Resource scheduling method and device, electronic equipment and storage medium

Abstract

The invention discloses a resource scheduling method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining adjustable load resource data of a power system in a preset time period, inputting the adjustable load resource data into a preset power generation side low-carbon scheduling model, and obtaining n groups of resource scheduling modes, determining n groups of power generation characteristic indexes corresponding to the n groups of resource scheduling modes, inputting the n groups of resource scheduling modes into a preset load side low-carbon scheduling model to obtain n groups of load side power consumption data, and determining n groups of load side power consumption characteristic indexes corresponding to the n groups of load side power consumption data, and determining n coordination degree values based on the n groups of power generation characteristic indexes and the n groups of load side power utilization characteristic indexes, determining the maximum coordination degree value in the n coordination degree values, determining a resource scheduling mode corresponding to the maximum coordination degree value, and obtaining a target resource scheduling mode. By adopting the embodiment of the invention, the coordination of resource scheduling of the power system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system resource scheduling, and in particular to a resource scheduling method, device, electronic equipment and storage medium. Background Art

[0002] The operation of the power system directly affects the level of greenhouse gas emissions. Under the traditional power system operation mode, the power generation side and the load side are usually independently planned and dispatched, lacking an effective coordination mechanism. This may lead to excessive reliance on traditional fossil energy on the power generation side, which not only causes excessive carbon emissions, but also fails to fully consider the dynamic changes in the load side demand, causing an imbalance in power supply and demand. In the process of electricity consumption, the load side also does not form a close interaction with the power generation side, making it difficult to adjust the electricity consumption behavior to help the low-carbon and efficient operation of the power system, resulting in low overall operating efficiency and serious waste of power resources. Therefore, how to improve the coordination of power system resource dispatch is an urgent problem to be solved. Summary of the invention

[0003] The embodiments of the present application provide a resource scheduling method, device, electronic device and storage medium, which improve the coordination of power system resource scheduling.

[0004] In a first aspect, an embodiment of the present application provides a resource scheduling method, including: Obtaining adjustable load resource data of the power system within a preset time period; Input the adjustable load resource data into a preset power generation side low-carbon scheduling model to obtain n groups of resource scheduling methods; n is an integer greater than 1; Determine the power generation characteristic indicators corresponding to the n groups of resource scheduling modes to obtain n groups of power generation characteristic indicators; each group of power generation characteristic indicators includes power supply on the power generation side, carbon emissions on the power generation side, and power generation cost on the power generation side; Inputting the n groups of resource scheduling methods into a preset load-side low-carbon scheduling model, obtaining load-side electricity consumption data corresponding to each group of resource scheduling methods in the n groups of resource scheduling methods, and obtaining n groups of load-side electricity consumption data; Determine the load side power consumption characteristic index corresponding to each set of load side power consumption data in the n sets of load side power consumption data, and obtain n sets of load side power consumption characteristic indexes; each set of load side power consumption characteristic indexes includes load side power demand, load side carbon emissions, and load side power consumption cost; Determine the coordination degree value corresponding to each of the n groups of resource scheduling modes based on the n groups of power generation characteristic indicators and the n groups of load-side power consumption characteristic indicators, and obtain n coordination degree values; the greater the coordination degree value of the resource scheduling mode, the better the resource scheduling effect of the resource scheduling mode; Determining the maximum coordination degree value among the n coordination degree values; Determine the resource scheduling method corresponding to the maximum coordination degree value, and obtain the target resource scheduling method.

[0005] In a second aspect, an embodiment of the present application provides a resource scheduling device, the device comprising: an acquisition unit and a processing unit; The acquisition unit is used to acquire the adjustable load resource data of the power system within a preset time period; The processing unit is used to input the adjustable load resource data into a preset power generation side low-carbon scheduling model to obtain n groups of resource scheduling methods; n is an integer greater than 1; Determine the power generation characteristic indicators corresponding to the n groups of resource scheduling modes to obtain n groups of power generation characteristic indicators; each group of power generation characteristic indicators includes power supply on the power generation side, carbon emissions on the power generation side, and power generation cost on the power generation side; Inputting the n groups of resource scheduling methods into a preset load-side low-carbon scheduling model, obtaining load-side electricity consumption data corresponding to each group of resource scheduling methods in the n groups of resource scheduling methods, and obtaining n groups of load-side electricity consumption data; Determine the load side power consumption characteristic index corresponding to each set of load side power consumption data in the n sets of load side power consumption data, and obtain n sets of load side power consumption characteristic indexes; each set of load side power consumption characteristic indexes includes load side power demand, load side carbon emissions, and load side power consumption cost; Determine the coordination degree value corresponding to each of the n groups of resource scheduling modes based on the n groups of power generation characteristic indicators and the n groups of load-side power consumption characteristic indicators, and obtain n coordination degree values; the greater the coordination degree value of the resource scheduling mode, the better the resource scheduling effect of the resource scheduling mode; Determining the maximum coordination degree value among the n coordination degree values; Determine the resource scheduling method corresponding to the maximum coordination degree value, and obtain the target resource scheduling method.

[0006] In a third aspect, an embodiment of the present invention provides an electronic device, comprising: a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor so that the electronic device executes the method of the first aspect.

[0007] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method of the first aspect.

[0008] In a fifth aspect, an embodiment of the present invention provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, so that a computer executes the method of the first aspect.

[0009] The implementation of the present invention has the following beneficial effects: It can be seen that the resource scheduling method described in the embodiment of the present invention first obtains the adjustable load resource data of the power system within a preset time period, and then inputs the adjustable load resource data into the preset power generation side low-carbon scheduling model to obtain n groups of resource scheduling methods, and then inputs the n groups of resource scheduling methods into the preset load side low-carbon scheduling model to obtain the load side power consumption data corresponding to each group of resource scheduling methods in the n groups of resource scheduling methods, and obtains n groups of load side power consumption data, and then determines the power generation characteristic index corresponding to the n groups of resource scheduling methods to obtain n groups of power generation characteristic indexes, and also determines the load side power consumption characteristic index corresponding to each group of load side power consumption data in the n groups of load side power consumption data to obtain n groups of load side power consumption characteristic indexes, and determines the coordination degree value corresponding to each group of resource scheduling methods in the n groups of resource scheduling methods based on the n groups of power generation characteristic indexes and the n groups of load side power consumption characteristic indexes, and obtains n coordination degree values, thereby determining the maximum coordination degree value among the n coordination degree values, and finally determining the resource scheduling method corresponding to the maximum coordination degree value to obtain the target resource scheduling method, thereby improving the coordination of power system resource scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the technical solutions in the implementation methods of the present application or the background technology, the drawings required for use in the implementation methods of the present application or the background technology will be described below.

[0011] Figure 1 It is a structural diagram of a resource scheduling system provided by an implementation method of the present application; Figure 2 is a flow chart of a resource scheduling method provided by an implementation method of the present application; Figure 3 is a flow chart for determining n coordination degree values ​​provided by an embodiment of the present application; Figure 4 It is a flow chart for determining a carbon emission reduction indicator provided by an implementation method of the present application; Figure 5 is a flow chart for determining a cost-effectiveness indicator provided by an implementation method of the present application; Figure 6 It is a flow chart of determining a coordination degree value corresponding to a first group of resource scheduling modes provided by an implementation method of the present application; Figure 7It is a structural diagram of a resource scheduling device provided in an embodiment of the present application; Figure 8 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0012] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the implementation mode of the present application will be clearly and completely described below in conjunction with the drawings in the implementation mode of the present application. Obviously, the described implementation mode is only a part of the implementation mode of the present application, not all the implementation modes. Based on the implementation mode in the present application, all other implementation modes obtained by ordinary technicians in the field without creative work are within the scope of protection of the present application.

[0013] The terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0014] Reference to "embodiment" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiment may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0015] See also Figure 1 , Figure 1 It is a structural diagram of a resource scheduling system provided in an embodiment of the present application. The resource scheduling system 100 includes a preset power generation side low-carbon scheduling model 101 and a preset load side low-carbon scheduling model 102.

[0016] In this embodiment, it is first necessary to construct a preset power generation side low-carbon scheduling model 101. The preset power generation side low-carbon scheduling model 101 is mainly composed of several constraint relationships, including objective function constraints, branch flow constraints, node power balance constraints, generator ramp constraints, and carbon emission intensity constraints.

[0017] Specifically, the objective function of the preset power generation side low-carbon dispatch model 101 satisfies the following constraints:

[0018]

[0019] in, is the objective function of the power generation side, is the unit time, For is a collection of time points with unit time intervals, For an engine that is generating electricity, is the set of all generators on the generation side, is the power generation cost function, is the penalty cost per unit of carbon emissions of the generator, for The carbon emission coefficient of the generator at the moment, for The active power output of the generator at the moment, and is the coefficient of the power generation cost function.

[0020] The branch power flow of the low-carbon dispatch model 101 on the power generation side meets the following constraints:

[0021]

[0022] in, and At the nodes The branch measured at Active power flow refers to the flow of active power transmitted in the power system branch, and reactive power flow refers to the flow of reactive power transmitted in the power system branch. and The directions are all nodes To Node , and They are Time Node and nodes The voltage, and They are Time Node and nodes The phase angle, For branch The conductivity, For branch The electrical susceptance, It is the collection of all branches of the system. It should be explained that the nodes in the branch flow of the low-carbon dispatch model 101 on the power generation side refer to the points in the power system used to describe various locations in the power grid. These points can be power plants, substations, load centers or connection points of transmission lines, etc. The branches in the branch flow of the low-carbon dispatch model 101 on the power generation side are power lines or elements connecting nodes for transmitting electric energy, including transmission lines, which are the main channels for transmitting electric energy, transmitting electric energy from power plants to various load centers or substations; transformers, which are used to change voltage levels to adapt to different transmission and power demand; and some other power equipment, such as reactors, capacitors, etc., which can also be used as branch elements to adjust the parameters and operating status of the power system.

[0023] The node power balance of the low-carbon dispatch model 101 on the power generation side satisfies the following constraints:

[0024]

[0025] in, for The reactive power output of the generator at the moment, , For Node Department The carbon emission intensity at the moment, for Time load The optimal active power is related to the carbon emission intensity of the load node, indicating that the user's load demand changes with the carbon emission intensity of the node. In the traditional power system optimization operation, the node load is fixed. The present invention considers that the user load is related to the carbon emission intensity of the node. When the carbon emission intensity of the node is high, the user will reduce the power load. Conversely, the user will increase the power load, so that the user actively adjusts his own power load according to the change of the carbon emission intensity of the node. For load The power factor, For Node The collection of all generators at the location, For Node The collection of all loads at the location, It is the collection of all nodes in the system.

[0026] The generator ramping of the low-carbon dispatch model 101 on the power generation side meets the following constraints:

[0027] in, is the lower limit of the generator output power climbing rate, It is the upper limit of the generator output power ramp rate.

[0028] The node voltage and phase of the low-carbon dispatch model 101 on the power generation side meet the following constraints:

[0029]

[0030] in, and For Node The lower limit of voltage and phase angle, and For Node The upper limit of voltage and phase angle.

[0031] The node carbon emission intensity of the power generation side low-carbon dispatch model 101 satisfies the following constraints:

[0032]

[0033]

[0034]

[0035] in, For Node Department The carbon emission intensity at the moment, For Node Department The carbon emission intensity at the moment, For Node a collection of associated generators, For Node The set of adjacent nodes, and Two auxiliary power flow variables are introduced to replace the real active power flow of the branch. , to avoid the influence of tidal direction and facilitate the calculation and solution of the model.

[0036] In this embodiment, it is also necessary to construct a preset load-side low-carbon scheduling model 102. The preset load-side low-carbon scheduling model 102 is mainly composed of several constraint relationships, including objective function constraints, power constraints of adjustable load resources, and total energy constraints consumed by adjustable load resources within a unit time period.

[0037] Specifically, the objective function of the preset load-side low-carbon dispatch model 102 satisfies the following constraints:

[0038] in, is the load side objective function, is the penalty cost per unit carbon emission of adjustable load, For Node Department The carbon emission intensity at the moment, for Time Node The electricity price at for Time load Active power consumed.

[0039] The power consumption of the adjustable load resources of the preset load-side low-carbon dispatch model 102 satisfies the following constraints:

[0040] in, for Time load The lower limit of power consumption, for Time load The upper limit of electrical power consumption.

[0041] The total energy consumed by the adjustable load resources in a unit time period of the preset load-side low-carbon dispatch model 102 satisfies the following constraints:

[0042] In the formula, for Adjustable load resources within a time period The lower limit of total energy consumption, for Adjustable load resources within a time period The upper limit of total energy consumption.

[0043] It should be explained that, in this embodiment, the preset low-carbon dispatch model 101 on the power generation side is a tool based on mathematical algorithms and rules, which is used to generate n groups of resource dispatching methods according to the input adjustable load resource data to achieve the low-carbon dispatching goal on the power generation side. It comprehensively considers various factors of the power generation system, and aims to optimize the allocation of power generation resources, while meeting the operation requirements of the power system, to reduce the carbon emissions and power generation costs on the power generation side as much as possible, and reasonably determine the power supply on the power generation side. The input of the preset low-carbon dispatch model 101 on the power generation side is the adjustable load resource data, which may include the power generation capacity, adjustable range, current load level of different power sources (such as thermal power, hydropower, wind power, photovoltaic power, etc.), and the load demand forecast of the power system in a preset time period, the transmission capacity of the power grid and other related information. The output of the preset low-carbon dispatch model 101 on the power generation side is n groups of resource dispatching methods, and each group of resource dispatching methods clarifies how various power generation resources (different types of power generation units, etc.) should be dispatched within a preset time period, such as when to start or stop a certain unit, how to adjust the power generation of each unit, etc., so as to achieve low-carbon and economic operation on the power generation side. The preset power generation side low-carbon dispatch model 101 dispatches resources by constructing branch flow constraints, node power balance constraints, click power generation capacity constraints, node voltage and phase constraints, node carbon emission intensity and other constraints to obtain a resource dispatching method that meets the conditions.

[0044] It should be explained that in this embodiment, the preset load-side low-carbon scheduling model 102 is used to calculate the load-side electricity consumption data corresponding to each set of resource scheduling methods according to the given n sets of resource scheduling methods, and further determine the load-side electricity consumption characteristic indicators. The purpose is to evaluate the impact of different resource scheduling methods on the user's electricity consumption from the perspective of the load side, including the load-side power demand, carbon emissions and electricity costs, so as to achieve low-carbon and economical electricity consumption on the load side. The input of the preset load-side low-carbon scheduling model 102 is n sets of resource scheduling methods. These scheduling methods determine the power supply situation on the power generation side, including information such as power supply and power supply price in different time periods, which is the input basis of the load-side low-carbon scheduling model. The output of the preset load-side low-carbon scheduling model 102 is n groups of load-side electricity consumption data and corresponding n groups of load-side electricity consumption characteristic indicators. Each group of load-side electricity consumption data describes the changes in electricity consumption behavior of various power-consuming equipment or user groups on the load side under the corresponding resource scheduling method, such as electricity consumption and power consumption in different time periods, and each group of load-side electricity consumption characteristic indicators is a comprehensive quantitative assessment of the load-side electricity consumption, including the load-side electricity demand (reflecting the degree to which the user's actual electricity demand is met), the load-side carbon emissions (taking into account the indirect carbon emissions generated during the electricity consumption process due to different electricity sources), and the load-side electricity cost (the fee paid by the user for electricity consumption). The preset load-side low-carbon dispatch model 102 needs to consider various factors on the load side, such as users' electricity consumption behavior patterns (electricity consumption habits of different user types, peak and off-peak periods of electricity consumption, etc.), electricity price response characteristics (users' reactions to different electricity price levels, such as adjusting electricity consumption time to reduce costs), energy efficiency characteristics of power equipment, etc., and the relationship between these factors is described by establishing corresponding mathematical models. For example, the change in user electricity consumption is calculated based on electricity prices and user demand elasticity, and the carbon emissions on the load side are calculated based on the carbon emission factor of the electricity source. Then, based on the input resource dispatch method, these models and relationships are combined to calculate the electricity consumption data and characteristic indicators on the load side.

[0045] See also Figure 2 , Figure 2 It is a flow chart of a resource scheduling method provided by an embodiment of the present application, including but not limited to the following steps: S201: Obtaining adjustable load resource data of the power system within a preset time period.

[0046] In this embodiment, smart meters and sensors can be installed at each load node of the power system. These devices can monitor and record the power consumption information of the load in real time, including parameters such as power, voltage, and current, and transmit these data to the data center through the communication network. After data processing and analysis, the adjustable load resource data is extracted. For example, the smart meters installed on large motors of industrial users and air conditioning systems of commercial buildings can accurately measure the power consumption of the equipment and provide data support for analyzing its adjustability. For some users who can participate in demand response, such as large industrial users and commercial parks, the power company can encourage users to actively report their adjustable load resource information, including adjustable power range, adjustment time, adjustment cost, etc., by formulating relevant policies and incentives. In the actual operation process, the user will feedback the load adjustment situation to the power company according to the dispatching requirements of the power system, so as to obtain real-time adjustable load resource data. It is also possible to use the power system operation monitoring and control platform, such as the energy management system, distribution management system, etc., to monitor the system's operating status in real time, obtain the load information of the entire network, and identify the adjustable load resources by screening and analyzing this information, and extract the corresponding data. For example, the load changes of each node in the power grid can be monitored in real time, and combined with the operating status and constraints of the equipment, it can be determined which loads are adjustable and the degree of adjustability.

[0047] The adjustable load resource data may include load type, load location, load capacity, load adjustable power range, load adjustment speed, load adjustment duration, load adjustment flexibility, load adjustment cost and other data.

[0048] S202: Input the adjustable load resource data into a preset power generation side low-carbon scheduling model to obtain n groups of resource scheduling methods.

[0049] In this embodiment, n is an integer greater than 1. It should be noted that the preset low-carbon dispatch model on the power generation side in this embodiment can be Figure 1 The preset power generation side low-carbon scheduling model 101 in the embodiment can input the adjustable load resource data into the preset power generation side low-carbon scheduling model 101 to obtain n groups of resource scheduling methods.

[0050] The adjustable load resource data contains various information about the adjustable loads in the power system, such as the type of load (industrial load, commercial load, residential load, etc.), the adjustable power range of the load, the adjustment speed, the adjustment cost, etc. These data are input into the preset low-carbon dispatch model on the power generation side so that the model can fully consider the adjustability of the load when making decisions on power generation resource dispatch, so as to better balance the relationship between power generation and power consumption. When the preset low-carbon dispatch model on the power generation side receives the adjustable load resource data, it will calculate and optimize based on its own algorithm and constraints. By solving the objective function and constraint equations, the model will output a variety of possible power generation resource dispatch schemes, i.e., n groups of resource dispatch methods. Each group of resource dispatch methods specifies in detail the specific operation strategies such as the power distribution and power generation time arrangement of different power generation equipment within the preset time period, so as to achieve low-carbon and economic operation on the power generation side. For example, a group of resource dispatch methods may be arranged during the load valley period to reduce the power generation of thermal power plants and increase the power generation proportion of wind farms and photovoltaic power stations, thereby reducing carbon emissions and power generation costs.

[0051] S203: Determine the power generation characteristic indicators corresponding to the n groups of resource scheduling modes, and obtain n groups of power generation characteristic indicators.

[0052] In this embodiment, each set of power generation characteristic indicators includes power supply on the power generation side, carbon emissions on the power generation side, and power generation costs on the power generation side. In order to comprehensively evaluate the operation and performance of the power generation side under different resource scheduling methods, it is necessary to quantify and describe it through specific indicators, which can reflect the key elements and influencing factors in the power generation process. The power supply on the power generation side refers to the amount of electricity actually supplied to the power system by the power generation side when a certain resource scheduling method is adopted. It is an important indicator to measure the ability of the power generation system to meet the load demand. The amount of power supply is directly related to whether the power system can meet the user's electricity demand. If the power supply is insufficient, it may lead to power shortages and affect production and life. If the power supply is excessive, it may cause waste of resources and increase in power generation costs. By analyzing the power supply on the power generation side under different resource scheduling methods, we can understand the ability and adaptability of each method in meeting load demand, and provide a basis for the reasonable arrangement of power generation plans. The carbon emissions on the power generation side refer to the amount of greenhouse gases such as carbon dioxide generated and emitted into the atmosphere due to the combustion of fossil fuels and other energy sources during the power generation process. It reflects the impact of power generation activities on the environment. With the global attention to environmental protection and climate change, reducing carbon emissions has become an important goal for the development of the power industry. Different resource scheduling methods may lead to different energy utilization efficiencies and carbon emissions. Analyzing the carbon emissions on the power generation side can help evaluate the environmental friendliness of each resource scheduling method, prompting the power system to give priority to low-carbon emission methods when scheduling resources to reduce the negative impact on the environment and achieve sustainable development. The power generation cost on the power generation side covers various costs incurred in the power generation process, including fuel costs, equipment maintenance costs, employee wages, equipment depreciation, etc. It reflects the economic input of power generation activities. The power generation cost is one of the important indicators that power companies pay attention to, which directly affects the economic benefits of the company and the price of power supply. By comparing the power generation costs under different resource scheduling methods, it can help the power system optimize resource allocation and choose a lower-cost scheduling method to improve power generation efficiency and reduce operating costs. It also helps to reasonably formulate electricity prices and ensure the stable operation of the power market.

[0053] By determining the power generation characteristic indicators corresponding to each of these n groups of resource scheduling methods, namely the power supply on the power generation side, the carbon emissions on the power generation side, and the power generation cost on the power generation side, it is possible to comprehensively evaluate the comprehensive performance of different resource scheduling methods on the power generation side from multiple dimensions, and provide detailed data support for further analysis and optimization of resource scheduling, so as to make more scientific and reasonable decisions and achieve coordinated development of the power system in terms of economy, environment and power supply reliability.

[0054] S204: Input the n groups of resource scheduling methods into a preset load-side low-carbon scheduling model to obtain load-side electricity consumption data corresponding to each group of resource scheduling methods in the n groups of resource scheduling methods, thereby obtaining n groups of load-side electricity consumption data.

[0055] In this embodiment, the preset load-side low-carbon dispatch model can be Figure 1 The preset load-side low-carbon scheduling model 102 can input the n groups of resource scheduling methods into the preset load-side low-carbon scheduling model 102 to obtain the load-side electricity consumption data corresponding to each group of resource scheduling methods in the n groups of resource scheduling methods, thereby obtaining n groups of load-side electricity consumption data.

[0056] Each set of load-side electricity consumption data describes the specific electricity consumption of each user or power-consuming equipment on the load side within a preset time period under the resource scheduling method, such as electricity consumption in different time periods, changes in power consumption, start and stop status of power-consuming equipment, etc. In this way, we can obtain n sets of load-side electricity consumption data, so as to fully understand the impact of different power generation side resource scheduling methods on load-side electricity consumption, which provides an important basis for the subsequent evaluation of the advantages and disadvantages of resource scheduling methods.

[0057] S205: Determine the load-side power consumption characteristic index corresponding to each group of load-side power consumption data in the n groups of load-side power consumption data, and obtain n groups of load-side power consumption characteristic indexes.

[0058] In this embodiment, each group of load-side electricity consumption characteristic indicators includes load-side electricity demand, load-side carbon emissions, and load-side electricity costs. For each group of load-side electricity consumption data, the corresponding load-side electricity consumption characteristic indicators must be determined separately, so that n groups of load-side electricity consumption characteristic indicators can be obtained. Through these indicators, the electricity consumption of the load side under different resource scheduling methods can be understood more comprehensively and deeply. The load-side electricity demand refers to the total amount of electricity required by the load side under the corresponding resource scheduling method. This indicator directly reflects the power demand of users or electrical equipment in a specific time period. It is a key indicator to measure whether the power system can meet the load requirements. For example, the operation of industrial production equipment and the use of household electrical appliances will generate a certain amount of electricity demand. The electricity demand of these different users and equipment is summed up to obtain the load-side electricity demand. The carbon emissions on the load side are the carbon emissions indirectly generated by electricity consumption during the electricity consumption process on the load side. Different electricity sources have different carbon emission factors. For example, the use of thermal power will produce higher carbon emissions, while the use of clean energy such as hydropower and wind power will have lower carbon emissions. By calculating the amount of electricity used by different power sources on the load side and the corresponding carbon emission factors, the carbon emissions on the load side can be obtained. This indicator is used to evaluate the impact of electricity use on the environment. The electricity cost on the load side refers to the cost of equipment maintenance required by users during the electricity use process and the cost of electricity consumption when transmitted on the load side.

[0059] S206: Determine the coordination degree value corresponding to each of the n groups of resource scheduling modes based on the n groups of power generation characteristic indicators and the n groups of load-side power consumption characteristic indicators, and obtain n coordination degree values.

[0060] In this implementation, the greater the coordination degree value of the resource scheduling method, the better the resource scheduling effect of the resource scheduling method. Figure 3 , Figure 3 A flowchart for determining n coordination degree values ​​provided by an embodiment of the present application includes but is not limited to the following steps: S301: Obtain the power supply on the power generation side, the carbon emissions on the power generation side, and the power generation cost on the power generation side corresponding to the first group of power generation characteristic indicators, and obtain the first power supply on the power generation side, the first carbon emissions on the power generation side, and the power generation cost on the first power generation side.

[0061] In this embodiment, the first group of power generation characteristic indicators are power generation characteristic indicators corresponding to the first group of resource scheduling methods among the n groups of power generation characteristic indicators, and the first group of resource scheduling methods is any one group of resource scheduling methods among the n groups of resource scheduling methods.

[0062] Each group of power generation characteristic indicators includes three specific indicators: power supply on the power generation side, carbon emissions on the power generation side, and power generation cost on the power generation side. The values ​​of these three indicators are extracted from the first group of power generation characteristic indicators to obtain the first power supply on the power generation side, the first carbon emissions on the power generation side, and the first power generation cost on the power generation side. This step is to subsequently analyze the specific situation of the power generation side under a specific resource scheduling method, extract key numerical information, and facilitate subsequent analysis.

[0063] S302: Obtain the load side power demand, load side carbon emissions, and load side electricity cost corresponding to the first group of load side power consumption characteristic indicators to obtain the first load side power demand, the first load side carbon emissions, and the first load side electricity cost.

[0064] In this embodiment, the first group of load-side power consumption characteristic indicators are the load-side power consumption characteristic indicators corresponding to the first group of power generation characteristic indicators among the n groups of load-side power consumption characteristic indicators.

[0065] Each group of load-side electricity consumption characteristic indicators includes three indicators: load-side electricity demand, load-side carbon emissions, and load-side electricity costs. The values ​​of these three indicators are extracted from the first group of load-side electricity consumption characteristic indicators to obtain the first load-side electricity demand, the first load-side carbon emissions, and the first load-side electricity costs. This is to obtain key electricity consumption information on the load side under a specific resource scheduling method, so as to conduct subsequent comprehensive analysis together with the power generation side information.

[0066] S303: Determine a power supply and demand balance rate based on the power supply at the first power generation side and the power demand at the first load side.

[0067] In this embodiment, the first power supply on the power generation side represents the power supplied by the power generation side under the first resource scheduling mode, and the first power demand on the load side represents the power demand on the load side under the resource scheduling mode.

[0068] The power supply and demand balance rate is an indicator used to measure the matching degree between the power supply on the generation side and the power demand on the load side. The calculation of the power supply and demand balance rate satisfies the following formula: Electricity supply and demand balance rate = power supply on the first generation side / power demand on the first load side; According to the above formula, the power supply and demand balance rate can be determined based on the power supply on the first power generation side and the power demand on the first load side.

[0069] The electricity supply and demand balance rate can reflect whether the supply and demand of the power system is balanced and the degree of balance under this resource scheduling method. If the electricity supply and demand balance rate is close to 1, it means that the power supply and demand are relatively matched; if the value is greater than 1, it may indicate an oversupply, and if it is less than 1, it may indicate an undersupply. Therefore, the closer the electricity supply and demand balance rate is to 1, the more balanced the supply and demand of the power system.

[0070] S304: Determine a carbon emission reduction indicator based on the first power generation side carbon emissions and the first load side carbon emissions.

[0071] In this implementation, see Figure 4 , Figure 4 This is a flow chart for determining a carbon emission reduction indicator provided by an embodiment of the present application, including but not limited to the following steps: S401: Obtain historical generation-side carbon emissions and historical load-side carbon emissions of the power system within a historical time period.

[0072] In this implementation, the end time of the historical time period is earlier than the start time of the preset time period, and the duration of the historical time period is the same as the duration of the preset time period.

[0073] By comparing the carbon emissions on the power system's generation and load sides in different time periods, the changes in the power system's carbon emissions are evaluated. The preset time period is the time period in which the resource scheduling to be studied is currently located, and the historical time period is used as a reference. The historical time period is the same length as the preset time period, and the end time of the former is earlier than the start time of the latter. This ensures that the two time periods are independent of each other and eliminates the interference of time period overlap on the analysis. On this basis, the carbon emissions generated on the power system's generation side and the carbon emissions indirectly generated by electricity consumption on the load side during the historical time period are collected.

[0074] S402: Determine the difference between the first power generation side carbon emissions and the historical power generation side carbon emissions to obtain the power generation side carbon emissions difference.

[0075] In this embodiment, the first power generation side carbon emissions come from the power generation characteristic indicators corresponding to the first group of resource scheduling methods within the preset time period. It is compared with the historical power generation side carbon emissions in the historical time period. By calculating the difference between the two, it is possible to understand the value of the reduction in power generation side carbon emissions compared with the historical situation after the first group of resource scheduling methods are adopted in the preset time period. This helps to analyze the direction and degree of the impact of the current resource scheduling method on the power generation side carbon emissions.

[0076] S403: Determine the difference between the first load-side carbon emissions and the historical load-side carbon emissions to obtain the load-side carbon emissions difference.

[0077] In this embodiment, the first load-side carbon emissions also come from the load-side electricity consumption characteristic indicators corresponding to the first group of resource scheduling methods within the preset time period. They are compared with the historical load-side carbon emissions in the historical time period. The difference in load-side carbon emissions is calculated to reflect the changes in carbon emissions generated by electricity consumption on the load side during the preset time period compared to historical conditions.

[0078] S404: Determine a carbon emission reduction value based on the carbon emission difference on the power generation side and the carbon emission difference on the load side.

[0079] In this embodiment, the previously obtained difference in carbon emissions on the power generation side and the difference in carbon emissions on the load side are comprehensively calculated. The calculation method can be a simple addition, or different weights can be assigned according to the importance of carbon emissions on the power generation side and the load side and then calculated. The carbon emissions reduction value obtained by calculation can reflect the overall changes in carbon emissions on the power generation side and the load side of the power system after adopting the first set of resource scheduling methods within the preset time period, and clarify whether carbon emissions are increasing or decreasing, as well as the specific magnitude of the change.

[0080] S405: Determine the carbon emission reduction indicator based on the carbon emission reduction value.

[0081] In this implementation, it may be a mapping relationship between a preset carbon emission reduction value and a carbon emission reduction index, and based on the mapping relationship, the carbon emission reduction index may be determined based on the carbon emission reduction value.

[0082] It can be seen that by calculating the difference in carbon emissions between the power generation side and the load side in different time periods, the carbon emission reduction value can be accurately obtained, and then the carbon emission reduction index can be determined, so as to quantitatively evaluate the emission reduction effect of the power system under a specific resource scheduling method, and intuitively reflect the impact of the current scheduling method on carbon emissions compared with historical situations. Calculating the difference in carbon emissions between the power generation side and the load side separately helps to clarify the respective roles of the power generation link and the load link in the change of carbon emissions, thereby determining the key links and responsible entities for emission reduction, and providing a basis for formulating targeted emission reduction measures. The carbon emission reduction index can provide data support for the optimization of resource scheduling methods. According to this indicator, it can be judged whether the current scheduling strategy is conducive to reducing carbon emissions, and then adjust and optimize the resource scheduling plan to improve the low-carbon operation level of the power system.

[0083] S305: Determine a cost-effectiveness index based on the power generation cost of the first power generation side and the power consumption cost of the first load side.

[0084] In this implementation, see Figure 5 , Figure 5 A flowchart for determining a cost-effectiveness indicator provided by an embodiment of the present application includes but is not limited to the following steps: S501: Determine a target electricity cost based on the first power generation side power generation cost and the first load side power consumption cost.

[0085] In this embodiment, the first power generation cost on the power generation side refers to the various costs paid by the power generation side to generate electricity under the first resource scheduling mode, including fuel costs, equipment maintenance costs, staff wages, etc. The first load side electricity cost refers to the cost that the load side user needs to bear for using electricity under the same first resource scheduling mode, such as electricity charges, possible additional maintenance costs of power equipment, etc. The target electricity cost can be obtained by adding the first power generation cost on the power generation side and the first load side electricity cost.

[0086] S502: Obtain the economic benefits corresponding to the first group of resource scheduling methods to obtain target economic benefits.

[0087] In this embodiment, the first group of resource scheduling methods will have various impacts on the operation of the power system, and these impacts will bring corresponding economic benefits. The sources of economic benefits may include multiple aspects. For example, through reasonable resource scheduling, the utilization efficiency of power generation equipment is improved, the power generation cost is reduced, thereby increasing profits, or by guiding the electricity consumption behavior on the load side, the configuration of power resources is optimized, the overall operation efficiency of the power system is improved, and additional economic benefits are brought. The economic benefits generated by the first group of resource scheduling methods are evaluated and calculated to obtain a specific value, which is the target economic benefit. The evaluation process may need to consider multiple factors, such as power generation income, changes in electricity costs, return on equipment investment, etc., and the final target economic benefit is determined by comprehensive analysis of these factors.

[0088] S503: Determine the cost-effectiveness index based on the target electricity cost and the target economic benefit.

[0089] In this implementation, the cost-effectiveness index is an important index used to measure the relationship between the cost and benefit of the power system under the first group of resource scheduling methods. It reflects the economic benefit (i.e., the target economic benefit) obtained under the condition of investing a certain cost (i.e., the target electricity cost). According to the two values ​​of the target electricity cost and the target economic benefit, the cost-effectiveness index is determined by a specific calculation method. A common calculation method may be to divide the target economic benefit by the target electricity cost, and the ratio obtained is the cost-effectiveness index. The larger the value of this index, the higher the economic benefit per unit cost under this resource scheduling method, and the better the cost-effectiveness of the resource scheduling method. On the contrary, it means that the cost-effectiveness is poor.

[0090] It can be seen that by comprehensively considering the power generation cost of the first power generation side and the power consumption cost of the first load side to determine the target power consumption cost, the cost factors of the two key links of power generation and power consumption are fully covered. The cost-effectiveness index is determined in combination with the target economic benefit, which can objectively and comprehensively evaluate the first group of resource scheduling methods as a whole, rather than focusing on the cost of power generation or power consumption in a one-sided way, making the evaluation results more scientific and accurate, so as to better measure the feasibility and rationality of the resource scheduling method at the economic level. The cost-effectiveness index provides a clear quantitative basis for resource scheduling decisions. When faced with a variety of resource scheduling methods, comparisons and screening can be made based on this index, and cost-effective methods can be given priority, which will help to reasonably allocate power resources, improve resource utilization efficiency, avoid waste of resources, and enable the power system to achieve a better state in economic operation.

[0091] S306: Determine the coordination degree value corresponding to the first group of resource scheduling methods based on the electricity supply and demand balance rate, the carbon emission reduction index and the cost-effectiveness index.

[0092] In this implementation, see Figure 6 , Figure 6 A flowchart of determining a coordination degree value corresponding to a first group of resource scheduling modes provided by an embodiment of the present application includes but is not limited to the following steps: S601: Determine the difference between the power supply and demand balance rate and a preset power supply and demand balance rate to obtain the power supply and demand balance rate difference.

[0093] In this embodiment, the electricity supply and demand balance rate is an indicator to measure the degree of balance between electricity supply and demand in the power system. The preset electricity supply and demand balance rate is an ideal balance state indicator value set in advance according to the planning and operation requirements of the power system. By calculating the difference between the actual electricity supply and demand balance rate and the preset electricity supply and demand balance rate, the gap between the current supply and demand situation of the power system and the ideal state can be clearly determined. This difference is the electricity supply and demand balance rate difference, which reflects the degree of deviation from the electricity supply and demand balance.

[0094] S602: When the electricity supply and demand balance rate difference is less than or equal to a preset electricity supply and demand balance rate difference, determine the coordination degree value corresponding to the first group of resource scheduling methods based on the carbon emission reduction index and the cost-effectiveness index.

[0095] In this embodiment, when the calculated electricity supply and demand balance rate difference is within an acceptable range, that is, less than or equal to the preset electricity supply and demand balance rate difference, it means that the supply and demand balance of the power system is relatively good and within an acceptable fluctuation range. At this time, the carbon emission reduction indicator and the cost-effectiveness indicator are further considered to determine the coordination degree value corresponding to the first group of resource scheduling methods.

[0096] Exemplarily, a first mapping relationship between a carbon emission reduction indicator and a coordination degree value, and a second mapping relationship between a cost-effectiveness indicator and a coordination degree value are obtained. Specifically, by analyzing a large amount of historical data, it is possible to observe which coordination degree values ​​different carbon emission reduction indicators and cost-effectiveness indicators correspond to, thereby summarizing the rules between them and forming corresponding mapping relationships, thereby obtaining a first mapping relationship between a carbon emission reduction indicator and a coordination degree value, and a second mapping relationship between a cost-effectiveness indicator and a coordination degree value.

[0097] Exemplarily, the first coordination degree value corresponding to the carbon emission reduction indicator is determined based on the first mapping relationship. Specifically, after obtaining the first mapping relationship between the carbon emission reduction indicator and the coordination degree value, the carbon emission reduction indicator in the first group of resource scheduling methods currently concerned is substituted into the mapping relationship, and the coordination degree value corresponding to this carbon emission reduction indicator, that is, the first coordination degree value, can be determined.

[0098] Exemplarily, the second coordination degree value corresponding to the cost-effectiveness indicator is determined based on the second mapping relationship. Specifically, according to the second mapping relationship between the cost-effectiveness indicator and the coordination degree value, the cost-effectiveness indicator in the first group of resource scheduling methods is substituted therein to obtain the coordination degree value corresponding to the cost-effectiveness indicator, that is, the second coordination degree value.

[0099] Exemplarily, a first weight corresponding to the first coordination degree value and a second weight corresponding to the second coordination degree value are determined, and the sum of the first weight and the second weight is 1. Specifically, if in the current power system planning, more attention is paid to the impact of carbon emission reduction on overall coordination, then a larger weight may be given to the first coordination degree value. If more attention is paid to cost-effectiveness, the weight of the second coordination degree value will be increased accordingly. The determination of weights usually requires comprehensive consideration of multiple factors to ensure that the evaluation results can accurately reflect the actual coordination of the resource scheduling method.

[0100] Exemplarily, the coordination degree value corresponding to the first group of resource scheduling methods is determined based on the first coordination degree value, the second coordination degree value, the first weight, and the second weight. Exemplarily, a reference coordination degree value is obtained by calculation based on the first coordination degree value, the second coordination degree value, the first weight, and the second weight. Specifically, the reference coordination degree value is calculated according to the following formula: Reference coordination degree value = first coordination degree value × first weight value + second coordination degree value × second weight value; According to the above formula, a reference coordination degree value may be obtained by performing calculation based on the first coordination degree value, the second coordination degree value, the first weight value and the second weight value.

[0101] Exemplarily, the proportion of renewable energy output corresponding to the first group of resource scheduling methods is determined. Specifically, the proportion of renewable energy output refers to the proportion of electricity provided by renewable energy (such as solar energy, wind energy, etc.) in the total power output under the first group of resource scheduling methods. Determining the proportion of renewable energy output can understand the degree of participation and importance of renewable energy in resource scheduling. A higher proportion of renewable energy output helps to improve the stability of energy supply, thereby improving the degree of coordination of resource scheduling methods. New energy such as solar energy and wind energy are widely distributed and renewable. When the proportion of renewable energy output is high, it can reduce dependence on traditional fossil energy to a certain extent and reduce the risks caused by resource shortages or supply interruptions. At the same time, multiple energy sources complement each other, making the power supply more flexible, enhancing the ability of the entire energy system to respond to emergencies and meet power demand in different periods, and promoting the coordination of resource scheduling in time and space. The increase in the proportion of renewable energy output is more environmentally friendly and can improve the degree of coordination of resource scheduling methods. The proportion of renewable energy output has a complex impact on the cost-effectiveness of resource scheduling methods, thereby affecting the degree of coordination.

[0102] Exemplarily, the target adjustment parameter corresponding to the new energy output proportion is determined. Specifically, it can be a mapping relationship between a preset new energy output proportion and an adjustment parameter. Based on the mapping relationship, the target adjustment parameter corresponding to the new energy output proportion can be determined.

[0103] Exemplarily, the reference coordination degree value is adjusted based on the target adjustment parameter to obtain the coordination degree value corresponding to the first group of resource scheduling modes. Specifically, the coordination degree value corresponding to the first group of resource scheduling modes is calculated according to the following formula: The coordination degree value corresponding to the first group of resource scheduling methods = reference coordination degree value × (1 + target adjustment parameter); According to the above formula, the reference coordination degree value can be adjusted based on the target adjustment parameter to obtain the coordination degree value corresponding to the first group of resource scheduling methods.

[0104] It can be seen that by comprehensively considering the first coordination degree value, the second coordination degree value, the first weight and the second weight to calculate the reference coordination degree value, factors of different dimensions such as carbon emission reduction and cost-effectiveness can be taken into consideration, avoiding evaluating the coordination of resource scheduling methods from only a single perspective, thereby more comprehensively and accurately reflecting the comprehensive performance of resource scheduling methods, explicitly incorporating the factor of the proportion of new energy output, and adjusting the reference coordination degree value by determining its corresponding target adjustment parameters, which can fully reflect the importance of new energy in resource scheduling. The development of new energy plays a key role in achieving energy transformation, reducing carbon emissions and ensuring sustainable energy supply. This method can encourage greater use of new energy in resource scheduling and promote energy The resource structure is transformed towards green and low-carbon, and the target adjustment parameters are dynamically determined according to the proportion of new energy output, and then the reference coordination degree value is adjusted, so that the evaluation results can be flexibly changed according to the actual situation. Different resource scheduling methods may differ in the use of new energy. Through this dynamic adjustment mechanism, the degree of coordination of various scheduling methods in different scenarios can be more accurately reflected, providing a more targeted basis for resource scheduling decisions. A higher proportion of new energy output usually corresponds to more favorable target adjustment parameters, which will prompt the resource scheduling plan to pay more attention to improving the utilization efficiency and proportion of new energy during the formulation and implementation process, while taking into account factors such as carbon emission reduction and cost-effectiveness, thereby achieving overall optimization and sustainable development of resource scheduling.

[0105] S603: When the power supply and demand balance rate difference is greater than the preset power supply and demand balance rate difference, determining that the coordination degree value corresponding to the first group of resource scheduling methods is 0.

[0106] In this embodiment, if the difference in the power supply and demand balance rate exceeds the preset range, that is, it is greater than the preset power supply and demand balance rate difference, this means that there is a large deviation in the supply and demand balance of the power system, and there may be more serious problems such as insufficient or excessive power supply. In this case, no matter how the resource scheduling method performs in terms of carbon emission reduction and cost-effectiveness, it is considered that the resource scheduling method is overall uncoordinated and cannot meet the most basic supply and demand balance requirements of the power system. Therefore, the coordination degree value corresponding to the first group of resource scheduling methods is directly determined to be 0, indicating that the resource scheduling method is not desirable under the current circumstances, and the resource scheduling method needs to be adjusted or re-planned to restore the balance of power supply and demand, and then consider other aspects of coordination on this basis.

[0107] It needs to be explained that, since the first group of resource scheduling methods is any one of the n groups of resource scheduling methods, the method for determining the coordination degree value corresponding to each group of resource scheduling methods in the n groups of resource scheduling methods is the same as the method for determining the coordination degree value corresponding to the first group of resource scheduling methods. Based on the method for determining the coordination degree value corresponding to the above-mentioned first group of resource scheduling methods, the coordination degree value corresponding to each group of resource scheduling methods in the n groups of resource scheduling methods can be determined based on the n groups of power generation characteristic indicators and the n groups of load-side power consumption characteristic indicators to obtain n coordination degree values.

[0108] S207: Determine the maximum coordination degree value among the n coordination degree values.

[0109] In this embodiment, among the obtained n coordination degree values, the coordination degree value with the largest value is found through comparison and screening. This largest coordination degree value represents the evaluation index value corresponding to the scheduling method with the best comprehensive performance among all calculated resource scheduling methods.

[0110] S208: Determine the resource scheduling method corresponding to the maximum coordination degree value, and obtain the target resource scheduling method.

[0111] In this implementation, after finding the maximum coordination degree value, the set of resource scheduling methods corresponding to this value is determined. This set of resource scheduling methods is considered to be the best resource scheduling method after considering various factors on the power generation side and the load side. It is used as the target resource scheduling method and can be used as a reference or implementation plan for actual power system resource scheduling to achieve optimized operation of the power system in terms of low carbon, economy, reliability and other aspects.

[0112] It can be seen that by obtaining adjustable load resource data and analyzing them from the power generation side and the load side respectively, the power generation characteristic indicators (such as power supply, carbon emissions, and power generation costs) and the load side power consumption characteristic indicators (such as power demand, carbon emissions, and electricity costs) are comprehensively considered. The performance of different resource scheduling methods in the power system can be comprehensively and systematically evaluated, avoiding the one-sidedness caused by evaluating from only a single perspective. The coordination degree value is determined for each group of resource scheduling methods, and the resource scheduling effect is quantified to make the different scheduling methods comparable. By comparing these quantified values, it is possible to intuitively judge which resource scheduling method is better, which provides a clear basis for resource scheduling decisions, and determines the target resource scheduling method corresponding to the maximum coordination degree value, which helps to find a resource scheduling plan that achieves the best balance between the power generation side and the load side, thereby achieving optimal allocation of power system resources, improving resource utilization efficiency, reducing overall costs, reducing carbon emissions, and improving the overall operation efficiency and benefits of the power system. Obtaining multiple sets of resource scheduling methods and their corresponding characteristic indicators and coordination degree values ​​can enable the power system to find the relatively optimal resource scheduling method when faced with different preset time periods, different adjustable load resource data and different system operation requirements, thereby enhancing the power system's ability to cope with various complex situations and meet diversified needs.

[0113] In summary, the implementation of the present invention has the following beneficial effects: It can be seen that the resource scheduling method described in the embodiment of the present invention first obtains the adjustable load resource data of the power system within a preset time period, and then inputs the adjustable load resource data into the preset power generation side low-carbon scheduling model to obtain n groups of resource scheduling methods, and then inputs the n groups of resource scheduling methods into the preset load side low-carbon scheduling model to obtain the load side power consumption data corresponding to each group of resource scheduling methods in the n groups of resource scheduling methods, and obtains n groups of load side power consumption data, and then determines the power generation characteristic index corresponding to the n groups of resource scheduling methods to obtain n groups of power generation characteristic indexes, and also determines the load side power consumption characteristic index corresponding to each group of load side power consumption data in the n groups of load side power consumption data to obtain n groups of load side power consumption characteristic indexes, and determines the coordination degree value corresponding to each group of resource scheduling methods in the n groups of resource scheduling methods based on the n groups of power generation characteristic indexes and the n groups of load side power consumption characteristic indexes, and obtains n coordination degree values, thereby determining the maximum coordination degree value among the n coordination degree values, and finally determining the resource scheduling method corresponding to the maximum coordination degree value to obtain the target resource scheduling method, thereby improving the coordination of power system resource scheduling.

[0114] See also Figure 7 , Figure 7It is a structural diagram of a resource scheduling device provided in an embodiment of the present application, wherein the resource scheduling device 700 comprises: an acquisition unit 701 and a processing unit 702; The acquisition unit 701 is used to acquire adjustable load resource data of the power system within a preset time period; The processing unit 702 is used to input the adjustable load resource data into a preset power generation side low-carbon scheduling model to obtain n groups of resource scheduling methods; n is an integer greater than 1; Determine the power generation characteristic indicators corresponding to the n groups of resource scheduling modes to obtain n groups of power generation characteristic indicators; each group of power generation characteristic indicators includes power supply on the power generation side, carbon emissions on the power generation side, and power generation cost on the power generation side; Inputting the n groups of resource scheduling methods into a preset load-side low-carbon scheduling model, obtaining load-side electricity consumption data corresponding to each group of resource scheduling methods in the n groups of resource scheduling methods, and obtaining n groups of load-side electricity consumption data; Determine the load side power consumption characteristic index corresponding to each set of load side power consumption data in the n sets of load side power consumption data, and obtain n sets of load side power consumption characteristic indexes; each set of load side power consumption characteristic indexes includes load side power demand, load side carbon emissions, and load side power consumption cost; Determine the coordination degree value corresponding to each of the n groups of resource scheduling modes based on the n groups of power generation characteristic indicators and the n groups of load-side power consumption characteristic indicators, and obtain n coordination degree values; the greater the coordination degree value of the resource scheduling mode, the better the resource scheduling effect of the resource scheduling mode; Determining the maximum coordination degree value among the n coordination degree values; Determine the resource scheduling method corresponding to the maximum coordination degree value, and obtain the target resource scheduling method.

[0115] In some possible implementations, in terms of determining the coordination degree value corresponding to each group of resource scheduling modes in the n groups of resource scheduling modes based on the n groups of power generation characteristic indicators and the n groups of load-side power consumption characteristic indicators, and obtaining n coordination degree values, the processing unit 702 is specifically used to: Obtaining the power supply on the power generation side, the carbon emissions on the power generation side, and the power generation cost on the power generation side corresponding to the first group of power generation characteristic indicators, and obtaining the first power supply on the power generation side, the first carbon emissions on the power generation side, and the first power generation cost on the power generation side; the first group of power generation characteristic indicators is the power generation characteristic indicators corresponding to the first group of resource scheduling methods in the n groups of power generation characteristic indicators, and the first group of resource scheduling methods is any group of resource scheduling methods in the n groups of resource scheduling methods; Obtain the load side power demand, load side carbon emissions, and load side power cost corresponding to the first group of load side power consumption characteristic indicators to obtain the first load side power demand, the first load side carbon emissions, and the first load side power cost; the first group of load side power consumption characteristic indicators are the load side power consumption characteristic indicators corresponding to the first group of power generation characteristic indicators in the n groups of load side power consumption characteristic indicators; Determining a power supply and demand balance rate based on the power supply at the first power generation side and the power demand at the first load side; Determining a carbon emission reduction index based on the first power generation side carbon emissions and the first load side carbon emissions; Determining a cost-effectiveness index based on the first power generation side power generation cost and the first load side power consumption cost; The coordination degree value corresponding to the first group of resource scheduling methods is determined based on the electricity supply and demand balance rate, the carbon emission reduction index and the cost-effectiveness index.

[0116] In some possible implementations, in determining the carbon emission reduction indicator based on the first power generation side carbon emissions and the first load side carbon emissions, the processing unit 702 is specifically configured to: Acquire the historical generation-side carbon emissions and the historical load-side carbon emissions of the power system in a historical time period; the end time of the historical time period is earlier than the start time of the preset time period, and the duration of the historical time period is the same as the duration of the preset time period; Determine the difference between the first power generation side carbon emissions and the historical power generation side carbon emissions to obtain the power generation side carbon emissions difference; Determine the difference between the first load-side carbon emissions and the historical load-side carbon emissions to obtain a load-side carbon emissions difference; Determine a carbon emission reduction value based on the difference between the carbon emission on the power generation side and the carbon emission on the load side; The carbon emission reduction indicator is determined based on the carbon emission reduction value.

[0117] In some possible implementations, in determining the cost-effectiveness indicator based on the first power generation side power generation cost and the first load side power consumption cost, the processing unit 702 is specifically configured to: Determine a target electricity cost based on the first power generation side power generation cost and the first load side power cost; Obtaining the economic benefits corresponding to the first group of resource scheduling methods to obtain target economic benefits; The cost-effectiveness index is determined based on the target electricity cost and the target economic benefit.

[0118] In some possible implementations, in determining the coordination degree value corresponding to the first group of resource scheduling modes based on the power supply and demand balance rate, the carbon emission reduction index and the cost-effectiveness index, the processing unit 702 is specifically configured to: Determine the difference between the power supply and demand balance rate and a preset power supply and demand balance rate to obtain the power supply and demand balance rate difference; When the power supply and demand balance rate difference is less than or equal to a preset power supply and demand balance rate difference, determining a coordination degree value corresponding to the first group of resource scheduling modes based on the carbon emission reduction index and the cost-effectiveness index; When the power supply and demand balance rate difference is greater than the preset power supply and demand balance rate difference, it is determined that the coordination degree value corresponding to the first group of resource scheduling methods is 0.

[0119] In some possible implementations, in determining the coordination degree value corresponding to the first group of resource scheduling modes based on the carbon emission reduction indicator and the cost-effectiveness indicator, the processing unit 702 is specifically configured to: Obtaining a first mapping relationship between a carbon emission reduction index and a coordination degree value, and a second mapping relationship between a cost-effectiveness index and a coordination degree value; Determine a first coordination degree value corresponding to the carbon emission reduction indicator based on the first mapping relationship; Determine a second coordination degree value corresponding to the cost-effectiveness indicator based on the second mapping relationship; Determine a first weight corresponding to the first coordination degree value and a second weight corresponding to the second coordination degree value; the sum of the first weight and the second weight is 1; The coordination degree value corresponding to the first group of resource scheduling modes is determined based on the first coordination degree value, the second coordination degree value, the first weight and the second weight.

[0120] In some possible implementations, in determining the coordination degree value corresponding to the first group of resource scheduling modes based on the first coordination degree value, the second coordination degree value, the first weight, and the second weight, the processing unit 702 is specifically configured to: Calculating based on the first coordination degree value, the second coordination degree value, the first weight value, and the second weight value to obtain a reference coordination degree value; Determine the proportion of new energy output corresponding to the first group of resource scheduling methods; Determine the target adjustment parameter corresponding to the new energy output ratio; The reference coordination degree value is adjusted based on the target adjustment parameter to obtain the coordination degree value corresponding to the first group of resource scheduling methods.

[0121] See also Figure 8 , Figure 8 Schematic diagram of the structure of an electronic device provided by the embodiment of the present application. Figure 8 As shown, the electronic device 800 includes a transceiver 801, a processor 802 and a memory 803. They are connected via a bus 804. The memory 803 is used to store computer programs and data, and the transceiver 801 can transmit the data stored in the memory 803 to the processor 802. The above program includes instructions for executing the following steps: Obtaining adjustable load resource data of the power system within a preset time period; Input the adjustable load resource data into a preset power generation side low-carbon scheduling model to obtain n groups of resource scheduling methods; n is an integer greater than 1; Determine the power generation characteristic indicators corresponding to the n groups of resource scheduling modes to obtain n groups of power generation characteristic indicators; each group of power generation characteristic indicators includes power supply on the power generation side, carbon emissions on the power generation side, and power generation cost on the power generation side; Inputting the n groups of resource scheduling methods into a preset load-side low-carbon scheduling model, obtaining load-side electricity consumption data corresponding to each group of resource scheduling methods in the n groups of resource scheduling methods, and obtaining n groups of load-side electricity consumption data; Determine the load side power consumption characteristic index corresponding to each set of load side power consumption data in the n sets of load side power consumption data, and obtain n sets of load side power consumption characteristic indexes; each set of load side power consumption characteristic indexes includes load side power demand, load side carbon emissions, and load side power consumption cost; Determine the coordination degree value corresponding to each of the n groups of resource scheduling modes based on the n groups of power generation characteristic indicators and the n groups of load-side power consumption characteristic indicators, and obtain n coordination degree values; the greater the coordination degree value of the resource scheduling mode, the better the resource scheduling effect of the resource scheduling mode; Determining the maximum coordination degree value among the n coordination degree values; Determine the resource scheduling method corresponding to the maximum coordination degree value, and obtain the target resource scheduling method.

[0122] In some possible implementations, in terms of determining the coordination degree value corresponding to each of the n groups of resource scheduling modes based on the n groups of power generation characteristic indicators and the n groups of load-side power consumption characteristic indicators to obtain n coordination degree values, the above program includes instructions for performing the following steps: Obtaining the power supply on the power generation side, the carbon emissions on the power generation side, and the power generation cost on the power generation side corresponding to the first group of power generation characteristic indicators, and obtaining the first power supply on the power generation side, the first carbon emissions on the power generation side, and the first power generation cost on the power generation side; the first group of power generation characteristic indicators is the power generation characteristic indicators corresponding to the first group of resource scheduling methods in the n groups of power generation characteristic indicators, and the first group of resource scheduling methods is any group of resource scheduling methods in the n groups of resource scheduling methods; Obtain the load side power demand, load side carbon emissions, and load side power cost corresponding to the first group of load side power consumption characteristic indicators to obtain the first load side power demand, the first load side carbon emissions, and the first load side power cost; the first group of load side power consumption characteristic indicators are the load side power consumption characteristic indicators corresponding to the first group of power generation characteristic indicators in the n groups of load side power consumption characteristic indicators; Determining a power supply and demand balance rate based on the power supply at the first power generation side and the power demand at the first load side; Determining a carbon emission reduction index based on the first power generation side carbon emissions and the first load side carbon emissions; Determining a cost-effectiveness index based on the first power generation side power generation cost and the first load side power consumption cost; The coordination degree value corresponding to the first group of resource scheduling methods is determined based on the electricity supply and demand balance rate, the carbon emission reduction index and the cost-effectiveness index.

[0123] In some possible implementations, in determining the carbon emission reduction indicator based on the first power generation side carbon emissions and the first load side carbon emissions, the program includes instructions for performing the following steps: Acquire the historical generation-side carbon emissions and the historical load-side carbon emissions of the power system in a historical time period; the end time of the historical time period is earlier than the start time of the preset time period, and the duration of the historical time period is the same as the duration of the preset time period; Determine the difference between the first power generation side carbon emissions and the historical power generation side carbon emissions to obtain the power generation side carbon emissions difference; Determine the difference between the first load-side carbon emissions and the historical load-side carbon emissions to obtain a load-side carbon emissions difference; Determine a carbon emission reduction value based on the difference between the carbon emission on the power generation side and the carbon emission on the load side; The carbon emission reduction indicator is determined based on the carbon emission reduction value.

[0124] In some possible implementations, in determining the cost-effectiveness indicator based on the first generation side power generation cost and the first load side power consumption cost, the program includes instructions for performing the following steps: Determine a target electricity cost based on the first power generation side power generation cost and the first load side power cost; Obtaining the economic benefits corresponding to the first group of resource scheduling methods to obtain target economic benefits; The cost-effectiveness index is determined based on the target electricity cost and the target economic benefit.

[0125] In some possible implementations, in terms of determining the coordination degree value corresponding to the first group of resource scheduling modes based on the power supply and demand balance rate, the carbon emission reduction index and the cost-effectiveness index, the above-mentioned program includes instructions for performing the following steps: Determine the difference between the power supply and demand balance rate and a preset power supply and demand balance rate to obtain the power supply and demand balance rate difference; When the power supply and demand balance rate difference is less than or equal to a preset power supply and demand balance rate difference, determining a coordination degree value corresponding to the first group of resource scheduling modes based on the carbon emission reduction index and the cost-effectiveness index; When the power supply and demand balance rate difference is greater than the preset power supply and demand balance rate difference, it is determined that the coordination degree value corresponding to the first group of resource scheduling methods is 0.

[0126] In some possible implementations, in terms of determining the coordination degree value corresponding to the first group of resource scheduling modes based on the carbon emission reduction indicator and the cost-effectiveness indicator, the above program includes instructions for performing the following steps: Obtaining a first mapping relationship between a carbon emission reduction index and a coordination degree value, and a second mapping relationship between a cost-effectiveness index and a coordination degree value; Determine a first coordination degree value corresponding to the carbon emission reduction indicator based on the first mapping relationship; Determine a second coordination degree value corresponding to the cost-effectiveness indicator based on the second mapping relationship; Determine a first weight corresponding to the first coordination degree value and a second weight corresponding to the second coordination degree value; the sum of the first weight and the second weight is 1; The coordination degree value corresponding to the first group of resource scheduling modes is determined based on the first coordination degree value, the second coordination degree value, the first weight and the second weight.

[0127] In some possible implementations, in terms of determining the coordination degree value corresponding to the first group of resource scheduling modes based on the first coordination degree value, the second coordination degree value, the first weight, and the second weight, the above-mentioned program includes instructions for performing the following steps: Calculating based on the first coordination degree value, the second coordination degree value, the first weight value, and the second weight value to obtain a reference coordination degree value; Determine the proportion of new energy output corresponding to the first group of resource scheduling methods; Determine the target adjustment parameter corresponding to the new energy output ratio; The reference coordination degree value is adjusted based on the target adjustment parameter to obtain the coordination degree value corresponding to the first group of resource scheduling methods.

[0128] It should be understood that the electronic devices in this application may include resource scheduling devices, smart phones (such as Android phones, iOS phones, Windows Phone phones, etc.), tablet computers, PDAs, laptop computers, mobile Internet devices MID (Mobile Internet Devices, MID for short) or wearable devices or servers, edge computing nodes, etc. The above electronic devices are only examples, not exhaustive, and include but are not limited to the above electronic devices.

[0129] The embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. The computer program is executed by a processor to implement part or all of the steps of any resource scheduling method recorded in the above method embodiment.

[0130] The present application also provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute part or all of the steps of any resource scheduling method described in the above method implementation.

[0131] It should be noted that, for the above-mentioned various method implementations, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that this application is not limited by the order of the actions described, because according to this application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the implementations described in the specification are all optional implementations, and the actions and modules involved are not necessarily required by this application.

[0132] In the above-mentioned embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0133] In the several embodiments provided in this application, it should be understood that the disclosed device can be implemented in other ways. For example, the device implementation described above is only schematic, such as the division of units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0134] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0135] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above integrated unit may be implemented in the form of hardware or in the form of a software program module.

[0136] If the integrated unit is implemented in the form of a software program module and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of each implementation method of the present application. The aforementioned memory includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk and other media that can store program codes.

[0137] A person skilled in the art may understand that all or part of the steps in the various methods of the above-mentioned embodiments may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable memory, and the memory may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0138] The above is a detailed introduction to the implementation methods of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above implementation methods is only used to help understand the method and core idea of ​​the present application. At the same time, for general technical personnel in this field, according to the idea of ​​the present application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A resource scheduling method, characterized in that: include: Obtaining adjustable load resource data of the power system within a preset time period; Input the adjustable load resource data into a preset power generation side low-carbon scheduling model to obtain n groups of resource scheduling methods; n is an integer greater than 1; Determine the power generation characteristic indicators corresponding to the n groups of resource scheduling modes to obtain n groups of power generation characteristic indicators; each group of power generation characteristic indicators includes power supply on the power generation side, carbon emissions on the power generation side, and power generation cost on the power generation side; Inputting the n groups of resource scheduling methods into a preset load-side low-carbon scheduling model, obtaining load-side electricity consumption data corresponding to each group of resource scheduling methods in the n groups of resource scheduling methods, and obtaining n groups of load-side electricity consumption data; Determine the load side power consumption characteristic index corresponding to each set of load side power consumption data in the n sets of load side power consumption data, and obtain n sets of load side power consumption characteristic indexes; each set of load side power consumption characteristic indexes includes load side power demand, load side carbon emissions, and load side power consumption cost; Determine the coordination degree value corresponding to each of the n groups of resource scheduling modes based on the n groups of power generation characteristic indicators and the n groups of load-side power consumption characteristic indicators, and obtain n coordination degree values; The greater the coordination degree value of the resource scheduling method, the better the resource scheduling effect of the resource scheduling method; Determining the maximum coordination degree value among the n coordination degree values; Determine the resource scheduling method corresponding to the maximum coordination degree value, and obtain the target resource scheduling method.

2. The method according to claim 1, characterized in that The determining of the coordination degree value corresponding to each of the n groups of resource scheduling modes based on the n groups of power generation characteristic indicators and the n groups of load-side power consumption characteristic indicators to obtain n coordination degree values ​​includes: Obtaining the power supply on the power generation side, the carbon emissions on the power generation side, and the power generation cost on the power generation side corresponding to the first group of power generation characteristic indicators, and obtaining the first power supply on the power generation side, the first carbon emissions on the power generation side, and the first power generation cost on the power generation side; the first group of power generation characteristic indicators is the power generation characteristic indicators corresponding to the first group of resource scheduling methods in the n groups of power generation characteristic indicators, and the first group of resource scheduling methods is any group of resource scheduling methods in the n groups of resource scheduling methods; Obtain the load side power demand, load side carbon emissions, and load side power cost corresponding to the first group of load side power consumption characteristic indicators to obtain the first load side power demand, the first load side carbon emissions, and the first load side power cost; the first group of load side power consumption characteristic indicators are the load side power consumption characteristic indicators corresponding to the first group of power generation characteristic indicators in the n groups of load side power consumption characteristic indicators; Determining a power supply and demand balance rate based on the power supply at the first power generation side and the power demand at the first load side; Determining a carbon emission reduction index based on the first power generation side carbon emissions and the first load side carbon emissions; Determining a cost-effectiveness index based on the first power generation side power generation cost and the first load side power consumption cost; The coordination degree value corresponding to the first group of resource scheduling methods is determined based on the electricity supply and demand balance rate, the carbon emission reduction index and the cost-effectiveness index.

3. The method according to claim 2, characterized in that The determining of the carbon emission reduction index based on the first power generation side carbon emissions and the first load side carbon emissions includes: Acquire the historical generation-side carbon emissions and the historical load-side carbon emissions of the power system in a historical time period; the end time of the historical time period is earlier than the start time of the preset time period, and the duration of the historical time period is the same as the duration of the preset time period; Determine the difference between the first power generation side carbon emissions and the historical power generation side carbon emissions to obtain the power generation side carbon emissions difference; Determine the difference between the first load-side carbon emissions and the historical load-side carbon emissions to obtain a load-side carbon emissions difference; Determine a carbon emission reduction value based on the difference between the carbon emission on the power generation side and the carbon emission on the load side; The carbon emission reduction indicator is determined based on the carbon emission reduction value.

4. The method according to claim 2, characterized in that The determining of the cost-effectiveness index based on the first power generation side power generation cost and the first load side power consumption cost includes: Determine a target electricity cost based on the first power generation side power generation cost and the first load side power cost; Obtaining the economic benefits corresponding to the first group of resource scheduling methods to obtain target economic benefits; The cost-effectiveness index is determined based on the target electricity cost and the target economic benefit.

5. The method according to any one of claims 2 to 4, characterized in that: The determining of the coordination degree value corresponding to the first group of resource scheduling modes based on the power supply and demand balance rate, the carbon emission reduction index and the cost-effectiveness index includes: Determine the difference between the power supply and demand balance rate and a preset power supply and demand balance rate to obtain the power supply and demand balance rate difference; When the power supply and demand balance rate difference is less than or equal to a preset power supply and demand balance rate difference, determining a coordination degree value corresponding to the first group of resource scheduling modes based on the carbon emission reduction index and the cost-effectiveness index; When the power supply and demand balance rate difference is greater than the preset power supply and demand balance rate difference, it is determined that the coordination degree value corresponding to the first group of resource scheduling methods is 0.

6. The method according to claim 5, characterized in that The determining the coordination degree value corresponding to the first group of resource scheduling modes based on the carbon emission reduction index and the cost-effectiveness index includes: Obtaining a first mapping relationship between a carbon emission reduction index and a coordination degree value, and a second mapping relationship between a cost-effectiveness index and a coordination degree value; Determine a first coordination degree value corresponding to the carbon emission reduction indicator based on the first mapping relationship; Determine a second coordination degree value corresponding to the cost-effectiveness indicator based on the second mapping relationship; Determine a first weight corresponding to the first coordination degree value and a second weight corresponding to the second coordination degree value; the sum of the first weight and the second weight is 1; The coordination degree value corresponding to the first group of resource scheduling modes is determined based on the first coordination degree value, the second coordination degree value, the first weight and the second weight.

7. The method according to claim 6, characterized in that The determining the coordination degree value corresponding to the first group of resource scheduling modes based on the first coordination degree value, the second coordination degree value, the first weight value, and the second weight value includes: Calculating based on the first coordination degree value, the second coordination degree value, the first weight value, and the second weight value to obtain a reference coordination degree value; Determine the proportion of new energy output corresponding to the first group of resource scheduling methods; Determine the target adjustment parameter corresponding to the new energy output ratio; The reference coordination degree value is adjusted based on the target adjustment parameter to obtain the coordination degree value corresponding to the first group of resource scheduling methods.

8. A resource scheduling device, characterized in that: The device comprises: an acquisition unit and a processing unit; The acquisition unit is used to acquire the adjustable load resource data of the power system within a preset time period; The processing unit is used to input the adjustable load resource data into a preset power generation side low-carbon scheduling model to obtain n groups of resource scheduling methods; n is an integer greater than 1; Determine the power generation characteristic indicators corresponding to the n groups of resource scheduling modes to obtain n groups of power generation characteristic indicators; each group of power generation characteristic indicators includes power supply on the power generation side, carbon emissions on the power generation side, and power generation cost on the power generation side; Inputting the n groups of resource scheduling methods into a preset load-side low-carbon scheduling model, obtaining load-side electricity consumption data corresponding to each group of resource scheduling methods in the n groups of resource scheduling methods, and obtaining n groups of load-side electricity consumption data; Determine the load side power consumption characteristic index corresponding to each set of load side power consumption data in the n sets of load side power consumption data, and obtain n sets of load side power consumption characteristic indexes; each set of load side power consumption characteristic indexes includes load side power demand, load side carbon emissions, and load side power consumption cost; Determine the coordination degree value corresponding to each of the n groups of resource scheduling modes based on the n groups of power generation characteristic indicators and the n groups of load-side power consumption characteristic indicators, and obtain n coordination degree values; the greater the coordination degree value of the resource scheduling mode, the better the resource scheduling effect of the resource scheduling mode; Determining the maximum coordination degree value among the n coordination degree values; Determine the resource scheduling method corresponding to the maximum coordination degree value, and obtain the target resource scheduling method.

9. An electronic device, characterized in that: The method comprises a processor, a memory, a communication interface and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, and the one or more programs include instructions for executing the steps in the method described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method according to any one of claims 1 to 7.

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