A park multi-energy resource scheduling method, system, equipment and medium

Abstract

The present application provides a method, system, equipment and medium for multi-energy resource scheduling in a park. The method determines the energy consumption complementarity between various energy sources in the park according to the complementary relationship of each energy consumption, and then constructs a supply relationship map of energy scheduling in the park through all the energy consumption complementarity and the conflict relationship of each energy consumption; constructs a demand relationship map of energy scheduling in the park according to the topological association between the demand data in the scheduling demand information and the various energy demands in the park; and performs graph fusion of energy reserves in resource scheduling through the supply relationship map and the demand relationship map to obtain the fusion interval of various energy reserves in the park, and dynamically schedules the multi-energy resources in the park based on the fusion interval of each energy reserve. Based on the above scheme, the graph fusion based on energy supply and demand information can be realized in the multi-energy resource scheduling of the park, thereby improving the supply and demand matching degree in the multi-energy resource scheduling of the park.

Description

Technical Field

[0001] The present application relates to the technical field of park resource scheduling, and more specifically, to a park multi-energy resource scheduling method, system, equipment and medium. Background Art

[0002] In recent years, with the rapid development of information technology, artificial intelligence, and big data, the scheduling of multi-energy resources in industrial parks has gradually become intelligent and automated. With the help of technologies such as the Internet of Things and machine learning, it is possible to monitor and predict the demand and supply of various types of energy in real time, optimize scheduling strategies, and provide new solutions for industrial park energy management, thereby improving the efficiency and reliability of energy scheduling.

[0003] In the multi-energy resource scheduling of industrial parks, traditional scheduling strategies often rely on historical data for demand forecasting. However, due to the large temporal and spatial volatility of energy demand, traditional methods often ignore the uncertainty and diversity of demand, resulting in demand forecast deviations, which in turn leads to energy oversupply or shortage. In addition, traditional scheduling strategies are usually scheduled in a "conservative" manner. In order to ensure the security of energy supply, a large reserve is often set to prevent gaps during peak demand. Although conservative strategies ensure security, they cause excessive redundancy in energy supply and lead to unnecessary waste of resources. The graph fusion method combines multi-dimensional information of energy supply and demand to refine and quantify the supply relationship and demand pattern. Therefore, how to realize graph fusion based on energy supply and demand information in industrial park multi-energy resource scheduling, thereby improving the supply and demand matching in industrial park multi-energy resource scheduling, is a difficult problem faced by the industry. Summary of the Invention

[0004] The present application provides a method, system, equipment and medium for multi-energy resource scheduling in a park, which can realize graph fusion based on energy supply and demand information in multi-energy resource scheduling in the park, thereby improving the supply and demand matching degree in multi-energy resource scheduling in the park.

[0005] In a first aspect, the present application provides a method for scheduling multi-energy resources in a park, comprising:

[0006] Collect resource supply information and scheduling demand information of various energy sources in the park;

[0007] Extracting the complementary and conflicting relationships between energy consumptions of various energy sources from the resource supply information, determining the complementary amounts of energy consumptions between various energy sources in the park based on the complementary relationships of each energy consumption, and then constructing a supply relationship map for energy scheduling in the park based on all the complementary amounts of energy consumptions and the conflicting relationships of each energy consumption;

[0008] Determine the demand pattern of the multi-energy resources in the park according to the demand data in the scheduling demand information, and construct a demand relationship map of energy scheduling in the park through the topological association relationship between the demand pattern and various energy demands in the park;

[0009] The energy reserves in resource scheduling are graph-fused through the supply relationship graph and the demand relationship graph to obtain the fusion intervals of various energy reserves in the park, and the multi-energy resources of the park are dynamically scheduled based on the fusion intervals of various energy reserves.

[0010] In some embodiments, extracting the complementary relationship and conflict relationship of energy consumption between various energy sources from the resource supply information specifically includes:

[0011] Extracting energy consumption complementation and conflict loss between various energy sources from the resource supply information;

[0012] Determine the complementary relationship between energy consumption of various energy sources through all complementary amounts of energy consumption;

[0013] The conflict relationship of energy consumption between various energy sources is determined through all conflicting losses.

[0014] In some embodiments, constructing a supply relationship map for energy scheduling in a park by analyzing all energy consumption complements and conflict relationships among various energy consumptions specifically includes:

[0015] Obtain the topological structure of energy supply in the park's multi-energy resources;

[0016] Extract the conflict loss between various energy sources from the conflict relationship of each energy consumption;

[0017] The relationship edges between the nodes in the topological structure of the energy supply are strength-constrained by means of the conflicting loss amounts and the complementary energy consumption amounts, thereby obtaining a supply relationship graph for energy scheduling in the park.

[0018] In some embodiments, determining the demand pattern of the multi-energy resource in the park according to the demand data in the scheduling demand information specifically includes:

[0019] Extracting temporal and spatial distribution characteristics of various energy demands from the demand data in the scheduling demand information;

[0020] The demand pattern of the park's multi-energy resources is determined through all temporal distribution characteristics and all spatial distribution characteristics.

[0021] In some embodiments, constructing a demand relationship map for energy scheduling in a park through the topological association between the demand pattern and various energy demands in the park specifically includes:

[0022] Identifying peak and valley characteristics and regional load characteristics of various energy demands in the park through the demand pattern;

[0023] Extract the topological structure and correlation value between energy demands in the park from the topological correlation relationship between various energy demands in the park;

[0024] By using all the associated values, peak and valley characteristics of various energy demands and regional load characteristics, the relationship edges between the nodes in the topological structure are constrained in strength, thereby obtaining a demand relationship map for energy scheduling in the park.

[0025] In some embodiments, the dynamic scheduling of the park's multi-energy resources based on the fusion interval of each energy reserve specifically includes:

[0026] For various energy sources in the park, when the energy reserves in the park are not within the fusion range of the reserves, the energy reserves in the park are replenished, thereby completing the dynamic scheduling of the park's multi-energy resources.

[0027] In some embodiments, the park's multi-energy resources include electricity, heat, cold, natural gas, and solar energy.

[0028] In a second aspect, the present application provides a campus multi-energy resource scheduling system, comprising:

[0029] The collection module is used to collect resource supply information and scheduling demand information of various energy sources in the park;

[0030] a processing module configured to extract the complementary and conflicting relationships between energy consumptions of various energy sources from the resource supply information, determine the complementary amounts of energy consumptions between various energy sources in the park based on the complementary relationships of the energy consumptions, and then construct a supply relationship map for energy scheduling in the park based on all the complementary amounts of energy consumptions and the conflicting relationships of the energy consumptions;

[0031] The processing module is further configured to determine a demand pattern for multi-energy resources in the park based on the demand data in the scheduling demand information, and to construct a demand relationship map for energy scheduling in the park based on the topological association between the demand pattern and various energy demands in the park;

[0032] The execution module is used to perform graph fusion on the energy reserves in resource scheduling through the supply relationship graph and the demand relationship graph, obtain the fusion interval of various energy reserves in the park, and dynamically schedule the multi-energy resources of the park based on the fusion interval of each energy reserve.

[0033] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the above-mentioned campus multi-energy resource scheduling method.

[0034] In a fourth aspect, the present application provides a computer-readable storage medium, in which instructions or codes are stored. When the instructions or codes are run on a computer, the computer implements the above-mentioned campus multi-energy resource scheduling method when executing.

[0035] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects:

[0036] The present application provides a method, system, device and medium for scheduling multi-energy resources in a park, which collects resource supply information and scheduling demand information of various energy sources in the park; extracts the complementary relationship and conflict relationship of energy consumption between various energy sources from the resource supply information, determines the energy consumption complementarity between various energy sources in the park based on the complementary relationship of each energy consumption, and then constructs a supply relationship map of energy scheduling in the park through all the energy consumption complementarity and the conflict relationship of each energy consumption; determines the demand pattern of multi-energy resources in the park based on the demand data in the scheduling demand information, and constructs a demand relationship map of energy scheduling in the park through the topological association relationship between the demand pattern and various energy demands in the park; performs graph fusion of energy reserves in resource scheduling through the supply relationship map and the demand relationship map to obtain the fusion interval of various energy reserves in the park, and dynamically schedules multi-energy resources in the park based on the fusion interval of each energy reserve.

[0037] It can be seen that in this application, the energy reserves in resource scheduling are fused through the supply relationship map and the demand relationship map, and the fusion intervals of various energy reserves in the park are obtained. The multi-energy resources of the park are dynamically adjusted based on the fusion intervals of each energy reserve. First, the process of determining the supply relationship map extracts the energy consumption complementarity and conflict relationship between various energy sources from the resource supply information, and then calculates the energy supply status and the interaction mode between supply and demand in the multi-energy resources of the park. The complementary amount of energy consumption reflects the synergistic relationship between different energy sources, while the conflict relationship reveals the resource conflicts that may arise when different energy sources are used at the same time. The supply strategies of various energy sources can be dynamically adjusted to minimize resource waste and conflict phenomena. The supply relationship map can provide a dynamic and adjustable energy supply framework for the park, so that the synergy between different energy sources can be achieved. can be fully utilized, thereby improving the supply and demand matching in the park's multi-energy resource scheduling; then, the establishment of the demand relationship map can accurately reflect the spatiotemporal characteristics, regional load characteristics and peak-valley fluctuation laws of each energy demand, so that it can identify energy demand patterns in different time periods and regions, and then provide suppliers with accurate demand forecasts, which will help the park to rationally plan energy supply, avoid energy supply imbalance, ensure that the supply can quickly respond to demand fluctuations during peak hours, and reduce unnecessary reserves and waste during periods of low demand. In addition, the demand relationship map can help the park flexibly adjust resource allocation strategies to adapt to the changing demand environment, and further improve the supply and demand matching of energy scheduling. In summary, based on the above scheme, the map fusion based on energy supply and demand information can be realized in the park's multi-energy resource scheduling, thereby improving the supply and demand matching in the park's multi-energy resource scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0039] Figure 1 is an exemplary flow chart of a campus multi-energy resource scheduling method according to some embodiments of the present application;

[0040] Figure 2 is a schematic diagram of the process of multi-energy resource scheduling in a park according to some embodiments of the present application;

[0041] Figure 3 is a schematic diagram of a process for implementing graph fusion according to some embodiments of the present application;

[0042] Figure 4is a structural diagram of a campus multi-energy resource scheduling system according to some embodiments of the present application;

[0043] Figure 5 It is a structural diagram of a computer device for implementing a campus multi-energy resource scheduling method according to some embodiments of the present application. DETAILED DESCRIPTION

[0044] In order to better understand the technical solution of the present application, the technical solution of the present application will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0045] refer to Figure 1 , which is an exemplary flow chart of a campus multi-energy resource scheduling method according to some embodiments of the present application. The campus multi-energy resource scheduling method mainly includes the following steps:

[0046] In step 101, resource supply information and scheduling demand information of various energy sources in the park are collected.

[0047] It should be noted that in this application, resource supply information represents the source, scale and time of energy supply in the park, and scheduling demand information represents the demand scale and time of energy consumption. The park's multi-energy resources include electricity, heat, cold energy, natural gas and solar energy.

[0048] In specific implementation, for various energy sources in the park, the supply records and demand records of energy within a specified time period (the default is the most recent month) can be collected, and the collection of all supply records can be used as the energy resource supply information, and the collection of all demand records can be used as the energy scheduling demand information. The resource supply information and scheduling demand information of various energy sources in the park can be obtained in the above manner. Among them, the supply record contains the supply replenishment amount and mutual loss amount of other energy sources in each supply. The supply replenishment amount represents the complementary value between different energy demands in the park resource scheduling. For example, there is demand complementarity between solar power generation and electricity in the power grid during low periods. The mutual loss amount represents the energy loss caused by mutual competition and resource surplus between different energy sources in the park. For example: when there is sufficient natural gas supply, excessive load on the power grid may lead to excessive electricity production and energy waste. There is a certain conflict loss between natural gas and electricity.

[0049] In some embodiments, reference Figure 2The figure is a schematic diagram of the multi-functional resource scheduling process for a campus, as shown in some embodiments of this application. This figure demonstrates an efficient resource management mechanism that optimizes resource allocation and configuration through real-time monitoring and business forecasting. First, the system uses real-time monitoring to track current resource usage and uses business forecasting to estimate short-term resource demand. This information is used to guide resource allocation and configuration decisions, ensuring that resources are properly scheduled based on user needs and system load.

[0050] In campus multi-energy resource scheduling, resources are divided into two categories: available resources and dormant resources. Available resources are currently available for allocation to users, while dormant resources are temporarily unused and on standby. Through intelligent management by the resource scheduling module, these resources are efficiently allocated to the virtual resource pool to meet user needs. As a centralized resource management platform, the virtual resource pool dynamically adjusts resource allocation based on real-time monitoring and business forecasting, ensuring efficient resource utilization and stable system operation.

[0051] In step 102, the complementary relationship and conflict relationship of energy consumption between various energy sources are extracted from the resource supply information, and the energy consumption complementarity between various energy sources in the park is determined based on the complementary relationship of each energy consumption, and then the supply relationship map of energy scheduling in the park is constructed through all the energy consumption complementarity and the conflict relationship of each energy consumption.

[0052] In some embodiments, extracting the complementary and conflicting relationships between energy consumptions of various energy sources from the resource supply information may be achieved by using the following steps:

[0053] Extracting energy consumption complementation and conflict loss between various energy sources from the resource supply information;

[0054] Determine the complementary relationship between energy consumption of various energy sources through all complementary amounts of energy consumption;

[0055] The conflict relationship of energy consumption between various energy sources is determined through all conflicting losses.

[0056] It should be noted that in this application, the conflict relationship represents the impact intensity of resource competition between different energy sources, and the conflict loss amount represents the energy loss caused by mutual competition and resource surplus between different energy sources in the park; the complementary relationship represents the mutual dependence between different energy supplies in the park, and the energy consumption complementarity amount represents the mutual complementation between different energy sources in the park.

[0057] In specific implementation, first, for various energy sources in the resource supply information, the mean of all supply supplementary amounts between the energy and other energy sources is extracted from each supply record of the resource supply information as the energy consumption complementarity amount, and the mean of all mutual losses between the energy and other energy sources is extracted from each supply record of the resource supply information as the conflict loss amount. Through the above method, the energy consumption complementarity and conflict loss amount between various energy sources and other energy sources can be obtained, and the energy consumption complementarity and conflict loss amount between various energy sources can be obtained; then, the set of all energy consumption complementarity amounts can be used as the complementary relationship of energy consumption between various energy sources; finally, the set of all conflict loss amounts can be used as the conflict relationship of energy consumption between various energy sources.

[0058] In some embodiments, the energy consumption complementarity between various energy sources in the park can be determined based on the complementary relationship between each energy consumption, which can be achieved in the following way: for various energy sources in the park, the energy consumption complementarity between each energy and other energy sources is obtained from the complementary relationship between each energy consumption. The energy consumption complementarity between various energy sources and other energy sources can be obtained through the above method, and the energy consumption complementarity between various energy sources in the park can be obtained.

[0059] It should be noted that the complementary amount of energy consumption reflects the synergistic relationship between different energy sources, while the conflict relationship reveals the resource conflicts that may arise when certain energy sources are used at the same time. The supply strategies of various energy sources can be dynamically adjusted to minimize resource waste and conflict. Specifically, when the supply of a certain energy source exceeds the demand, the system can adjust the energy flow and allocate redundant resources to other scarce energy sources in a priority manner, optimize the supply and demand relationship, and thus avoid energy supply shortages or excessive waste.

[0060] In some embodiments, constructing a supply relationship map for energy scheduling in a park by comparing all energy consumption complements and conflict relationships between various energy consumptions can be achieved by the following steps:

[0061] Obtain the topological structure of energy supply in the park's multi-energy resources;

[0062] Extract the conflict loss between various energy sources from the conflict relationship of each energy consumption;

[0063] The relationship edges between the nodes in the topological structure of the energy supply are strength-constrained by means of the conflicting loss amounts and the complementary energy consumption amounts, thereby obtaining a supply relationship graph for energy scheduling in the park.

[0064] It should be noted that in this application, the supply relationship map is a map that represents the supply relationship between multiple energy resources in the park; the topological structure of energy supply refers to the distribution nodes of various energy sources in the park and the interaction relationship between the distribution nodes. The nodes of the topological structure represent different energy sources, and the edges represent the relationship between energy flow or energy conversion.

[0065] In specific implementation, first, the topological structure of the energy supply in the multi-energy resources of the park can be obtained from the central control console of the park; then, the conflict loss between various energy sources is extracted from the conflict relationship of each energy consumption; finally, for the relationship edge between each node in the topological structure of the energy supply, the energy corresponding to the two nodes of the relationship edge is obtained, and the difference between the complementary amount and the conflict loss between the two energy sources can be used as the strength constraint value of the relationship edge. The strength constraint value of the relationship edge between each node in the topological structure of the energy supply can be obtained in the above way, thereby completing the strength constraint of the relationship edge in the topological structure of the energy supply, and the topological structure of the energy supply after the strength constraint can be used as the supply relationship map of energy scheduling in the park.

[0066] In step 103, the demand pattern of the multi-energy resources in the park is determined according to the demand data in the scheduling demand information, and a demand relationship map of energy scheduling in the park is constructed through the topological association relationship between the demand pattern and various energy demands in the park.

[0067] In some embodiments, determining the demand pattern of the multi-energy resources in the park based on the demand data in the scheduling demand information can be achieved by using the following steps:

[0068] Extracting temporal and spatial distribution characteristics of various energy demands from the demand data in the scheduling demand information;

[0069] The demand pattern of the park's multi-energy resources is determined through all temporal distribution characteristics and all spatial distribution characteristics.

[0070] It should be noted that, in this application, the demand pattern refers to the overall regularity of various energy demands in the park in time and space; the time domain distribution characteristics represent the changes in various energy demands in the park in different time periods, and the time domain distribution characteristics reflect the fluctuation characteristics of energy demand over time, such as: peak hours, low hours and periodic fluctuations; the spatial distribution characteristics represent the spatial differences in various energy demands in the park between different regions or different energy users, and the spatial distribution characteristics describe the spatial distribution characteristics of energy demand, such as some areas have higher demand for electricity or heat energy, while other areas are relatively low.

[0071] In specific implementation, first, for various energy sources, the set of energy demand records in the scheduling demand information can be used as demand data, so that the time series analysis algorithm can be used to extract the peak period, low period, periodic fluctuation and regional demand of each demand record in the demand data. The set of all peak periods, low period and periodic fluctuations can be used as the time domain distribution characteristics of energy demand, and a clustering algorithm (for example: K-means clustering) can be used to cluster the regional demand of different areas in the park in the demand data, so as to identify the regional demand of cluster areas in different cluster clusters. The set of all regional demand can be used as the spatial distribution characteristics of energy demand. The time domain distribution characteristics and spatial distribution characteristics of various energy demands can be obtained in the above manner; then, the set of all time domain distribution characteristics and all spatial distribution characteristics can be used as the demand pattern of multi-energy resources in the park.

[0072] In some embodiments, constructing a demand relationship map for energy scheduling in a park by using the topological association between the demand pattern and various energy demands in the park can be achieved by the following steps:

[0073] Identifying peak and valley characteristics and regional load characteristics of various energy demands in the park through the demand pattern;

[0074] Extract the topological structure and correlation value between energy demands in the park from the topological correlation relationship between various energy demands in the park;

[0075] By using all the associated values, peak and valley characteristics of various energy demands and regional load characteristics, the relationship edges between the nodes in the topological structure are constrained in strength, thereby obtaining a demand relationship map for energy scheduling in the park.

[0076] It should be noted that in this application, the demand relationship map; the topological association relationship refers to the distribution nodes of various energy demands in the park and the demand association between the distribution nodes; the topological structure represents the distribution structure of the energy demand nodes in the park; the association value; the peak and valley characteristics reflect the intensity and regularity of demand fluctuations in the park; the regional load characteristics represent the distribution characteristics of energy demand in each area of ​​the park.

[0077] In specific implementation, first, for various energy demands, the time domain distribution characteristics and spatial distribution characteristics of energy demands are obtained from the demand pattern, and then the overlapping periods are screened out from all peak periods in the time domain distribution characteristics as peak sub-features, and the overlapping periods are screened out from all valley periods in the time domain distribution characteristics as underestimated sub-features. The collection of peak sub-features and valley sub-features can be used as the peak and valley characteristics of energy demand, and the mean of all regional demands can be obtained from the spatial distribution characteristics as the regional load characteristics of energy demand; then, the topological association relationship of energy supply in the multi-energy resources of the park can be obtained from the central console of the park, and the spatial structure of the distribution nodes of various energy demands in the topological association relationship can be used as the topological structure between the energy demands in the park, and the topological association relationship can be used as the topological structure between the energy demands in the park. The initial quantitative value of the demand association between distribution nodes is used as the association value between energy demands in the park; finally, a shortest path model based on graph theory algorithm is initialized, and the peak and valley characteristics of various energy demands can be used as the time domain constraint adjustment in the shortest path model, and the regional load characteristics of various energy demands can be used as the spatial constraint adjustment in the shortest path model. The topological structure is used as the constraint target of the shortest path model, and each association value is used as the initial path length of the constraint target in the shortest path model. The shortest path model is used to perform strength constraints on the relationship edges between each node in the constraint target. The results of the strength constraints of the shortest path model can be updated to the relationship edges in the topological structure, so that the updated topological structure can be used as the demand relationship map for energy scheduling in the park.

[0078] It should be noted that the shortest path model is a graph theory algorithm model that aims to meet the time domain, spatial constraints and topological structure goals in the multi-energy resource scheduling of the park by optimizing the path length. The shortest path model uses the peak and valley characteristics of energy demand as time domain constraints, and the regional load characteristics of energy demand as spatial constraints. It uses the topological structure of the park as the constraint target, and calculates the shortest path between each node through the shortest path algorithm. During the calculation process, the association value is used as the initial path length of each relationship edge in the constraint target. The shortest path algorithm optimizes energy flow and scheduling efficiency by adjusting the strength of the path (for example, distance). Finally, by updating the strength value of the relationship edge in the topological structure, a new demand relationship map is formed to help more accurately reflect the dynamic changes and optimization results of energy scheduling in the park.

[0079] In step 104, the energy reserves in resource scheduling are graph-fused through the supply relationship graph and the demand relationship graph to obtain the fusion intervals of various energy reserves in the park, and the multi-energy resources of the park are dynamically scheduled based on the fusion intervals of each energy reserve.

[0080] In some embodiments, the energy reserves in resource scheduling are integrated through the supply relationship map and the demand relationship map to obtain the integration interval of various energy reserves in the park. Figure 3 The figure is a schematic diagram of the process of implementing graph fusion in some embodiments of the present application. In this embodiment, graph fusion can be implemented using the following steps:

[0081] In step 1041, the supply influencing factor and demand influencing factor of energy reserves in resource scheduling are determined based on the supply and demand relationship of various energy sources in the park;

[0082] In step 1042, fusion information of various energy reserves in the park is determined based on the supply influencing factor and the demand influencing factor;

[0083] In step 1043, the supply relationship map and the demand relationship map are fused based on the fusion information to obtain the fusion interval of various energy reserves in the park.

[0084] It should be noted that, in this application, the supply impact factor refers to the degree of influence of the energy supply situation in the park on the reserve demand; the demand impact factor refers to the degree of influence of the fluctuation of energy demand in the park on the energy reserve; the supply and demand relationship represents the mutual dependence and influence relationship between the energy supply situation and demand pattern in the park, and the supply and demand relationship includes energy redundancy, supply adequacy, demand volatility and demand concentration. Energy redundancy represents the proportion of the redundant part in the energy supply of the park. Energy redundancy = (supply - demand) / demand. When calculating specifically, the supply and demand at each moment in the historical records can be collected to calculate the average of a large number of energy redundancies. As the energy redundancy in the supply and demand relationship; the degree to which the energy supply in the park can meet the demand, supply adequacy = total supply / total demand. When calculating specifically, the total supply and total demand within a specified time period (the default is the most recent day) can be collected for calculation; demand volatility reflects the intensity of changes in energy demand in the park over time. Demand in areas with greater volatility is unstable and requires more reserves and scheduling flexibility. The standard deviation of all collected demands can be used as demand volatility; demand concentration represents the spatial distribution characteristics of energy demand in the park. The ratio of the standard deviation of all collected demands to the mean can be used as demand concentration.

[0085] In specific implementation, first, the supply influencing factor and demand influencing factor of energy reserves in resource scheduling are determined through the supply and demand relationship of various energy sources in the park. This can be achieved in the following way, namely: for various energy sources in the park, the energy redundancy, supply adequacy, demand volatility and demand concentration of energy can be obtained from the supply and demand relationship of energy. The ratio of supply adequacy to energy redundancy can be used as the impact of energy reserves on supply, and the ratio of demand concentration to demand volatility can be used as the impact of energy reserves on demand. The impact of various energy reserves on supply and demand can be obtained in the above way. The set of the impact of all energy reserves on supply is used as the value range of the supply influencing factor of energy reserves in resource scheduling, and the supply influencing factor of energy reserves in resource scheduling can be obtained. The set of the impact of all energy reserves on demand is used as the value range of the demand influencing factor of energy reserves in resource scheduling, and the demand influencing factor of energy reserves in resource scheduling can be obtained.

[0086] Then, in the specific implementation, the fusion information of various energy reserves in the park determined according to the supply influencing factor and the demand influencing factor can be achieved in the following way, namely: all value sets of the supply influencing factor and all value sets of the demand influencing factor are used as the fusion information of various energy reserves in the park; it should be noted that, in this application, the fusion information refers to the weight information of the impact of the supply and demand relationship of various energy sources in the park on the reserve.

[0087] Finally, in a specific implementation, the supply relationship map and the demand relationship map are fused based on the fusion information to obtain the fusion interval of various energy reserves in the park. This can be achieved in the following manner: for various energy sources in the park, one energy source is selected from other energy sources as a comparison energy source, the strength value of the relationship edge between the energy corresponding node and the comparison energy corresponding node is obtained from the supply relationship map as the supply relationship value between the energy and the comparison energy, the strength value of the relationship edge between the energy corresponding node and the comparison energy corresponding node is obtained from the demand relationship map as the demand relationship value between the energy and the comparison energy, the value of the supply impact factor and the value of the demand impact factor of the energy are obtained from the fusion information, and the two values ​​are used as the weights of the supply relationship value and the demand relationship value respectively, and the sum of the weights of the supply relationship value and the demand relationship value is calculated as the fusion value of the energy in the park and the comparison energy. Repeat the above steps to select other energy sources for comparison to obtain the fusion value between the energy and other energy sources. The range from the minimum value to the maximum value of all fusion values ​​can be used as the fusion interval of the energy reserve. The fusion interval of various energy reserves can be obtained in the above manner, wherein the fusion interval reflects the reasonable range of various resource reserves in energy scheduling.

[0088] In some embodiments, dynamic scheduling of multi-energy resources in a park based on the fusion interval of each energy reserve can be achieved by the following steps:

[0089] For various energy sources in the park, when the energy reserves in the park are not within the fusion range of the reserves, the energy reserves in the park are replenished, thereby completing the dynamic scheduling of the park's multi-energy resources.

[0090] In specific implementation, for various energy sources in the park, when the energy reserves in the park are within the fusion range of the reserves, the reserves are maintained; when the energy reserves in the park are not within the fusion range of the reserves, the energy reserves in the park are supplemented until the energy reserves in the park are within the fusion range of the reserves. The dynamic scheduling of the park's multi-energy resources can be completed through the above-mentioned method.

[0091] In addition, in another aspect of the present application, in some embodiments, the present application provides a park multi-energy resource scheduling system, referring to Figure 4 This figure is a schematic diagram of the structure of a campus multi-energy resource scheduling system according to some embodiments of the present application. The campus multi-energy resource scheduling system includes: a collection module 201, a processing module 202 and an execution module 203, which are described as follows:

[0092] Collection module 201, in this application, the collection module 201 is mainly used to collect resource supply information and scheduling demand information of various energy sources in the park;

[0093] Processing module 202, in this application, is used to extract the complementary and conflicting relationships between energy consumptions of various energy sources from the resource supply information, determine the energy consumption complementary amounts between various energy sources in the park based on the complementary relationships of each energy consumption, and then construct a supply relationship map for energy scheduling in the park based on all the energy consumption complementary amounts and the conflicting relationships of each energy consumption;

[0094] It should be noted that the processing module 202 is further configured to determine a demand pattern for multi-energy resources in the park based on the demand data in the scheduling demand information, and to construct a demand relationship map for energy scheduling in the park based on the topological association between the demand pattern and various energy demands in the park.

[0095] Execution module 203. In this application, execution module 203 is mainly used to perform graph fusion of energy reserves in resource scheduling through the supply relationship graph and the demand relationship graph, obtain the fusion interval of various energy reserves in the park, and dynamically schedule the multi-energy resources of the park based on the fusion interval of each energy reserve.

[0096] The above describes in detail the examples of the campus multi-energy resource scheduling method, system, equipment and medium provided by the embodiments of the present application. It can be understood that in order to realize the above functions, the corresponding device includes a hardware structure and / or software module corresponding to the execution of each function. It should be easily appreciated by those skilled in the art that, in combination with the units and algorithm steps of each example described in the embodiments disclosed herein, the present application can be implemented in the form of hardware or a combination of hardware and computer software. Whether a function is executed in the form of hardware or computer software driving hardware depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0097] In some embodiments, the present application also provides a computer device, which includes a memory and a processor, the memory is used to store computer programs, and the processor is used to call and run the computer programs from the memory, so that the computer device executes the above-mentioned campus multi-energy resource scheduling method.

[0098] In some embodiments, reference Figure 5 The dotted line in the figure indicates that the unit or module is optional. The figure is a structural diagram of a computer device for implementing a multi-energy resource scheduling method for a park according to an embodiment of the present application. The multi-energy resource scheduling method for a park described in the above embodiment can be Figure 5 The computer device shown in the figure is implemented, and the computer device includes at least one processor 301, a memory 302 and at least one communication unit 305. The computer device can be a terminal device, a server or a chip.

[0099] The processor 301 may be a general-purpose processor or a dedicated processor. For example, the processor 301 may be a central processing unit (CPU), which may be used to control the computer device, execute software programs, and process data from the software programs. The computer device may also include a communication unit 305 for inputting (receiving) and outputting (transmitting) signals.

[0100] For example, the computer device may be a chip, the communication unit 305 may be an input and / or output circuit of the chip, or the communication unit 305 may be a communication interface of the chip, and the chip may be a component of a terminal device, a network device, or other device.

[0101] For another example, the computer device may be a terminal device or a server, and the communication unit 305 may be a transceiver of the terminal device or the server, or the communication unit 305 may be a transceiver circuit of the terminal device or the server.

[0102] The computer device may include one or more memories 302, on which a program 304 is stored. The program 304 can be executed by the processor 301 to generate instructions 303, so that the processor 301 executes the method described in the above method embodiment according to the instructions 303. Optionally, data (such as a target audit model) can also be stored in the memory 302. Optionally, the processor 301 can also read data stored in the memory 302. The data can be stored at the same storage address as the program 304, or at a different storage address from the program 304.

[0103] The processor 301 and the memory 302 may be provided separately or integrated together, for example, integrated on a system on chip (SOC) of a terminal device.

[0104] It should be understood that each step of the above method embodiment can be completed by a hardware-based logic circuit or software-based instructions in the processor 301. The processor 301 can be a CPU, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, such as discrete gates, transistor logic devices, or discrete hardware components.

[0105] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0106] For example, in some embodiments, the present application also provides a computer-readable storage medium, which stores instructions or codes. When the instructions or codes are run on a computer, the computer implements the above-mentioned campus multi-energy resource scheduling method when executing.

[0107] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0108] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A multi-energy resource scheduling method for a park, characterized in that: The steps include: Collect resource supply information and scheduling demand information of various energy sources in the park; Extracting the complementary and conflicting relationships between energy consumptions of various energy sources from the resource supply information, determining the complementary amounts of energy consumptions between various energy sources in the park based on the complementary relationships of each energy consumption, and then constructing a supply relationship map for energy scheduling in the park based on all the complementary amounts of energy consumptions and the conflicting relationships of each energy consumption; Determine the demand pattern of the multi-energy resources in the park according to the demand data in the scheduling demand information, and construct a demand relationship map of energy scheduling in the park through the topological association relationship between the demand pattern and various energy demands in the park; By using the supply relationship map and the demand relationship map, the energy reserves in resource scheduling are integrated to obtain the integration intervals of various energy reserves in the park, and the multi-energy resources of the park are dynamically scheduled based on the integration intervals of various energy reserves; The supply relationship map of energy scheduling in the park is constructed through all energy consumption complementarities and conflict relationships of various energy consumptions, specifically including: Obtain the topological structure of energy supply in the park's multi-energy resources; Extract the conflict loss between various energy sources from the conflict relationship of each energy consumption; By using each conflict loss amount and each energy consumption complement amount, the relationship edge of each node in the energy supply topology structure is constrained in strength, thereby obtaining a supply relationship map of energy scheduling in the park; The construction of a demand relationship map for energy scheduling in the park through the topological association between the demand pattern and various energy demands in the park specifically includes: Identifying peak and valley characteristics and regional load characteristics of various energy demands in the park through the demand pattern; Extract the topological structure and correlation value between energy demands in the park from the topological correlation relationship between various energy demands in the park; By using all the correlation values, peak and valley characteristics of various energy demands, and regional load characteristics, the relationship edges between the nodes in the topological structure are constrained in strength, thereby obtaining a demand relationship map for energy scheduling in the park; The energy reserves in resource scheduling are graph-fused by using the supply relationship graph and the demand relationship graph to obtain the fusion intervals of various energy reserves in the park, specifically including: Determine the supply and demand factors of energy reserves in resource scheduling through the supply and demand relationships of various energy sources in the park; Determine fusion information of various energy reserves in the park according to the supply influencing factor and the demand influencing factor; The supply relationship map and the demand relationship map are fused based on the fusion information to obtain the fusion intervals of various energy reserves in the park.

2. The method according to claim 1, wherein Extracting the complementary relationship and conflict relationship of energy consumption between various energy sources from the resource supply information specifically includes: Extracting energy consumption complementation and conflict loss between various energy sources from the resource supply information; Determine the complementary relationship between energy consumption of various energy sources through all complementary amounts of energy consumption; The conflict relationship of energy consumption between various energy sources is determined through all conflicting losses.

3. The method according to claim 1, wherein Determining the demand pattern of the park's multi-energy resources based on the demand data in the scheduling demand information specifically includes: Extracting temporal and spatial distribution characteristics of various energy demands from the demand data in the scheduling demand information; The demand pattern of the park's multi-energy resources is determined through all temporal distribution characteristics and all spatial distribution characteristics.

4. The method according to claim 1, wherein Dynamic scheduling of multi-energy resources in the park based on the fusion interval of each energy reserve includes: For various energy sources in the park, when the energy reserves in the park are not within the fusion range of the reserves, the energy reserves in the park are replenished, thereby completing the dynamic scheduling of the park's multi-energy resources.

5. The method according to claim 1, wherein The multi-energy resources of the park include electricity, heat, cold, natural gas and solar energy.

6. A multi-energy resource scheduling system for a park, which uses the method according to any one of claims 1 to 5 to perform multi-energy resource scheduling for the park, characterized in that: The system includes: The collection module is used to collect resource supply information and scheduling demand information of various energy sources in the park; a processing module configured to extract the complementary and conflicting relationships between energy consumptions of various energy sources from the resource supply information, determine the complementary amounts of energy consumptions between various energy sources in the park based on the complementary relationships of the energy consumptions, and then construct a supply relationship map for energy scheduling in the park based on all the complementary amounts of energy consumptions and the conflicting relationships of the energy consumptions; The processing module is further configured to determine a demand pattern for multi-energy resources in the park based on the demand data in the scheduling demand information, and to construct a demand relationship map for energy scheduling in the park based on the topological association between the demand pattern and various energy demands in the park; The execution module is used to perform graph fusion on the energy reserves in resource scheduling through the supply relationship graph and the demand relationship graph, obtain the fusion interval of various energy reserves in the park, and dynamically schedule the multi-energy resources of the park based on the fusion interval of each energy reserve.

7. A computer device, characterized in that: The computer device includes a memory and a processor, the memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device executes the campus multi-energy resource scheduling method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores instructions or codes, and when the instructions or codes are executed on a computer, the computer implements the campus multi-energy resource scheduling method according to any one of claims 1 to 5.

Images