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

Abstract

The invention discloses a coordinated operation method of a multi-element resource polymer cluster and a related device. The method comprises the following steps: acquiring m resource polymers in a target power system; m MRA models corresponding to the m resource aggregates are determined; acquiring a first sales electricity price; m first loads and m first powers corresponding to the m resource aggregates in the first future time period are predicted through the m MRA models; adjusting the first sales electricity price based on the m first loads and the m first powers to obtain a second sales electricity price of the first future time period; predicting m second loads and m second powers corresponding to m resource aggregates in a second future time period through m MRA models; determining m differences between the m second powers and the m first powers; and determining a target sales electricity price according to the m difference values and the second sales electricity price. By adopting the method and the device, the internal coordination efficiency of the multi-element resource polymer cluster is improved.

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, social electricity consumption has increased dramatically, and the power system is facing many changes and challenges. On the power supply side, renewable energy represented by photovoltaics and wind power has developed rapidly, and the proportion of installed capacity has increased year by year; on the user side, massive load resources have gradually been transformed into controllable resources, and the power system has changed from "source follows load" to "source and load interaction". In this context, Multi-Resource Aggregator (MRA), which aggregates decentralized power generation, energy storage and load-side resources, has become an important part of the power system. At the same time, the optimal scheduling of MRA has also become an important research topic.

[0003] At present, most scheduling technologies are aimed at scheduling a single MRA, ignoring the coordinated operation between multi-resource aggregator clusters (Multi-Resource Aggregator Cluster, MRAC), and cannot give full play to the synergy 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 coordinated operation method of a multi-resource aggregate cluster, including: Obtain resource aggregates in the target power system to obtain m resource aggregates; m is an integer greater than 1; Determine the MRA model corresponding to each of the m resource aggregates to obtain m MRA models; Obtain the first sales electricity price; Predicting the load and power of the m resource aggregates in a first future period by using the m MRA models to obtain m first loads and m first powers; Adjusting the first sales electricity price based on the m first loads and the m first powers to obtain a second sales electricity price for the first future period; Predicting the load and power of the m resource aggregates in a second future period by 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; Determine a difference between the m second powers and a corresponding first power among the m first powers to obtain m difference values; 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-demand balance.

[0006] 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: 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 used to determine the MRA model corresponding to each of the m resource aggregates to obtain m MRA models; The acquisition unit is further used to acquire the first sales electricity price; The coordination control unit is used to predict the load and power of the m resource aggregates in the first future period through 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 the second sales electricity price in the first future period; predict the load and power of the m resource aggregates in the second future period through 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 used to determine the target sales electricity price for the second future period according to the m differences and the second sales electricity price, and to instruct to sell electricity at the target sales electricity price so as to achieve a supply and demand balance in the electricity market.

[0007] 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.

[0008] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program for electronic data exchange, wherein the computer program enables a computer to execute part or all of the steps described in the first aspect of the embodiment of the present application.

[0009] In a fifth aspect, an embodiment of the present application provides a computer program product, wherein the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to execute some or all of the steps described in the first aspect of the embodiment of the present application. The computer program product may be a software installation package.

[0010] The implementation of this application has the following beneficial effects: It can be seen that the coordinated operation method of the multi-resource aggregate cluster described in the present application adjusts the first sales electricity price to obtain the second sales electricity price according to 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

[0011] 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.

[0012] Figure 1 It is a structural schematic diagram of an electronic device provided in an embodiment of the present application; Figure 2 is a schematic diagram of a scenario of an electronic device provided in an embodiment of the present application; Figure 3 It is a flow chart of a coordinated operation method of a multi-resource aggregate cluster provided in an embodiment of the present application; Figure 4 is a structural schematic diagram of a target power system provided in an embodiment of the present application; Figure 5 It is a flow chart of another coordinated operation method of a multi-resource aggregate cluster provided in an embodiment of the present application; Figure 6 is a flow chart of an electricity price adjustment method provided in an embodiment of the present application; Figure 7 It is a block diagram of the functional units of a coordinated operation device of a multi-resource aggregate cluster provided in an embodiment of the present application; Figure 8 It is a schematic diagram of the structure of another electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0013] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0014] 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.

[0015] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article indicates that the associated objects before and after are in an "or" relationship. The "plurality" appearing in the embodiments of the present application refers to two or more.

[0016] In the embodiments of the present application, "at least one item" or similar expressions refer to any combination of these items, including any combination of single items or plural items, and refer to one or more, and multiple refers to two or more. For example, at least one item of a, b, or c can represent the following seven situations: a, b, c, a and b, a and c, b and c, a, b, and c. Among them, each of a, b, and c can be an element or a set containing one or more elements.

[0017] The "connection" that appears 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.

[0018] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments 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.

[0019] 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, laptop computers, video matrices, monitoring platforms, mobile Internet devices (mobile internet devices, MID) or wearable devices, etc. The above are only examples and not exhaustive, including but not limited to the above devices.

[0020] Of course, the above-mentioned electronic device can also be a server, for example, a cloud server.

[0021] The following is an explanation of the relevant contents, concepts, meanings, technical issues, technical solutions, beneficial effects, etc. involved in the embodiments of the present application.

[0022] First, some professional terms involved in this application are explained: Multi-Resource Aggregator (MRA): 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 stations, small wind turbines, etc.), energy storage equipment (such as battery energy storage systems), and controllable loads (such as industrial interruptible loads, smart home appliances, etc.). MRA uses technical means and management strategies to uniformly manage and dispatch these decentralized resources, thereby achieving optimal allocation and coordinated operation of resources, and improving the flexibility, reliability, and economy of the power system.

[0023] Multi-Resource Aggregator Cluster (MRAC): A group formed by further aggregation of multiple multi-resource aggregators (MRAs). Different MRAs may aggregate power resources of different types or regions. MRAC brings these MRAs together to achieve larger-scale resource integration and coordinated control. It can optimize the dispatch of power resources in a wider range, enhance the ability of the power system to cope with complex working conditions and large-scale load changes, and play a greater role in participating in power market transactions and providing ancillary services.

[0024] MRA model: It is a mathematical model or analysis model established for a multi-resource aggregate. This model is used to describe the characteristics, operating rules and interactions of various resources within the MRA. By collecting and analyzing historical data, real-time operating data and other information of various resources in the MRA, combined with the physical laws and constraints of the power system, a model that can accurately reflect the behavior and performance of the MRA is constructed. This model can be used to predict key parameters such as load demand and power output of the MRA under different working conditions, providing an important basis for the planning, operation and dispatch of the power system and market trading decisions.

[0025] Load: In the power system, load refers to the total amount of electricity demanded by power users or power-consuming equipment at a certain moment. It can be classified from different perspectives. From the perspective of user type, it can be divided into industrial load, commercial load, residential load, etc.; from the perspective of time characteristics, there are peak load, valley load and average load, etc. The size and variation characteristics of the load have an important impact on the planning, operation and dispatching of the power system. The power system needs to reasonably arrange power generation and transmission according to the load demand to ensure the reliability and stability of power supply.

[0026] Power: refers to the work done or energy transmitted per unit time. In the power system, power is divided into active power, reactive power and apparent power. Active power is the power actually consumed by electrical equipment to realize the conversion of electrical energy and other forms of energy (such as mechanical energy, thermal energy, etc.), with watts (W) as the unit; reactive power is mainly used to establish and maintain the magnetic field in electrical equipment. It does not directly do work, but has an important impact on the voltage stability and power quality of the power system. The unit is var; apparent power is the vector sum of active power and reactive power, reflecting the capacity of electrical equipment, and the unit is volt-ampere (VA). Power balance and control is one of the key factors for the stable operation of power systems.

[0027] See also Figure 1 , Figure 1 : 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: The communication module is used to collect real-time data of each resource aggregate, such as power, load and other information, during the coordinated operation of the 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. At the same time, it can also send information such as control instructions generated by the electronic device to the corresponding resource aggregate to achieve two-way data interaction. In addition, the communication module can also communicate with external systems (such as power market trading platforms, other power system control centers, etc.) to obtain external key information such as the first sales electricity price, and can also send out the operating status of the power system and relevant data of the resource aggregate, so as to participate in power market transactions, accept dispatch instructions from the superior system, etc., and promote the coordination of the multi-resource aggregate cluster with the external environment.

[0028] The modeling module is used to build the MRA model. According to the characteristics and historical data of each resource aggregate in the multi-resource aggregate cluster, the corresponding MRA model is built. These models can accurately describe the operating rules, load and power change characteristics of each resource aggregate, and provide a basis for subsequent prediction and analysis. In addition, the modeling module can also use the constructed MRA model to predict the load and power of each resource aggregate in the future. By inputting relevant influencing factor data (such as time, weather, historical electricity consumption patterns, etc.) into the model, the predicted load and power values ​​are output, providing important decision-making basis for electricity price regulation and coordinated control.

[0029] 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 first sales electricity price obtained, so as to achieve the regulation of the electricity market price and guide the reasonable allocation of power resources. In addition, the coordination control module can also issue corresponding control instructions according to 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 resource aggregates of the power generation type are controlled to increase the power output, and the resource aggregates of the controllable load type are controlled to reduce the power consumption; otherwise, the opposite adjustment is made to achieve the balance of supply and demand in the electricity market and ensure the efficient and coordinated operation of the multi-resource aggregate cluster.

[0030] 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 the electronic device obtains the data, it 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: Obtain resource aggregates in the target power system to obtain m resource aggregates; m is an integer greater than 1; Determine the MRA model corresponding to each of the m resource aggregates to obtain m MRA models; Obtain the first sales electricity price; Predicting the load and power of the m resource aggregates in a first future period by using the m MRA models to obtain m first loads and m first powers; Adjusting the first sales electricity price based on the m first loads and the m first powers to obtain a second sales electricity price for the first future period; Predicting the load and power of the m resource aggregates in a second future period by 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; Determine a difference between the m second powers and a corresponding first power among the m first powers to obtain m difference values; 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-demand balance.

[0031] 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 aggregation cluster provided in the embodiment of the present application.

[0032] See also Figure 3 , Figure 3 1 is a flow chart of a coordinated operation method of a multi-resource aggregate cluster provided in an embodiment of the present application. The method can be applied to electronic devices, including but not limited to the following steps: S301. Acquire resource aggregates in a target power system to obtain m resource aggregates; m is an integer greater than 1.

[0033] In the embodiment of the present application, the target power system may be a physical power grid, or may be a virtual power grid.

[0034] In a specific embodiment, the electronic device can communicate or physically connect 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.

[0035] Optional, see Figure 4 , Figure 4 It 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 a multi-resource aggregate cluster provided in an embodiment of the present application.

[0036] Optionally, in the first future period, the time scale of the dispatch can be 1h. At this stage, the MRA individual takes into account its own internal supply and demand balance and optimizes the dispatch with the goal of minimizing the dispatch cost. The objective function includes: the output cost of distributed generator sets, the cost of interruptible load call, the cost of transferable load call, the energy storage cost, and the cost of market purchase and sale of electricity. The objective function is as follows:

[0037] In the formula, is the operating cost of the i-th MRA; The output cost of distributed generators, The output cost of distributed generators, Calling cost for transferable load, The cost of energy storage; Represents the cost of purchasing and selling electricity.

[0038] in, It can be expressed as follows:

[0039] In the formula, represents the amount of electricity purchased by the target power system, represents the amount of electricity sold by the target power system; The electricity price for market transactions.

[0040] S302: Determine the MRA model corresponding to each of the m resource aggregates to obtain m MRA models.

[0041] 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, conversion efficiency, etc. need to be collected; then, MRA modeling can be performed based on the acquired information to obtain m MRA models.

[0042] Optional, see Figure 5 , Figure 5 is a flow chart of another coordinated operation method of a multi-resource aggregate cluster provided in an embodiment of the present application, such as 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: S21, 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 set, interruptible load, transferable load, energy storage; S22, obtaining a first power generation device corresponding to the first resource aggregate; S23, obtaining first equipment parameter data corresponding to the first power generation equipment; S24: Construct an MRA model corresponding to the first resource aggregate according to the first device parameter data.

[0043] 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.

[0044] In a specific embodiment, the first resource type corresponding to the first resource aggregate can be determined first. Specifically, the number, 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.

[0045] 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 integrated to obtain the first device parameter data. For example, assuming that there are three solar photovoltaic panels in the first resource aggregate, and the rated powers are 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, it is necessary to perform weighted summation based on the real-time power generation data of each device, and the weight can be determined according to the rated power ratio of the device; finally, the MRA model corresponding to the first resource aggregate can be constructed based on the first device parameter data.

[0046] In this way, by constructing the MRA model corresponding to the first resource aggregate, the various resources and equipment in the first resource aggregate can be considered as a whole, and the interactions and synergies between them can be considered. For example, in a resource aggregate that includes photovoltaics, wind power, and energy storage, the MRA model can analyze the changes in photovoltaic and wind power generation under different weather conditions, as well as how energy storage equipment regulates charging and discharging to maintain the power balance and stable operation of the system, thereby achieving system-level optimization and management of the entire resource aggregate.

[0047] Let's take an example: (1) The first resource type is photovoltaic Photovoltaic power generation technology is an energy technology that uses solar cells to directly convert solar radiation into electrical energy. It has the technical characteristics of 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., MRA model) is as follows:

[0048] In the formula, is the photovoltaic output power; , , They are the maximum output power, light intensity and ambient temperature under standard test conditions; , It is the light intensity and temperature in the current environment; is the temperature coefficient, usually taken as .

[0049] (2) The first resource type is wind power Wind power generation technology is an energy technology that uses wind power to drive the rotation of wind turbines, converts wind energy into mechanical energy, and then converts mechanical energy into electrical energy through generators. The core of the wind power generation model is the wind turbine, and its power generation mainly depends on the wind speed and the design characteristics of the wind rotor. The functional relationship between wind power generation power and wind speed is shown below:

[0050]

[0051]

[0052] In the formula, is the output power of the fan; is the rated power of the wind turbine, , , , are the cut-in wind speed, cut-out wind speed, rated wind speed and actual wind speed of the fan respectively; k1 is the linear gain coefficient, indicating ~ In the interval, the slope of the wind turbine output power increases with wind speed; k2 is the intercept of the modified linear relationship, so that the wind turbine output power is transitions smoothly to a non-zero value.

[0053] (3) The first resource type is distributed generators Distributed generator sets (hereinafter referred to as units) refer to power generation equipment that can adjust output power at any time according to power demand. Common ones include diesel generators and coal-fired units. Diesel generators use diesel combustion to drive the engine, which drives the generator to generate electricity. They have the characteristics of rapid start-up and stable output. They are suitable for emergency power supply or scenarios with large load fluctuations. Coal-fired units burn coal to heat water to generate steam, drive steam turbines to generate electricity, and are widely used in basic load power supply. The power generation cost of distributed generator sets usually has a quadratic function relationship with the power generation. The specific function formula (i.e., MRA model) is as follows:

[0054] Where: , Respectively represent the operating cost and actual output of the unit; , They respectively represent the operating cost coefficient of the units.

[0055] Functions of distributed generators The constraints that must be met include unit output constraints and ramp constraints:

[0056]

[0057] In the formula, , Respectively represent the upper and lower limits of the unit output; , They respectively represent the upper and lower limits of the unit's climbing grade.

[0058] (4) The first resource type is interruptible load (IL) Interruptible load refers to the power load that can be temporarily interrupted according to the needs of the power grid or in an emergency during the operation of the power system. Usually, this type of load is signed by users (such as industrial electricity, commercial facilities, etc.), and users agree to actively reduce or stop electricity consumption under certain conditions when the power supply is tight. In this way, the power system can alleviate the pressure of supply and demand and enhance the stability of the system during peak load periods or when the power grid fails. The management of interruptible loads helps to reduce system operating costs and improve the reliability of power supply. The MRA model corresponding to interruptible loads is as follows:

[0059]

[0060] In the formula, Indicates the IL user call cost; They represent the unit compensation cost of IL user load interruption respectively; It indicates the load interruption amount of IL users; They respectively represent the maximum interruption upper limit of load.

[0061] (5) The first resource type is transferable load (TL) Transferable load refers to the load that can be flexibly transferred between different time periods or different regions in the power system. The characteristic of this load is that users can transfer part of the load to the time period or area with lower grid load during the peak period of power demand, thereby optimizing the operation of the grid. Transferable load is usually applied to loads with flexible scheduling capabilities, such as industrial production lines, cold chain storage or large-scale air-conditioning systems. By effectively managing transferable loads, the peak-to-valley difference of electricity can be reduced, the economy and stability of the power system can be improved, and excessive load during peak periods can be avoided. The MRA model is as follows:

[0062]

[0063]

[0064]

[0065] In the formula, represents the calling cost of TL users; represents the unit compensation cost for TL user load transfer; , The load increase and decrease of TL users should be the same in the whole dispatch period T; Indicates the maximum load transfer upper limit.

[0066] (6) The first resource type is energy storage The operating costs and constraints of the energy storage equipment (i.e., the MRA model) are shown below.

[0067]

[0068]

[0069]

[0070]

[0071]

[0072]

[0073] In the formula, The operating cost of energy storage; , They are respectively the energy storage charging and discharging power; 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.

[0074] It needs to 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.

[0075] S303: Obtain a first sales electricity price.

[0076] In the embodiment of the present application, the website of the electricity market operator can be accessed to query the current sales electricity price, that is, the first sales electricity price, from the website.

[0077] S304: Predict the load and power of the m resource aggregates in the first future period by using the m MRA models to obtain m first loads and m first powers.

[0078] In an embodiment of the present application, the first future time period can be preset or defaulted in advance, and the first future time period is a long-term time 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 a long-term prediction.

[0079] In a specific embodiment, historical data of m resource aggregates may be collected first. The historical data may include load and power data. These data should cover as long a time range as possible to reflect the operation law and seasonal changes of the resource aggregates. At the same time, external factor data related to load and power are collected, such as weather data (temperature, humidity, wind speed, etc.), date type (working day, rest day), holiday information, etc. Then, the forecast data of the first future period may be obtained, for example, forecast weather data, date type and other data. According to the input requirements of the MRA model, the historical data and the forecast data of the first future period are combined into an input vector to obtain m input vectors. Each input vector should contain enough information so that the MRA model can learn the change law of load and power. The m input vectors are input into the corresponding MRA model in the m MRA models to obtain m first loads and m first powers.

[0080] S305: Adjust the first sales electricity price based on the m first loads and the m first powers to obtain a second sales electricity price for the first future period.

[0081] In an embodiment of the present application, the supply and demand situation of the electricity market can be judged by m first loads and m first powers, 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 reduced.

[0082] 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: A1. determining a sum of first loads according to the m first loads; A2. determining a first power sum according to the m first powers; A3. Determine a difference between the first load sum and the first power sum to obtain a first difference; A4. Determine a target market state according to the first difference; the target market state includes one of the following: a state where supply exceeds demand, a balanced state, and a state where supply falls short of demand; A5. When the target market state does not include the equilibrium state, obtaining a target electricity price adjustment coefficient; 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; A7. 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 as follows:

[0083] in, represents the second sales electricity price; Indicated in The electricity price sold in the round iteration; Indicated in The amount of electricity purchase willingness of the target power system in the round iteration; Indicated in The power 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.

[0084] 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 means that the target market state is in an equilibrium state; if the first difference is greater than 0, it means 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 means that the target market state is in a state where supply exceeds demand.

[0085] 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.

[0086] In this way, by calculating the sum of m first loads and the sum of m first powers respectively, the electricity demand and power generation capacity of multiple resource aggregates can be summarized. This allows power market participants (such as power grid operators, power sales companies, etc.) to understand the overall scale of power supply and demand in the entire market from a macro perspective, providing a quantitative data basis for subsequent decision-making; when the target market state is not in equilibrium, the target electricity price adjustment coefficient is obtained, and the first sales electricity price is adjusted to obtain the second sales electricity price by combining the number of iterations of the electricity price using the preset electricity price iteration formula. This mechanism can adjust electricity prices in a timely and flexible manner according to the actual supply and demand situation in the market. When supply exceeds demand, electricity prices are lowered to stimulate electricity consumption and reduce excess power generation; when supply is less than demand, electricity prices are raised to suppress excessive demand and guide users to use electricity reasonably.

[0087] Optionally, the step of obtaining the target electricity price adjustment coefficient may include the following steps: B1. Obtaining the operating cost of each of the m resource aggregates to obtain m operating costs; B2. determining a target operating cost according to the m operating costs; B3. determining initial sales revenue according to the first power sum, the first sales electricity price and a preset sales period; B4. Determine the initial profit coefficient based on the target operating cost and the initial sales revenue; B5. Determine the reference electricity price adjustment coefficient corresponding to the initial profit coefficient; B6. Obtaining historical price adjustment data corresponding to the target power system; B7, determining the target season corresponding to the current moment; B8. predicting the target price change rate corresponding to the target season based on the historical price adjustment data; B9. Adjust the reference electricity price adjustment coefficient according to the target price change rate to obtain the target electricity price adjustment coefficient.

[0088] In the embodiment of the present application, the preset sales period can be preset in advance or defaulted.

[0089] 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, the m operating costs can be added together to obtain the target operating cost.

[0090] Furthermore, the initial sales revenue can be determined according to the first power sum, the first sales electricity price and the preset sales period. Specifically, the first power sum can be multiplied by the preset sales period to obtain the first power generation, and then the first power generation can be multiplied by the first sales electricity price to obtain the initial sales revenue. Then, the initial profit coefficient can be calculated according to the target operating cost and the initial sales revenue. The specific calculation formula is as follows:

[0091] Where k is the initial return coefficient, is the weight of the ith 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 ith 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.

[0092] 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 according to the date. Further, the target price change rate corresponding to the target season can be predicted according to 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: Target electricity price adjustment coefficient = reference electricity price adjustment coefficient × (1 + target price change rate); According to the above formula, the target electricity price adjustment coefficient can be obtained.

[0093] In this way, by obtaining the operating costs of m resource aggregates and determining the target operating costs, we can fully and carefully understand the cost structure of the resource end of the entire power system. The operating costs of different resource aggregates are significantly different. For example, photovoltaic aggregates are mainly equipment depreciation and a small amount of maintenance costs, while thermal power aggregates involve multiple costs such as fuel and environmental protection. Integrating these cost data will help operators accurately grasp the scale and distribution of costs and provide data support for subsequent decision-making. In addition, comprehensively considering the operating costs, benefits, market history and seasonal factors of resource aggregates to determine the target electricity price adjustment coefficient can enable electricity prices to adapt to dynamic changes in the electricity market in a timely manner. Whether it is cost fluctuations, changes in demand or changes in the market competition environment, this series of analysis and calculations can be used to adjust electricity price strategies and maintain the flexibility and adaptability of enterprises in the market.

[0094] Optionally, predicting the target price change rate corresponding to the target season according to the historical price adjustment data may include the following steps: C1. Extracting the price adjustment data corresponding to the target season from the historical price adjustment data to obtain i-segment price adjustment data; i is a positive integer; C2. Perform straight line fitting according to the i-segment price adjustment data and their corresponding adjustment moments to obtain i straight lines; C3. Determine the slope corresponding to each of the i straight lines to obtain i slopes; C4. Determine the average slope corresponding to the i slopes; C5. determining a reference price change rate according to the average slope; C6. Obtaining the average power generation power of the target power system in the target season; C7. Determine the deviation between the average power generation and the preset power generation to obtain a target deviation; C8. Determine the target optimization factor corresponding to the target deviation; C9. Adjust the reference price change rate according to the target optimization factor to obtain the target price change rate.

[0095] In the embodiment of the present application, the preset power generation power can be preset or defaulted in advance.

[0096] 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 are 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 are 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.

[0097] 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 is determined based on the operating data. The average power generation can be obtained by dividing the total power generation by the number of days in the target season; then, the deviation between the average power generation and the preset power generation can be determined. The specific calculation formula is as follows: Target deviation = (average power generation - preset power generation) / preset power generation; 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, the mapping relationship between the preset deviation and the 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: Target price change rate = reference price change rate × (1 + target optimization factor); According to the above formula, the target price change rate can be obtained.

[0098] In this way, by extracting the price adjustment data corresponding to the target season from the historical price adjustment data, we can focus on the price changes in a specific season. The electricity demand and supply conditions in different seasons are different, and the price change patterns are also different. This can eliminate the interference of other seasonal factors and more accurately analyze the characteristics of electricity prices in the target season.

[0099] S306. Predict the load and power of the m resource aggregates in a second future period by 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.

[0100] In an embodiment of the present application, the time length of the second future time period can be a fixed value, and the second future time period is a short-term time 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 time period belongs to a short-term prediction.

[0101] 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.

[0102] S307: Determine the difference between the m second powers and a corresponding first power among the m first powers to obtain m difference values.

[0103] 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.

[0104] S308. Determine the target sales electricity price for the second future period according to the m differences and the second sales electricity price, and instruct to sell electricity at the target sales electricity price so that the electricity market reaches a balance between supply and demand.

[0105] In an embodiment of the present application, the second sales electricity price can be adjusted according to the m differences to obtain a 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.

[0106] 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: D1. Determine 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; D2. When the target power supply-demand ratio is equal to 1, obtaining the target power purchase price of the target power system, and determining the target power sales price according to the target power purchase price; D3. When the target electricity 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.

[0107] 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: ①MRA balance of payments; ② The purchase price of electricity (i.e. the purchase price of electricity) is greater than or equal to the sales price of electricity (i.e. the sales price of electricity); ③When the supply-demand ratio is 1, the purchase and sale prices of electricity are the same.

[0108] It should be explained that, since the continuous adjustment of the sales price may cause the transaction 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 conduct of the transaction, it is necessary to set the constraints of the transaction price:

[0109] in, Indicated 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.

[0110] In a specific embodiment, the power supply and demand ratios corresponding to m MRA models can be determined according to the m differences to obtain the target power supply and demand ratio. Specifically, the differences greater than or equal to 0 among the m differences can be determined first 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 according to the first difference sum and the second difference sum. The calculation formula is as follows:

[0111] Among them, R represents the target power supply-demand ratio, S represents the first difference sum; and D represents the second difference sum.

[0112] It needs to be explained that a difference of the m differences greater than 0 indicates that the power generation of the resource aggregate is greater than the demand, that is, there is surplus electricity; a difference less than 0 indicates that the power generation of the resource aggregate is less than the demand, that is, there is a shortage of electricity. In other words, S represents surplus electricity and D represents shortage of electricity.

[0113] 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.

[0114] In this way, by tracking the changes in the power supply and demand ratio in real time or regularly, market dynamics can be captured in a timely manner. Over time, the supply and demand of electricity will fluctuate due to factors such as the status of power generation equipment and changes in user electricity consumption behavior. By continuously calculating the target power supply and demand ratio, changes can be quickly detected so that electricity prices can be adjusted in a timely manner.

[0115] For an example, see Figure 6 , Figure 6 This is a flow chart of a method for adjusting electricity prices provided in an embodiment of the present application. First, "obtain the supply-demand ratio of the target power system" to determine whether "supply-demand ratio = 0" is true. If the supply-demand ratio is really equal to 0 (Y branch), it means that the supply and demand of the power system have reached a balanced state. At this time, the operation taken is to "set the purchase price of electricity for the target power system to the sales price of electricity". This is because under the ideal state of supply and demand balance, directly using the purchase price of electricity as the sales price of electricity can not only ensure that the power company operates reasonably on the basis of cost, but also make the electricity price relatively stable, and will not change significantly due to market supply and demand fluctuations. If the supply-demand ratio is not equal to 0 (N branch), it means that the power system is in a state of imbalance between supply and demand. Whether the supply exceeds demand or the supply is less than demand, the sales price of electricity needs to be adjusted. At this point, the process enters the link of "adjusting the sales price of electricity according to the preset power adjustment rules to obtain the target sales price of electricity". The preset power adjustment rules are formulated based on market laws and aim to adjust the supply and demand relationship in the power market through price levers. 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.

[0116] Optionally, when the target power supply-demand ratio is less than 1, adjusting the second sales power price according to a preset power price adjustment rule to obtain the target sales power price may include the following steps: E1. According to 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:

[0117] 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; E2. Determine a second expression that the target sales electricity price satisfies according to the preset electricity price adjustment rule; the second expression is as follows:

[0118] in, represents the target electricity purchase price; represents the second sales electricity price; E3. Determine the target sales electricity price according to the first expression and the second expression. The calculation formula of the target sales electricity price is as follows:

[0119] In the embodiment of the present application, in the power market, when the ratio of power supply to demand (supply-demand ratio) changes, the sales price will change in the opposite direction. For example, the more sufficient the power supply is relative to the demand (the supply-demand ratio becomes larger), the sales price will tend to be lower; and the more insufficient the power supply is relative to the demand (the supply-demand ratio becomes smaller), the sales price will generally increase, that is, there is an inverse relationship between the sales price and the supply-demand ratio. According to this inverse relationship, the first expression corresponding to the target sales price can be determined as follows:

[0120] 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; 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:

[0121] in, represents the target electricity purchase price; represents the second sales electricity price; Finally, the variables a and b can be solved according to the first expression and the second expression, and further, the target sales electricity price can be determined. The calculation formula of the target sales electricity price is as follows:

[0122] In addition, if the power purchaser's demand is not met, the power purchaser will also conduct power transactions with m resource aggregates. Therefore, according to rule ①, the MRA balance of payments can be obtained:

[0123] Electricity purchase price for:

[0124] Optionally, when the target power supply-demand ratio is greater than 1, adjusting the second sales power price according to a preset power price adjustment rule to obtain the target sales power price may include the following steps: Calculate the ratio of the power shortage D to the surplus power S:

[0125] According to the preset electricity price adjustment rules, the electricity purchase price at this time can be determined The expression is:

[0126] 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:

[0127] According to the third expression and rule ①MRA balance of payments, the target sales price and purchase price can be obtained:

[0128]

[0129] The implementation of this application has the following beneficial effects: It can be seen that the coordinated operation method of the multi-resource aggregate cluster described in the present application adjusts the first sales electricity price to obtain the second sales electricity price according to 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.

[0130] See also Figure 7 , Figure 7 : is a functional unit composition block diagram of a coordinated operation device 700 of a multi-resource aggregate cluster provided in an embodiment of the present application, wherein the coordinated operation device 700 of the multi-resource aggregate cluster includes: an acquisition unit 701, a determination unit 702, and a coordination control unit 703, wherein: The acquisition unit 701 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 702 is used to determine the MRA model corresponding to each of the m resource aggregates to obtain m MRA models; The acquisition unit 701 is further used to acquire the first sales electricity price; The coordination control unit 703 is used to predict the load and power of the m resource aggregates in the first future period through 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 the second sales electricity price in the first future period; predict the load and power of the m resource aggregates in the second future period through 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 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; The coordination control unit 703 is further configured to determine a target sales electricity price for the second future period according to the m differences and the second sales electricity price, and instruct to sell electricity at the target sales electricity price so as to achieve a supply-demand balance in the electricity market.

[0131] 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.

[0132] See also Figure 8 , Figure 8 It 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, the memory and the communication interface may be interconnected through a bus; the one or more programs are stored in the memory and configured to be executed by the processor; in the embodiment of the present application, the program includes enabling the electronic device to execute part or all of the steps of any method recorded in the method embodiment.

[0133] 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, wherein 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.

[0134] The embodiment of the present application also provides a computer program product, the computer program product includes a non-transitory computer-readable storage medium storing a computer program, the computer program is operable to cause a computer to execute some or all of the steps of any method described in the method embodiment. The computer program product may be a software installation package, and the computer includes an electronic device.

[0135] 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 the present application is not limited by the described order of actions, because according to the present 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 the present application.

[0136] In the above 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.

[0137] In the several embodiments provided in the present application, it should be understood that the disclosed device can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the above-mentioned 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.

[0138] Those skilled in the art can understand that to implement all or part of the processes in the above-mentioned embodiments, the processes can be completed by computer programs to instruct related hardware, and the programs can be stored in computer-readable storage media. When the programs are executed, they can include the processes of the above-mentioned method embodiments. The aforementioned storage media include: ROM or random access memory RAM, magnetic disk or optical disk and other media that can store program codes.

[0139] The steps of the method or algorithm described in the embodiments of the present application can be implemented in hardware or by executing software instructions by a processor. The software instructions can be composed of corresponding software modules, and the software modules can be stored in RAM, flash memory, ROM, EPROM, electrically erasable programmable read-only memory (electrically EPROM, EEPROM), registers, hard disks, mobile hard disks, read-only compact disks (CD-ROMs) 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 a component of the processor. The processor and the storage medium can be located in an ASIC. In addition, the ASIC can be located in a terminal device or a management device. Of course, the processor and the storage medium can also exist in a terminal device or a management device as discrete components.

[0140] Those skilled in the art should be aware 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 by software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiments of the present application is generated in whole or in part.

[0141] The above-mentioned computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instruction can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instruction can be transmitted from a website site, computer, server or data center to another website site, computer, server or data center by wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by the computer or a data storage device such as a server or data center that includes one or more available media integrated. Among them, the available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a digital video disc (DVD)), or a semiconductor medium (for example, a solid state disk (SSD)), etc.

[0142] The modules / units included in the devices and products described in the above embodiments may be software modules / units or hardware modules / units, or may be partially software modules / units and partially hardware modules / units. For example, for the devices and products applied to or integrated in the chip, the modules / units included therein may all be implemented in the form of hardware such as circuits, or at least some of the modules / units may be implemented in the form of software programs, which run on the processor integrated inside the chip, and the remaining (if any) modules / units may be implemented in the form of hardware such as circuits; for the devices and products applied to or integrated in the 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 chip, circuit module, etc.) or different components of the chip module, or at least some of the modules / units may be implemented in the form of software programs. It is implemented in the form of a software program that runs on a processor integrated inside the chip module, and the remaining (if any) modules / units can be implemented in 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 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 of the modules / units can be implemented in the form of a software program that runs on a processor integrated inside the terminal equipment, and the remaining (if any) modules / units can be implemented in hardware such as circuits.

[0143] 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 the specific implementation method of the embodiments of the present application and is not intended to limit the protection scope of the embodiments of the present application. Any modifications, equivalent substitutions, improvements, etc. made on the basis of the technical solutions of the embodiments of the present application should be included in the protection scope of the embodiments of the present application.

Claims

1. A coordinated operation method 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 the MRA model corresponding to each of the m resource aggregates to obtain m MRA models; Obtain the first sales electricity price; Predicting the load and power of the m resource aggregates in a first future period by using the m MRA models to obtain m first loads and m first powers; Adjusting the first sales electricity price based on the m first loads and the m first powers to obtain a second sales electricity price for the first future period; Predicting the load and power of the m resource aggregates in a second future period by using the m MRA models to obtain m second loads and m second powers; The starting time of the second future time period is the ending time of the first future time period; Determine a difference between the m second powers and a corresponding first power among the m first powers to obtain m difference values; 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-demand balance.

2. The method according to claim 1, characterized in that The determining of the MRA model corresponding to each of the m resource aggregates to obtain m MRA models includes: 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 set, interruptible load, transferable load, energy storage; Acquire a first power generation device corresponding to the first resource aggregate; Acquire 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, characterized in that 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: Determine a first load sum according to the m first loads; Determine a first power sum according to the m first powers; Determine a difference between the first load sum and the first power sum to obtain a first difference; Determine a target market state according to the first difference; the target market state includes one of the following: a state where supply exceeds demand, a balanced state, and a state where supply falls short of demand; When the target market state does not include the equilibrium state, obtaining a target electricity price adjustment coefficient; Determine the number of times the electricity price has been iterated at the current moment, and obtain 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; Indicated in The electricity price sold in the round iteration; Indicated in The amount of electricity purchase willingness of the target power system in the round iteration; Indicated in The power 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.

4. The method according to claim 3, characterized in that The step of obtaining 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 according to the m operating costs; determining initial sales revenue according to the first power sum, 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; Determine the reference electricity price adjustment coefficient corresponding to the initial profit coefficient; Acquiring 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.

5. The method according to claim 4, characterized in that The predicting the target price change rate corresponding to the target season according to the historical price adjustment data includes: Extracting the price adjustment data corresponding to the target season from the historical price adjustment data to obtain i segments of price adjustment data; i is a positive integer; Perform straight line fitting according to the i-segment 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 an average slope corresponding to the i slopes; determining a reference price change rate according to the average slope; Obtaining the average power generation power of the target power system in the target season; Determining a deviation between the average power generation and a preset power generation to obtain a target deviation; Determine 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.

6. The method according to any one of claims 1 to 5, characterized in that: The determining the target sales electricity price according to the m differences and the second sales electricity price comprises: Determine the power supply-demand ratios corresponding to the m MRA models according to the m differences, and obtain a target power supply-demand ratio; When the target power supply-demand ratio is equal to 1, obtaining a target power purchase price of the target power system, and determining the target power sales price according to the target power purchase price; When the target electricity 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.

7. The method according to claim 6, characterized in that When the target power supply-demand ratio is less than 1, adjusting the second sales power price according to a preset power price adjustment rule to obtain the target sales power price includes: According to 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: 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; According to the preset electricity price adjustment rule, a second expression satisfied by the target sales electricity price is determined; the second expression is as follows: in, represents the target electricity purchase price; represents the second sales electricity price; The target sales electricity price is determined according to the first expression and the second expression. The calculation formula of the target sales electricity price is as follows: 。 8. A coordinated operation device for a multi-resource aggregate cluster, characterized in that: The device comprises: 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 used to determine the MRA model corresponding to each of the m resource aggregates to obtain m MRA models; The acquisition unit is further used to acquire the first sales electricity price; The coordination control unit is used to predict the load and power of the m resource aggregates in the first future period through 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 the second sales electricity price in the first future period; predict the load and power of the m resource aggregates in the second future period through 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 used to determine the target sales electricity price for the second future period according to the m differences and the second sales electricity price, and to instruct to sell electricity at the target sales electricity price so as to achieve a supply and demand balance in the electricity market.

9. 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, the programs comprising instructions for executing the steps in the method according to any one of claims 1 to 7.

10. 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 7.

Images