically reflecting the risks under natural disasters such as typhoons and ice storms.

[0086] S2: Based on the industrial type and energy consumption characteristics of the park, determine the preset characteristic parameters corresponding to various park nodes. Specifically, the park is divided into five types: technology, cultural and creative industries, high-end manufacturing, heavy industry and comprehensive industries. For each type, a structured and quantifiable operational characteristic parameter is established, namely the preset characteristic parameter (including response speed coefficient, adjustable load ratio and critical load ratio).

[0087] S3: Construct a target distribution network resource allocation model with multiple park nodes under multiple scenarios. Specifically, this includes: based on the common operating constraints applicable to the target distribution network and the unique operating constraints corresponding to each of the multiple park nodes, taking the total operating cost of the target distribution network under multiple operating scenarios during the prediction period as the objective function, minimizing the function value of the objective function, optimizing the initial resource allocation strategy of the target distribution network during the prediction period, and determining the target resource allocation strategy. The specific implementation process is the same as the aforementioned embodiments and will not be repeated here.

[0088] S4: Resource allocation strategy output and adaptive control, specifically including: based on the target resource allocation strategy, and through the communication and control interface, it is sent to the park-level energy management system and the distribution network control master station in real time to realize the closed-loop linkage from mathematical optimization to physical execution.

[0089] S5: Resource allocation strategy evaluation and model rolling correction, specifically including: based on the actual running data after the target resource allocation strategy is executed, adopting rolling optimization and online learning mechanisms to dynamically correct model parameters and constraint boundaries, providing more accurate input basis for multi-scenario generation and model optimization in the next cycle, and realizing the model's adaptive iteration to the external environment and running behavior.

[0090] This embodiment also provides a resource allocation device for a power distribution network, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the terms "module" and "device" can refer to a combination of software and / or hardware that performs a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0091] According to an embodiment of the present invention, an apparatus embodiment for implementing the above-described resource allocation method for a power distribution network is also provided. Figure 4 This is a schematic diagram of the structure of a resource allocation device for a power distribution network according to an embodiment of the present invention, as shown below. Figure 4 As shown, the resource allocation device for the aforementioned power distribution network includes: an operation scenario acquisition module 400, an objective function determination module 402, and an objective resource allocation strategy determination module 404, wherein:

[0092] The operation scenario acquisition module 400 is used to obtain multiple operation scenarios of the target distribution network during the forecast period based on the historical operation data of various park nodes in the target distribution network and the meteorological forecast information of the area where the target distribution network is located. The historical operation data includes at least the load data, power generation output data and fault data of the corresponding park nodes during the historical period. Multiple operation scenarios correspond to multiple different fault levels, and multiple park nodes correspond to multiple different industry types. The historical period is the period of the first predetermined duration before the current period, and the forecast period is the period of the second predetermined duration after the current period.

[0093] The objective function determination module 402 is connected to the operation scenario acquisition module 400. It is used to determine the objective function of the target distribution network based on multiple operation scenarios and the preset characteristic parameters corresponding to multiple park nodes. The preset characteristic parameters include the response speed coefficient, adjustable load ratio and critical load ratio of the corresponding park node. The objective function represents the total operating cost of the target distribution network under multiple operation scenarios during the prediction period.

[0094] The target resource allocation strategy determination module 404 is connected to the objective function determination module 402. It is used to optimize the initial resource allocation strategy of the target distribution network during the forecast period with the minimum function value of the objective function as the optimization objective, so as to obtain the target resource allocation strategy of the target distribution network during the forecast period.

[0095] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0096] It should be noted that the above-mentioned scenario acquisition module 400, objective function determination module 402, and objective resource configuration strategy determination module 404 correspond to steps S102 to S106 in the embodiments. The instances and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the content disclosed in the above embodiments. It should be noted that the above modules, as part of the device, can run in a computer terminal.

[0097] It should be noted that the optional or preferred implementation methods of this embodiment can be found in the relevant descriptions in the embodiments, and will not be repeated here.

[0098] The aforementioned resource allocation device for the power distribution network may also include a processor and a memory. The aforementioned operating scenario acquisition module 400, objective function determination module 402, and objective resource allocation strategy determination module 404 are all stored in the memory as program modules, and the processor executes the aforementioned program modules stored in the memory to realize the corresponding functions.

[0099] The processor contains a core that retrieves the corresponding program modules from memory. One or more cores may be configured. Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip.

[0100] According to an embodiment of this application, an embodiment of a non-volatile storage medium is also provided. Optionally, in this embodiment, the non-volatile storage medium includes a stored program, wherein, when the program runs, it controls the device where the non-volatile storage medium is located to execute any of the above-mentioned power distribution network resource allocation methods.

[0101] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals, and the non-volatile storage medium includes stored programs.

[0102] Optionally, during program execution, the device containing the non-volatile storage medium may be controlled to execute any of the above-mentioned power distribution network resource allocation methods.

[0103] According to an embodiment of this application, an embodiment of a processor is also provided. Optionally, in this embodiment, the processor is used to run a program, wherein the program executes any of the above-described resource allocation methods for a power distribution network.

[0104] According to an embodiment of this application, an embodiment of a computer program product is also provided. Optionally, in this embodiment, the computer program product includes a computer program that, when executed by a processor, implements the resource allocation method steps of any of the above-described power distribution networks.

[0105] Optionally, when the above-mentioned computer program product is executed on a data processing device, it is suitable for executing any of the above-mentioned resource allocation methods for power distribution networks.

[0106] like Figure 5 As shown, an embodiment of the present invention provides an electronic device 10, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it executes any of the above-described resource allocation methods for a power distribution network.

[0107] The order of the above embodiments of the present invention is merely for description and does not represent the superiority or inferiority of the embodiments.

[0108] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0109] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of modules described above can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between modules, and may be electrical or other forms.

[0110] The modules described above as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0111] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0112] If the aforementioned integrated modules are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable non-volatile storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a non-volatile storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned non-volatile storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0113] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A resource allocation method for a power distribution network, characterized in that, include: Based on the historical operating data of various park nodes in the target distribution network and the meteorological forecast information of the area where the target distribution network is located, multiple operating scenarios of the target distribution network during the forecast period are obtained. The historical operating data includes at least the load data, power generation output data and fault data of the corresponding park nodes during the historical period. The multiple operating scenarios correspond to multiple different fault levels, and the multiple park nodes correspond to multiple different industry types. The historical period is a period of a first predetermined duration before the current period, and the forecast period is a period of a second predetermined duration after the current period. Based on the various operating scenarios and the preset characteristic parameters corresponding to each of the various park nodes, the objective function of the target distribution network is determined. The preset characteristic parameters include the response speed coefficient, adjustable load ratio, and critical load ratio of the corresponding park node. The objective function represents the total operating cost of the target distribution network under the various operating scenarios during the prediction period. The initial resource allocation strategy of the target distribution network during the forecast period is optimized with the minimum function value of the objective function as the optimization objective, so as to obtain the target resource allocation strategy of the target distribution network during the forecast period.

2. The method according to claim 1, characterized in that, The objective function for determining the target distribution network based on the various operating scenarios and the preset characteristic parameters corresponding to each of the various park nodes includes: Based on the power supply capacity attenuation degree corresponding to each of the various operating scenarios, the power purchase price of the target distribution network from the upper-level transmission network during the predicted period is obtained. The power supply capacity attenuation degree is used to indicate the proportion of the decrease in the power supply capacity of the target distribution network relative to the normal power supply capacity in the case of a fault in the upper-level transmission network in the corresponding operating scenario. Based on the preset lifespan cost, the response speed coefficient corresponding to each of the various park nodes, the adjustable load ratio, and the critical load ratio, the energy storage lifespan cost corresponding to each of the various park nodes is obtained, wherein the energy storage lifespan cost represents the lifespan cost of each unit of electricity of the energy storage device in the corresponding park node due to scheduling loss. Based on the preset penalty cost and the critical load ratio corresponding to each of the various park nodes, the load penalty price of the target distribution network during the forecast period is obtained, wherein the load penalty price represents the penalty price borne for implementing load interruption for each unit of electricity; Based on the response speed coefficients corresponding to the various park nodes, the electricity sales price in the electricity market during the predicted period is obtained. Based on the response speed coefficients and multiple target distances corresponding to the various park nodes, the transmission prices of multiple parks in the target distribution network during the prediction period are obtained, wherein the multiple target distances represent the distance between any two park nodes among the various park nodes, and the multiple transmission prices of multiple parks represent the incentive price for transmitting a unit of electricity between any two park nodes. The objective function is determined based on the electricity purchase price, the load penalty price, the electricity sales price, the transmission prices of the multiple parks, and the energy storage lifetime cost corresponding to each of the multiple park nodes.

3. The method according to claim 2, characterized in that, The objective function is determined based on the electricity purchase price, the load penalty price, the electricity sales price, the transmission prices of multiple industrial parks, and the energy storage lifetime cost corresponding to each of the various industrial park nodes, including: The following parameters are determined: self-owned generator generation cost, node purchase power for each of the various operating scenarios, self-owned generator generation, energy storage discharge power, energy storage charging power, load reduction, first transmission power, and second transmission power. Specifically, node purchase power represents the amount of electricity purchased by the electrical nodes of the target distribution network from the upstream transmission network under the corresponding operating scenario; self-owned generator generation cost represents the cost per unit of electricity consumed by the self-owned generator in the target distribution network; self-owned generator generation represents the generation of the target park node of the target distribution network under the corresponding operating scenario; the target park node is a park node containing the self-owned generator; energy storage discharge power represents the discharge power of the energy storage device in the corresponding park node under the corresponding operating scenario; energy storage charging power represents the charging power of the energy storage device in the corresponding park node under the corresponding operating scenario; load reduction represents the active power that the load of the electrical node is allowed to be reduced under the corresponding operating scenario; first transmission power represents the amount of electricity transmitted from the corresponding park node to the upstream transmission network under the corresponding operating scenario; and second transmission power represents the amount of electricity transmitted between any two park nodes. The objective function is determined based on the electricity purchase price, the load penalty price, the electricity sales price, the transmission prices of multiple parks, the generation cost of the self-owned generating unit, the energy storage life cost corresponding to each of the multiple park nodes, and the node purchase volume, the self-owned generating unit generation, the energy storage discharge power, the energy storage charging power, the load reduction, the first transmission volume, and the second transmission volume corresponding to each of the multiple operating scenarios.

4. The method according to claim 3, characterized in that, When there are multiple electrical nodes and multiple target park nodes, the objective function is determined based on the electricity purchase price, the load penalty price, the electricity sales price, the transmission prices of the multiple parks, the generation cost of the self-owned generating units, the energy storage lifespan cost corresponding to each of the various park nodes, and the node purchase volume, self-owned generating unit generation, energy storage discharge power, energy storage charging power, load reduction, the first transmission volume, and the second transmission volume corresponding to each of the various operating scenarios. This includes: Based on the electricity purchase price, the load penalty price, the electricity sales price, the transmission prices of multiple parks, the generation cost of self-owned generating units, the energy storage lifespan cost corresponding to each of the various park nodes, and the node purchase volume, self-owned generating unit generation, energy storage discharge power, energy storage charging power, load reduction, first transmission volume, and second transmission volume corresponding to each of the various operating scenarios, the objective function is determined in the following manner: ； in, This represents the function value of the objective function. Let represent the weight corresponding to any given running scenario, 'a' represent the index of that given running scenario, and 'A' represent the number of the various running scenarios. This represents the electricity purchase price at any one of the multiple prediction times included in the prediction period, where t represents the index of any one prediction time, and T represents the number of the multiple prediction times. This represents the electricity purchase amount of any electrical node among multiple electrical nodes at any given predicted time under any given operating scenario, where n represents the index of any electrical node and N represents the number of multiple electrical nodes. This represents the power generation cost of the self-contained generating unit at any given predicted time. This represents the self-provided generator power generation of any target park node among multiple target park nodes under any operating scenario at any given predicted time, where g represents the index of any target park node, and G represents the number of multiple target park nodes. This represents the energy storage lifetime cost of any one type of park node among the various park nodes, where i represents the index of any one type of park node, and I represents the number of the various park nodes. This represents the energy storage discharge power of any type of park node at any given prediction time under any given operating scenario. This represents the energy storage charging power of any type of park node at any given prediction time under any given operating scenario. The load penalty price represents the price at any given forecast time. This represents the load reduction amount of any electrical node at any given predicted time under any given operating scenario. This represents the response speed coefficient of any type of park node. This represents the electricity price at any given predicted time. This represents the first transmitted power of any type of park node at any predicted time under any operating scenario. This represents the electricity transmission price of the park at any given predicted time. The term j represents the second transmitted power transmitted by any type of park node to a specified park node under any operating scenario at any predicted time, where j represents the index of the specified park node.

5. The method according to claim 1, characterized in that, The optimization of the initial resource allocation strategy of the target distribution network during the forecast period, with the goal of minimizing the function value of the objective function, yields the target resource allocation strategy of the target distribution network during the forecast period, including: Determine the common operating constraints applicable to the target distribution network; The unique operational constraints corresponding to each of the various park nodes are determined, wherein the unique operational constraints represent specific constraints applicable to the corresponding park node; Based on the common operational constraints and their respective unique operational constraints, the initial resource allocation strategy is optimized with the goal of minimizing the function value of the objective function, thereby obtaining the target resource allocation strategy.

6. The method according to claim 5, characterized in that, The determination of common operating constraints applicable to the target distribution network includes: Determine the first common operating constraint applicable to the lines in the target distribution network; Determine a second common operating constraint applicable to the electrical nodes in the target distribution network; A third common operating constraint is determined for the target park node in the target distribution network, wherein the target park node is a park node that includes self-owned generating units; A fourth common operating constraint is determined that applies to the various park nodes in the target distribution network.

7. The method according to claim 6, characterized in that, When the target distribution network has multiple lines and multiple electrical nodes, determining the first common operating constraint applicable to the lines in the target distribution network includes: The first common operational constraint is determined as follows: The power parameters of any line in any of the multiple prediction times included in the prediction period under any operating scenario at any prediction time do not exceed a preset power parameter threshold. The power parameters include at least: current, active power and reactive power. ； ； ； ； ； ； ； ； in, This represents the active power flowing from any electrical node among multiple electrical nodes in the target distribution network to the first electrical node under any operating scenario at any given predicted time. Let represent the active power flowing from the second electrical node to the first electrical node at any predicted time under any operating scenario, where 'a' represents the index of the operating scenario, 't' represents the index of the predicted time, 'd' represents the index of the first electrical node, and 'e' represents the index of the first electrical node. Indicates the index of the second electrical node. This represents the resistance of any of the stated lines. The reactance of any of the lines is represented by , and de represents the index of any of the lines including any of the electrical nodes and the first electrical node. This represents the reactive power flowing from any electrical node to the first electrical node at any predicted time under any operating scenario. This represents the square of the current of any line at any given prediction time under any given operating scenario. This indicates the on / off state of any line at any predicted time under any operating scenario. Represents the first preset constant. This represents the square of the voltage of any electrical node at any given predicted time under any given operating scenario. This represents the squared value of the first electrical node at any given prediction time under any given operating scenario. Denotes the Euclidean norm. This represents the active power flow of any of the lines. This represents the reactive power flow of any of the aforementioned lines. This represents the active energy stored by the energy storage device of any one type of park node among the various park nodes at any given predicted time, under any given operating scenario; i represents the index of any one type of park node; and I represents the number of the various park nodes. This represents the active energy stored by the energy storage device of any type of park node in any operating scenario at the next prediction time after any prediction time. This represents the energy storage charging power of any type of park node at any given prediction time under any given operating scenario. This represents the energy storage discharge power of any type of park node at any given prediction time under any given operating scenario. This indicates the time interval between any given prediction time and the next prediction time. This indicates the charging efficiency of the energy storage device. The discharge efficiency of the energy storage device is represented by N, where N represents the number of the plurality of electrical nodes. This represents the set of power nodes used in any of the aforementioned operating scenarios. This represents the flow of traffic from any electrical node to the first electrical node at any predicted time under any operating scenario. This indicates whether a power supply path is formed between the second electrical node and the first electrical node at any predicted time. It is a binary variable. This indicates the formation of the power supply path. This indicates that the power supply path has not been formed; This indicates the number of the multiple lines. This represents the load demand of any electrical node at any given predicted time under any given operating scenario.

8. The method according to claim 6, characterized in that, When there are multiple electrical nodes, determining the second common operating constraint applicable to the electrical nodes in the target distribution network includes: The second common operational constraint is determined as follows: The voltage of any electrical node among multiple electrical nodes at any prediction time within the prediction period is within the preset safe voltage range under any operating scenario. In any of the above operating scenarios, the difference in load reduction between any two adjacent prediction times for any electrical node in the plurality of prediction times does not exceed a preset response speed coefficient; In any of the operating scenarios, the load reduction of any electrical node at any predicted time shall not exceed the product of the load power at the corresponding time and the preset load shedding ratio.

9. The method according to claim 6, characterized in that, When there are multiple target park nodes and multiple electrical nodes, determining the third common operating constraint applicable to the target park nodes in the target distribution network includes: The third common operational constraint is determined as follows: The self-provided generating capacity of any target park node in any of the multiple prediction times included in the prediction period, under any operating scenario, shall not exceed the preset self-provided generating capacity. In any of the above operating scenarios, the difference in the power generation of the self-provided unit of any target park node between two adjacent prediction times in the plurality of prediction times does not exceed the preset power generation difference. ； ； in, This represents the node purchase amount of any electrical node among multiple electrical nodes at any given predicted time under any given operating scenario. This represents the power generation of distributed photovoltaic units at any type of park node under any operating scenario at any predicted time, where 'a' represents the index of any operating scenario, 't' represents the index of any predicted time, 'n' represents the index of any electrical node, 'i' represents the index of any type of park node, and 'I' represents the number of the various types of park nodes. This represents the self-owned generator output of any target park node at any predicted time under any operating scenario, where g represents the index of any target park node. This represents the energy storage discharge power of any type of park node at any predicted time under any operating scenario. This represents the energy storage charging power of any type of park node at any predicted time under any operating scenario. This represents the load reduction amount of any electrical node at any given predicted time under any given operating scenario. This represents the load power of any electrical node at any predicted time under any operating scenario. The second power transmission amount delivered by any type of park node to the designated park node at any predicted time under any operating scenario. The second power transmission amount delivered by the designated park node to the park node under any operating scenario at any predicted time. This represents the index of the specified park node.

10. The method according to claim 6, characterized in that, When there are multiple electrical nodes, determining the fourth common operating constraint applicable to the various park nodes in the target distribution network includes: The fourth common operational constraint is determined as follows: The energy storage parameters of any one of the multiple park nodes in any one of the multiple prediction times included in the prediction period do not exceed the preset energy storage parameter threshold in any one of the multiple park nodes under any one of the operating scenarios. The energy storage parameters include at least: the active energy stored by the energy storage device, the energy storage charging power and the energy storage discharging power. In any of the operating scenarios, the first power transmission of any park node at any predicted time shall not exceed the product of the preset power sales coefficient and the preset power transmission in the corresponding operating scenario. In any of the operating scenarios, the node purchase volume of any of the park nodes at any predicted time shall not exceed the product of the preset purchase coefficient and the preset purchase volume in the corresponding operating scenario. In any of the above operating scenarios, the product of the first power transmission of any park node at any predicted time and the power purchase of the node at the corresponding time is equal to zero. In any of the operating scenarios, the unidirectional power transmission between any two park nodes among the multiple park nodes at any predicted time shall not exceed the preset power transmission capacity. In any of the above operating scenarios, the amount of electricity delivered by any type of park node to the designated park node at any predicted time is equal to the amount of electricity received by the designated park node from the any type of park node at the corresponding time. At any given predicted time, the temperature of any type of park node in any given operating scenario does not exceed the preset temperature. The power generation of the distributed photovoltaic units of any park node at any predicted time under any operating scenario shall not exceed the preset power generation of the photovoltaic units. Under any of the operating scenarios, the active power that can be reduced for the load of any of the park nodes at any predicted time shall not exceed the product of the preset load ratio and the preset reduction active power for any of the park nodes. ； ； in, This indicates whether the energy storage device of any type of park node is in a charging state under any operating scenario at any predicted time, where 'a' represents the index of any operating scenario and 't' represents the index of any predicted time. It is a binary variable. This indicates that it is in a charging state. This indicates that the device is not in the charging state. This indicates whether the energy storage device of any type of park node is in a discharging state under any operating scenario at any predicted time. It is a binary variable. This indicates that the system is in the aforementioned discharge state. This indicates that the device is not in the aforementioned discharge state; This indicates whether the energy storage device at any given park node is operational under any given operating scenario at any given predicted time. It is a binary variable. This indicates that it is in operation. This indicates that it is not yet in operation; This refers to the temperature of any type of park node at any given prediction time under any given operating scenario. This refers to the temperature of any type of park node in any operating scenario at the next prediction time after any prediction time. This represents the preset temperature control coefficient of any type of park node under any operating scenario. This represents the active power of the temperature-controlled load of any type of park node under any operating scenario at any given prediction time. The active power of the temperature-controlled load of any park node under any operating scenario is represented by the next prediction time after any prediction time.

11. The method according to claim 5, characterized in that, The determination of the unique operational constraints corresponding to each of the various park nodes includes: If any one of the various park nodes is a science and technology park node, and there are multiple science and technology park nodes, then the unique operational constraints of any one of the multiple science and technology park nodes are determined as follows: The active power that can be reduced by the first load of any science park node under any operating scenario at any prediction time during the prediction period is equal to zero, where the first load represents the load related to power outage. In any of the operating scenarios, the active power that the second load of any science park node is allowed to be reduced at any predicted time shall not exceed the difference between the load power at the corresponding time and the active power that the first load is allowed to be reduced, wherein the second load represents the load that is not related to the power outage. In any of the operating scenarios, the sum of the power generation of the distributed photovoltaic units, the power generation of the self-provided units, and the energy storage discharge power of any technology park node at any predicted time exceeds the active power that the first load is allowed to be reduced at the corresponding time.

12. The method according to claim 5, characterized in that, The determination of the unique operational constraints corresponding to each of the various park nodes includes: When any one of the various park nodes is a cultural and creative park node, and there are multiple cultural and creative park nodes, the unique operational constraints of any one of the multiple cultural and creative park nodes are determined as follows: ； ； in, This represents the active power that the load of any cultural and creative park node can be reduced under any operating scenario at any prediction time among multiple prediction times included in the prediction period. 'o' represents the index of any cultural and creative park node, 't' represents the index of any prediction time, and 'a' represents the index of any operating scenario. This indicates whether any cultural and creative park node has activity at any predicted time under any operating scenario. It is a binary variable. This indicates that the activity exists. This indicates that the activity does not exist; This represents the preset load reduction ratio for any node in the cultural and creative park under any given operating scenario. This represents the load power of any cultural and creative park node at any predicted time under any operating scenario. This represents the amount of electricity transmitted from any cultural and creative park node to the upper-level power grid at any given predicted time and under any given operating scenario. This represents the preset power supply for any node in the cultural and creative park.

13. The method according to claim 5, characterized in that, When there are multiple target park nodes, and these target park nodes include park nodes with self-provided generating units, determining the unique operational constraints corresponding to each of the various park nodes includes: When any one of the various park nodes is a high-end manufacturing park node, and there are multiple high-end manufacturing park nodes, the unique operational constraints of any one of the multiple high-end manufacturing park nodes are determined as follows: ； ； ； in, This represents the amount of electricity transmitted by any high-end manufacturing park node to the upper-level power grid at any prediction time within the multiple prediction times included in the prediction period, under any operating scenario. q represents the index of the high-end manufacturing park node, t represents the index of any prediction time, and a represents the index of any operating scenario. This represents the power generation of any target park node among multiple target park nodes at any given prediction time under any given operating scenario, where g represents the index of any target park node, and G represents the number of multiple target park nodes. This represents the charging power of the energy storage device in any node of the high-end manufacturing park at any predicted time under any operating scenario. This indicates whether the load of any high-end manufacturing park node under any given operating scenario occurs at any predicted time. It is a binary variable. This indicates that the resection has occurred. This indicates that the resection did not occur; This represents the active power that the load of any high-end manufacturing park node can be reduced under any operating scenario at any given predicted time. Represents the first preset constant. This represents the second preset constant.

14. The method according to claim 5, characterized in that, The determination of the unique operational constraints corresponding to each of the various park nodes includes: When any one of the various park nodes is a heavy industrial park node, and there are multiple heavy industrial park nodes, the unique operational constraints of any one of the multiple heavy industrial park nodes are determined as follows: The power output of the cogeneration unit at any heavy industrial park node under any operating scenario at any prediction time during the prediction period shall not exceed the preset power output range. In any of the operating scenarios, the electrical power of the cogeneration unit at any heavy industrial park node at any predicted time is equal to the product of the thermal power at the corresponding time and the preset heat-to-power ratio. Under any of the above operating scenarios, the heat load of any heavy industrial park node at any predicted time shall not exceed the electrical power at the corresponding time.

15. The method according to claim 5, characterized in that, The determination of the unique operational constraints corresponding to each of the various park nodes includes: When any one of the various types of park nodes is a comprehensive park node, and there are multiple comprehensive park nodes, the unique operational constraints of any one of the multiple comprehensive park nodes are determined as follows: The charging power of the electric vehicle reversible charging pile at any integrated park node in any operating scenario at any prediction time during the prediction period shall not exceed the preset charging power of the charging equipment. The discharge power of the electric vehicle reversible charging pile at any integrated park node at any predicted time under any operating scenario shall not exceed the preset discharge power of the discharge device. At any predicted time, the product between the discharge power and the charging power of the electric vehicle reversible charging pile at any integrated park node under any operating scenario is equal to zero.

16. An electronic device, characterized in that, It includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the resource allocation method for the distribution network of any one of claims 1 to 15.

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