Distribution network energy management method and related device considering underlying resources

Abstract

The present application discloses a distribution network energy management method and related devices considering underlying resources. The method includes: determining the underlying resources in the target distribution network to obtain type-a underlying resources; determining the first index sets corresponding to each type of underlying resources in the type-a underlying resources to obtain a first index sets; determining a comprehensive performance indicators based on the a first index sets and a preset comprehensive performance indicator formula; aggregating the type-a underlying resources according to the a comprehensive performance indicators to obtain a fast response aggregate; constructing a target energy balance model corresponding to the fast response aggregate; determining a target energy management scheme according to the target energy balance model and a preset management objective; and managing the target distribution network based on the target energy management scheme. By using the present application, precise management of the underlying resources is achieved.

Description

Technical Field

[0001] This application relates to the technical field of distribution network control, and particularly to a distribution network energy management method and related devices considering underlying resources. Background Art

[0002] With the adjustment of the global energy structure and the continuous growth of power demand, the distribution network is facing a series of severe challenges. Traditional distribution networks mainly rely on centralized power generation and one-way power transmission, making it difficult to meet the growing diverse electricity consumption demands and the complexity brought about by the large-scale access of new energy.

[0003] Currently, the management of underlying resources (such as new energy units, energy storage devices, etc.) by the power grid is often too simple, lacking precise management of the characteristics of various resources. Therefore, how to achieve precise management of underlying resources has become an urgent problem to be solved. Summary of the Invention

[0004] The embodiments of this application provide a distribution network energy management method and related devices considering underlying resources, achieving precise management of underlying resources.

[0005] In a first aspect, the embodiments of this application provide a distribution network energy management method considering underlying resources, the method including:

[0006] Determine the underlying resources in the target distribution network to obtain a types of underlying resources; a is an integer greater than 1;

[0007] Determine the first index set corresponding to each type of underlying resource among the a types of underlying resources to obtain a first index sets;

[0008] Based on the a first index sets and a preset comprehensive performance index formula, determine a comprehensive performance indexes;

[0009] Aggregate the a types of underlying resources according to the a comprehensive performance indexes to obtain a fast response aggregate;

[0010] Construct a target energy balance model corresponding to the fast response aggregate;

[0011] According to the target energy balance model and a preset management objective, determine a target energy management plan;

[0012] Manage the target distribution network based on the target energy management plan.

[0013] In a second aspect, the embodiments of this application provide a distribution network energy management device considering underlying resources, the device including: a determination unit, a control unit, and a management unit, where:

[0014] The determining unit is configured to determine the underlying resources in the target distribution network to obtain type-a underlying resources, where a is an integer greater than 1; determine the first index set corresponding to each type of underlying resources in the type-a underlying resources to obtain a first index sets;

[0015] The control unit is configured to determine a comprehensive performance indicators based on the a first index sets and a preset comprehensive performance indicator formula; aggregate the type-a underlying resources according to the a comprehensive performance indicators to obtain a fast response aggregate; construct a target energy balance model corresponding to the fast response aggregate; determine a target energy management solution according to the target energy balance model and a preset management objective;

[0016] The management unit is configured to manage the target distribution network based on the target energy management solution.

[0017] In a third aspect, an embodiment of the present application provides an electronic device, including: a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for executing the steps in the first aspect of the embodiments of the present application.

[0018] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute some or all of the steps described in the first aspect of the embodiments of the present application.

[0019] In a fifth aspect, an embodiment of the present application provides a computer program product, where the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to enable a computer to execute some or all of the steps described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package.

[0020] Implementing the present application has the following beneficial effects:

[0021] It can be seen that in the distribution network energy management method considering underlying resources described in this application, by determining the underlying resources in the target distribution network, type-a underlying resources are obtained; determining the first index set corresponding to each type of underlying resource in the type-a underlying resources to obtain a first index sets; determining a comprehensive performance indicators based on the a first index sets and a preset comprehensive performance indicator formula; aggregating the type-a underlying resources according to the a comprehensive performance indicators to obtain a fast response aggregate; constructing a target energy balance model corresponding to the fast response aggregate; determining a target energy management plan according to the target energy balance model and a preset management target; managing the target distribution network based on the target energy management plan. Thus, by constructing a target energy balance model corresponding to the underlying resources and solving and optimizing the target energy balance model according to the preset management target, an optimal management strategy for the underlying resources, that is, the target energy management plan, is obtained. Therefore, precise management of the underlying resources is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of this application or the background art, the following will describe the drawings required to be used in the embodiments of this application or the background art.

[0023] Figure 1 is a schematic structural diagram of an electronic device provided by an embodiment of this application;

[0024] Figure 2 is a schematic scenario diagram of an electronic device provided by an embodiment of this application;

[0025] Figure 3 is a flowchart of a distribution network energy management method considering underlying resources provided by an embodiment of this application;

[0026] Figure 4 is a schematic structural diagram of a target distribution network provided by an embodiment of this application;

[0027] Figure 5 is a flowchart of another distribution network energy management method considering underlying resources provided by an embodiment of this application;

[0028] Figure 6 is a schematic structural diagram of a first particle swarm provided by an embodiment of this application;

[0029] Figure 7 is a block diagram of the functional units of a distribution network energy management device considering underlying resources provided by an embodiment of this application;

[0030] Figure 8 is a schematic structural diagram of another electronic device provided by an embodiment of this application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] To enable those skilled in the art to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of this application.

[0032] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but optionally further includes steps or units not listed, or optionally further includes other steps or units inherent to these processes, methods, products, or devices.

[0033] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this article indicates that the associated objects before and after are in an "or" relationship. The "multiple" that appears in the embodiments of this application refers to two or more.

[0034] The "at least one (item)" or its similar expression in the embodiments of this application refers to any combination of these items, including any combination of single items (pieces) or plural items (pieces), referring to one or more, and multiple refers to two or more. For example, at least one (item) of a, b, or c can represent the following seven situations: a, b, c, a and b, a and c, b and c, a, b, and c. Among them, each of a, b, and c can be an element or a set containing one or more elements.

[0035] The "connection" that appears in the embodiments of this application refers to various connection methods such as direct connection or indirect connection to achieve communication between devices. This application does not make any limitations on this.

[0036] Referring to "embodiment" in this article means that the specific features, structures, or characteristics described in combination with the embodiment can be included in at least one embodiment of this application. The appearance of this phrase in various positions in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment mutually exclusive with other embodiments. Those skilled in the art explicitly and implicitly understand that the embodiments described herein can be combined with other embodiments.

[0037] The electronic devices described in the embodiments of this application may include smart phones (such as Android phones, iOS phones, Windows Phone phones, etc.), tablet computers, handheld computers, laptop computers, video matrices, monitoring platforms, mobile internet devices (MID), or wearable devices, etc. The above are only examples, not an exhaustive list, including but not limited to the above devices.

[0038] Of course, the above electronic device may also be a server, for example, a cloud server.

[0039] The relevant content, concepts, meanings, technical problems, technical solutions, beneficial effects, etc. involved in the embodiments of this application will be described below.

[0040] First, some professional terms involved in this application will be explained:

[0041] Distribution network: It is an important part of the power system, which distributes electric power from the transmission network or power plant to each power user. The distribution network mainly consists of distribution substations, high-voltage distribution lines, distribution transformers, low-voltage distribution lines, and electric energy metering devices, etc. Its function is to distribute electric energy safely, reliably, and economically to the user side to meet the electricity consumption needs of various users.

[0042] Underlying resources in the distribution network: It refers to various available resources directly facing users or at the end of power distribution in the distribution network. These resources include distributed power sources, such as solar photovoltaic panels, small wind turbines, biomass power generation devices, etc.; energy storage devices, such as lithium-ion battery energy storage systems, lead-acid battery energy storage systems, flow battery energy storage systems, etc.; and various adjustable loads, such as electric vehicles, air conditioners, electric water heaters, etc., which can adjust their own electricity consumption behaviors according to the needs of the power grid.

[0043] Energy balance model: It is a mathematical model used to describe the energy flow and conversion relationship in the distribution network. This model is based on the principle of energy conservation and considers factors such as the electric energy output on the power generation side in the distribution network, power losses during power transmission, energy conversion efficiency in the transformation link, load consumption in the distribution system, and charge and discharge of energy storage devices. By establishing corresponding mathematical expressions, it ensures that the input and output of energy in the distribution network always remain balanced under different operating states, thus providing an important theoretical basis and analysis tool for the planning, operation, and control of the distribution network.

[0044] Particle Swarm Optimization Algorithm: A stochastic search and optimization algorithm based on swarm intelligence. In this algorithm, the solutions to problems are simulated as particles in the search space. Each particle has its own position and velocity, representing a potential solution to the problem. The particles search for the optimal solution in the search space by continuously updating their positions and velocities, and the update is based on the information of the particle's own historical optimal position and the historical optimal position of the entire swarm. The particle swarm optimization algorithm has the advantages of simple principle, fast convergence speed, and easy implementation, and is widely used in the optimization problems of distribution networks, such as economic dispatch of distribution networks, unit commitment, and power grid planning. It can be used to solve complex non-linear optimization problems to improve the operation efficiency and economy of distribution networks.

[0045] Output: Refers to the electric energy power actually generated by power generation equipment (such as thermal power generators, hydroelectric generators, wind turbines, solar photovoltaic panels, etc.) per unit time. For example, a thermal power unit with a rated power of 100 megawatts actually generates 80 megawatts of power at a certain moment, then the output of this unit at this time is 80 megawatts.

[0046] Load: Refers to the total power consumed by all electrical equipment in the power system at a certain moment. The load has obvious time and space characteristics, and the load size and composition vary greatly in different time periods (such as day and night, weekdays and holidays) and different regions. The change of the load will have an important impact on the operation of the power grid. When the load increases, if the output of the power generation equipment cannot keep up in time, it will lead to problems such as a decrease in the power grid frequency and a reduction in voltage; conversely, when the load decreases, there may be a situation of over-generation.

[0047] Please refer to Figure 1 , Figure 1 which is a schematic structural diagram of an electronic device provided by an embodiment of the present application. It can be seen that the electronic device may include: a communication module, a modeling module, a management module, etc., which are not limited herein. Among them:

[0048] The communication module is used to collect underlying resource information, responsible for communicating with various underlying resources in the distribution network, collecting real-time data such as the power generation power of distributed power sources, the state of charge of energy storage devices, and the power consumption of adjustable loads, providing basic information for subsequent analysis and management; it can also transmit control instructions, sending the control instructions generated by the electronic device to the underlying resources, such as adjusting the output of distributed power sources, controlling the charging and discharging of energy storage devices, and regulating the power consumption of adjustable loads, to achieve real-time control of the underlying resources. In addition, it can also communicate with other systems in the distribution network, such as substation automation systems and transmission line monitoring systems, to obtain the overall information of the power grid operation, and at the same time transmit the relevant information of the underlying resources to other systems to achieve information sharing and coordinated operation.

[0049] A modeling module, which is used to construct an energy balance model of the distribution network according to the collected underlying resource data, the structure and operation principle of the distribution network, and in combination with the resource data of the underlying resources, considering links such as the input, conversion, storage, and output of energy, to ensure that the model can accurately reflect the energy flow in the distribution network.

[0050] A management module, which is used to determine the objectives of energy management according to the operation requirements of the distribution network and user needs, such as improving power supply reliability, reducing operation costs, and reducing environmental pollution. Then, based on the energy balance model established by the modeling module, an optimization algorithm is used to calculate the optimal energy management plan to achieve the management objectives, including the scheduling plan of the underlying resources, the charge and discharge strategies of energy storage devices, etc.; finally, the underlying resources are scheduled and managed according to the optimal energy management plan.

[0051] Please refer to Figure 2 , Figure 2 which is a schematic diagram of the scenario of an electronic device provided by an embodiment of the present application. It can be seen that as the operator, the staff can operate the electronic device, and the electronic device can establish a physical or communication connection with the target distribution network to achieve data interaction and obtain the underlying resources and related data in the target distribution network. When the staff needs to manage the target distribution network, the electronic device can execute the distribution network energy management method considering the underlying resources provided by the present application to manage the target distribution network. The specific steps are as follows:

[0052] Determine the underlying resources in the target distribution network to obtain type-a underlying resources; a is an integer greater than 1;

[0053] Determine the first index set corresponding to each type of underlying resource in the type-a underlying resources to obtain a first index sets;

[0054] Based on the a first index sets and a preset comprehensive performance index formula, determine a comprehensive performance indexes;

[0055] Aggregate the type-a underlying resources according to the a comprehensive performance indexes to obtain a fast response aggregate;

[0056] Construct a target energy balance model corresponding to the fast response aggregate;

[0057] Determine a target energy management plan according to the target energy balance model and a preset management objective;

[0058] Manage the target distribution network based on the target energy management plan.

[0059] It should be noted that the above electronic device can execute some or all of the steps of the distribution network energy management method considering the underlying resources provided by the embodiment of the present application.

[0060] Please refer to Figure 3 , Figure 3 which is a flowchart of a distribution network energy management method considering underlying resources provided by an embodiment of the present application; this method can be applied to Figure 1 the electronic device shown in

[0061] S301. Determine the underlying resources in the target distribution network to obtain a type of underlying resources; a is an integer greater than 1.

[0062] In the embodiment of the present application, the target distribution network can be a physical distribution network or a virtual distribution network.

[0063] In a specific embodiment, the electronic device can communicate or be physically connected to the target distribution network, obtain the authorization permission of the target distribution network, and then can access the database of the target distribution network to collect the operation data of each resource in the target distribution network, including information such as power, voltage, current, load, etc. Then, based on the collected data and the characteristics of the resources, various underlying resources in the target distribution network are identified to obtain a type of underlying resources, such as energy storage devices, air conditioning devices, etc., which are not limited herein.

[0064] Optionally, please refer to Figure 4 , Figure 4 which is a schematic structural diagram of a target distribution network provided by an embodiment of the present application. It can be seen that the target distribution network can include: a controller, a new energy unit, an air conditioning device, a data center, an energy storage device, an electric vehicle, etc. Among them, the controller can be used to execute some or all of the steps in the distribution network energy management method considering underlying resources provided by the embodiment of the present application.

[0065] S302. Determine the first index set corresponding to each type of underlying resource in the a types of underlying resources to obtain a first index sets.

[0066] In the embodiment of the present application, each first index set includes at least one index.

[0067] In a specific embodiment, the resource data corresponding to each type of underlying resource in the a types of underlying resources can be obtained first, and then the corresponding first index sets are calculated based on these resource data, so as to obtain a first index sets.

[0068] Optionally, please refer to Figure 5 , Figure 5 which is a flowchart of another distribution network energy management method considering underlying resources provided by an embodiment of the present application. As shown in Figure 5 , step S302, the determining the first index set corresponding to each type of underlying resource in the a types of underlying resources to obtain a first index sets may include the following steps:

[0069] S21. Determine the first device corresponding to the first underlying resource; the first underlying resource is any one of the underlying resources of type a.

[0070] S22. Obtain the first device data corresponding to the first device.

[0071] S23. Determine the first index set corresponding to the first device according to the first device data and the preset index calculation formula set.

[0072] In the embodiments of the present application, the first device may be one of the following: new energy units (such as photovoltaic devices, wind power devices, etc.), air conditioning devices, data centers, electric vehicles, etc., which are not limited herein; the preset index calculation formula set may be preset in advance or by default.

[0073] In a specific embodiment, the first device corresponding to the first underlying resource may be determined first. Specifically, the mapping relationship between the preset underlying resources and devices may be pre-stored, and the first device corresponding to the first underlying resource may be determined based on this mapping relationship; then, the first device data corresponding to the first device may be obtained. Specifically, relevant data may be collected through smart meters and various sensors installed near or directly connected to the first device. The smart meter may measure data such as the power consumption and power factor of the first device; temperature sensors, humidity sensors, pressure sensors, etc. may respectively collect the operating environment parameters or internal physical parameters of the first device, so as to obtain the first device data; finally, the first device data may be substituted into each calculation formula in the preset index calculation formula set to obtain at least one index, and this at least one index constitutes the first index set corresponding to the first device.

[0074] In an embodiment, the preset index calculation formula set may include: regulation ability coefficient calculation formula, response time calculation formula, cost-benefit calculation formula, etc., which are not limited herein; the specific formulas included in the preset index calculation formula set may be as follows:

[0075] (1) Regulation ability

[0076] ① The regulation ability coefficient calculation formula is as follows:

[0077]

[0078] In the formula, represents the regulation ability coefficient of the device, represents the maximum regulation power of the device, represents the minimum regulation time of the device.

[0079] ② The regulation power ratio calculation formula is as follows:

[0080]

[0081] In the formula, represents the regulation power ratio, represents the actual regulation power of the i-th device, represents the rated power of the i-th device. This index is used to measure the proportion of the actual regulation power of the device in its rated power, reflecting the relative magnitude of the device's regulation ability.

[0082] (2) Response time

[0083] ① The response time calculation formula is as follows:

[0084]

[0085] In the formula, represents the response time, represents the time when the control command is issued, represents the response time of the device.

[0086] ② Average response time

[0087] For multiple similar devices, their average response time can be calculated. This index is used to evaluate the overall response speed of this type of device.

[0088] (3) Cost-benefit

[0089] ① The cost-benefit ratio calculation formula is as follows:

[0090] Cost-benefit ratio = total cost / total benefit;

[0091] In the formula, the cost-benefit ratio is denoted as R cb , the total cost includes equipment transformation and upgrade costs, operation and maintenance costs, etc., and the total benefit includes the income obtained from participating in auxiliary services, the electricity cost savings brought by energy conservation, etc.

[0092] ② The investment payback period calculation formula is as follows:

[0093] Investment payback period = total investment cost / annual net income;

[0094] This index is used to evaluate the investment recovery time of the device's participation in interaction.

[0095] In this way, by determining the first device corresponding to the first underlying resource and obtaining its data, the actual operating conditions of specific resources can be analyzed targeted. For example, for the underlying resource of solar photovoltaic, after obtaining the device data such as photovoltaic panels and inverters, using the preset formula to calculate indicators such as power generation efficiency and power stability, the power generation capacity and performance level of this solar photovoltaic resource can be accurately evaluated.

[0096] S303. Determine a comprehensive performance indicators based on the a first indicator sets and a preset comprehensive performance indicator formula.

[0097] In the embodiment of the present application, the a first indicator sets can be respectively substituted into the preset comprehensive performance indicator formula for calculation to obtain a comprehensive performance indicators.

[0098] Optionally, in step S303, the determining a comprehensive performance indicators based on the a first indicator sets and a preset comprehensive performance indicator formula may include the following steps:

[0099] A1. Obtain a target first indicator set; the target first indicator set includes: a target first adjustment ability coefficient, a target first response time, and a target first cost-benefit ratio; the target first indicator set is any one of the a first indicator sets;

[0100] A2. Determine a second device corresponding to the target first indicator set;

[0101] A3. Determine the importance value of each indicator in the target first indicator set for the second device to obtain a target first importance value set;

[0102] A4. Obtain a target usage scenario corresponding to the target distribution network;

[0103] A5. Adjust the target first importance value set according to the target usage scenario to obtain a target importance value set;

[0104] A6. Determine a target first weight set corresponding to the target first indicator set according to the target importance value set;

[0105] A7. Determine a comprehensive performance indicator corresponding to the target first indicator set according to the target first weight set, the target first indicator set, and the preset comprehensive performance indicator formula; where the preset comprehensive performance indicator formula is specifically as follows:

[0106]

[0107] Where I device represents the comprehensive performance indicator, w 1 represents the weight corresponding to the target first adjustment ability coefficient in the target first weight set, w 2 represents the weight corresponding to the target first response time in the target first weight set, w 3 represents the weight corresponding to the target first cost-benefit ratio in the target first weight set, K adj represents the target first adjustment ability coefficient, Kadj,max represents the maximum adjustment ability coefficient in the a first index sets, T response represents the target first response time, R cb represents the target first cost-benefit ratio.

[0108] In the embodiments of the present application, the importance degree value is a quantitative value used to measure the importance degree of an index. For example, the value range of the importance degree value can be 0 to 10, and the larger the importance degree value, the more important the index.

[0109] In a specific embodiment, the target first index set can be obtained; then, the second device corresponding to the target first index set can be determined. Specifically, the original data other than index calculation for the target first index set can be determined first, and the second device can be determined according to the data source of the original data; then, the importance degree value of each index in the target first index set for the second device can be determined to obtain the target first importance degree value set. Specifically, a preset importance analysis method can be used to analyze the target first index set of the second device to obtain the target first importance degree value set. For example, the preset importance analysis method can be the analytic hierarchy process. First, a hierarchical structure model can be established. The second device can be used as the target layer, and each index in the target first index set can be used as the index layer. Then, by comparing two by two, the judgment matrix of the relative importance degree between each index can be determined. Finally, the eigenvector and eigenvalue of the judgment matrix can be calculated by mathematical methods to obtain the importance degree value of each index for the second device, that is, the target first importance degree value set.

[0110] Furthermore, the target usage scenario corresponding to the target distribution network can be obtained. Specifically, the users served by the target distribution network can be obtained, and the user types of these users can be determined. For example, residential users, commercial users, industrial users, etc. Then, the target usage scenario can be determined according to the user type. For example, the target usage scenario corresponding to residential users is the residential power supply scenario; then, the target first importance degree value set can be adjusted according to the target usage scenario to obtain the target importance degree value set; furthermore, the target first weight set corresponding to the target first index set can be determined according to the target importance degree value set. Specifically, the mapping relationship between the preset importance degree value and the weight can be stored in advance, and the target first weight set corresponding to the target importance degree value set can be determined based on this mapping relationship.

[0111] Finally, the target first weight set and the target first index set can be brought into a preset comprehensive performance index formula for calculation. The specific form of the preset comprehensive performance index formula is as follows:

[0112]

[0113] According to the above formula, the comprehensive performance index corresponding to the target first index set can be obtained. I device 。

[0114] In this way, by obtaining the target first index set, covering key indicators such as the adjustment ability coefficient, response time, cost-benefit ratio, etc., the performance of the second device can be comprehensively measured from multiple dimensions. For example, the target first adjustment ability coefficient can reflect the adaptability of the device to different working conditions, the target first response time can reflect the rapid response degree of the device, and the target first cost-benefit ratio helps to evaluate the economy of the device operation, so as to achieve accurate evaluation of the device performance.

[0115] Optionally, in step A5, the adjustment of the target first importance value set according to the target usage scenario to obtain the target importance value set may include the following steps:

[0116] B1. Obtain the first importance value and its corresponding first index; the first importance value is any importance value in the target first importance value set;

[0117] B2. Obtain the target usage duration corresponding to the second device;

[0118] B3. Obtain the target environmental parameters of the second device in the target usage scenario;

[0119] B4. Determine the first optimization factor corresponding to the target usage duration;

[0120] B5. Determine the second optimization factor corresponding to the target environmental parameters;

[0121] B6. Determine the second importance value according to the first optimization factor, the second optimization factor and the first importance value;

[0122] B7. Determine the third importance value of the first index in the target usage scenario;

[0123] B8. Determine the importance value corresponding to the first importance value in the target importance value set according to the first importance value and the third importance value.

[0124] In the embodiments of the present application, the target environmental parameters may include at least one of the following: temperature, humidity, light intensity, wind speed, air pressure, etc., which are not limited herein.

[0125] In a specific embodiment, the first importance value and its corresponding first index may be obtained first; then, the target usage duration corresponding to the second device may be obtained. Specifically, the power distribution network operation data may be obtained from the database of the target power distribution network, and then the data related to the second device, such as the operation times of the second device and the operation duration of each operation, may be extracted from the power distribution network operation data. Through the statistical analysis of these data, the total operation duration of the second device in the past, that is, the target usage duration, may be understood; then, the target environmental parameters of the second device in the target usage scenario may be obtained. Specifically, in the target usage scenario, corresponding environmental monitoring devices may be arranged in the target usage scenario, and the environmental parameters may be obtained in real time. For example, a temperature and humidity sensor may be used to measure the temperature and humidity, a barometer may be used to measure the air pressure, and a light intensity meter may be used to measure the light intensity, etc. These monitoring devices detect the second device, and thus, the target environmental parameters may be obtained.

[0126] Then, the first optimization factor corresponding to the target usage duration may be determined. Specifically, the mapping relationship between the preset usage duration and the optimization factor may be stored in advance, and the first optimization factor corresponding to the target usage duration may be determined based on this mapping relationship; then, the second optimization factor corresponding to the target environmental parameters may be determined. Similarly, the mapping relationship between the preset environmental parameters and the optimization factor may be stored in advance, and the second optimization factor corresponding to the target environmental parameters may be determined based on this mapping relationship; the value ranges of both the first optimization factor and the second optimization factor may be -0.3 to 0.3; further, calculations may be performed according to the first optimization factor, the second optimization factor, and the first importance value. The specific calculation formula is as follows:

[0127] The second importance value = the first importance value × (1 + the first optimization factor) × (1 + the second optimization factor);

[0128] According to the above formula, the second importance value may be obtained; then, the third importance value of the first index in the target usage scenario may be determined. Specifically, the importance value of the first index in the target usage scenario may be analyzed through a preset importance analysis method to obtain the third importance value; finally, the importance value corresponding to the first importance value in the target importance value set may be determined according to the first importance value and the third importance value. Specifically, a first weight (for example, 0.7) may be assigned to the first importance value, and a second weight (for example, 0.3) may be assigned to the third importance value. Weighted operations are performed according to the first weight, the second weight, the first importance value, and the third importance value to obtain the fourth importance value, and the fourth importance value is the importance value corresponding to the first importance value in the target importance value set.

[0129] In this way, by obtaining the target usage duration and target environmental parameters and respectively determining the corresponding first optimization factor and second optimization factor, the impacts of device usage time and the environment on the device can be taken into consideration. For example, a longer device usage duration may lead to performance degradation or an increased failure probability, while adverse environmental parameters such as high temperature and high humidity may affect the device's lifespan and operating stability. Doing so can more comprehensively evaluate the importance of device-related metrics.

[0130] S304. Aggregate the a types of underlying resources according to the a comprehensive performance metrics to obtain a fast response aggregate.

[0131] In the embodiments of the present application, the comprehensive performance metrics greater than the preset comprehensive performance metric among the a comprehensive performance metrics can be first determined to obtain at least one comprehensive performance metric, the underlying resources corresponding to the at least one comprehensive performance metric in the a types of underlying resources can be determined to obtain at least one type of underlying resource, and the at least one type of underlying resource can be aggregated together to obtain a fast response aggregate.

[0132] It should be noted that the fast response aggregate can be used to meet the emergency power regulation requirements of the target distribution network. For example, when the grid frequency fluctuates rapidly, the energy storage devices and electric vehicles in the fast response aggregate can quickly charge and discharge to stabilize the frequency; in addition, the underlying resources other than the at least one type of underlying resource among the a types of underlying resources can be aggregated into a basic guarantee aggregate, which can be used for the daily load balancing and basic auxiliary services (such as peak shaving services, frequency modulation services) of the target distribution network.

[0133] For example, in the frequency modulation service, according to the grid frequency deviation , using the frequency modulation coefficient calculate the frequency modulation power of each device , as shown in the following formula:

[0134]

[0135] Considering that the frequency modulation response characteristics of different devices are different, a preset frequency modulation response coefficient is introduced, and the actual frequency modulation power provided by the i-th device is as shown in the following formula:

[0136]

[0137] The frequency modulation effect can be evaluated by the frequency recovery time and the frequency stability. Among them, the frequency stability can be measured by calculating the standard deviation of the frequency fluctuation, as follows:

[0138]

[0139] In the formula, represents the grid frequency at time t, represents the average value of the grid frequency, and n represents the number of sampling points.

[0140] When the grid frequency drops, the active power output of conventional power generation equipment (such as thermal power generation equipment, hydropower generation equipment, etc.) can be increased; when the frequency rises, the output of the power generation equipment can be reduced. At the same time, for distributed power sources with fast adjustment capabilities, such as small gas turbines, energy storage devices, etc., the power can also be quickly adjusted according to the frequency deviation to participate in frequency modulation.

[0141] S305. Construct the target energy balance model corresponding to the fast response aggregate.

[0142] In the embodiments of the present application, the operation data of the fast response aggregate can be obtained, and the target energy balance model can be constructed according to the operation data.

[0143] Optionally, the fast response aggregate may include type-b underlying resources; b is a positive integer less than or equal to a; step S305, constructing the target energy balance model corresponding to the fast response aggregate may include the following steps:

[0144] S51. Determine the devices corresponding to each underlying resource in the type-b underlying resources to obtain b devices; the b devices include: new energy units, electric vehicles, and energy storage devices.

[0145] S52. Determine the constraint conditions corresponding to each device in the b devices to obtain b constraint conditions.

[0146] S53. Determine the objective function corresponding to the fast response aggregate; the objective function is specifically as follows:

[0147]

[0148] Among them, J represents the objective function, P d,i represents the actual power of the i-th new energy unit, P ev,j represents the actual power of the j-th electric vehicle, SOC k represents the actual state of charge of the k-th energy storage device, P s represents the actual power of the s-th other device among the b devices except for new energy units, electric vehicles, and energy storage devices; represents the expected power of the i-th new energy unit, represents the expected power of the j-th electric vehicle, represents the expected state of charge of the k-th energy storage device, Denote the expected power of the s-th other device among the b devices except for the new energy unit, electric vehicle, and energy storage device; Denote the sum of squares of power differences between different types of devices; λ 1. λ 2. λ 3. λ 4. λ 5 are all preset weight coefficients;

[0149] S54. Determine the target energy balance model according to the b constraint conditions and the objective function.

[0150] In the embodiments of the present application, the expected power refers to the power value that should be achieved under ideal conditions and is preset for various devices.

[0151] In a specific embodiment, the devices corresponding to each underlying resource in the b types of underlying resources can be determined first to obtain b devices. Specifically, according to the preset mapping relationship between the underlying resources and the devices, the b devices corresponding to the b types of underlying resources can be determined; it should be noted that the b devices may include, in addition to new energy units, electric vehicles, and energy storage devices, data centers, air conditioning devices, etc., which are not limited herein; then, the constraint conditions corresponding to each device among the b devices can be determined to obtain b constraint conditions. Specifically, the technical manuals of each of the b devices can be obtained. The device manufacturer will clearly list the various constraint conditions of the device in the technical manual, such as the working voltage range, current limit, temperature and humidity requirements, storage capacity limit, maximum processing speed, etc. For example, the manual of a server device will indicate the voltage for its normal operation as 110~220V, the environmental temperature as 5~35°C, etc. According to this technical manual, b constraint conditions can be obtained; then, the objective function corresponding to the fast response aggregate can be determined; the specific objective function is as follows:

[0152]

[0153] Finally, the b constraint conditions and the objective function can be combined to obtain the target energy balance model.

[0154] For example, for new energy devices, the power generation power needs to meet the following constraints:

[0155]

[0156] Among them, is the maximum power generation power of the new energy device;

[0157] For electric vehicles, the charging power needs to meet the following constraints:

[0158] Pev,min ≤ P ev ≤ P ev,max

[0159] Among them, P ev,min is limited by the minimum output power of the charging pile, while P ev,max is limited by the maximum charging power of the vehicle battery;

[0160] For energy storage devices, the charge and discharge power needs to meet the following constraints:

[0161]

[0162] Among them, is the maximum charge and discharge power.

[0163] And the state of charge of the energy storage device meets the following constraints:

[0164] SOC min ≤SOC≤S OC max

[0165] Among them, SOC min is the minimum state of charge, SOC max is the maximum state of charge.

[0166] For air conditioning equipment, the following constraints need to be met:

[0167] The cooling / heating power adjustment range of the air conditioner is P ac,min , P ac,max ; P ac,min is the minimum operating power of the air conditioner compressor, P ac,max is the full-load operating power of the compressor. Its adjustment is affected by the ambient temperature, set temperature, and air conditioner energy efficiency ratio. When the difference between the ambient temperature and the set temperature is large, the air conditioner needs to operate at a higher power; for air conditioners with a high energy efficiency ratio, the power adjustment range is relatively small under the same cooling / heating demand.

[0168] For the data center, the following constraints need to be met:

[0169] ① Server power adjustment range:

[0170] The server power adjustment range is related to the server load rate, and the adjustable range is P dc,min ,P dc,max . P dc,min is the power when the server is idle, P dc,max is the full-load power of the server. The power of the server is affected by the traffic volume. When the traffic volume increases, the server load rate rises and the power increases. At the same time, the energy-saving mode setting of the server also affects its power regulation. After the energy-saving mode is turned on, the power limit will be reduced.

[0171] ② Power adjustment range of the cooling system:

[0172] The power adjustment range of the cooling system is P cool,min , P cool,max , P cool,min is the power for the cooling system to maintain the minimum cooling requirement, P cool,max is the power for the cooling system to run at full capacity. The power of the cooling system is affected by the heat generated by the server and the ambient temperature. When the heat generated by the server increases or the ambient temperature rises, the cooling system needs to increase the power to ensure the normal operation of the server.

[0173] ③ Adjustable capacity The calculation formula is as follows:

[0174]

[0175] Among them, represents the start time, the starting time point of the integral operation, representing the starting position of the time for calculating the adjustable capacity, represents the end time, the end time point of the integral operation, that is, the termination position of the time for calculating the adjustable capacity.

[0176] In this way, by determining the objective function of the fast-response aggregator and constructing the target energy balance model in combination with the device constraint conditions, the performance of the energy system can be optimized as a whole. For example, if the objective function is to maximize the energy utilization efficiency, the power generation of new energy units, the charging time of electric vehicles, and the charge and discharge strategies of energy storage devices can be reasonably arranged through the model, so that the energy is optimally allocated among different devices, reducing energy waste and improving the overall energy efficiency of the system.

[0177] S306. Determine the target energy management plan according to the target energy balance model and the preset management target.

[0178] In the embodiments of the present application, the target energy balance model can be solved based on the preset management target to obtain multiple energy management plans, and then the optimal energy management plan, that is, the target energy management plan, can be selected from these multiple energy management plans.

[0179] Optionally, in step S306, determining the target energy management scheme according to the target energy balance model and the preset management target may include the following steps:

[0180] S61. Initialize a particle swarm according to preset parameters to obtain a first particle swarm; the first particle swarm includes c particles; c is an integer greater than 1; the position vector of each particle represents an energy management scheme;

[0181] S62. Determine the fitness value corresponding to each of the c particles according to the target energy balance model to obtain c fitness values;

[0182] S63. Obtain the historical optimal fitness value corresponding to each of the c particles to obtain c historical optimal fitness values;

[0183] S64. Determine c current optimal fitness values according to the c historical optimal fitness values and the c fitness values;

[0184] S65. Determine a first global optimal fitness value according to the c current optimal fitness values;

[0185] S66. Determine a first energy management scheme corresponding to the first global optimal fitness value;

[0186] S67. Determine whether the first energy management scheme meets the preset management target;

[0187] S68. If it meets, determine the first energy management scheme as the target energy management scheme;

[0188] S69. If it does not meet, iterate and update the first particle swarm according to a preset particle swarm update formula until the updated first particle swarm meets a preset iteration termination condition; determine a second global optimal fitness value corresponding to the updated first particle swarm; determine a second energy management scheme corresponding to the second global optimal fitness value as the target energy management scheme.

[0189] In the embodiments of the present application, the preset parameters, the preset management target, the preset particle swarm update formula, and the preset iteration termination condition can all be preset in advance or by default. Among them, the preset parameters may include at least one of the following: inertia weight, first learning factor, second learning factor, etc., which are not limited herein.

[0190] In a specific embodiment, a particle swarm can be initialized according to preset parameters to obtain a first particle swarm. Specifically, within the value range of the position vector, an initial position vector can be randomly generated for each particle. For example, if the elements in the position vector represent the power of a device, the value range is (0, Pmax ), a random number generation function can be used to generate the value of each element within this range, thereby obtaining the initial positions of the particles. Similarly, within the value range of the velocity vector, i.e., (V min , V max ), an initial velocity vector can be randomly generated for each particle. A random number generation method similar to that for generating the position vector can be adopted. In this way, the first particle swarm can be obtained.

[0191] It should be explained that the position vector of each particle represents an energy management scheme, and each dimension of the position vector corresponds to a control parameter in the energy management scheme. For example, in a simple energy system including a solar panel, a wind turbine, and a storage battery, if an energy management scheme is to be formulated, the position vector may include control parameters such as the power generation of the solar panel, the power generation of the wind turbine, the charging power of the storage battery, and the discharging power of the storage battery. Then the dimension of the position vector is 4; in this way, the position vector can comprehensively describe the states of the key elements in the energy management scheme.

[0192] For example, please refer to Figure 6 , Figure 6 which is a schematic structural diagram of a first particle swarm provided by an embodiment of the present application. As shown in Figure 6 , the first particle swarm may include: a first particle, a second particle, a third particle, a fourth particle, etc. Each particle represents its state in the form of coordinates. For example, the first particle is represented as (x1, v1), where x1 represents the position of the first particle in the solution space and v1 represents the velocity of the first particle. Similarly, the other particles are respectively represented as (x2, v2), (x3, v3), and (x4, v4). The arrows in the figure represent the movement directions of the particles. For example, the arrow of the first particle points to the right, indicating that the particle moves to the right in the solution space, and so on.

[0193] It should be explained that in the particle swarm optimization algorithm, these particles continuously move and adjust their positions in the solution space to find the optimal solution through iteration. The position of a particle corresponds to a potential solution to the problem (i.e., an energy management scheme), while the velocity controls the movement direction and step size of the particle in the solution space.

[0194] Next, the fitness value corresponding to each of the c particles can be determined according to the target energy balance model, obtaining c fitness values. Specifically, the position vector corresponding to each of the c particles can be determined first, obtaining c position vectors. Then, these c position vectors are substituted into the objective function in the target energy balance model to obtain c function values, that is, c fitness values. Next, the historical optimal fitness value corresponding to each of the c particles can be obtained, obtaining c historical optimal fitness values. Specifically, since the first particle swarm is initialized, the historical optimal fitness value corresponding to each particle at this time is its current fitness value.

[0195] It should be explained that during the iteration of the particle swarm algorithm, for each particle, after calculating a new fitness value each time, it is necessary to compare it with the historical optimal fitness value previously recorded by the particle. If the new fitness value is better (according to the optimization goal of the fitness function, such as a lower new cost when minimizing the cost, or a higher new efficiency when maximizing the efficiency), then update the historical optimal fitness value of the particle to the newly calculated fitness value; otherwise, the historical optimal fitness value remains unchanged.

[0196] Next, c current optimal fitness values can be determined according to the c historical optimal fitness values and the c fitness values. Specifically, the comparison rule can be determined first according to the preset management goal, and then, according to this comparison rule, the c historical optimal fitness values are compared with the corresponding fitness values among the c fitness values to obtain c current optimal fitness values. For example, assuming that the preset management goal is to minimize the operating cost of the target distribution network, the comparison rule is: when comparing two fitness values, the smaller value is considered better.

[0197] Furthermore, the first global optimal fitness value can be determined according to the c current optimal fitness values. Specifically, the minimum value among the c current optimal fitness values can be selected as the first global optimal fitness value. Then, the first energy management scheme corresponding to the first global optimal fitness value can be determined. For example, the particle corresponding to the first global optimal fitness value can be determined to obtain the target particle, the position vector of the target particle can be obtained to obtain the target position vector, and the first energy management scheme corresponding to the target position vector can be determined. Then, it can be judged whether the first energy management scheme meets the preset management goal. If it meets, the first energy management scheme is determined as the target energy management scheme.

[0198] If it does not meet, the first particle swarm is iteratively updated according to the preset particle swarm update formula until the updated first particle swarm meets the preset iteration termination condition. Specifically, since the particle swarm optimization algorithm is a conventional technology, it will not be elaborated here. Next, the second global optimal fitness value corresponding to the updated first particle swarm can be determined, and the second energy management scheme corresponding to the second global optimal fitness value is determined as the target energy management scheme.

[0199] In this way, through the simultaneous search of multiple particles in the first particle swarm in the search space, each particle represents an energy management scheme, which can start from different initial points, explore different regions of the solution space, reduce the risk of falling into local optimal solutions, and contribute to finding an energy management scheme closer to the global optimum, thereby achieving a more optimal allocation of energy.

[0200] S307. Manage the target distribution network based on the target energy management scheme.

[0201] In the embodiments of the present application, the underlying resources in the target distribution network can be managed according to the target energy management scheme, the operating status of the underlying resource devices can be monitored in real time, and the power generation can be adjusted. For example, when the light intensity or wind speed changes, the control parameters of the photovoltaic inverter or the fan can be adjusted to make it generate electricity as per the plan as much as possible.

[0202] Optionally, after managing the target distribution network based on the target energy management scheme, the method further includes the following steps:

[0203] S71. Determine the aggregate peak shaving capacity corresponding to the fast response aggregate; the calculation formula for the aggregate peak shaving capacity is as follows:

[0204]

[0205] Wherein, P reg represents the aggregate peak shaving capacity, represents the peak shaving capacity of the i-th device;

[0206] S72. Determine the peak shaving accuracy of each of the b devices to obtain b peak shaving accuracies;

[0207] S73. Determine the target peak shaving strategy according to the aggregate peak shaving capacity and the b peak shaving accuracies;

[0208] S74. Obtain the grid operation data of the target distribution network within a preset time period;

[0209] S75. Determine whether the target distribution network needs peak shaving according to the grid operation data;

[0210] S76. If peak shaving is required, control the fast response aggregate to perform peak shaving according to the target peak shaving strategy, so as to keep the power generation and power consumption in the target distribution network balanced.

[0211] In the embodiments of the present application, the peak shaving capacity of the fast response aggregate can be determined. Specifically, the peak shaving capacity of each device in the fast response aggregate can be obtained to get b peak shaving capacities. Then, the peak shaving capacity of the aggregate is calculated based on these b peak shaving capacities. The calculation formula is as follows:

[0212]

[0213] Next, the peak shaving accuracy of each device among the b devices can be determined to get b peak shaving accuracies. Specifically, the actual peak shaving power and the expected peak shaving power of each device can be obtained to get b actual peak shaving powers and b expected peak shaving powers. Then, b peak shaving accuracies are calculated. The calculation formula of the peak shaving accuracy is as follows:

[0214]

[0215] Among them, represents the i-th peak shaving accuracy, represents the i-th actual peak shaving power, represents the i-th expected peak shaving power; By calculating according to the above formula b times, b peak shaving accuracies can be obtained; Next, the target peak shaving strategy can be determined based on the peak shaving capacity of the aggregate and the b peak shaving accuracies. For example, the target peak shaving strategy can be: preferentially call the devices with fast response speed and high peak shaving accuracy for peak shaving; Then, the grid operation data of the target distribution network within a preset time period can be obtained. Specifically, it can be queried from the database of the target distribution network with the preset time period as the query range to get the grid operation data; Further, it can be determined whether the target distribution network needs peak shaving based on the grid operation data. Specifically, monitor the voltage levels of each node of the target distribution network. When the voltage exceeds the allowable range, it may be due to overloading or unreasonable power output of the power source. At this time, it is necessary to adjust the power distribution through peak shaving to improve voltage stability. For example, when the voltage of a certain area is too low due to excessive load, the load in this area can be reduced or the power output of the nearby power source can be increased to raise the voltage. On the contrary, when the voltages are all within the allowable range, it means everything is normal and no peak shaving is required; If necessary, control the fast response aggregate to perform peak shaving according to the target peak shaving strategy so that the power generation and power consumption in the target distribution network are balanced.

[0216] It should be noted that the fast response aggregator can also respond to the frequency regulation requirements of the target distribution network. The frequency of the target distribution network is closely related to the balance of active power. When there is an imbalance in active power in the system, such as a sudden increase in load or a sudden decrease in power generation output, the system frequency will decrease; conversely, the frequency will increase. In order to maintain the stable operation of the power system frequency within the specified range, it is necessary to adjust the power generation output or load size in a timely manner to restore the balance of active power, which generates the frequency regulation requirements. The fast response aggregator integrates various types of adjustable resources together, coordinates and manages these resources in a unified manner, and can easily perform frequency regulation to meet the frequency regulation requirements of the target distribution network. For example, it can connect distributed photovoltaics, small wind power generation, electric vehicle battery energy storage, and some interruptible industrial loads to the distribution network and monitor their operating status and available adjustment capabilities in real time.

[0217] In summary, the distribution network energy management method considering underlying resources described in this application determines the underlying resources in the target distribution network to obtain a types of underlying resources; determines the first index set corresponding to each type of underlying resource in the a types of underlying resources to obtain a first index sets; determines a comprehensive performance index based on the a first index sets and a preset comprehensive performance index formula; aggregates the a types of underlying resources according to the a comprehensive performance index to obtain a fast response aggregator; constructs a target energy balance model corresponding to the fast response aggregator; determines a target energy management plan according to the target energy balance model and a preset management target; manages the target distribution network based on the target energy management plan. In this way, by constructing a target energy balance model corresponding to the underlying resources and solving and optimizing the target energy balance model according to the preset management target, an optimal management strategy for the underlying resources, that is, the target energy management plan, is obtained, thereby realizing the precise management of the underlying resources.

[0218] Please refer to Figure 7 , Figure 7 is a functional unit composition block diagram of a distribution network energy management device 700 considering underlying resources provided by an embodiment of this application. The distribution network energy management device 700 considering underlying resources includes: a determination unit 701, a control unit 702, and a management unit 703, where:

[0219] The determination unit 701 is configured to determine the underlying resources in the target distribution network to obtain a types of underlying resources; a is an integer greater than 1; determine the first index set corresponding to each type of underlying resource in the a types of underlying resources to obtain a first index sets;

[0220] The control unit 702 is configured to determine a comprehensive performance metrics based on the a first metric sets and a preset comprehensive performance metric formula; aggregate the a types of underlying resources according to the a comprehensive performance metrics to obtain a fast response aggregate; construct a target energy balance model corresponding to the fast response aggregate; and determine a target energy management solution according to the target energy balance model and a preset management objective.

[0221] The management unit 703 is configured to manage the target distribution network based on the target energy management solution.

[0222] In the embodiment of the present application, the distribution network energy management device 700 considering underlying resources may further execute some or all of the steps of any of the methods described in the above method embodiments.

[0223] Please refer to Figure 8 , Figure 8 FIG. is a schematic structural diagram of an electronic device provided in an embodiment of the present application. The electronic device may include a processor, a memory, a communication interface, and one or more programs. The processor, the memory, and the communication interface may be connected to each other through a bus. The above one or more programs are stored in the above memory and are configured to be executed by the above processor. In the embodiment of the present application, the above programs include steps that cause the electronic device to execute some or all of the steps of any of the methods described in the above method embodiments.

[0224] The embodiment of the present application further provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program for electronic data exchange, and the computer program causes a computer to execute some or all of the steps of any of the methods described in the above method embodiments. The above computer includes an electronic device.

[0225] The embodiment of the present application further provides a computer program product. The above computer program product includes a non-transitory computer-readable storage medium storing a computer program. The above computer program is operable to cause a computer to execute some or all of the steps of any of the methods described in the above method embodiments. The above computer program product may be a software installation package, and the above computer includes an electronic device.

[0226] It should be noted that, for the foregoing method embodiments, for simplicity of description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present application is not limited by the described action sequence, because according to the present application, certain steps may be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present application.

[0227] In the above embodiments, the descriptions of the respective embodiments have their own emphases. For parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0228] In several embodiments provided by the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the above division of units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical or other form.

[0229] Those of ordinary skill in the art can understand all or part of the processes in the methods of the above embodiments. These processes can be completed by relevant hardware instructed by a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. The foregoing storage medium includes: ROM or random access memory RAM, magnetic disk, or optical disk and other various media that can store program codes.

[0230] The steps of the methods or algorithms described in the embodiments of the present application can be implemented in a hardware manner or by a processor executing software instructions. The software instructions can be composed of corresponding software modules. The software modules can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (EEPROM), registers, hard disk, removable hard disk, compact disc read-only memory (CD-ROM), or any other form of storage medium well known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC. In addition, the ASIC can be located in a terminal device or a management device. Of course, the processor and the storage medium can also exist as discrete components in the terminal device or the management device.

[0231] Those skilled in the art should be able to realize that in the above one or more examples, the functions described in the embodiments of the present application can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part.

[0232] The above computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wirelessly (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that the computer can access or a data storage device such as a server or data center that includes one or more integrated available media.

[0233] Among them, the available medium can be a magnetic medium (such as a floppy disk, hard disk, magnetic tape), an optical medium (such as a digital video disc (DVD)), or a semiconductor medium (such as a solid state disk (SSD)), etc.

[0234] Each device and product described in the above embodiments includes various modules / units, which can be software modules / units, hardware modules / units, or can be partially software modules / units and partially hardware modules / units. For example, for each device and product applied to or integrated into a chip, each module / unit it includes can be implemented in a hardware manner such as a circuit. Or, at least some of the modules / units can be implemented in the form of a software program that runs on a processor integrated inside the chip, and the remaining (if any) part of the modules / units can be implemented in a hardware manner such as a circuit; for each device and product applied to or integrated into a chip module, each module / unit it includes can be implemented in a hardware manner such as a circuit, and different modules / units can be located in the same component (such as a chip, circuit module, etc.) or different components of the chip module. Or, at least some of the modules / units can be implemented in the form of a software program that runs on a processor integrated inside the chip module, and the remaining (if any) part of the modules / units can be implemented in a hardware manner such as a circuit; for each device and product applied to or integrated into a terminal device, each module / unit it includes can be implemented in a hardware manner such as a circuit, and different modules / units can be located in the same component (such as a chip, circuit module, etc.) or different components inside the terminal device. Or, at least some of the modules / units can be implemented in the form of a software program that runs on a processor integrated inside the terminal device, and the remaining (if any) part of the modules / units can be implemented in a hardware manner such as a circuit.

[0235] The specific embodiments described above further elaborate on the objectives, technical solutions, and beneficial effects of the embodiments of the present application. It should be understood that the above description is only the specific embodiments of the embodiments of the present application and is not used to limit the protection scope of the embodiments of the present application. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solutions of the embodiments of the present application shall be included in the protection scope of the embodiments of the present application.

Claims

1. A distribution network energy management method considering underlying resources, characterized in that, The method includes: Determine the underlying resources in the target distribution network to obtain type-a underlying resources; a is an integer greater than 1; Determine the first index set corresponding to each type of underlying resource in the type-a underlying resources to obtain a first index sets; Determine a comprehensive performance indicators based on the a first index sets and a preset comprehensive performance index formula; Aggregate the type-a underlying resources according to the a comprehensive performance indicators to obtain a fast response aggregate; Construct a target energy balance model corresponding to the fast response aggregate; Determine a target energy management scheme according to the target energy balance model and a preset management objective; Manage the target distribution network based on the target energy management scheme; Among them, the determining a comprehensive performance indicators based on the a first index sets and a preset comprehensive performance index formula includes: Obtain a target first index set; the target first index set includes: a target first regulation ability coefficient, a target first response time, and a target first cost-benefit ratio; the target first index set is any one of the a first index sets; Determine the second device corresponding to the target first index set; Determine the importance degree value of each index in the target first index set for the second device to obtain a target first importance degree value set; Obtain the target usage scenario corresponding to the target distribution network; Adjust the target first importance degree value set according to the target usage scenario to obtain a target importance degree value set; Determine the target first weight set corresponding to the target first index set according to the target importance degree value set; Determine the comprehensive performance indicator corresponding to the target first index set according to the target first weight set, the target first index set, and the preset comprehensive performance index formula; where the preset comprehensive performance index formula is specifically as follows: Among them, I device represents the comprehensive performance index, w 1 represents the weight corresponding to the target first adjustment ability coefficient in the target first weight set, w 2 represents the weight corresponding to the target first response time in the target first weight set, w 3 represents the weight corresponding to the target first cost-benefit ratio in the target first weight set, K adj represents the target first adjustment ability coefficient, K adj,max represents the maximum adjustment ability coefficient in the a first index sets, T response represents the target first response time, R cb represents the target first cost-benefit ratio.

2. The method according to claim 1, characterized in that, The determining the first index set corresponding to each type of underlying resource in the type-a underlying resources to obtain a first index sets includes: Determine the first device corresponding to the first underlying resource; the first underlying resource is any one of the type-a underlying resources; Obtain the first device data corresponding to the first device; Determine the first index set corresponding to the first device according to the first device data and a preset index calculation formula set.

3. The method according to claim 1, characterized in that, The adjusting the target first importance degree value set according to the target usage scenario to obtain a target importance degree value set includes: Obtain a first importance degree value and its corresponding first index; the first importance degree value is any importance degree value in the target first importance degree value set; Obtain the target usage duration corresponding to the second device; Obtain the target environmental parameters of the second device in the target usage scenario; Determine the first optimization factor corresponding to the target usage duration; Determine the second optimization factor corresponding to the target environmental parameters; Determine a second importance degree value according to the first optimization factor, the second optimization factor, and the first importance degree value; Determine the third importance degree value of the first index in the target usage scenario; Determine the importance value corresponding to the first importance value in the target importance value set according to the first importance value and the third importance value.

4. The method according to claim 1 or 2, characterized in that The fast response aggregate includes b types of underlying resources; b is a positive integer less than or equal to a; constructing the target energy balance model corresponding to the fast response aggregate includes: Determine the devices corresponding to each underlying resource in the b types of underlying resources, obtaining b devices; the b devices include: new energy units, electric vehicles, and energy storage devices. Determine the constraint conditions corresponding to each device in the b devices, obtaining b constraint conditions. Determine the objective function corresponding to the fast response aggregate; the specific objective function is as follows: Among them, J represents the objective function, P d,i represents the actual power of the i-th new energy unit, P ev,j represents the actual power of the j-th electric vehicle, SOC k represents the actual state of charge of the k-th energy storage device, P s represents the actual power of the s-th other device among the b devices except for the new energy units, electric vehicles, and energy storage devices; represents the desired power of the i-th new energy unit, represents the desired power of the j-th electric vehicle, represents the desired state of charge of the k-th energy storage device, represents the desired power of the s-th other device among the b devices except for the new energy units, electric vehicles, and energy storage devices; represents the sum of squares of power differences between different types of devices; λ 1, λ 2, λ 3, λ 4, λ 5 are all preset weight coefficients; Determine the target energy balance model according to the b constraint conditions and the objective function.

5. The method according to claim 1 or 2, characterized in that, The determining the target energy management plan according to the target energy balance model and the preset management objective includes: Initialize the particle swarm according to preset parameters to obtain a first particle swarm; the first particle swarm includes c particles; c is an integer greater than 1; the position vector of each particle represents an energy management plan. Determine the fitness value corresponding to each particle in the c particles according to the target energy balance model, obtaining c fitness values. Obtain the historical optimal fitness value corresponding to each particle in the c particles, obtaining c historical optimal fitness values. Determine c current optimal fitness values according to the c historical optimal fitness values and the c fitness values. Determine a first global optimal fitness value according to the c current optimal fitness values. Determine the first energy management plan corresponding to the first global optimal fitness value. Determine whether the first energy management plan meets the preset management objective. If it meets, determine the first energy management plan as the target energy management plan. If it does not meet, iteratively update the first particle swarm according to the preset particle swarm update formula until the updated first particle swarm meets the preset iteration termination condition; determine the second global optimal fitness value corresponding to the updated first particle swarm; determine the second energy management plan corresponding to the second global optimal fitness value as the target energy management plan.

6. The method according to claim 4, wherein After managing the target distribution network based on the target energy management plan, the method further includes: Determine the aggregate peak shaving capacity corresponding to the fast response aggregate; the calculation formula of the aggregate peak shaving capacity is as follows: Among them, P reg represents the peak shaving capacity of the aggregate, represents the peak shaving capacity of the i-th device; Determine the peak shaving accuracy of each device in the b devices, obtaining b peak shaving accuracies. Determine the target peak shaving strategy according to the aggregate peak shaving capacity and the b peak shaving accuracies. Obtain the grid operation data of the target distribution network within a preset time period. Determine whether the target distribution network needs peak shaving according to the grid operation data. If it is necessary, control the fast response aggregate to perform peak shaving according to the target peak shaving strategy, so that the power generation and power consumption in the target distribution network are balanced.

7. A distribution network energy management device considering underlying resources, characterized in that, The device includes: a determination unit, a control unit, and a management unit, where: The determining unit is configured to determine the underlying resources in the target distribution network to obtain type-a underlying resources, where a is an integer greater than 1; determine the first index set corresponding to each type of underlying resources in the type-a underlying resources to obtain a first index sets; The control unit is configured to determine a comprehensive performance indicators based on the a first index sets and a preset comprehensive performance indicator formula; aggregate the type-a underlying resources according to the a comprehensive performance indicators to obtain a fast response aggregate; construct a target energy balance model corresponding to the fast response aggregate; determine a target energy management solution according to the target energy balance model and a preset management objective; The management unit is configured to manage the target distribution network based on the target energy management solution; Wherein, in terms of determining the a comprehensive performance indicators based on the a first index sets and the preset comprehensive performance indicator formula, the control unit is specifically configured to: Obtain a target first index set, where the target first index set includes a target first regulation ability coefficient, a target first response time, and a target first cost-benefit ratio; the target first index set is any one of the a first index sets; Determine the second device corresponding to the target first index set; Determine the importance degree value of each index in the target first index set for the second device to obtain a target first importance degree value set; Obtain a target usage scenario corresponding to the target distribution network; Adjust the target first importance degree value set according to the target usage scenario to obtain a target importance degree value set; Determine a target first weight set corresponding to the target first index set according to the target importance degree value set; Determine the comprehensive performance indicator corresponding to the target first index set according to the target first weight set, the target first index set, and the preset comprehensive performance indicator formula, where the preset comprehensive performance indicator formula is specifically as follows: Among them, I device represents the comprehensive performance index, w 1 represents the weight corresponding to the target first adjustment ability coefficient in the target first weight set, w 2 represents the weight corresponding to the target first response time in the target first weight set, w 3 represents the weight corresponding to the target first cost-benefit ratio in the target first weight set, K adj represents the target first adjustment ability coefficient, K adj,max represents the maximum adjustment ability coefficient in the a first index sets, T response represents the target first response time, R cb represents the target first cost-benefit ratio.

8. An electronic device, characterized in that, Including: A processor, a memory, a communication interface, and one or more programs; The one or more programs are stored in the memory and configured to be executed by the processor, and the programs include instructions for executing the steps in the method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, Store a computer program for electronic data exchange, where the computer program causes the computer to execute the method according to any one of claims 1-6.

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