Coordinated operation method and related device of multi-resource aggregate cluster

Abstract

This application discloses a coordinated operation method and related apparatus for a multi-resource aggregate cluster, including: obtaining m resource aggregates in a target power system; determining m MRA models corresponding to the m resource aggregates; obtaining a first sales electricity price; predicting m first loads and m first powers corresponding to the m resource aggregates in a first future period using the m MRA models; adjusting the first sales electricity price based on the m first loads and m first powers to obtain a second sales electricity price in the first future period; predicting m second loads and m second powers corresponding to the m resource aggregates in a second future period using the m MRA models; determining m differences between the m second powers and the m first powers; and determining a target sales electricity price based on the m differences and the second sales electricity price. This application improves the coordination efficiency within the multi-resource aggregate cluster.

Description

Technical Field

[0001] The present application relates to the field of power system control technology, and in particular to a coordinated operation method and related devices for a multi-resource aggregate cluster. Background Art

[0002] With the establishment of a modern society and the surge in electricity consumption, the power system faces numerous changes and challenges. On the power supply side, renewable energy sources, represented by photovoltaics and wind power, are developing rapidly, with the proportion of installed capacity increasing annually. On the user side, vast load resources are gradually being transformed into controllable resources, and the power system is shifting from a "source follows load" model to a "source-load interaction" model. Against this backdrop, multi-resource aggregators (MRAs), which aggregate distributed generation, energy storage, and load-side resources, have become a crucial component of the power system. Furthermore, the optimal scheduling of MRAs has become a key research topic.

[0003] At present, most scheduling technologies are aimed at scheduling a single MRA, ignoring the collaborative operation between multi-resource aggregator clusters (MRACs), and cannot give full play to the synergistic effect between various entities, resulting in low coordination efficiency. Summary of the Invention

[0004] The embodiments of the present application provide a coordinated operation method and related devices for a multi-resource aggregate cluster, thereby improving the coordination efficiency within the multi-resource aggregate cluster.

[0005] In a first aspect, an embodiment of the present application provides a method for coordinated operation of a multi-resource aggregate cluster, comprising:

[0006] Obtain resource aggregates in the target power system to obtain m resource aggregates; m is an integer greater than 1;

[0007] Determine an MRA model corresponding to each of the m resource aggregates to obtain m MRA models;

[0008] Obtain the first sales electricity price;

[0009] Predicting the loads and powers of the m resource aggregates in a first future period using the m MRA models to obtain m first loads and m first powers;

[0010] Adjusting the first electricity sales price based on the m first loads and the m first powers to obtain a second electricity sales price for the first future period;

[0011] Predicting the load and power of the m resource aggregates in a second future period using the m MRA models to obtain m second loads and m second powers; the start time of the second future period is the end time of the first future period;

[0012] determining a difference between the m second powers and a corresponding first power among the m first powers to obtain m differences;

[0013] The target electricity sales price for the second future period is determined according to the m differences and the second electricity sales price, and an instruction is given to sell electricity at the target electricity sales price so that the electricity market reaches a supply and demand balance.

[0014] In a second aspect, an embodiment of the present application provides a coordinated operation device for a multi-resource aggregate cluster, the device comprising: an acquisition unit, a determination unit, and a coordination control unit, wherein:

[0015] The acquisition unit is used to acquire resource aggregates in the target power system to obtain m resource aggregates; m is an integer greater than 1;

[0016] The determining unit is configured to determine an MRA model corresponding to each of the m resource aggregates to obtain m MRA models;

[0017] The obtaining unit is further configured to obtain a first electricity sales price;

[0018] The coordination control unit is configured to predict the load and power of the m resource aggregates in a first future period using the m MRA models to obtain m first loads and m first powers; adjust the first sales electricity price based on the m first loads and the m first powers to obtain a second sales electricity price in the first future period; predict the load and power of the m resource aggregates in a second future period using the m MRA models to obtain m second loads and m second powers; the start time of the second future period is the end time of the first future period;

[0019] The determining unit is further configured to determine a difference between the m second powers and a corresponding first power among the m first powers, to obtain m differences;

[0020] The coordination control unit is further configured to determine a target electricity sales price for the second future period based on the m differences and the second electricity sales price, and instruct to sell electricity at the target electricity sales price so as to achieve a supply and demand balance in the electricity market.

[0021] In a third aspect, an embodiment of the present application 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, and the program includes instructions for executing the steps in the first aspect of the embodiment of the present application.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the above-mentioned computer-readable storage medium stores a computer program for electronic data exchange, wherein the above-mentioned computer program enables a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application.

[0023] In a fifth aspect, embodiments of the present application provide a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is operable to cause a computer to perform some or all of the steps described in the first aspect of the embodiments of the present application. The computer program product may be a software installation package.

[0024] The implementation of this application has the following beneficial effects:

[0025] It can be seen that the coordinated operation method of the multi-resource aggregate cluster described in this application adjusts the first sales electricity price to obtain the second sales electricity price based on the predicted m first loads and m first powers. This process can link the forecast of the resource aggregate with the electricity price. When it is predicted that the load is high or the power supply is tight, the electricity price is appropriately increased to suppress some unnecessary electricity demand; when the load is low or the power supply is sufficient, the electricity price is lowered to encourage users to increase electricity consumption. Through the economic lever of electricity price, the power system or users are guided to reasonably adjust their electricity consumption behavior, thereby achieving a preliminary balance between electricity supply and demand, reducing the coordination cost and resource waste caused by the imbalance between supply and demand in the power system, and improving the coordination efficiency within the multi-resource aggregate cluster. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the background technology, the drawings required for use in the embodiments of the present application or the background technology will be described below.

[0027] Figure 1 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application;

[0028] Figure 2 This is a schematic diagram of a scenario of an electronic device provided in an embodiment of the present application;

[0029] Figure 3 This is a flow chart of a method for coordinated operation of a multi-resource aggregate cluster provided in an embodiment of the present application;

[0030] Figure 4 is a structural diagram of a target power system provided in an embodiment of the present application;

[0031] Figure 5 This is a flow chart of another method for coordinated operation of a multi-resource aggregate cluster provided by an embodiment of the present application;

[0032] Figure 6 This is a flow chart of an electricity price adjustment method provided in an embodiment of the present application;

[0033] Figure 7 This is a block diagram of the functional units of a coordinated operation device for a multi-resource aggregate cluster provided by an embodiment of the present application;

[0034] Figure 8 It is a structural diagram of another electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0035] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0036] The terms "first," "second," and the like in the specification and claims of this application and the accompanying drawings are used to distinguish between different objects, not to describe a particular order. Furthermore, the terms "including," "having," and any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or elements is not limited to the listed steps or elements but may optionally include steps or elements not listed, or may optionally include other steps or elements inherent to the process, method, product, or apparatus.

[0037] It should be understood that the term "and / or" in this document is merely a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document indicates that the associated objects are in an "or" relationship. The "plurality" appearing in the embodiments of this application refers to two or more.

[0038] In the embodiments of the present application, "at least one item" or similar expressions refers to any combination of these items, including any combination of single items or plural items, and refers to one or more, and multiple refers to two or more. For example, at least one item (item) of a, b, or c can represent the following seven situations: a, b, c, a and b, a and c, b and c, a, b, and c. Among them, each of a, b, and c can be an element or a set containing one or more elements.

[0039] The "connection" appearing in the embodiments of the present application refers to various connection methods such as direct connection or indirect connection to achieve communication between devices, and the embodiments of the present application do not impose any limitations on this.

[0040] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0041] The electronic devices described in the embodiments of the present application may include smart phones (such as Android phones, iOS phones, Windows Phone phones, etc.), tablet computers, PDAs, laptops, video matrices, monitoring platforms, mobile internet devices (MIDs) or wearable devices, etc. The above are only examples and not exhaustive, including but not limited to the above devices.

[0042] Of course, the above-mentioned electronic device can also be a server, for example, a cloud server.

[0043] The following describes the relevant contents, concepts, meanings, technical issues, technical solutions, beneficial effects, etc. involved in the embodiments of this application.

[0044] First, some professional terms involved in this application are explained:

[0045] A Multi-Resource Aggregator (MRA) is an entity or concept that integrates and aggregates multiple different types of power-related resources. These resources can include distributed power sources (such as solar photovoltaic power plants and small wind turbines), energy storage devices (such as battery energy storage systems), and controllable loads (such as industrial interruptible loads and smart appliances). MRAs use technical means and management strategies to unify the management and scheduling of these dispersed resources, achieving optimal resource allocation and coordinated operation, thereby improving the flexibility, reliability, and cost-effectiveness of the power system.

[0046] Multi-Resource Aggregator Cluster (MRAC): A cluster formed by the aggregation of multiple Multi-Resource Aggregators (MRAs). Different MRAs may aggregate power resources of different types or regions. MRACs bring these MRAs together to achieve larger-scale resource integration and coordinated control. This allows for optimized dispatch of power resources across a wider range of areas, enhancing the power system's ability to cope with complex operating conditions and large-scale load fluctuations, and playing a greater role in participating in power market transactions and providing ancillary services.

[0047] The MRA model is a mathematical or analytical model built for a multi-resource aggregate. It describes the characteristics, operating patterns, and interactions of various resources within the MRA. By collecting and analyzing historical and real-time operational data from various resources within the MRA and integrating it with the physical laws and constraints of the power system, a model is constructed that accurately reflects the MRA's behavior and performance. This model can be used to predict key parameters such as load demand and power output under different operating conditions, providing an important basis for power system planning, operational scheduling, and market trading decisions.

[0048] Load: In power systems, load refers to the total amount of electricity demanded by power users or devices at a given moment. It can be categorized from various perspectives. Based on user type, it can be divided into industrial, commercial, and residential loads; based on temporal characteristics, it can be divided into peak load, off-peak load, and average load. The magnitude and variability of loads have a significant impact on power system planning, operation, and scheduling. Power systems must rationally arrange power generation and transmission based on load demand to ensure reliable and stable power supply.

[0049] Power refers to the work done or energy transferred per unit time. In power systems, power is divided into active power, reactive power, and apparent power. Active power is the power actually consumed by electrical equipment to convert electrical energy into other forms of energy (such as mechanical energy and thermal energy). It is measured in watts (W). Reactive power is primarily used to establish and maintain the magnetic field in electrical equipment. It does not directly perform work, but has a significant impact on the voltage stability and power quality of the power system. Its unit is var. Apparent power is the vector sum of active power and reactive power, reflecting the capacity of the electrical equipment. Its unit is volt-ampere (VA). Power balance and control are key factors in the stable operation of power systems.

[0050] See also Figure 1 , Figure 1This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. It can be seen that the electronic device may include: a communication module, a modeling module, a coordination control module, etc., which are not limited here, wherein:

[0051] The communication module is used to collect real-time data, such as power and load, from each resource aggregate during the coordinated operation of a multi-resource aggregate cluster. The communication module is responsible for receiving this data from multiple resource aggregates and transmitting it to the electronic device for processing. Simultaneously, it can also send information such as control instructions generated by the electronic device to the corresponding resource aggregate, enabling two-way data exchange. Furthermore, the communication module can communicate with external systems (such as power market trading platforms and other power system control centers) to obtain key external information such as the initial sales electricity price. It can also transmit information such as the power system's operating status and relevant data about resource aggregates to facilitate participation in power market transactions and receive dispatch instructions from higher-level systems, thereby promoting collaboration between the multi-resource aggregate cluster and the external environment.

[0052] The modeling module is used to construct MRA models based on the characteristics and historical data of each resource aggregate in the multi-resource aggregate cluster. These models accurately describe the operating patterns, load, and power variations of each resource aggregate, providing a foundation for subsequent forecasting and analysis. Furthermore, the modeling module can leverage these constructed MRA models to predict the future load and power of each resource aggregate. By inputting relevant influencing factor data (such as time, weather, and historical electricity usage patterns), the model outputs predicted load and power values, providing important decision-making basis for electricity price regulation and coordinated control.

[0053] The coordination control module is used to adjust the electricity price according to specific rules based on the load and power data of each resource aggregate predicted by the modeling module and the obtained first sales electricity price, thereby achieving price regulation in the electricity market and guiding the rational allocation of electricity resources. In addition, the coordination control module can also issue corresponding control instructions based on the results of the electricity price adjustment and the operating status of each resource aggregate to coordinate the operation of each resource aggregate in the multi-resource aggregate cluster. For example, when the target sales electricity price is high, the power generation resource aggregate is controlled to increase power output, and the controllable load resource aggregate is controlled to reduce power consumption; conversely, the opposite adjustment is made to achieve a balance between supply and demand in the electricity market and ensure the efficient and coordinated operation of the multi-resource aggregate cluster.

[0054] See also Figure 2 , Figure 2This is a scene diagram of an electronic device provided in an embodiment of the present application. It can be seen that the staff, as the operating subject, can operate the electronic device, for example, execute the two key instructions of "connecting to the power system" and "starting coordination". The electronic device is the intermediate bridge connecting the staff and the power distribution network. Two options are displayed on the electronic device, one is "connecting to the power system" and the other is "starting coordination". "Connecting to the power grid" means that the electronic device can establish a connection with the target power system, realize data interaction, and obtain the operating parameters, equipment status and other information of the target power system; "starting coordination" means that after obtaining the data, the electronic device can adjust the equipment in the target power system according to the built-in algorithm. Specifically, the electronic device can execute the coordinated operation method of the multi-resource aggregate cluster provided in the embodiment of the present application to coordinate the target power system. The specific steps are as follows:

[0055] Obtain resource aggregates in the target power system to obtain m resource aggregates; m is an integer greater than 1;

[0056] Determine an MRA model corresponding to each of the m resource aggregates to obtain m MRA models;

[0057] Obtain the first sales electricity price;

[0058] Predicting the loads and powers of the m resource aggregates in a first future period using the m MRA models to obtain m first loads and m first powers;

[0059] Adjusting the first electricity sales price based on the m first loads and the m first powers to obtain a second electricity sales price for the first future period;

[0060] Predicting the load and power of the m resource aggregates in a second future period using the m MRA models to obtain m second loads and m second powers; the start time of the second future period is the end time of the first future period;

[0061] determining a difference between the m second powers and a corresponding first power among the m first powers to obtain m differences;

[0062] The target electricity sales price for the second future period is determined according to the m differences and the second electricity sales price, and an instruction is given to sell electricity at the target electricity sales price so that the electricity market reaches a supply and demand balance.

[0063] It should be explained that the above-mentioned electronic device can execute part or all of the steps of the coordinated operation method of the multi-resource aggregate cluster provided in the embodiment of the present application.

[0064] See also Figure 3 , Figure 3This is a flow chart of a coordinated operation method of a multi-resource aggregate cluster provided by an embodiment of the present application. The method can be applied to electronic devices and includes but is not limited to the following steps:

[0065] S301. Obtain resource aggregates in a target power system to obtain m resource aggregates; m is an integer greater than 1.

[0066] In the embodiment of the present application, the target power system may be a physical power grid, or may be a virtual power grid.

[0067] In a specific embodiment, the electronic device can communicate or be physically connected with the target power system, and use smart meters, sensors and other detection equipment to collect the operating data of various resources in the target power system, including power, voltage, current, load and other information. Then, based on the collected data and the characteristics of the resources, various types of resources in the target power system are identified. For example, different types of distributed power sources are distinguished according to the power generation principle; the type of controllable load is determined according to the function and adjustability of the power-consuming equipment, etc., thereby obtaining a variety of resources. Furthermore, the identified resources can be classified according to certain rules. For example, resources with similar adjustment characteristics or response speeds are classified into one category. Based on the classification and characteristics of the resources, resources of the same type are combined into resource aggregates, thereby obtaining m resource aggregates.

[0068] Optional, see Figure 4 , Figure 4 This is a structural diagram of a target power system provided in an embodiment of the present application. It can be seen that the target power system may include: a controller, transferable loads, interruptible loads, distributed generator sets, photovoltaic, wind power, energy storage and other equipment, wherein the controller can be used to execute part or all of the steps in the coordinated operation method of the multi-resource aggregate cluster provided in an embodiment of the present application.

[0069] Optionally, within the first future period, the scheduling time scale can be 1 hour. During this phase, the MRA individual takes into account its own internal supply and demand balance and optimizes scheduling with the goal of minimizing scheduling costs. The objective function includes: the output cost of distributed generators, the cost of interruptible load deployment, the cost of transferable load deployment, the cost of energy storage, and the cost of market electricity purchase and sales. The objective function is as follows:

[0070]

[0071] Where, is the operating cost of the i-th MRA; Output cost of distributed generators, Output cost of distributed generators, Calling cost for transferable load, is the energy storage cost; Represents the cost of purchasing and selling electricity.

[0072] in, It can be expressed as follows:

[0073]

[0074] Where, Indicates the amount of electricity purchased by the target power system, Indicates the amount of electricity sold by the target power system; The electricity price for market transactions.

[0075] S302: Determine the MRA model corresponding to each of the m resource aggregates to obtain m MRA models.

[0076] In an embodiment of the present application, detailed information of each of the m resource aggregates can be obtained. For example, for a resource aggregate including distributed power sources, data such as the type of power source (solar energy, wind energy, etc.), rated power, and conversion efficiency need to be collected; then, MRA modeling can be performed based on the obtained information to obtain m MRA models.

[0077] Optional, see Figure 5 , Figure 5 This is a flow chart of another coordinated operation method of a multi-resource aggregate cluster provided by an embodiment of the present application. Figure 5 As shown, step S302, determining the MRA model corresponding to each of the m resource aggregates to obtain m MRA models, may include the following steps:

[0078] S21. Determine a first resource type corresponding to the first resource aggregate; the first resource type includes one of the following: photovoltaic, wind power, distributed generator, interruptible load, transferable load, and energy storage;

[0079] S22. Acquire a first power generation device corresponding to the first resource aggregate;

[0080] S23. Acquire first device parameter data corresponding to the first power generation device;

[0081] S24: Construct an MRA model corresponding to the first resource aggregate according to the first device parameter data.

[0082] In the embodiment of the present application, the first device parameter data may include at least one of the following: rated capacity, rated power, power factor, etc., which are not limited here.

[0083] In a specific embodiment, the first resource type corresponding to the first resource aggregate can be determined first. Specifically, the quantity, capacity or power proportion of each type of resource in the first resource aggregate can be counted to determine the dominant resource type, that is, the first resource type. For example, if the total capacity of distributed power sources in a resource aggregate is the largest, then the resource type of the aggregate can be preliminarily determined to be a distributed generator set; then, the first power generation device corresponding to the first resource aggregate can be obtained. Specifically, the first power generation device can be a virtual power generation device. All power generation devices contained in the first resource aggregate can be obtained first, and these power generation devices can be virtualized into one power generation device to obtain the first power generation device.

[0084] Next, the first device parameter data corresponding to the first power generation device can be obtained. Specifically, the device parameter data of all power generation devices in the first resource aggregate can be obtained to obtain multiple device parameter data. Then, the multiple device parameter data are fused to obtain the first device parameter data. For example, assuming that there are three solar photovoltaic panels in the first resource aggregate, with rated powers of 300W, 350W, and 400W respectively, the rated power of the virtual power generation device is 300+350+400=1050W. When considering the actual power generation power, a weighted sum is required based on the real-time power generation data of each device, and the weight can be determined according to the proportion of the rated power of the device. Finally, the MRA model corresponding to the first resource aggregate can be constructed based on the first device parameter data.

[0085] By constructing an MRA model for the first resource aggregate, the various resources and devices within the first resource aggregate can be considered as a whole, taking into account their interactions and synergies. For example, in a resource aggregate comprising photovoltaics, wind power, and energy storage, the MRA model can analyze the power variations of photovoltaics and wind power under different weather conditions, as well as how energy storage devices regulate charging and discharging to maintain system power balance and stable operation. This allows for system-level optimization and management of the entire resource aggregate.

[0086] Let’s take an example:

[0087] (1) The first resource type is photovoltaic

[0088] Photovoltaic power generation technology is an energy technology that uses solar cells to directly convert solar radiation into electrical energy. It is characterized by being clean, green, and highly reliable. The output characteristics of photovoltaic power generation are closely related to environmental factors. Its output power model (i.e., the MRA model) is shown below:

[0089]

[0090] Where, is the photovoltaic output power; 、 、 are the maximum output power, light intensity and ambient temperature under standard test conditions; 、 is the light intensity and temperature in the current environment; is the temperature coefficient, usually taken .

[0091] (2) The first resource type is wind power

[0092] Wind power generation technology utilizes wind power to drive the rotation of wind turbines, converting wind energy into mechanical energy, which is then converted into electrical energy through generators. The core of the wind power generation model is the wind turbine, whose power generation depends primarily on wind speed and the design characteristics of the rotor. The functional relationship between wind power generation and wind speed is shown below:

[0093]

[0094]

[0095]

[0096] Where, is the output power of the fan; is the rated power of the wind turbine, 、 、 、 They are the cut-in wind speed, cut-out wind speed, rated wind speed and actual wind speed of the wind turbine respectively; k1 is the linear gain coefficient, which means ~ In the interval, the slope of wind turbine output power increases with wind speed; k2 is the intercept of the modified linear relationship, which makes the wind turbine output power decrease at the cut-in wind speed. transitions smoothly to a non-zero value.

[0097] (3) The first resource type is distributed generators

[0098] Distributed generator sets (hereinafter referred to as units) refer to power generation equipment that can adjust output power at any time based on power demand. Common examples include diesel generators and coal-fired units. Diesel generators use diesel combustion to drive the engine, which in turn drives the generator to generate electricity. They feature rapid startup and stable output, making them suitable for emergency power supply or scenarios with large load fluctuations. Coal-fired units, on the other hand, burn coal to heat water to generate steam, which drives the steam turbine to generate electricity. They are widely used in baseload power supply. The power generation cost of a distributed generator set typically has a quadratic function relationship with the power generated. The specific function formula (i.e., the MRA model) is as follows:

[0099]

[0100] Where: 、 Respectively represent the operating cost and actual output of the unit; 、 They represent the operating cost coefficients of the units respectively.

[0101] Functions of distributed generators The constraints that must be met include unit output constraints and ramp constraints:

[0102]

[0103]

[0104] Where, 、 Respectively represent the upper and lower limits of the unit output; 、 They respectively represent the upper and lower limits of the unit's climbing capacity.

[0105] (4) The first resource type is interruptible load (IL)

[0106] Interruptible loads refer to power loads that can be temporarily interrupted during power system operation based on grid demand or emergency situations. Typically, users (such as industrial users and commercial facilities) sign contracts for these loads, agreeing under specific conditions to proactively reduce or cease power consumption when power supply is tight. This allows the power system to alleviate supply and demand pressures during peak load periods or when grid failures occur, enhancing system stability. Managing interruptible loads helps reduce system operating costs and improve power supply reliability. The MRA model for interruptible loads is as follows:

[0107]

[0108]

[0109] Where, Indicates the IL user call cost; They represent the unit compensation cost of IL user load interruption respectively; Indicates the load interruption amount of IL users; They represent the maximum interruption limit of load respectively.

[0110] (5) The first resource type is transferable load (TL)

[0111] Transferable loads refer to loads that can be flexibly transferred between different time periods or regions within the power system. This type of load is characterized by users being able to shift part of the load to periods or regions with lower grid loads during peak power demand periods, thereby optimizing grid operation. Transferable loads are typically applied to loads with flexible dispatch capabilities, such as industrial production lines, cold chain storage, or large-scale air conditioning systems. By effectively managing transferable loads, the difference between peak and valley power levels can be reduced, improving the economic efficiency and stability of the power system and avoiding excessive loads during peak periods. The MRA model is as follows:

[0112]

[0113]

[0114]

[0115]

[0116] Where, represents the call cost of TL users; represents the unit compensation cost for TL user load transfer; 、 The load adjustment amount and the load reduction amount of the TL user should be the same in the entire scheduling period T; Indicates the maximum load transfer upper limit.

[0117] (6) The first resource type is energy storage

[0118] The operating costs and constraints of the energy storage equipment (i.e., the MRA model) are shown below.

[0119]

[0120]

[0121]

[0122]

[0123]

[0124]

[0125] Where, The operating cost of energy storage; 、 are the energy storage charging and discharging power respectively; is the unit operating cost of energy storage; The upper limit of storage, charge and discharge; is the energy storage capacity; 、 Respectively represent the upper and lower limits of energy storage capacity; is the energy storage charging and discharging efficiency; t represents time.

[0126] It should be explained that the first resource type may also include multiple resource types. For example, assuming that the first resource aggregate includes photovoltaics and wind power, the first resource type includes photovoltaics and wind power. Photovoltaic and wind power can be modeled separately, and then the various resources (i.e., photovoltaics and wind power) can be integrated into a comprehensive model, thereby obtaining the MRA model corresponding to the first resource aggregate.

[0127] S303: Obtain a first sales electricity price.

[0128] In the embodiment of the present application, the website of the power market operator can be accessed to query the current sales electricity price, that is, the first sales electricity price, from the website.

[0129] S304 : Predict the loads and powers of the m resource aggregates in a first future period using the m MRA models to obtain m first loads and m first powers.

[0130] In an embodiment of the present application, the first future time period can be preset or defaulted in advance. The first future time period is a long-term period, for example, the next year, the next month, the next week, etc., which is not limited here; therefore, predicting the load and power of the first future time period belongs to long-term prediction.

[0131] In a specific embodiment, historical data can be collected from m resource aggregates. This historical data can include load and power data. This data should cover as long a timeframe as possible to reflect the operational patterns and seasonal variations of the resource aggregates. Simultaneously, data related to external factors related to load and power can be collected, such as weather data (temperature, humidity, wind speed, etc.), date types (weekdays, weekends), and holiday information. Next, forecast data for a first future time period can be obtained, such as forecast weather data, date types, and other data. Based on the input requirements of the MRA model, the historical data and forecast data for the first future time period are combined into an input vector, resulting in m input vectors. Each input vector should contain sufficient information for the MRA model to learn the variation patterns of load and power. These m input vectors are then input into corresponding MRA models among the m MRA models to obtain m first loads and m first powers.

[0132] S305: Adjust the first electricity sales price based on the m first loads and the m first powers to obtain a second electricity sales price for the first future period.

[0133] In an embodiment of the present application, m first loads and m first powers can be used to judge the supply and demand situation of the electricity market, and the first sales electricity price can be adjusted according to the supply and demand situation to obtain the second sales electricity price for the first future period. For example, if supply exceeds demand, the first sales electricity price can be adjusted down.

[0134] Optionally, step S305, adjusting the first sales electricity price based on the m first loads and the m first powers to obtain a second sales electricity price, may include the following steps:

[0135] A1. Determine a total first load based on the m first loads;

[0136] A2. Determine a first power sum according to the m first powers;

[0137] A3. Determine a difference between the first load sum and the first power sum to obtain a first difference;

[0138] A4. Determine a target market state based on the first difference; the target market state includes one of the following: an oversupply state, an equilibrium state, or a undersupply state;

[0139] A5. When the target market state does not include the equilibrium state, obtaining a target electricity price adjustment coefficient;

[0140] A6. Determine the number of times the current electricity price has been iterated, and obtain the number of times the electricity price has been iterated;

[0141] A7. Adjust the first sales electricity price according to the target electricity price adjustment coefficient, the number of electricity price iterations, and a preset electricity price iteration formula to obtain a second sales electricity price. The preset electricity price iteration formula is as follows:

[0142]

[0143] in, represents the second sales electricity price; Indicates in The electricity price sold in the round iteration; Indicates in The electricity purchase willingness of the target power system in the round iteration; Indicates in The electricity sales willingness of the target power system in the round iteration; represents the target electricity price adjustment coefficient; is the number of iterations of the electricity price.

[0144] In an embodiment of the present application, the sum of m first loads can be calculated first to obtain the first load sum, and the sum of m first powers can be calculated to obtain the first power sum; then, the first difference can be obtained by subtracting the first power sum from the first load sum; then, the target market state can be determined based on the first difference. Specifically, if the first difference is equal to 0, it indicates that the target market state is in equilibrium; if the first difference is greater than 0, it indicates that the target market state is in a state where supply is less than demand; and if the first difference is less than 0, it indicates that the target market state is in a state where supply exceeds demand.

[0145] When the target market state does not include an equilibrium state, the target electricity price adjustment coefficient can be obtained; then, the number of times the electricity price has been iterated at the current moment can be determined to obtain the number of times the electricity price has been iterated. Specifically, the electricity price adjustment data can be obtained from the database of the electronic device, and the counter can be initialized to 0. Each time an electricity price adjustment is found in the electricity price adjustment data, the counter is increased by 1, and the electricity price adjustment data is traversed. The final value of the counter is the number of times the electricity price has been iterated; finally, the first sales electricity price can be adjusted according to the target electricity price adjustment coefficient, the number of times the electricity price has been iterated, and the preset electricity price iteration formula to obtain the second sales electricity price.

[0146] In this way, by separately calculating the sum of m primary loads and m primary powers, the electricity demand and generation capacity of multiple resource aggregates can be aggregated. This allows electricity market participants (such as grid operators and electricity retail companies) to understand the overall scale of electricity supply and demand in the entire market from a macro perspective, providing a quantitative data foundation for subsequent decision-making. When the target market state is not in equilibrium, the target electricity price adjustment coefficient is obtained. Combined with the number of electricity price iterations, the first sales price is adjusted using a preset electricity price iteration formula to obtain the second sales price. This mechanism enables timely and flexible adjustment of electricity prices based on actual market supply and demand. When supply exceeds demand, electricity prices are lowered to stimulate electricity consumption and reduce overgeneration; when supply falls short, prices are raised to curb excessive demand and guide users to use electricity rationally.

[0147] Optionally, obtaining the target electricity price adjustment coefficient may include the following steps:

[0148] B1. Obtaining the operating cost of each of the m resource aggregates to obtain m operating costs;

[0149] B2. determining a target operating cost based on the m operating costs;

[0150] B3. determining initial sales revenue based on the first total power, the first sales electricity price, and a preset sales period;

[0151] B4. Determining an initial profit coefficient based on the target operating cost and the initial sales revenue;

[0152] B5. Determine the reference electricity price adjustment coefficient corresponding to the initial profit coefficient;

[0153] B6. Obtaining historical price adjustment data corresponding to the target power system;

[0154] B7. Determine the target season corresponding to the current moment;

[0155] B8. Predicting the target price change rate corresponding to the target season based on the historical price adjustment data;

[0156] B9. Adjust the reference electricity price adjustment coefficient according to the target price change rate to obtain the target electricity price adjustment coefficient.

[0157] In the embodiment of the present application, the preset sales period can be preset in advance or defaulted.

[0158] In a specific embodiment, the operating cost of each of the m resource aggregates can be obtained to obtain m operating costs. Specifically, the average purchase cost, daily maintenance cost, power generation cost, etc. of each of the m resource aggregates can be obtained. By combining these costs, m operating costs can be obtained; then, the target operating cost can be determined based on the m operating costs. Specifically, these m operating costs can be added together to obtain the target operating cost.

[0159] Furthermore, the initial sales revenue can be determined based on the first total power, the first sales electricity price, and the preset sales period. Specifically, the first total power can be multiplied by the preset sales period to obtain the first power generation amount. Then, the first power generation amount can be multiplied by the first sales electricity price to obtain the initial sales revenue. Next, the initial profit coefficient can be calculated based on the target operating cost and the initial sales revenue. The specific calculation formula is as follows:

[0160]

[0161] Among them, k is the initial profit coefficient, is the weight of the i-th resource aggregate in the above m resource aggregates (which can be determined based on the proportion of power generation capacity, sales revenue, etc.); is the sales revenue of the i-th resource aggregate in the initial sales revenue; is the operating cost of the i-th resource aggregate in the target operating cost; according to the above formula, the initial profit coefficient can be obtained.

[0162] Next, the reference electricity price adjustment coefficient corresponding to the initial profit coefficient can be determined. Specifically, the mapping relationship between the preset profit coefficient and the electricity price adjustment coefficient can be pre-stored, and the reference electricity price adjustment coefficient corresponding to the initial profit coefficient can be determined based on the mapping relationship. The value range of the reference electricity price adjustment coefficient can be -0.5~0.5; then, the historical price adjustment data corresponding to the target power system can be obtained. Specifically, the change data of its sales electricity price, that is, the historical price adjustment data, can be obtained from the database of the target power system. Then, the target season corresponding to the current moment can be determined. Specifically, the date of the current moment can be obtained and the target season can be determined based on the date. Furthermore, the target price change rate corresponding to the target season can be predicted based on the historical price adjustment data; finally, the reference electricity price adjustment coefficient can be adjusted according to the target price change rate. The specific calculation formula is as follows:

[0163] Target electricity price adjustment coefficient = reference electricity price adjustment coefficient × (1 + target price change rate);

[0164] According to the above formula, the target electricity price adjustment coefficient can be obtained.

[0165] By obtaining the operating costs of m resource aggregates and determining target operating costs, a comprehensive and detailed understanding of the cost structure of the entire power system's resource side is achieved. Operating costs vary significantly across resource aggregates. For example, for photovoltaic aggregates, they primarily consist of equipment depreciation and a small amount of maintenance costs, while for thermal power aggregates, they involve a variety of costs, such as fuel and environmental protection. Integrating this cost data helps operators accurately understand the scale and distribution of costs, providing data support for subsequent decision-making. Furthermore, by comprehensively considering the operating costs and revenues of each resource aggregate, as well as market history and seasonal factors, the target electricity price adjustment coefficient can be determined to ensure that electricity prices adapt to dynamic changes in the power market. This series of analyses and calculations allows operators to adjust electricity pricing strategies to respond to cost fluctuations, changes in demand, and shifts in the market competition environment, maintaining flexibility and adaptability in the market.

[0166] Optionally, predicting the target price change rate corresponding to the target season based on the historical price adjustment data may include the following steps:

[0167] C1. Extracting price adjustment data corresponding to the target season from the historical price adjustment data to obtain i segments of price adjustment data, where i is a positive integer;

[0168] C2. Perform straight line fitting based on the i segments of price adjustment data and their corresponding adjustment times to obtain i straight lines;

[0169] C3. Determine the slope corresponding to each of the i straight lines to obtain i slopes;

[0170] C4. Determine the average slope corresponding to the i slopes;

[0171] C5. determining a reference price change rate based on the average slope;

[0172] C6. Obtaining the average generated power of the target power system in the target season;

[0173] C7. Determine the deviation between the average power generation and the preset power generation to obtain a target deviation;

[0174] C8. Determine the target optimization factor corresponding to the target deviation;

[0175] C9. Adjust the reference price change rate according to the target optimization factor to obtain the target price change rate.

[0176] In the embodiment of the present application, the preset power generation power can be preset in advance or defaulted.

[0177] In a specific embodiment, price adjustment data corresponding to the target season can be extracted from the historical price adjustment data to obtain i segments of price adjustment data. Specifically, the time range corresponding to the target season can be determined first. For example, if the target season is summer, June, July, and August can usually be determined as the time range of summer. Then, the data in the historical price adjustment data that is within the time range of the target season can be filtered out according to the date to obtain i segments of price adjustment data; then, a straight line fitting can be performed based on the i segments of price adjustment data and their corresponding adjustment moments to obtain i straight lines. Taking the first price adjustment data as an example, the first price adjustment data is a segment of price adjustment data in the i segments of price adjustment data. All prices in the first price adjustment data can be determined first to obtain multiple prices, and then multiple adjustment moments corresponding to the multiple prices can be determined. The multiple prices and corresponding adjustment moments in the multiple adjustment moments can be combined to obtain multiple coordinate points. The least squares method can be used to perform a straight line fitting on the multiple coordinate points to obtain the first straight line.

[0178] Next, the slope corresponding to each of the i straight lines can be calculated to obtain i slopes; then, the average of the i slopes, that is, the average slope, can be calculated; then, the reference price change rate can be determined based on the average slope. Specifically, since the i straight lines represent the changing relationship between price and time, the average slope can be directly used as the reference price change rate; further, the average power generation of the target power system in the target season can be obtained. Specifically, the operating data of each power generation equipment in the system in the target season can be obtained from the database of the target power system. The total power generation of the target power system in the target season can be determined based on the operating data. The average power generation power can be obtained by dividing the total power generation power by the number of days in the target season; then, the deviation between the average power generation power and the preset power generation power can be determined. The specific calculation formula is as follows:

[0179] Target deviation = (average power generation - preset power generation) / preset power generation;

[0180] According to the above formula, the target deviation can be obtained; then, the target optimization factor corresponding to the target deviation can be determined. Specifically, a mapping relationship between a preset deviation and an optimization factor can be pre-stored, and the target optimization factor corresponding to the target deviation can be determined based on the mapping relationship. The value range of the target optimization factor can be -0.3~0.3; finally, the reference price change rate can be adjusted according to the target optimization factor. The specific calculation formula is as follows:

[0181] Target price change rate = reference price change rate × (1 + target optimization factor);

[0182] According to the above formula, the target price change rate can be obtained.

[0183] By extracting price adjustment data corresponding to the target season from historical price adjustment data, we can focus on price fluctuations in a specific season. Since electricity demand and supply vary across seasons, price fluctuation patterns also vary. This approach eliminates interference from other seasonal factors and allows for a more accurate analysis of electricity price characteristics for the target season.

[0184] S306. Predict the load and power of the m resource aggregates in a second future period using the m MRA models to obtain m second loads and m second powers; the start time of the second future period is the end time of the first future period.

[0185] In an embodiment of the present application, the time length of the second future period can be a fixed value, and the second future period is a short-term period, for example, one day, one hour, 30 minutes, etc., which is not limited here; therefore, predicting the load and power of the second future period belongs to a short-term prediction.

[0186] In a specific embodiment, the load and power of m resource aggregates in the second future period can be predicted by m MRA models to obtain m second loads and m second powers. Specifically, the prediction method of m second loads and m second powers can be the same as the prediction method of the above-mentioned m first loads and m first powers, which will not be repeated here.

[0187] S307: Determine the difference between the m second powers and a corresponding first power among the m first powers to obtain m differences.

[0188] In the embodiment of the present application, since the result of short-term prediction is more reliable than the result of long-term prediction, m differences can be obtained by subtracting the corresponding first power from the m first powers from the m second powers.

[0189] S308: Determine a target electricity sales price for the second future period based on the m differences and the second electricity sales price, and instruct to sell electricity at the target electricity sales price so as to achieve a supply-demand balance in the electricity market.

[0190] In an embodiment of the present application, the second sales electricity price can be adjusted according to the m differences to obtain the target sales electricity price for the second future period. Then, the target power system or staff can be instructed to sell electricity at the target sales electricity price to achieve a balance between supply and demand in the electricity market.

[0191] Optionally, step S308, determining the target sales electricity price according to the m differences and the second sales electricity price, may include the following steps:

[0192] D1. Determine the power supply and demand ratios corresponding to the m MRA models based on the m differences to obtain a target power supply and demand ratio;

[0193] D2. When the target power supply-demand ratio is equal to 1, obtaining a target power purchase price for the target power system, and determining the target power sales price based on the target power purchase price;

[0194] D3. When the target power supply-demand ratio is not equal to 1, the second sales electricity price is adjusted according to a preset electricity price adjustment rule to obtain the target sales electricity price.

[0195] In the embodiment of the present application, the preset electricity price adjustment rules may be preset or defaulted in advance. Specifically, the preset electricity price adjustment rules may include the following three rules:

[0196] ①MRA balance of revenue and expenditure;

[0197] ② The electricity purchase price (i.e. the electricity purchase price) is greater than or equal to the electricity sales price (i.e. the electricity sales price);

[0198] ③When the supply-demand ratio is 1, the purchase and sale prices of electricity are the same.

[0199] It should be explained that since the continuous adjustment of the sales electricity price may cause the transaction electricity price driven by the supply and demand relationship to exceed the purchase price or sales price of the power system, in order to ensure the normal operation of the transaction, it is necessary to set the constraints of the transaction electricity price:

[0200]

[0201] in, Indicates in The electricity price sold in the round iteration; The price of electricity purchased from the grid, The price of electricity sold to the grid.

[0202] In a specific embodiment, the power supply and demand ratios corresponding to m MRA models can be determined based on the m differences to obtain a target power supply and demand ratio. Specifically, the differences greater than or equal to 0 among the m differences can be first determined to obtain p differences, and the differences less than 0 among the m differences can be determined to obtain q differences, where p and q are both positive integers, and p+q=m. Then, the sum of the p differences can be calculated to obtain a first difference sum, and the sum of the q differences can be calculated to obtain a second difference sum. Then, the target power supply and demand ratio can be calculated based on the first difference sum and the second difference sum. The calculation formula is as follows:

[0203]

[0204] Among them, R represents the target power supply-demand ratio, S represents the first difference sum; and D represents the second difference sum.

[0205] It should be explained that if the difference among the m differences is greater than 0, it means that the power generation of the resource aggregate is greater than the demand, that is, there is surplus power. If the difference is less than 0, it means that the power generation of the resource aggregate is less than the demand, that is, there is a shortage of power. In other words, S represents surplus power and D represents shortage of power.

[0206] When the target electricity supply-demand ratio is equal to 1, the target electricity purchase price of the target power system is obtained, and the target electricity purchase price can be directly used as the target sales electricity price; when the target electricity supply-demand ratio is not equal to 1, the second sales electricity price can be adjusted according to the preset electricity price adjustment rules to obtain the target sales electricity price.

[0207] This allows for real-time or periodic tracking of changes in the power supply-demand ratio to capture market dynamics. Over time, power supply and demand fluctuate due to factors such as the status of power generation equipment and changes in consumer behavior. Continuously calculating the target power supply-demand ratio allows for rapid detection of changes, allowing for timely adjustments to power prices.

[0208] For an example, see Figure 6 , Figure 6This is a flowchart of a method for adjusting electricity prices provided in an embodiment of the present application. First, the process "obtains the supply-demand ratio of the target power system" and determines whether "supply-demand ratio = 0" is true. If the supply-demand ratio is truly equal to 0 (branch Y), it means that the supply and demand of the power system have reached a balanced state. At this point, the action taken is to "set the purchase price of electricity for the target power system to the sales price." This is because, under ideal conditions of supply and demand balance, directly using the purchase price as the sales price ensures that power companies operate reasonably based on cost while also keeping electricity prices relatively stable and preventing significant fluctuations due to market supply and demand fluctuations. If the supply-demand ratio is not equal to 0 (branch N), it indicates that the power system is in a state of supply-demand imbalance. Whether supply exceeds demand or supply falls short, the sales price needs to be adjusted. At this point, the process enters the step of "adjusting the sales price according to the preset electricity adjustment rules to obtain the target sales price." The preset electricity adjustment rules are formulated based on market laws and aim to regulate the supply and demand relationship in the electricity market through price leverage. For example, when supply exceeds demand, the sales price of electricity may be lowered to stimulate electricity consumption; when supply is less than demand, the sales price of electricity may be raised to curb excessive demand and prompt the electricity market to return to balance.

[0209] Optionally, when the target power supply-demand ratio is less than 1, adjusting the second sales electricity price according to a preset electricity price adjustment rule to obtain the target sales electricity price may include the following steps:

[0210] E1. Based on the inverse relationship between the sales electricity price and the supply-demand ratio, the first expression corresponding to the target sales electricity price is determined as follows:

[0211]

[0212] in, represents the target electricity sales price, R represents the target electricity supply-demand ratio, a and b are variables, and b is not equal to 0;

[0213] E2. Determine a second expression that the target sales electricity price satisfies based on the preset electricity price adjustment rule; the second expression is as follows:

[0214]

[0215] in, represents the target electricity purchase price; represents the second sales electricity price;

[0216] E3. Determine the target electricity sales price based on the first expression and the second expression. The calculation formula of the target electricity sales price is as follows:

[0217]

[0218] In the embodiments of the present application, in the electricity market, when the ratio of electricity supply to demand (supply-demand ratio) changes, the electricity sales price will change in the opposite direction. For example, the more abundant the electricity supply is relative to demand (the supply-demand ratio increases), the lower the electricity sales price will tend to be; and the less abundant the electricity supply is relative to demand (the supply-demand ratio decreases), the higher the electricity sales price will tend to be. In other words, there is an inverse relationship between the electricity sales price and the supply-demand ratio. Based on this inverse relationship, the first expression corresponding to the target electricity sales price can be determined as follows:

[0219]

[0220] in, represents the target electricity sales price, R represents the target electricity supply-demand ratio, a and b are variables, and b is not equal to 0;

[0221] According to the preset electricity price adjustment rules and the constraints of the transaction electricity price, it can be seen that when the supply and demand are balanced, the purchase price and the sales price should be consistent and taken as the middle value of the grid electricity price; when R=0, the grid electricity price can only be used. Purchase, the second expression that the target sales electricity price satisfies can be determined; the second expression is as follows:

[0222]

[0223] in, represents the target electricity purchase price; represents the second sales electricity price;

[0224] Finally, the variables a and b can be solved according to the first and second expressions. Further, the target sales electricity price can be determined. The calculation formula of the target sales electricity price is as follows:

[0225]

[0226] In addition, if the electricity purchaser's demand is not met, the electricity purchaser will also conduct electricity transactions with m resource aggregates. Therefore, according to rule ①, the MRA balance of payments can be obtained:

[0227]

[0228] Electricity purchase price for:

[0229]

[0230] Optionally, when the target power supply-demand ratio is greater than 1, adjusting the second sales electricity price according to a preset electricity price adjustment rule to obtain the target sales electricity price may include the following steps:

[0231] Calculate the ratio of the power shortage D to the surplus power S:

[0232]

[0233] According to the preset electricity price adjustment rules, the electricity purchase price at this time can be determined The expression is:

[0234]

[0235] Among them, c and d are variables, and d is not equal to 0. According to the preset electricity price adjustment rules and the constraints of the transaction electricity price, it can be seen that when the supply and demand are balanced, that is, when X=1, the purchase price and the sales price should be consistent and taken as the middle value of the grid electricity price; when X=0, MRA is based on the grid purchase price. Sell, we can get the third expression:

[0236]

[0237] According to the third expression and rule ①MRA balance of income and expenditure, the target sales price and purchase price can be obtained:

[0238]

[0239]

[0240] The implementation of this application has the following beneficial effects:

[0241] It can be seen that the coordinated operation method of the multi-resource aggregate cluster described in this application adjusts the first sales electricity price to obtain the second sales electricity price based on the predicted m first loads and m first powers. This process can link the forecast of the resource aggregate with the electricity price. When it is predicted that the load is high or the power supply is tight, the electricity price is appropriately increased to suppress some unnecessary electricity demand; when the load is low or the power supply is sufficient, the electricity price is lowered to encourage users to increase electricity consumption. Through the economic lever of electricity price, the power system or users are guided to reasonably adjust their electricity consumption behavior, thereby achieving a preliminary balance between electricity supply and demand, reducing the coordination cost and resource waste caused by the imbalance between supply and demand in the power system, and improving the coordination efficiency within the multi-resource aggregate cluster.

[0242] See also Figure 7 , Figure 7 This is a block diagram of the functional units of a coordinated operation device 700 for a multi-resource aggregate cluster provided in an embodiment of the present application. The coordinated operation device 700 for a multi-resource aggregate cluster includes: an acquisition unit 701, a determination unit 702, and a coordination control unit 703, wherein:

[0243] The acquisition unit 701 is configured to acquire resource aggregates in the target power system to obtain m resource aggregates; m is an integer greater than 1;

[0244] The determining unit 702 is configured to determine an MRA model corresponding to each of the m resource aggregates to obtain m MRA models;

[0245] The acquisition unit 701 is further configured to acquire a first electricity sales price;

[0246] The coordination control unit 703 is configured to predict the load and power of the m resource aggregates in a first future period using the m MRA models to obtain m first loads and m first powers; adjust the first sales electricity price based on the m first loads and the m first powers to obtain a second sales electricity price in the first future period; predict the load and power of the m resource aggregates in a second future period using the m MRA models to obtain m second loads and m second powers; the start time of the second future period is the end time of the first future period;

[0247] The determining unit 702 is further configured to determine a difference between the m second powers and a corresponding first power among the m first powers, to obtain m differences;

[0248] The coordination control unit 703 is further configured to determine a target electricity sales price for the second future period based on the m differences and the second electricity sales price, and instruct to sell electricity at the target electricity sales price so as to achieve a supply and demand balance in the electricity market.

[0249] In the embodiment of the present application, the coordinated operation device 700 of the above-mentioned multi-resource aggregate cluster can execute part or all of the steps of any method recorded in the above-mentioned method embodiment.

[0250] See also Figure 8 , Figure 8 This is a structural diagram of an electronic device provided in an embodiment of the present application. The electronic device may include a processor, a memory, a communication interface and one or more programs. The processor, memory and communication interface may be interconnected through a bus; the above one or more programs are stored in the above memory and are configured to be executed by the above processor; in an embodiment of the present application, the above program includes enabling the electronic device to execute part or all of the steps of any method recorded in the above method embodiment.

[0251] An embodiment of the present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to execute part or all of the steps of any method described in the above method embodiments, and the above computer includes an electronic device.

[0252] The present application also provides a computer program product comprising a non-transitory computer-readable storage medium storing a computer program, wherein the computer program is operable to cause a computer to perform some or all of the steps of any of the methods described in the above method embodiments. The computer program product may be a software installation package, and the computer may comprise an electronic device.

[0253] It should be noted that for the aforementioned method embodiments, 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 embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by this application.

[0254] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0255] In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, 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.

[0256] Those skilled in the art will appreciate that all or part of the process steps in the above-described method embodiments can be implemented by a computer program instructing the relevant hardware. The program can be stored in a computer-readable storage medium, and when executed, the program can include the process steps in the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM or random access memory (RAM), magnetic disks, or optical disks.

[0257] The steps of the method or algorithm described in the embodiments of the present application can be implemented in hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, which can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (EEPROM), registers, hard disk, removable hard disk, CD-ROM, or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be an integral part of the processor. The processor and storage medium can be located in an ASIC. In addition, the ASIC can be located in a terminal device or a management device. Of course, the processor and storage medium can also exist as discrete components in the terminal device or the management device.

[0258] Those skilled in the art will appreciate that in one or more of the above examples, the functions described in the embodiments of the present application can be implemented in whole or in part through software, hardware, firmware, or any combination thereof. When implemented using software, they can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part.

[0259] The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer, or a data storage device such as a server or data center that integrates one or more available media. The available media can include magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., digital video discs (DVDs)), or semiconductor media (e.g., solid-state drives (SSDs)).

[0260] The modules / units included in the various devices and products described in the above embodiments may be software modules / units, hardware modules / units, or partly software modules / units and partly hardware modules / units. For example, for various devices and products applied to or integrated into a chip, the modules / units included therein may all be implemented in the form of hardware such as circuits, or at least part of the modules / units may be implemented in the form of software programs, which run on a processor integrated inside the chip, and the remaining (if any) modules / units may be implemented in the form of hardware such as circuits; for various devices and products applied to or integrated into a chip module, the modules / units included therein may all be implemented in the form of hardware such as circuits, and different modules / units may be located in the same component (such as a chip, circuit module, etc.) or different components of the chip module, or at least part of the modules / units may be It is implemented in the form of a software program, which runs on the processor integrated inside the chip module, and the remaining (if any) modules / units can be implemented in the form of hardware such as circuits; for various devices and products applied to or integrated in the terminal equipment, the various modules / units contained therein can be implemented in the form of hardware such as circuits, and different modules / units can be located in the same component (for example, chip, circuit module, etc.) or different components in the terminal equipment, or at least some modules / units can be implemented in the form of a software program, which runs on the processor integrated inside the terminal equipment, and the remaining (if any) modules / units can be implemented in the form of hardware such as circuits.

[0261] The specific implementation methods described above further illustrate the purpose, technical solutions and beneficial effects of the embodiments of the present application. It should be understood that the above description is only a specific implementation method of the embodiments of the present application and is not intended to limit the scope of protection of the embodiments of the present application. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solutions of the embodiments of the present application should be included in the scope of protection of the embodiments of the present application.

Claims

1. A method for coordinated operation of a multi-resource aggregate cluster, characterized in that: include: Obtain resource aggregates in the target power system to obtain m resource aggregates; m is an integer greater than 1; Determine an MRA model corresponding to each of the m resource aggregates to obtain m MRA models; Obtain the first sales electricity price; Predicting the loads and powers of the m resource aggregates in a first future period using the m MRA models to obtain m first loads and m first powers; Adjusting the first electricity sales price based on the m first loads and the m first powers to obtain a second electricity sales price for the first future period; Predicting the loads and powers of the m resource aggregates in a second future period using the m MRA models to obtain m second loads and m second powers; The start time of the second future time period is the end time of the first future time period; determining a difference between the m second powers and a corresponding first power among the m first powers to obtain m differences; determining a target electricity sales price for the second future period based on the m differences and the second electricity sales price, and instructing the sale of electricity at the target electricity sales price so as to achieve a supply-demand balance in the electricity market; The adjusting the first sales electricity price based on the m first loads and the m first powers to obtain a second sales electricity price includes: determining a first load sum according to the m first loads; Determine a first power sum according to the m first powers; determining a difference between the first load sum and the first power sum to obtain a first difference; Determine a target market state based on the first difference; the target market state includes one of the following: an oversupply state, an equilibrium state, and a undersupply state; When the target market state does not include the equilibrium state, obtaining a target electricity price adjustment coefficient; Determining the number of times the electricity price at the current moment has been iterated to obtain the number of times the electricity price has been iterated, specifically, obtaining electricity price adjustment data, traversing the electricity price adjustment data to determine the number of times the electricity price has been iterated; The first sales electricity price is adjusted according to the target electricity price adjustment coefficient, the number of electricity price iterations, and a preset electricity price iteration formula to obtain a second sales electricity price; the preset electricity price iteration formula is specifically as follows: in, represents the second sales electricity price; Indicates in The electricity price sold in the round iteration; Indicates in The electricity purchase willingness of the target power system in the round iteration; Indicates in The electricity sales willingness of the target power system in the round iteration; represents the target electricity price adjustment coefficient; is the number of iterations of the electricity price.

2. The method according to claim 1, wherein The determining of the MRA model corresponding to each of the m resource aggregates to obtain m MRA models includes: Determining a first resource type corresponding to the first resource aggregate; the first resource type includes one of the following: photovoltaic, wind power, distributed generator, interruptible load, transferable load, and energy storage; Acquire a first power generation device corresponding to the first resource aggregate; Acquiring first device parameter data corresponding to the first power generation device; An MRA model corresponding to the first resource aggregate is constructed according to the first device parameter data.

3. The method according to claim 1, wherein The obtaining of the target electricity price adjustment coefficient includes: Obtaining the operating cost of each of the m resource aggregates to obtain m operating costs; determining a target operating cost based on the m operating costs; determining an initial sales revenue according to the first total power, the first sales electricity price, and a preset sales period; determining an initial profit coefficient based on the target operating cost and the initial sales revenue; Determining a reference electricity price adjustment coefficient corresponding to the initial profit coefficient; Obtaining historical price adjustment data corresponding to the target power system; Determining a target season corresponding to the current moment; Predicting the target price change rate corresponding to the target season based on the historical price adjustment data; The reference electricity price adjustment coefficient is adjusted according to the target price change rate to obtain the target electricity price adjustment coefficient.

4. The method according to claim 3, wherein The predicting the target price change rate corresponding to the target season based on the historical price adjustment data includes: Extracting price adjustment data corresponding to the target season from the historical price adjustment data to obtain i segments of price adjustment data, where i is a positive integer; Perform straight line fitting based on the i segments of price adjustment data and their corresponding adjustment moments to obtain i straight lines; Determine the slope corresponding to each of the i straight lines to obtain i slopes; Determine the average slope corresponding to the i slopes; determining a reference price change rate based on the average slope; Obtaining the average generated power of the target power system in the target season; Determining a deviation between the average generated power and a preset generated power to obtain a target deviation; Determining a target optimization factor corresponding to the target deviation; The reference price change rate is adjusted according to the target optimization factor to obtain the target price change rate.

5. The method according to any one of claims 1 to 4, characterized in that The determining the target sales electricity price according to the m differences and the second sales electricity price includes: determining the power supply and demand ratios corresponding to the m MRA models according to the m differences, to obtain a target power supply and demand ratio; When the target power supply-demand ratio is equal to 1, obtaining a target power purchase price for the target power system, and determining the target power sales price according to the target power purchase price; When the target power supply-demand ratio is not equal to 1, the second sales electricity price is adjusted according to a preset electricity price adjustment rule to obtain the target sales electricity price.

6. A coordinated operation device for a multi-resource aggregate cluster, characterized in that: The device includes: an acquisition unit, a determination unit, and a coordination control unit, wherein: The acquisition unit is used to acquire resource aggregates in the target power system to obtain m resource aggregates; m is an integer greater than 1; The determining unit is configured to determine an MRA model corresponding to each of the m resource aggregates to obtain m MRA models; The obtaining unit is further configured to obtain a first electricity sales price; The coordination control unit is configured to predict the load and power of the m resource aggregates in a first future period using the m MRA models to obtain m first loads and m first powers; adjust the first sales electricity price based on the m first loads and the m first powers to obtain a second sales electricity price in the first future period; predict the load and power of the m resource aggregates in a second future period using the m MRA models to obtain m second loads and m second powers; the start time of the second future period is the end time of the first future period; The determining unit is further configured to determine a difference between the m second powers and a corresponding first power among the m first powers, to obtain m differences; The coordination control unit is further configured to determine a target electricity sales price for the second future period based on the m differences and the second electricity sales price, and instruct to sell electricity at the target electricity sales price so as to achieve a supply-demand balance in the electricity market; In the aspect of adjusting the first sales electricity price based on the m first loads and the m first powers to obtain the second sales electricity price, the coordination control unit is specifically configured to: determining a first load sum according to the m first loads; Determine a first power sum according to the m first powers; determining a difference between the first load sum and the first power sum to obtain a first difference; Determine a target market state based on the first difference; the target market state includes one of the following: an oversupply state, an equilibrium state, and a undersupply state; When the target market state does not include the equilibrium state, obtaining a target electricity price adjustment coefficient; Determining the number of times the electricity price at the current moment has been iterated to obtain the number of times the electricity price has been iterated, specifically, obtaining electricity price adjustment data, traversing the electricity price adjustment data to determine the number of times the electricity price has been iterated; The first sales electricity price is adjusted according to the target electricity price adjustment coefficient, the number of electricity price iterations, and a preset electricity price iteration formula to obtain a second sales electricity price; the preset electricity price iteration formula is specifically as follows: in, represents the second sales electricity price; Indicates in The electricity price sold in the round iteration; Indicates in The amount of electricity purchase willingness of the target power system in the round iteration; Indicates in The electricity sales willingness of the target power system in the round iteration; represents the target electricity price adjustment coefficient; is the number of iterations of the electricity price.

7. An electronic device, characterized in that: include: a processor, a memory, a communication interface, and one or more programs; The one or more programs are stored in the memory and configured to be executed by the processor, wherein the programs include instructions for executing the steps of the method according to any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that A computer program for electronic data exchange is stored, wherein the computer program enables a computer to execute the method according to any one of claims 1 to 5.

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