Power distribution network multi-energy coordinated planning method and system based on big data

Abstract

The invention provides a power distribution network multi-energy coordinated planning method and system based on big data, and the method comprises the steps: carrying out the prediction analysis based on the available resource amount of energy resources and the load demand amount of energy loads in a to-be-coordinated region covered by a power distribution network, and obtaining the future available amount and the future demand amount; performing energy supply and demand distribution situation analysis based on the future available amount and the future demand in combination with the user behavior mode to obtain an energy supply and demand situation analysis result; based on an energy supply and demand situation analysis result, performing energy planning by taking the minimum energy transmission loss and meeting the basic load demand as targets, and determining a bidirectional energy flow path; based on the energy supply and demand situation analysis result, the bidirectional energy flow path and the energy storage parameters of the energy storage devices in the to-be-coordinated area, scheduling decision making is carried out, and an energy storage device scheduling result is obtained; and controlling coordinated operation of the multi-energy equipment based on an energy supply and demand situation analysis result and an energy storage equipment scheduling result. According to the invention, the optimal coordinated operation of the multi-energy system is realized.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and in particular to a method and system for multi-energy coordinated planning of power distribution networks based on big data. Background Technology

[0002] With the continuous growth of energy demand and the increasing requirements for energy efficiency and sustainability, multi-energy coordinated planning technology for distribution networks has gradually become a research hotspot. Existing multi-energy coordinated planning methods for distribution networks are typically based on the traditional unidirectional energy flow model, with the power system as the core and other energy systems such as natural gas and heat as auxiliary systems. This model achieves the coordinated supply of multiple energy sources through pre-set rules and fixed energy conversion paths. However, the unidirectional energy flow model cannot fully utilize the flexibility of distributed energy sources and struggles to cope with the intermittency and volatility of renewable energy generation, resulting in poor energy supply stability. On the other hand, fixed energy conversion paths lack adaptability to real-time changes in energy supply and demand, failing to dynamically adjust energy allocation according to energy demand in different time periods and regions. This makes it difficult to further improve energy efficiency, thus hindering the optimal coordinated operation of multi-energy systems. Summary of the Invention

[0003] This invention provides a method and system for multi-energy coordinated planning of power distribution networks based on big data, aiming to achieve optimal coordinated operation of multi-energy systems.

[0004] In a first aspect, the present invention provides a multi-energy coordinated planning method for power distribution networks based on big data, including: Based on the energy resource availability and energy load demand in the area to be coordinated within the distribution network coverage, a predictive analysis is conducted to obtain the future availability and future demand. Based on the future available quantity and the future demand quantity, combined with the user behavior patterns in the area to be coordinated, an energy supply and demand distribution trend analysis is performed to obtain the energy supply and demand trend analysis results. Based on the energy supply and demand situation analysis results, energy planning is carried out with the goal of minimizing energy transmission losses and meeting basic load requirements, and a two-way energy flow path from the supply side to the demand side is determined. Based on the energy supply and demand situation analysis results, the bidirectional energy flow path, and the energy storage parameters of the energy storage devices in the area to be coordinated, scheduling decisions are made to obtain the energy storage device scheduling results. Based on the energy supply and demand situation analysis results and the energy storage equipment scheduling results, the coordinated operation of multiple energy devices is controlled.

[0005] Secondly, the present invention also provides a big data-based multi-energy coordinated planning system for distribution networks, applied to the big data-based multi-energy coordinated planning method for distribution networks as described in the first aspect; the big data-based multi-energy coordinated planning system for distribution networks includes: The demand forecasting and analysis module is used to forecast and analyze the availability of energy resources and the load demand of energy load within the coordinated area covered by the distribution network, so as to obtain the future availability and future demand. The supply and demand situation analysis module is used to analyze the energy supply and demand distribution situation based on the future available quantity and the future demand quantity, combined with the user behavior patterns in the area to be coordinated, and to obtain the energy supply and demand situation analysis results. The energy path planning module is used to perform energy planning based on the energy supply and demand situation analysis results, with the goal of minimizing energy transmission losses and meeting basic load requirements, and to determine the bidirectional energy flow path from the supply side to the demand side. The energy dispatch decision module is used to make dispatch decisions based on the energy supply and demand situation analysis results, the bidirectional energy flow path and the energy storage parameters of the energy storage devices in the area to be coordinated, and to obtain the energy storage device dispatch results. The equipment coordination operation module is used to control the coordinated operation of multiple energy devices based on the energy supply and demand situation analysis results and the energy storage device scheduling results.

[0006] Thirdly, the present invention also provides an electronic device, comprising: a memory for storing computer software programs; and a processor for reading and executing the computer software programs, thereby realizing the big data-based multi-energy coordinated planning method for power distribution networks as described above.

[0007] Fourthly, the present invention also provides a non-transitory computer-readable storage medium storing a computer software program, which, when executed by a processor, implements the multi-energy coordinated planning method for power distribution networks based on big data as described above.

[0008] Fifthly, the present invention provides a computer program product, including a computer program that, when executed by a processor, implements any of the above-mentioned big data-based multi-energy coordinated planning methods for power distribution networks.

[0009] The big data-based multi-energy coordinated planning method for power distribution networks provided in this invention analyzes the energy supply and demand distribution based on the future availability of energy resources, the future demand of energy load, and user behavior patterns. This effectively addresses the intermittency and volatility of renewable energy, improving energy supply stability. Furthermore, bidirectional energy flow path planning breaks the limitations of unidirectional flow, enabling flexible allocation of energy across different regions and devices. Scheduling decisions and multi-energy device collaborative operation are then made based on the bidirectional energy flow paths, energy supply and demand analysis results, and energy storage parameters. This allows for dynamic adjustment of energy allocation, full utilization of various energy resources, and improved energy efficiency, achieving efficient and stable coordinated planning of multi-energy systems in the power distribution network. Ultimately, this results in optimal coordinated operation of the multi-energy system. Attached Figure Description

[0010] Figure 1 This is a flowchart illustrating the multi-energy coordinated planning method for power distribution networks based on big data, provided in an embodiment of the present invention. Figure 2 This is a schematic diagram of the structure of the multi-energy coordinated planning system for power distribution networks based on big data provided in an embodiment of the present invention; Figure 3 An embodiment diagram of the electronic device provided in this invention; Figure 4 An embodiment diagram of a computer-readable storage medium provided in accordance with the present invention. Detailed Implementation

[0011] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. 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 are within the scope of protection of the present invention.

[0012] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0013] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0014] See Figure 1 , Figure 1 This is a flowchart illustrating the multi-energy coordinated planning method for distribution networks based on big data provided by the present invention. In this embodiment of the invention, the executing entity of the multi-energy coordinated planning method for distribution networks based on big data is the energy planning system. Therefore, the multi-energy coordinated planning method for distribution networks based on big data includes: Step 10: Based on the availability of energy resources and the load demand of energy load within the area to be coordinated covered by the distribution network, predictive analysis is performed to obtain the future availability and future demand.

[0015] Optionally, the energy planning system collects the available resources of various energy resources (such as solar energy, wind energy, and grid power supply) in the area to be coordinated. In this embodiment of the invention, the available resources are historical data, including resource output at different time periods, meteorological data (affecting renewable energy sources such as solar and wind energy), and grid dispatch plans. At the same time, it collects the load demand of energy loads, which covers the electricity and gas demand data of different user types such as residents, businesses, and industries at different time periods.

[0016] Furthermore, the energy planning system employs time series analysis and machine learning algorithms (such as LSTM long short-term memory networks and ARIMA autoregressive integrated moving average models) to consider the impact of factors such as seasons, weather, and holidays on energy resources and load demand. It then performs predictive analysis on the collected data on energy resource availability and energy load demand, constructing a predictive model. This model forecasts the availability of energy resources and the demand for energy load over a future period (e.g., the next week or month), yielding the future availability of energy resources and the future demand for energy load.

[0017] In one embodiment, in an industrial park in a city, an energy planning system collects solar power generation data (including daily power generation and solar irradiance at different times), wind power generation data (wind speed, wind direction, and power generation), grid power supply data (daily power supply and electricity price information), and electricity load data of enterprises within the park (peak and off-peak electricity consumption periods and electricity consumption based on production shifts). Using an LSTM algorithm, meteorological data such as solar irradiance and wind speed, along with time-series data, are used as input to predict the availability of solar and wind energy for the coming week. Based on enterprise production plans and historical electricity consumption data, the system predicts the park's electricity load demand for the coming week. For example, it predicts that by 12:00-14:00 next Tuesday, the available solar energy will be 500kW, the available wind energy 300kW, and the grid power supply 1000kW; while the park's electricity load demand during that period is expected to be 1800kW.

[0018] Step 20: Analyze the energy supply and demand distribution based on future available energy and future demand, combined with user behavior patterns within the region to be coordinated, to obtain the energy supply and demand situation analysis results.

[0019] Furthermore, the energy planning system analyzes future available energy and future demand in conjunction with the behavioral patterns of different user types within the area to be coordinated. These user behavioral patterns include residents' daily electricity consumption patterns (such as morning and evening peak hours), commercial users' electricity consumption characteristics during business hours, and industrial users' planned electricity demand for production. In this embodiment of the invention, the energy supply and demand are matched spatially and temporally according to different time periods and geographical locations (dividing the area to be coordinated into several sub-regions). By comparing the differences in energy supply and demand in each sub-region at different times, the tightness or surplus of energy supply and demand is analyzed, and the results of the energy supply and demand situation analysis are obtained, as detailed in steps 201 to 205.

[0020] Continuing with the industrial park case study, businesses within the park primarily operate from 8:00 AM to 8:00 PM on weekdays, resulting in peak electricity consumption; while surrounding residential areas experience peak electricity consumption from 6:00 PM to 10:00 PM. Dividing the industrial park into three sub-regions (A, B, and C), analysis of data for the coming week reveals that sub-region A experiences insufficient solar and wind power supply from 10:00 AM to 12:00 PM on weekdays, leading to significant pressure on the power grid and a tight energy supply; sub-region B has a relatively sufficient energy supply from 7:00 PM to 9:00 PM, but some energy waste exists.

[0021] Step 30: Based on the analysis results of energy supply and demand, energy planning is carried out with the goal of minimizing energy transmission losses and meeting basic load requirements, and the bidirectional energy flow path from the supply side to the demand side is determined.

[0022] Furthermore, the energy planning system establishes an optimization model with the goal of minimizing energy transmission losses and meeting basic load requirements. The optimization model considers factors such as the geographical location of the energy supply side (e.g., solar power plants, wind farms, grid access points) and the demand side (each user area), as well as the parameters of the energy transmission lines (e.g., line length, resistance, transmission capacity).

[0023] Furthermore, the model is solved to find the optimal bidirectional energy flow path from the supply side to the demand side. During the solution process, priority is given to ensuring the basic load demand. For excess energy, its return path is planned to achieve rational energy allocation and efficient utilization, as detailed in steps 301 to 306.

[0024] Step 40: Based on the energy supply and demand situation analysis results, the two-way energy flow path, and the energy storage parameters of the energy storage devices in the area to be coordinated, a scheduling decision is made to obtain the energy storage device scheduling results.

[0025] Furthermore, the energy planning system makes scheduling decisions based on the energy supply and demand situation analysis results and the two-way energy flow path, combined with the energy storage parameters (charge and discharge power limit and current energy storage capacity) of the energy storage devices in the area to be coordinated. In this embodiment of the invention, when the energy supply is excessive, the system determines the current energy storage capacity and charge and discharge power limit of the energy storage devices, and decides whether to charge the energy storage devices and the amount of charging power. When the energy supply is insufficient, the system arranges the energy storage devices to discharge to supplement the energy gap based on the remaining power and discharge power limit of the energy storage devices, thus obtaining the charging and discharging strategies of the energy storage devices at different times, i.e., the energy storage device scheduling results, as specifically described in steps 401 to 404.

[0026] Step 50: Control the coordinated operation of multiple energy devices based on the analysis results of energy supply and demand situation and the scheduling results of energy storage devices.

[0027] Furthermore, based on the analysis results of energy supply and demand and the scheduling results of energy storage devices, the energy planning system sends control commands to multiple energy devices (such as solar power plants, wind farms, gas turbines, energy storage devices, etc.) to coordinate the operating status of each energy device. When there is an energy surplus, the output power of some energy devices is reduced, and the surplus energy is prioritized to be stored in energy storage devices or transported to other demand areas. When there is an energy shortage, the output power of energy devices is increased, and the energy storage devices are started to discharge to ensure the stability of energy supply in the area to be coordinated and to meet user needs, as detailed in steps 501 to 505.

[0028] This invention analyzes the energy supply and demand distribution based on the future availability of energy resources and the future demand for energy load, combined with user behavior patterns. This effectively addresses the intermittency and volatility of renewable energy, improving energy supply stability. Bidirectional energy flow path planning breaks the limitations of unidirectional flow, enabling flexible energy allocation across different regions and devices. Furthermore, based on the bidirectional energy flow paths, energy supply and demand analysis results, and energy storage parameters, scheduling decisions and multi-energy device collaborative operation are made. This allows for dynamic adjustment of energy allocation, full utilization of various energy resources, and improved energy efficiency. It achieves efficient and stable coordinated planning of multiple energy sources in the distribution network, thereby realizing optimal coordinated operation of the multi-energy system.

[0029] In one embodiment, steps 201 to 205 are described as follows: Step 201: Calculate the future available quantity and future demand quantity using different time periods and different regions as scales to obtain the energy base flow ratio of each region at different time periods.

[0030] Optionally, the energy planning system can divide the area to be coordinated into multiple sub-regions (such as areas A, B, and C) according to geographical boundaries, and divide the future timeline into multiple time periods (such as in 1-hour units).

[0031] Furthermore, for each time period within each sub-region, the energy planning system calculates the ratio of future available energy to future demand, obtaining the energy base flow ratio for each region at different time periods. The specific calculation formula is as follows: .in, Indicates the first The region in the first Energy base flow ratio for a given time period Indicates the first The region in the first Future available quantity for a given time period Indicates the first The region in the first Future demand over a specific time period.

[0032] In one embodiment, in the case of an industrial park, the park is divided into three sub-regions: A, B, and C, and the time for the next week is divided in hours. The energy availability of sub-region A at 10:00 AM on a weekday is calculated to be 800kW, and the demand is 1200kW. Therefore, the energy base flow ratio for sub-region A during this period is 800 / 1200≈0.67. Sub-region B has an availability of 1500kW and a demand of 1000kW during the same period, with a base flow ratio of 1500 / 1000≈1.5.

[0033] Step 202: For each time period in each region, adjust the basic energy flow ratio based on the activity level of each user behavior pattern to obtain the first adjusted energy flow ratio.

[0034] Furthermore, the energy planning system pre-establishes activity models for different user behavior patterns (residential, commercial, and industrial). The activity level is determined by statistically analyzing the energy consumption proportion of each user type at different time periods using historical data. For each time period in each region, the energy planning system non-linearly adjusts the energy base flow ratio based on the activity level of each user behavior pattern within that time period. This embodiment of the invention employs a piecewise function model, mapping different adjustment coefficients according to the activity level intervals. .

[0035] in, Indicates user type, Indicates the first The user type in the first Adjustment coefficients for each time period Indicates the first The user type in the first Activity level over a specific time period.

[0036] Furthermore, the energy planning system adjusts the basic energy flow ratio based on the adjustment coefficient for each time period in each region, thus obtaining the first adjusted energy flow ratio. The specific formula is as follows: .

[0037] Continuing with the above embodiment, during the 10:00 AM workday in sub-region A, the activity level of industrial users is 0.9 (in a high-energy-consuming production phase), while the activity levels of residential and commercial users are relatively low. Based on the piecewise function described above, the adjustment coefficient for industrial users is 1.2, and the adjustment coefficient for residential and commercial users is 1.0. Therefore, the energy flow ratio after the first adjustment is 0.67 * 1.2 * 1.0 * 1.0 = 0.804.

[0038] Step 203: Adjust the first adjusted energy flow ratio based on the energy fluctuation coefficient to obtain the second adjusted energy flow ratio.

[0039] Furthermore, within each time period, the energy planning system sums up the first adjusted energy flow ratio for each region to obtain the total energy flow. Simultaneously, it averages the sums up the first adjusted energy flow ratio for each region within each time period to obtain the average total energy flow.

[0040] Furthermore, the energy planning system calculates the quotient based on the sum of total energy flow and the sum of average total energy flow to obtain the energy fluctuation coefficient for each region in each time period. The specific formula is as follows: .

[0041] in, Indicates the first The region in the first Energy fluctuation coefficient over a given time period Indicates the number of regions. Indicates the number of time periods.

[0042] Furthermore, the energy planning system adjusts the first adjusted energy flow ratio based on the energy fluctuation coefficient of each region in each time period to obtain the second adjusted energy flow ratio of each region in each time period, as shown in the following formula: ,in, Indicates the first The region in the first The second adjusted energy flow ratio for a given time period. Continuing with the above embodiment, at 10:00 AM on a weekday, the total energy flow of the entire industrial park is 5000 kW, and the average total energy flow is 4000 kW. Therefore, the energy fluctuation coefficient of the industrial park during this time period is 5000 / 4000 = 1.25. The first adjusted flow ratio of sub-region A during this time period is 0.804, and the second adjusted flow ratio is 0.804^1.25 ≈ 0.74.

[0043] Step 204: Adjust the second adjusted energy flow ratio based on the energy flow ratio of adjacent regions and adjacent time periods to obtain the third adjusted energy flow ratio.

[0044] Furthermore, considering the spatial and temporal continuity of energy supply and demand, the energy planning system analyzes the energy supply and demand situation in adjacent regions and time periods, and makes corresponding adjustments to the preliminary judgment results. Therefore, it obtains the energy flow ratio of adjacent regions for each region and calculates the neighborhood flow ratio based on the energy flow ratio of adjacent regions. The specific formula is as follows: Furthermore, the energy planning system obtains the energy flow ratio of each region in adjacent time periods, and calculates the flow ratio of adjacent time periods based on the energy flow ratio of adjacent time periods. The specific formula is as follows: .in, and These are the association weights for adjacent regions and adjacent time periods, respectively.

[0045] Furthermore, the energy planning system calculates the spatiotemporal flow ratio by averaging the neighborhood flow ratio and the neighboring time period flow ratio. The specific formula is as follows: .

[0046] Furthermore, the energy planning system adjusts the second adjusted energy flow ratio based on the spatiotemporal flow ratio to obtain the third adjusted energy flow ratio for each region in each time period. The specific formula is as follows: .

[0047] Continuing with the above embodiment, the second adjusted flow ratio of sub-region A at 10:00 AM on a weekday is 0.74, and the spatiotemporal flow ratio of its neighboring regions B and C during the same period and between 9:00 AM and 11:00 AM is 0.85. Calculated using the above formula, the third adjusted flow ratio is 0.74*[1+(0.85-0.74) / 0.85]≈0.84.

[0048] Step 205: If the third adjusted energy flow ratio in the target area during the target time period is less than the first energy flow ratio threshold, then the energy supply and demand situation analysis result for the target area during the target time period is determined to be a state of tight energy supply. If the third adjusted energy flow ratio in the target area during the target time period is greater than the second energy flow ratio threshold, then the energy supply and demand situation analysis result for the target area during the target time period is determined to be a state of surplus energy supply. The second energy flow ratio threshold is greater than the first energy flow ratio threshold.

[0049] Furthermore, a first energy flow ratio threshold (e.g., 0.8) and a second energy flow ratio threshold (e.g., 1.2) are preset. If the third adjusted energy flow ratio of the target area during the target time period is less than the first threshold, it is determined that the energy supply is tight; if it is greater than the second threshold, it is determined that the energy supply is excessive; if it is between the two, it is considered that the supply and demand are balanced. Continuing with the above embodiment, if the third adjusted flow ratio of sub-region A at 10:00 on a weekday is 0.84, which is between 0.8 and 1.2, the energy supply and demand situation of sub-region A during this time period is determined to be balanced; if the third adjusted flow ratio of sub-region B during the same time period is 1.3, which is greater than 1.2, it is determined to be in a state of excess energy supply.

[0050] This invention analyzes the distribution of energy supply and demand based on the future availability of energy resources and the future demand for energy load, combined with user behavior patterns. This approach can effectively address the intermittency and volatility of renewable energy and improve the stability of energy supply.

[0051] In one embodiment, steps 301 to 306 are described as follows: Step 301: Based on the energy supply and demand situation analysis results, determine the initial energy transmission path from the supply side to the demand side. The initial energy transmission path includes connections between different geographical regions and transmission links between different energy carriers.

[0052] Optionally, the energy planning system, based on the analysis of energy supply and demand, will conduct preliminary connection planning between the supply side (such as solar power plants and wind farms) and the demand side (various user areas). Taking into account the spatial distribution of geographical areas and the transmission characteristics of different energy carriers (electricity, hydrogen, etc.), multiple initial energy transmission paths will be generated from the supply side to the demand side. For example, for electricity transmission, priority will be given to connecting areas that are closer and have well-developed power grid infrastructure; for hydrogen transmission, the feasibility of laying dedicated pipelines will be considered.

[0053] Continuing with the industrial park case, the energy planning system, based on the energy supply and demand situation, identifies three initial energy transmission paths: Path 1 connects solar power plant A to sub-region A via high-voltage transmission lines; Path 2 connects wind farm B to sub-region B via hydrogen pipelines; Path 3 connects grid access point C to both sub-region A and sub-region C, utilizing the existing power distribution network.

[0054] Step 302: For each initial energy transmission path, determine the initial transmission loss tendency value based on the physical characteristics and energy carrier characteristics of each transmission link in the path, and correct the initial transmission loss tendency value based on the energy demand of different regions at different times to obtain the first corrected loss tendency value.

[0055] Furthermore, for each initial energy transmission path, the energy planning system calculates the initial transmission loss tendency value based on the physical characteristics of each transmission link in the path (such as line length, resistance, and pipe diameter) and the characteristics of the energy carrier (skin effect of electrical energy, leakage rate of hydrogen energy) using the transmission loss theory formula. The specific formula for the initial transmission loss tendency value is as follows: .

[0056] in, Indicates the first The initial transmission loss tendency value of the initial energy transmission path. This indicates the number of routes in the initial energy transmission path. Indicates the first Section line resistance, Indicates the first Line length, Indicates the first Section line current, Indicates the first Transmission power of a line segment.

[0057] Furthermore, the energy planning system corrects the initial transmission loss tendency value based on the energy demand of different regions at different times using a nonlinear correction function, obtaining a first corrected loss tendency value. The formula for calculating the first corrected loss tendency value is as follows: .

[0058] in, For the first The region in the first Demand for a specific time period This represents the maximum demand across all regions over all time periods. This is a preset correction factor. >0.

[0059] In one embodiment, for path 1 (solar power station A connecting sub-region A), its line resistance R = 0.1. / km, length L=5km, transmission current I=100A, transmission power P=1000kW, the calculated initial transmission loss tendency is (0.1*5*100) 2 ) / 1000=5. Sub-region A's demand at 10:00 AM on weekdays is 1200kW, and the park's maximum demand is 2000kW. Let... =1.2, then the first corrected loss tendency value is 5*(1+1200 / 2000). 1.2 ≈8.2.

[0060] Step 303: Based on the forward and reverse energy flow of the path, the first corrected loss tendency value is corrected to obtain the second corrected loss tendency value. The minimum value of the ratio of the maximum transmission capacity to the load demand in each time period is determined as the load satisfaction index of the path in meeting the basic load demand at the demand end in different time periods.

[0061] Furthermore, the energy planning system acquires the forward energy flow (from the supply side to the demand side) and reverse energy flow (such as the discharge return flow from energy storage devices) of the path, and performs a second correction on the first corrected loss tendency value based on the forward and reverse energy flow of the path to obtain the second corrected loss tendency value. The specific formula is as follows: .in, This is a preset correction factor. >0.

[0062] Furthermore, the energy planning system calculates the ratio of the maximum transmission capacity of the path to the load demand in each time period, and takes the minimum value among these ratios as the load sufficiency index. Therefore, the formula for calculating the load sufficiency index is: .

[0063] in, Indicates the first The load fulfillment capacity index of the initial energy transmission path. Indicates the first The initial energy transmission path during the period Maximum transmission capacity Indicates the time period The load demand.

[0064] Step 304: Based on the second corrected loss tendency value and load fulfillment capacity index of each initial energy transmission path, determine the candidate energy transmission path.

[0065] Furthermore, the energy planning system considers the transmission loss tendency of each initial energy transmission path. and load fulfillment index The initial energy transmission paths are screened, removing paths with excessively high transmission loss tendency and low load fulfillment capacity index, and retaining paths with better overall performance to obtain candidate energy transmission paths. The screening rule in this embodiment is as follows: Let... and For a pre-set threshold, where, and The value is determined based on the actual situation. > and < At that time, the initial energy transmission path Excluded from subsequent analysis; otherwise, retain the initial energy transmission path. .

[0066] Step 305: For each candidate energy transmission path, construct an objective function based on the second corrected loss tendency value and the energy transmission state variables in different time periods, and construct constraints based on the energy transmission state variables, maximum transmission capacity and load demand in different time periods.

[0067] Furthermore, for each candidate energy transmission path, considering the dynamic changes in energy supply and demand (such as the intermittency of new energy power generation and real-time fluctuations in user load), a dynamic path optimization model is constructed to determine the most suitable energy flow path at different times. In this embodiment of the invention, it is assumed that... For the time period path The energy transmission state variable, when the path During the period When there is energy transmission, =1, otherwise =0. Therefore, the objective function is The constraints include: (Ensure the time period is met) (load demand), of which, This indicates the number of candidate energy transmission paths. Indicates the total number of time periods. Representing a path During the period Maximum transmission capacity Indicates time period The energy demand. Therefore, it can be understood that the dynamic path optimization model (objective function) aims to minimize transmission loss while ensuring that the basic load demand is met in each time period.

[0068] Step 306: Solve the objective function of each candidate energy transmission path based on the constraints to obtain the optimal energy flow path, and determine the optimal energy flow path as a bidirectional energy flow path from the supply side to the demand side.

[0069] Furthermore, the energy planning system solves its objective function based on the constraints of each candidate energy transmission path. In this embodiment of the invention, heuristic algorithms are used to solve the objective function to obtain the optimal bidirectional energy flow path from the supply side to the demand side for each candidate energy transmission path at different time periods.

[0070] Furthermore, this can be understood as finding the value of the objective function that is minimized through iterative calculations while satisfying all constraints. The combination of values. When the algorithm converges, the obtained... A path with a value of 1 is within the time period. The optimal energy flow path is determined as a bidirectional energy flow path from the supply side to the demand side. Therefore, the bidirectional energy flow path is the one that can meet the basic load demand and minimize energy transmission loss.

[0071] This invention establishes basic connections based on supply and demand dynamics during initial path planning. Subsequently, through multi-layered adjustments based on transmission loss and load demand, feasible candidate paths are selected. The construction of the objective function and constraints combines practical engineering limitations with optimization objectives, and utilizes heuristic algorithms to ensure a globally optimal solution. Therefore, it comprehensively considers transmission loss, load fluctuations, and bidirectional flow, reducing energy transmission losses, improving energy supply reliability, and achieving efficient allocation and rational flow of energy resources.

[0072] In one embodiment, steps 401 to 404 are described as follows: Step 401: Based on the upper limit of charging and discharging power and the current energy storage capacity of each energy storage device in each time period, construct the initial state matrix of each energy storage device in each time period, and adjust the initial state matrix based on the first influence coefficient of the energy storage potential of each energy storage device based on the energy supply and demand situation analysis results of each time period, to obtain the first updated state matrix of each energy storage device in each time period.

[0073] Optionally, for each energy storage device within the area to be coordinated, the energy planning system will determine its charging power limit for each time period. Upper limit of discharge power and current energy storage capacity The initial state matrix is ​​constructed by means of the energy storage capacity, the upper limit of charging power, and the upper limit of discharging power. Therefore, the initial state matrix can be represented as follows: .

[0074] Furthermore, based on the analysis of energy supply and demand trends for each time period, the energy planning system determines the primary influence coefficient reflecting energy storage potential. When there is an energy surplus, A value greater than 1 encourages energy storage devices to charge; when energy supply is insufficient, A value less than 1 triggers the energy storage device to discharge. By performing exponential adjustments on key parameters such as energy storage capacity in the initial state matrix, the first updated state matrix is ​​obtained. The formula for calculating the first updated state matrix is: .in, Indicates the energy storage device number. Indicates the column number of the matrix element.

[0075] In one embodiment, in an industrial park, the current energy storage capacity of energy storage device E at 10:00 AM on a weekday is: =500kWh, maximum charging power =100kW, upper limit of discharge power =120kW, construct the initial state matrix as follows Given the known energy supply shortage during this period, the first impact factor is... =0.8, then the state matrix after the first update is = .

[0076] Step 402: Based on the bidirectional energy flow path in each time period, determine the target energy flow path adjacent to the area where each energy storage device is located, and based on the energy flow amount of each path in the target energy flow path in each time period, the second influence coefficient on the energy storage device, and the total energy flow of all paths in each time period, determine the state influence coefficient of the target energy flow path on each energy storage device in each time period.

[0077] Furthermore, the energy planning system identifies target energy flow paths adjacent to the area where each energy storage device is located based on the bidirectional energy flow paths for each time period. For each target energy flow path, the energy flow volume for each time period is obtained. Combined with the second impact coefficient on energy storage devices And the total energy flow of all target energy flow paths during that period. The following formula is used to determine the state impact coefficient of the target energy flow path on each energy storage device in each time period. : .in, The weighting of the impact of different paths on the state of energy storage devices is set based on factors such as the degree of correlation between the path and the energy storage device and the energy type.

[0078] Continuing with the above embodiment, there are two adjacent target energy flow paths in the area where energy storage device E is located. The energy flow volume of path X at 10:00 AM on a weekday is... =300kW, Second Influence Coefficient =0.6; the energy flow of path Y is =200kW, second influence coefficient =0.4. Total energy flow along all target energy flow paths. =300 + 200 = 500kW. Therefore, the state influence coefficient... .

[0079] Step 403: Adjust the first updated state matrix based on the state influence coefficient of each energy storage device in each time period to obtain the second updated state matrix of each energy storage device in each time period.

[0080] Furthermore, the energy planning system utilizes the state impact coefficient of each energy storage device at each time period. The first updated state matrix is ​​then adjusted. A second updated state matrix is ​​obtained by performing a logarithmic transformation on the elements of the first updated state matrix and combining this with state influence coefficients. This further aligns the energy storage device's state with the energy flow environment. The formula for calculating the second updated state matrix is ​​as follows:

[0081] Continuing with the above embodiments, for the energy storage device E, the state matrix after the first update at 10:00 AM on a weekday... State influence coefficient =0.52, then the state matrix after the second update is .

[0082] Step 404: Based on the second updated state matrix and energy demand of each energy storage device in each time period, a scheduling decision is made for each energy storage device to obtain the energy storage device scheduling result for each time period.

[0083] Furthermore, the energy planning system makes scheduling decisions for each energy storage device based on the second updated state matrix and energy demand of each energy storage device in each time period, and obtains the energy storage device scheduling results for each time period, as shown in steps 4041 to 4044.

[0084] By integrating energy supply and demand dynamics, energy flow paths, and energy storage device parameters, this invention can respond more flexibly and efficiently to dynamic changes in energy, enabling dynamic adjustments to energy allocation, full utilization of various energy resources, improved regulation capabilities and overall operating efficiency of energy storage devices, optimized allocation of energy resources, and improved energy utilization efficiency to achieve efficient and stable coordinated planning of multiple energy sources in the power distribution network.

[0085] In one embodiment, steps 4041 to 4044 are described as follows: Step 4041: Determine the energy supply path for supplying energy to the area where each energy storage device is located during each time period, and the energy acquisition path for acquiring energy from the area where each energy storage device is located during each time period.

[0086] Optionally, based on the bidirectional energy flow path and energy supply and demand situation, the energy planning system identifies the energy supply path to supply energy to the region and the energy acquisition path to obtain energy from the region (such as the discharge output of the energy storage device) for each time period and the region where the energy storage device is located. Taking into account factors such as transmission loss, remaining transmission capacity, and energy type compatibility, the system selects the optimal combination of energy supply path and energy acquisition path for each energy storage device in each time period.

[0087] Continuing with the industrial park case study, at 10:00 AM on a weekday, the energy supply in the area where energy storage device E is located is tight. The energy planning system determines the energy supply path as follows: power is supplied to the area from grid access point C via a dedicated transmission line; the energy acquisition path is: energy storage device E supplies power to surrounding businesses via the park's distribution lines. The transmission loss of the line from grid access point C to the area is low, and the current remaining transmission capacity can meet some of the demand; the distribution lines connected to energy storage device E can quickly deliver electricity to the demand side.

[0088] Step 4042: Based on the energy demand for each time period, the second updated state matrix of each energy storage device for each time period, and the positive energy flow of each path in the energy supply path and energy acquisition path of each energy storage device for each time period, determine the charging and discharging demand coefficient of each energy storage device for each time period.

[0089] Furthermore, the energy planning system combines the energy demand for each time period, the second updated state matrix of the energy storage devices, and the positive energy flow of the energy supply and energy acquisition paths during that time period to calculate the charging and discharging demand coefficient of each energy storage device for each time period using the following formula. Among them, the charge and discharge demand coefficient is used to quantify the charge and discharge demand of energy storage devices under the current energy supply and demand environment.

[0090] .

[0091] in, The sum of positive energy flows across all energy supply paths during this period; the available capacity of the energy storage device is determined based on the relevant parameters in the second updated state matrix; the charge / discharge adjustment factor is dynamically set according to the urgency of energy supply and demand. When energy supply is tight, the adjustment factor is greater than 1, prompting the energy storage device to discharge more; when energy supply is surplus, the adjustment factor is less than 1, encouraging the energy storage device to charge.

[0092] Continuing with the above embodiment, at 10:00 AM on a weekday, the energy demand in the area where energy storage device E is located in the industrial park is 1200kW, and the total forward energy flow along the energy supply path is 800kW. The second updated state matrix of energy storage device E reflects its available capacity as approximately 400kWh (after calculation and conversion). Due to the current tight energy supply, the charge / discharge adjustment factor is set to 1.2. Therefore, the charge / discharge demand coefficient... This indicates that the energy storage device needs to discharge to a large extent to meet the region's energy demand.

[0093] Step 4043: Based on the charging and discharging demand coefficient of each energy storage device in each time period, the status of the energy storage device, and the second updated state matrix, determine the preliminary scheduling result of each energy storage device in each time period.

[0094] Furthermore, the energy planning system determines the preliminary scheduling result based on the charging and discharging demand coefficient of each energy storage device in each time period, the current state of the energy storage device (such as the current state of charge and charging / discharging power limits), and the second updated state matrix, through the following logic: If the charging and discharging demand coefficient... >0, and the current state of charge of the energy storage device is greater than the minimum state of charge. Then the energy storage device is determined to be in a discharge state, and the discharge power is... Based on the charge / discharge demand coefficient and the upper limit of the discharge power of the energy storage device calculate: .

[0095] Furthermore, if the charging and discharging demand coefficient <0, and the current state of charge of the energy storage device is less than the maximum state of charge. Then the energy storage device is determined to be in a charging state, and the charging power is... Based on the absolute value of the charge / discharge demand coefficient and the upper limit of the charging power of the energy storage device calculate: .

[0096] If the above conditions are not met, the energy storage device will maintain its current state (neither charging nor discharging).

[0097] Continuing with the energy storage device E, the charging and discharging demand factor at 10 AM on weekdays. =0.83, current state of charge is 60%, which is greater than the minimum state of charge of 20%, and the upper limit of discharge power. =120kW. Therefore, the preliminary dispatch result indicates that energy storage device E is in a discharging state, with a discharge power of... =99.6kW.

[0098] Step 4044: Adjust the preliminary scheduling results based on the maximum state of charge, minimum state of charge, and state of charge in the previous time period of each energy storage device as state of charge constraints, to obtain the energy storage device scheduling results for each energy storage device in each time period.

[0099] Furthermore, the energy planning system uses the maximum state of charge of each energy storage device. Minimum state of charge and the state of charge of the previous period. As a constraint on the state of charge, the preliminary scheduling results are adjusted as follows: the predicted state of charge at the end of the current time period is calculated based on the preliminary scheduling results. : .

[0100] in, This refers to the charging and discharging power in the preliminary scheduling results (charging is positive, discharging is negative). For the duration of the period, This represents the maximum energy storage capacity of the energy storage device. The charging and discharging efficiency of energy storage devices.

[0101] Furthermore, if > Therefore, the final energy storage device scheduling result is adjusted as follows: Energy storage device scheduling result = Reduce charging power or stop charging; if < Therefore, the final energy storage device scheduling result is adjusted as follows: Energy storage device scheduling result = Reduce the discharge power or stop discharging until... This yields the final energy storage device scheduling results.

[0102] The embodiments of the present invention can more precisely balance energy supply and demand, make reasonable use of energy storage equipment capacity, and effectively avoid overcharging and over-discharging of energy storage equipment while ensuring stable energy supply, thereby improving the service life of energy storage equipment and the overall operating efficiency of the energy system.

[0103] In one embodiment, steps 501 to 505 are described as follows: Step 501: Based on the operating parameters of multiple energy devices in the area to be coordinated for each time period, construct an initial equipment operation matrix for each energy device for each time period. The operating parameters include rated power, energy conversion efficiency, and equipment operating temperature.

[0104] Optionally, the energy planning system collects operating parameters for multiple energy devices (such as solar power plants, wind farms, gas turbines, and energy storage devices) within the area to be coordinated, including rated power, for each time period. Energy conversion efficiency and equipment operating temperature These parameters are organized in matrix form to construct the initial equipment operation matrix for each energy device in each time period. Therefore, the initial equipment operation matrix is ​​represented as: .

[0105] Each row corresponds to the operating parameters of an energy device at a certain time period, and each column represents the rated power, energy conversion efficiency, and equipment operating temperature, respectively.

[0106] Continuing with the industrial park case study, at 10:00 AM on a weekday, solar power station A has a rated power of 800kW, an energy conversion efficiency of 25%, and an operating temperature of 35°C. C; Gas turbine B has a rated power of 1000kW, an energy conversion efficiency of 38%, and an operating temperature of 120°C. C. Then the initial equipment operation matrix of solar power station A during this period is: The initial equipment operation matrix for gas turbine B is as follows: .

[0107] Step 502: Adjust the initial equipment operation matrix based on the energy supply and demand situation analysis results for each time period to obtain the first adjusted equipment operation matrix for each energy device in each time period.

[0108] Furthermore, the energy planning system adjusts the initial equipment operation matrix based on the energy supply and demand situation analysis results for each time period. When energy supply is excessive, the rated power parameters of some energy equipment are reduced, and the energy conversion efficiency is adjusted (e.g., reducing equipment operating load to improve efficiency); when energy supply is insufficient, the rated power limit of some equipment is appropriately increased (within the equipment's safe range), and the energy conversion efficiency is adjusted to increase energy output. This embodiment of the invention uses a nonlinear function to adjust the matrix elements to obtain the first adjusted equipment operation matrix.

[0109] In one embodiment, the energy supply and demand situation coefficient is set as follows: When there is an energy surplus, <1; When energy supply is insufficient, >1. Adjust the formula as follows: .

[0110] in, According to A nonlinear function whose value changes, used to adjust energy conversion efficiency.

[0111] As of 10:00 AM on weekdays, the energy supply in the industrial park remains tight, and the energy supply and demand situation coefficient is [not specified]. =1.2. For solar power plant A, its first adjusted equipment operation matrix is: Rated power: 800 * 1.2 = 960 kW; Energy conversion efficiency: 0.25+ (Assuming) =0.03) =0.28; Equipment operating temperature maintained at 35°C C, that is, the first adjusted equipment operation matrix is .

[0112] Step 503: Based on the energy storage device scheduling results, charging and discharging status, and the third influence coefficient on the operating parameters of each energy storage device in each time period, the first adjusted device operation matrix is ​​adjusted to obtain the second adjusted device operation matrix.

[0113] Furthermore, the energy planning system considers the energy storage device's scheduling results (charging, discharging, or idle) and charging / discharging status in each time period, as well as the third influence coefficient on the operating parameters of each energy device. A second adjustment is then made to the equipment operation matrix after the first adjustment. When the energy storage device is charging, the output power of some power generation equipment can be appropriately reduced; when the energy storage device is discharging, it can work in conjunction with the power generation equipment to meet energy demand. The relevant equipment operating parameters are adjusted accordingly. The calculation formula for the second adjusted equipment operation matrix is ​​as follows: .

[0114] in, This represents the energy storage impact factor, which is determined based on the charging and discharging state of the energy storage device. It is -1 when charging, 1 when discharging, and 0 when idle.

[0115] Continuing on a weekday at 10:00 AM, energy storage device E is in a discharging state (energy storage impact factor is 1), and its third impact coefficient on solar power station A is... =0.1. The first adjusted equipment operation matrix of solar power plant A is: Then the second adjusted equipment operation matrix is: Rated power: 960*(1+0.1*1)=1056kW; Energy conversion efficiency: 0.28*(1+0.1*1)=0.308; Equipment operating temperature: 35*(1+0.1*0)=35 C, that is, the second adjusted equipment operation matrix is .

[0116] Step 504: Based on the second adjusted equipment operation matrix of each energy device in each time period, determine the cooperative operation adaptability of different energy devices in each time period.

[0117] Furthermore, based on the second adjusted equipment operation matrix for each energy device in each time period, the energy planning system determines the degree of cooperative operation suitability by calculating the correlation and complementarity between the operating parameters of different energy devices. Optionally, in this embodiment of the invention, a cosine similarity algorithm combined with device characteristic weights is used to construct a formula for calculating the cooperative operation adaptability: .

[0118] in, and Represents the numbering of different energy equipment. This represents the number of runtime parameters (n=3 here). Weights for each operating parameter (set according to equipment type and energy demand, such as rated power weight) =0.5, Energy Conversion Efficiency Weight =0.3, weight of equipment operating temperature =0.2).

[0119] Continue calculating the compatibility of coordinated operation between solar power plant A and gas turbine B at 10:00 AM on a weekday. The second adjusted equipment operation matrix for solar power plant A is as follows: The second adjusted equipment operation matrix for gas turbine B is (assuming the adjusted matrix is...). ),but .

[0120] Step 505: Control the operation of multiple energy devices based on the adaptiveness of the coordinated operation of different energy devices in each time period.

[0121] Furthermore, the energy planning system controls the operation of multiple energy devices based on the compatibility of different energy devices in each time period, as specifically in steps 5051 to 5054.

[0122] This invention, through the construction of an equipment operation matrix, the combination of energy supply and demand and energy storage scheduling adjustment matrix, and the calculation of collaborative operation adaptability, controls equipment operation accordingly, forming a complete multi-energy equipment coordinated operation mechanism. Therefore, it can deeply explore the collaborative potential between multi-energy equipment, dynamically optimize equipment operation parameters and combination methods, effectively improve overall operating efficiency, reduce energy loss, enhance the stability and reliability of energy supply, realize the efficient collaborative utilization of multiple energy sources in complex environments, improve energy utilization efficiency, and achieve efficient and stable coordinated planning of multiple energy sources in the distribution network.

[0123] In one embodiment, steps 5051 to 5054 are described as follows: Step 5051: Based on the compatibility of different energy devices in each time period and the importance of each energy device, determine the priority of collaborative operation of different energy device combinations in each time period.

[0124] Optionally, the energy planning system sets an importance index (range 0-1) for each energy device. This index is determined based on factors such as the device's stability and irreplaceability in energy supply. For example, the grid connection point, as a stable basic energy supply device, has an importance index of 0.9; while some small distributed energy devices have an importance index of 0.3. Furthermore, the energy planning system integrates the cooperative operation adaptability of different energy device combinations in each time period with the device importance index, and uses a nonlinear product-power model to determine the cooperative operation priority. The specific formula is as follows: .

[0125] in, This refers to the number of devices in the energy equipment portfolio. and The adjustment coefficient ( >0, >0), determined through optimization using historical data, is used to balance the influence weights of fit and importance.

[0126] Continuing at 10:00 AM on a weekday in the industrial park, the compatibility of energy equipment combination 1 (solar power station A, gas turbine B) is 0.85 and 0.78, respectively, and the equipment importance is 0.6 and 0.8, respectively; the compatibility of energy equipment combination 2 (energy storage device E, small wind turbine F) is 0.65 and 0.55, respectively, and the equipment importance is 0.4 and 0.3, respectively. Let... =0.7, =0.3. The priority of coordinated operation of energy equipment combination 1 is: 0.81, the priority for coordinated operation of energy equipment combination 2 is: 0.49, therefore, the collaborative operation priority of energy equipment combination 1 is higher than that of combination 2.

[0127] Step 5052: Based on the energy demand for each time period, the priority of coordinated operation of different energy equipment combinations in each time period, and the operating status of energy equipment in each energy equipment combination, determine the initial coordinated operation power of each energy equipment in each time period.

[0128] Furthermore, based on the energy demand for each time period, the coordinated operation priority of different energy equipment combinations, and the operating status of each energy equipment in the equipment combination (such as whether it can be started immediately, its current fault status, etc.), the energy planning system determines the initial coordinated operating power of each energy equipment in each time period through a target programming model. Specifically, the energy demand is first allocated to each equipment combination according to the priority of coordinated operation: allocation amount .

[0129] Where G represents the total number of equipment combinations.

[0130] Then, within each equipment group, the allocation amount is distributed to each equipment proportionally based on the equipment's rated power and operating status, using the following formula: .

[0131] in, The value can be 0 (fault or inoperable) or 1 (normal operation).

[0132] Continuing with the above embodiment, at 10:00 AM on a working day, the energy demand of the industrial park is 1500kW, with energy equipment combinations 1 (priority 0.81) and 2 (priority 0.49). Therefore, the allocation for combination 1 is 935kW. In combination 1, solar power plant A has a rated power of 1000kW (operating state 1), and gas turbine B has a rated power of 800kW (operating state 1). The initial coordinated operating power of solar power plant A is 519kW, and the initial coordinated operating power of gas turbine B is 416kW.

[0133] Step 5053: Adjust the initial coordinated operating power based on the grid capacity limit, heating network carrying capacity limit and equipment heating conversion efficiency of each energy device in each time period as operating constraints to obtain the final coordinated operating power of each energy device in each time period.

[0134] Furthermore, the energy planning system limits the grid capacity of each energy device at each time period. Heat network carrying capacity limitations (if heating equipment is involved), ) and equipment heat conversion efficiency ( As an operational constraint, the initial coordinated operating power is adjusted.

[0135] For power output devices, the following must be met: .

[0136] For heating equipment, the following requirements must be met: .

[0137] Optionally, in this embodiment of the invention, the Lagrange multiplier method is used to construct a constrained optimization function. Under the constraint conditions, the initial coordinated operating power is iteratively adjusted with the objective of minimizing energy production costs or maximizing energy utilization efficiency to obtain the final coordinated operating power. .

[0138] In one embodiment, for the current operating power ,like Then the final coordinated operating power .

[0139] like Ultimately, coordinate operating power .

[0140] Continuing with the above example, assume that the grid capacity limit for solar power plant A is 600kW, and its initial coordinated operating power of 519kW remains unchanged, not exceeding the limit; the grid capacity limit for gas turbine B is 450kW, and its initial coordinated operating power of 416kW also remains unchanged, not exceeding the limit. If gas turbine B also has a heating function, the heating network capacity is limited to 120GJ / h, and the heating conversion efficiency is 0.3. The heat output corresponding to the power generation of gas turbine B is 416kW * 3600s = 1.5 * 10 6 kJ = 1.5 GJ / h (1 hour), which does not exceed the capacity limit of the heating network. Therefore, the final coordinated operating power of gas turbine B is still 416 kW.

[0141] Step 5054: Control the operation of multiple energy devices based on the final coordinated operating power of each energy device in each time period.

[0142] Furthermore, the energy planning system converts the final coordinated operating power of each energy device in each time period into control commands and sends them to the corresponding multi-energy devices. Upon receiving the commands, the devices adjust their own operating parameters (such as power generation and fuel input) to operate according to the final coordinated operating power, thus achieving coordinated operation control of the multi-energy devices. Continuing with the above embodiment, at 10:00 AM on a weekday, the energy planning system sends a command to solar power plant A, controlling it to generate electricity at 519 kW; and sends a command to gas turbine B, controlling it to generate electricity at 416 kW, thereby achieving coordinated operation of the two devices during that time period to meet the energy needs of the industrial park.

[0143] The embodiments of the present invention can dynamically adapt to changes in energy demand, fully consider equipment characteristics and operating limitations, realize deep collaboration and efficient operation of multiple energy devices, effectively improve the stability and economy of energy supply, reduce energy loss and cost waste caused by unreasonable equipment operation, improve energy utilization efficiency, and realize efficient and stable coordinated planning of multiple energy sources in the distribution network.

[0144] Furthermore, the multi-energy coordinated planning system for distribution networks based on big data provided by the present invention will be described below. The multi-energy coordinated planning system for distribution networks based on big data described below can be referred to in correspondence with the multi-energy coordinated planning method for distribution networks based on big data described above.

[0145] Optional, refer to Figure 2 , Figure 2 This is a schematic diagram of the structure of the big data-based multi-energy coordinated planning system for power distribution networks provided by the present invention. The big data-based multi-energy coordinated planning system for power distribution networks includes: The demand forecasting and analysis module 210 is used to forecast and analyze the availability of energy resources and the load demand of energy load in the area to be coordinated within the coverage of the distribution network, so as to obtain the future availability and future demand. The supply and demand situation analysis module 220 is used to analyze the energy supply and demand distribution situation based on future available quantity and future demand quantity combined with user behavior patterns in the area to be coordinated, and obtain the energy supply and demand situation analysis results. The energy path planning module 230 is used to perform energy planning based on the results of energy supply and demand situation analysis, with the goal of minimizing energy transmission loss and meeting basic load demand, and to determine the bidirectional energy flow path from the supply side to the demand side. The energy dispatch decision module 240 is used to make dispatch decisions based on the energy supply and demand situation analysis results, bidirectional energy flow paths and energy storage parameters of energy storage devices in the area to be coordinated, and to obtain the dispatch results of energy storage devices. The equipment coordination operation module 250 is used to control the coordinated operation of multiple energy devices based on the analysis results of energy supply and demand and the scheduling results of energy storage devices.

[0146] This invention analyzes the energy supply and demand distribution based on the future availability of energy resources and the future demand for energy load, combined with user behavior patterns. This effectively addresses the intermittency and volatility of renewable energy, improving energy supply stability. Bidirectional energy flow path planning breaks the limitations of unidirectional flow, enabling flexible energy allocation across different regions and devices. Furthermore, based on the bidirectional energy flow paths, energy supply and demand analysis results, and energy storage parameters, scheduling decisions and multi-energy device collaborative operation are made. This allows for dynamic adjustment of energy allocation, full utilization of various energy resources, and improved energy efficiency. It achieves efficient and stable coordinated planning of multiple energy sources in the distribution network, thereby realizing optimal coordinated operation of the multi-energy system.

[0147] Please see Figure 3 , Figure 3 An embodiment diagram of an electronic device provided in accordance with the present invention. For example... Figure 3 As shown, this embodiment of the invention provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor 320. When the processor 320 executes the computer program 311, it performs the following steps: Based on the energy resource availability and energy load demand in the area to be coordinated within the distribution network coverage, a predictive analysis is conducted to obtain the future availability and future demand. Based on the future availability and future demand, combined with user behavior patterns in the region to be coordinated, an analysis of the energy supply and demand distribution is conducted to obtain the results of the energy supply and demand analysis. Based on the analysis of energy supply and demand, energy planning is carried out with the goal of minimizing energy transmission losses and meeting basic load requirements, and a two-way energy flow path from the supply side to the demand side is determined. Based on the analysis results of energy supply and demand, two-way energy flow paths, and energy storage parameters of energy storage devices in the area to be coordinated, scheduling decisions are made to obtain the scheduling results of energy storage devices. Control the coordinated operation of multiple energy devices based on the analysis results of energy supply and demand and the scheduling results of energy storage devices.

[0148] Please see Figure 4 , Figure 4 An embodiment diagram of a computer-readable storage medium provided in accordance with an embodiment of the present invention is shown. Figure 4 As shown, this embodiment provides a computer-readable storage medium 400 on which a computer program 311 is stored. When the computer program 311 is executed by a processor, it performs the following steps: Based on the energy resource availability and energy load demand in the area to be coordinated within the distribution network coverage, a predictive analysis is conducted to obtain the future availability and future demand. Based on the future availability and future demand, combined with user behavior patterns in the region to be coordinated, an analysis of the energy supply and demand distribution is conducted to obtain the results of the energy supply and demand analysis. Based on the analysis of energy supply and demand, energy planning is carried out with the goal of minimizing energy transmission losses and meeting basic load requirements, and a two-way energy flow path from the supply side to the demand side is determined. Based on the analysis results of energy supply and demand, two-way energy flow paths, and energy storage parameters of energy storage devices in the area to be coordinated, scheduling decisions are made to obtain the scheduling results of energy storage devices. Control the coordinated operation of multiple energy devices based on the analysis results of energy supply and demand and the scheduling results of energy storage devices.

[0149] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer is able to execute the big data-based multi-energy coordinated planning method for power distribution networks provided by the above methods. The method includes: Based on the energy resource availability and energy load demand in the area to be coordinated within the distribution network coverage, a predictive analysis is conducted to obtain the future availability and future demand. Based on the future availability and future demand, combined with user behavior patterns in the region to be coordinated, an analysis of the energy supply and demand distribution is conducted to obtain the results of the energy supply and demand analysis. Based on the analysis of energy supply and demand, energy planning is carried out with the goal of minimizing energy transmission losses and meeting basic load requirements, and a two-way energy flow path from the supply side to the demand side is determined. Based on the analysis results of energy supply and demand, two-way energy flow paths, and energy storage parameters of energy storage devices in the area to be coordinated, scheduling decisions are made to obtain the scheduling results of energy storage devices. Control the coordinated operation of multiple energy devices based on the analysis results of energy supply and demand and the scheduling results of energy storage devices.

[0150] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0151] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0152] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-energy coordinated planning method for power distribution networks based on big data, characterized in that, include: Based on the energy resource availability and energy load demand in the area to be coordinated within the distribution network coverage, a predictive analysis is conducted to obtain the future availability and future demand. Based on the future available quantity and the future demand quantity, combined with the user behavior patterns in the area to be coordinated, an energy supply and demand distribution trend analysis is performed to obtain the energy supply and demand trend analysis results. Based on the energy supply and demand situation analysis results, energy planning is carried out with the goal of minimizing energy transmission losses and meeting basic load requirements, and a two-way energy flow path from the supply side to the demand side is determined. Based on the energy supply and demand situation analysis results, the bidirectional energy flow path, and the energy storage parameters of the energy storage devices in the area to be coordinated, scheduling decisions are made to obtain the energy storage device scheduling results. Based on the energy supply and demand situation analysis results and the energy storage equipment scheduling results, the coordinated operation of multiple energy devices is controlled.

2. The method for multi-energy coordinated planning of power distribution networks based on big data as described in claim 1, characterized in that, The energy supply and demand distribution analysis is performed based on the future available quantity and the future demand, combined with user behavior patterns within the area to be coordinated, to obtain the energy supply and demand situation analysis results, including: The future available quantity and the future demand quantity are calculated using different time periods and different regions as scales to obtain the energy base flow ratio of each region at different time periods; For each time period in each region, the energy base flow ratio is adjusted based on the activity level of each user behavior pattern to obtain the first adjusted energy flow ratio; The first adjusted energy flow ratio is adjusted based on the energy fluctuation coefficient to obtain the second adjusted energy flow ratio; the energy fluctuation coefficient is determined based on the sum of energy flows and the sum of average energy flows. The second adjusted energy flow ratio is adjusted based on the energy flow ratio of adjacent regions and adjacent time periods to obtain the third adjusted energy flow ratio; If the third adjusted energy flow ratio in the target area during the target time period is less than the first energy flow ratio threshold, then the energy supply and demand situation analysis result for the target area during the target time period is determined to be that the energy supply is in a tight state; if the third adjusted energy flow ratio in the target area during the target time period is greater than the second energy flow ratio threshold, then the energy supply and demand situation analysis result for the target area during the target time period is determined to be that the energy supply is in a surplus state; the second energy flow ratio threshold is greater than the first energy flow ratio threshold.

3. The method for multi-energy coordinated planning of power distribution networks based on big data as described in claim 1, characterized in that, The energy storage parameters include the upper limit of charging and discharging power and the current energy storage capacity; The scheduling decision based on the energy supply and demand situation analysis results, the bidirectional energy flow path, and the energy storage parameters of the energy storage devices in the area to be coordinated, to obtain the energy storage device scheduling results, includes: Based on the upper limit of charging and discharging power and the current energy storage capacity of each energy storage device in each time period, an initial state matrix of each energy storage device in each time period is constructed. Based on the energy supply and demand situation analysis results of each time period, the first influence coefficient of the energy storage potential of each energy storage device is adjusted to obtain the first updated state matrix of each energy storage device in each time period. Based on the bidirectional energy flow path in each time period, the target energy flow path adjacent to the area where each energy storage device is located is determined. Based on the energy flow amount of each path in the target energy flow path in each time period, the second influence coefficient on the energy storage device, and the total energy flow of all paths in each time period, the state influence coefficient of the target energy flow path on each energy storage device in each time period is determined. Based on the state influence coefficient of each energy storage device in each time period, the first updated state matrix for each time period is adjusted to obtain the second updated state matrix for each energy storage device in each time period; Scheduling decisions are made for each energy storage device based on its second updated state matrix and energy demand for each time period, resulting in the energy storage device scheduling results for each time period.

4. The method for multi-energy coordinated planning of power distribution networks based on big data as described in claim 3, characterized in that, The scheduling decision for each energy storage device is made based on the second updated state matrix and energy demand of each device in each time period, resulting in the energy storage device scheduling result for each time period, including: Determine the energy supply path for supplying energy to the area where each energy storage device is located during each time period, and the energy acquisition path for obtaining energy from the area where each energy storage device is located during each time period; Based on the energy demand for each time period, the second updated state matrix of each energy storage device for each time period, and the positive energy flow of each path in the energy supply path and energy acquisition path of each energy storage device for each time period, the charging and discharging demand coefficient of each energy storage device for each time period is determined. Based on the charging and discharging demand coefficient of each energy storage device in each time period, the status of the energy storage device, and the second updated status matrix, the preliminary scheduling results of each energy storage device in each time period are determined. The initial scheduling results are adjusted based on the maximum state of charge, minimum state of charge, and state of charge in the previous time period for each energy storage device, to obtain the energy storage device scheduling results for each energy storage device in each time period.

5. The method for multi-energy coordinated planning of power distribution networks based on big data according to claim 1, characterized in that, The method of controlling the coordinated operation of multiple energy devices based on the energy supply and demand situation analysis results and the energy storage device scheduling results includes: Based on the operating parameters of multiple energy devices in the area to be coordinated in each time period, an initial equipment operation matrix is ​​constructed for each energy device in each time period; the operating parameters include rated power, energy conversion efficiency, and equipment operating temperature; The initial equipment operation matrix is ​​adjusted based on the energy supply and demand situation analysis results for each time period to obtain the first adjusted equipment operation matrix for each energy device in each time period; Based on the energy storage device scheduling results, charging and discharging status, and the third influence coefficient on the operating parameters of each energy device in each time period, the first adjusted device operation matrix is ​​adjusted to obtain the second adjusted device operation matrix. Based on the second adjusted equipment operation matrix of each energy device in each time period, the cooperative operation adaptability of different energy devices in each time period is determined; Controlling the operation of multiple energy devices based on the compatibility of their coordinated operation in each time period.

6. The method for multi-energy coordinated planning of power distribution networks based on big data according to claim 5, characterized in that, The control of multi-energy device operation based on the adaptive coordination of different energy devices in each time period includes: Based on the compatibility of different energy devices in each time period and the importance of each energy device, the priority of collaborative operation of different energy device combinations in each time period is determined. Based on the energy demand in each time period, the priority of coordinated operation of different energy equipment combinations in each time period, and the operating status of energy equipment in each energy equipment combination, the initial coordinated operation power of each energy equipment in each time period is determined. The initial coordinated operating power is adjusted based on the grid capacity limit, heating network carrying capacity limit, and equipment heating conversion efficiency of each energy device in each time period as operating constraints, so as to obtain the final coordinated operating power of each energy device in each time period; The operation of multiple energy devices is controlled based on the final coordinated operating power of each energy device in each time period.

7. The method for multi-energy coordinated planning of distribution networks based on big data according to any one of claims 1 to 6, characterized in that, Based on the energy supply and demand situation analysis results, energy planning is carried out with the goal of minimizing energy transmission losses and meeting basic load requirements. This includes determining bidirectional energy flow paths from the supply side to the demand side, including: Based on the energy supply and demand situation analysis results, the initial energy transmission path from the supply side to the demand side is determined; the initial energy transmission path includes connections between different geographical regions and transmission links for different energy carriers. For each initial energy transmission path, an initial transmission loss tendency value is determined based on the physical characteristics and energy carrier characteristics of each transmission link in the path. The initial transmission loss tendency value is then corrected based on the energy demand of different regions at different times to obtain a first corrected loss tendency value. The first modified loss tendency value is corrected based on the forward and reverse energy flow of the path to obtain the second modified loss tendency value. The minimum value of the ratio of the maximum transmission capacity to the load demand in each time period is determined as the load satisfaction index of the path in meeting the basic load demand at the demand end in different time periods. Candidate energy transmission paths are determined based on the second corrected loss tendency value and load fulfillment capacity index of each initial energy transmission path. For each candidate energy transmission path, an objective function is constructed based on the second corrected loss tendency value and the energy transmission state variables in different time periods, and constraints are constructed based on the energy transmission state variables, maximum transmission capacity and load demand in different time periods. The objective function of each candidate energy transmission path is solved based on the constraints of that path to obtain the optimal energy flow path, which is then defined as a bidirectional energy flow path from the supply side to the demand side.

8. A multi-energy coordinated planning system for power distribution networks based on big data, characterized in that, The method for multi-energy coordinated planning of distribution networks based on big data, as described in any one of claims 1 to 7, is applied; the system for multi-energy coordinated planning of distribution networks based on big data includes: The demand forecasting and analysis module is used to forecast and analyze the availability of energy resources and the load demand of energy load within the coordinated area covered by the distribution network, so as to obtain the future availability and future demand. The supply and demand situation analysis module is used to analyze the energy supply and demand distribution situation based on the future available quantity and the future demand quantity, combined with the user behavior patterns in the area to be coordinated, and to obtain the energy supply and demand situation analysis results. The energy path planning module is used to perform energy planning based on the energy supply and demand situation analysis results, with the goal of minimizing energy transmission losses and meeting the basic load demand, and to determine the bidirectional energy flow path from the supply side to the demand side. The energy dispatch decision module is used to make dispatch decisions based on the energy supply and demand situation analysis results, the bidirectional energy flow path and the energy storage parameters of the energy storage devices in the area to be coordinated, and to obtain the energy storage device dispatch results. The equipment coordination operation module is used to control the coordinated operation of multiple energy devices based on the energy supply and demand situation analysis results and the energy storage device scheduling results.

9. An electronic device, comprising: Memory, used to store computer software programs; A processor for reading and executing the computer software program, characterized in that, when the processor executes the computer software program, it implements the multi-energy coordinated planning method for power distribution networks based on big data as claimed in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium, wherein a computer software program is stored therein, characterized in that, When the computer software program is executed by the processor, it implements the big data-based multi-energy coordinated planning method for power distribution networks as described in any one of claims 1 to 7.

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