Optimization scheduling method and related device based on multi-resource aggregation

Abstract

The present application provides an optimization scheduling method and related device based on a multi-resource aggregate, the method comprising: obtaining m sub-models; obtaining historical electricity price data and predicting intraday electricity prices to obtain a reference predicted electricity price; determining a reference day-ahead market bidding model based on the m sub-models and the reference predicted electricity price; processing electricity price uncertainty and source-load uncertainty in the reference day-ahead market bidding model to obtain a second objective function and second constraints, and determining a target day-ahead market bidding model and a day-ahead clearing result; calculating the day-ahead clearing result to obtain an intraday deviation penalty cost; determining an intraday real-time scheduling model based on the intraday deviation penalty cost; and adjusting the day-ahead clearing result according to the intraday real-time scheduling model to obtain an intraday scheduling result. By processing electricity price uncertainty and source-load uncertainty and performing real-time optimization, the accuracy and real-time performance of resource scheduling are improved.

Description

Technical Field

[0001] The present application relates to the technical field of resource scheduling, and in particular to an optimization scheduling method based on a multi-resource aggregate and related devices. Background Art

[0002] Currently, most existing models use deterministic models, assuming that renewable energy output, load demand, and the market are known and fixed. This ignores the impact of uncertainty on system operation and fails to effectively address the frequent uncertainties and fluctuations in the market, leading to deviations in scheduling results. Furthermore, most existing scheduling models focus only on single-stage optimization, typically performing scheduling only during the day-ahead phase while ignoring real-time adjustments during the intraday phase. This model fails to fully consider the overall operational strategy of multiple resource aggregates in a market environment.

[0003] Therefore, how to improve the accuracy and real-time performance of resource scheduling needs to be solved urgently. Summary of the Invention

[0004] The embodiment of the present application provides an optimization scheduling method and related devices based on a multi-resource aggregate, which facilitates improving the accuracy of the day-ahead clearing results by processing the uncertainty of electricity prices and source-load uncertainties, and optimizes them in real time through an intraday deviation penalty cost mechanism, thereby improving the accuracy and real-time performance of resource scheduling.

[0005] In a first aspect, an embodiment of the present application provides an optimization scheduling method based on a multi-resource aggregate, the method comprising:

[0006] Obtain m sub-models, where the m sub-models are obtained by training m MRA models using m resource data in the day-ahead phase; each resource data corresponds to a resource; and m is an integer greater than 1;

[0007] Obtaining historical electricity price data within a preset time period, and predicting the electricity price for the intraday phase based on the historical electricity price data to obtain a reference predicted electricity price; the end time of the preset time period is earlier than the start time of the day-ahead phase; and the end time of the day-ahead phase is earlier than the start time of the intraday phase;

[0008] Determining a reference day-ahead market bidding model based on the m sub-models and the reference predicted electricity price; the reference day-ahead market bidding model includes a first objective function and a first constraint condition;

[0009] Performing electricity price uncertainty processing on the first objective function to obtain a second objective function;

[0010] Performing source-load uncertainty processing on the first constraint condition to obtain a second constraint condition;

[0011] determining a target day-ahead market bidding model according to the second objective function and the second constraint condition;

[0012] Determine the day-ahead clearing result based on the target day-ahead market bidding model;

[0013] Calculate the day-ahead clearing result according to the preset intraday deviation penalty cost mechanism to obtain the intraday deviation penalty cost;

[0014] Based on the intraday deviation penalty cost and according to a preset cost correction scheme, a model is constructed to obtain an intraday real-time scheduling model;

[0015] The day-ahead clearing result is adjusted according to the intraday real-time scheduling model to obtain an intraday scheduling result.

[0016] In a second aspect, an embodiment of the present application provides an optimization scheduling device based on a multi-resource aggregate, the device comprising a first acquisition module, a second acquisition module, a first determination module, a processing module, a second determination module, a calculation module, a construction module, and an adjustment module, wherein:

[0017] The first acquisition module is used to acquire m sub-models, where the m sub-models are obtained by training m MRA models using m resource data in the day-ahead phase; each resource data corresponds to a resource; and m is an integer greater than 1;

[0018] The second acquisition module is configured to acquire historical electricity price data within a preset time period, and predict the electricity price for the intraday phase based on the historical electricity price data to obtain a reference predicted electricity price; the end time of the preset time period is earlier than the start time of the day-ahead phase; and the end time of the day-ahead phase is earlier than the start time of the intraday phase;

[0019] The first determination module is configured to determine a reference day-ahead market bidding model based on the m sub-models and the reference predicted electricity price; the reference day-ahead market bidding model includes a first objective function and a first constraint condition;

[0020] The processing module is configured to perform electricity price uncertainty processing on the first objective function to obtain a second objective function; and perform source-load uncertainty processing on the first constraint condition to obtain a second constraint condition;

[0021] The second determining module is configured to determine a target day-ahead market bidding model based on the second objective function and the second constraint condition; and determine a day-ahead clearing result based on the target day-ahead market bidding model;

[0022] The calculation module is configured to calculate the day-ahead clearing result according to a preset intraday deviation penalty cost mechanism to obtain an intraday deviation penalty cost;

[0023] The construction module is used to construct a model based on the intraday deviation penalty cost and a preset cost correction scheme to obtain an intraday real-time scheduling model;

[0024] The adjustment module is used to adjust the day-ahead clearing result according to the intraday real-time scheduling model to obtain an intraday scheduling result.

[0025] 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 comprises instructions for executing the steps of any method of the first aspect of the embodiment of the present application.

[0026] 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 part or all of the steps described in any method of the first aspect of the embodiment of the present application.

[0027] 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 execute some or all of the steps described in any method of the first aspect of the embodiments of the present application. The computer program product may be a software installation package.

[0028] By implementing the embodiments of the present application, electricity price uncertainty and source-load uncertainty are processed to facilitate improving the accuracy of the day-ahead clearing results, and are optimized in real time through the intraday deviation penalty cost mechanism, thereby improving the accuracy and real-time performance of resource scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0030] Figure 1 This is an application scenario diagram of resource scheduling of a multi-resource aggregate provided by an embodiment of the present application;

[0031] Figure 2 This is a schematic diagram of the structure of a multi-resource aggregate provided in an embodiment of the present application;

[0032] Figure 3This is a system architecture diagram of a resource scheduling system provided by an embodiment of the present application;

[0033] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application;

[0034] Figure 5 This is a flow chart of an optimization scheduling method based on a multi-resource aggregate provided in an embodiment of the present application;

[0035] Figure 6 This is a flow chart of a source-load uncertainty processing method provided by an embodiment of the present application;

[0036] Figure 7 This is a flow chart of building a real-time scheduling model for a day, provided by an embodiment of the present application;

[0037] Figure 8 This is a block diagram of the functional modules of an optimization scheduling device based on a multi-resource aggregate provided in an embodiment of the present application. DETAILED DESCRIPTION

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] The following are the explanations of the relevant terms involved in this application:

[0045] A Multiresource Aggregator (MRA) refers to an organization or entity that integrates, coordinates and uniformly manages multiple different types of power resources.

[0046] Currently, most existing models use deterministic models, assuming that renewable energy output, load demand, and the market are known and fixed. This ignores the impact of uncertainty on system operation and fails to effectively address the frequent uncertainties and fluctuations in the market, leading to biased scheduling results. Furthermore, most existing scheduling models focus solely on single-stage optimization, typically performing scheduling only during the day-ahead phase while ignoring real-time adjustments during the intraday phase. These models fail to fully consider the overall operational strategies of diverse resource aggregates in a market environment. Therefore, improving the accuracy and real-time nature of resource scheduling is an urgent issue.

[0047] In order to solve the above problems, the embodiment of the present application provides an optimization scheduling method based on a multi-resource aggregate and a related device, obtaining m sub-models, wherein the m sub-models are obtained by training m MRA models through m resource data in the day-ahead stage; each resource data corresponds to a resource; m is an integer greater than 1; historical electricity price data in a preset time period is obtained, and the electricity price of the intraday stage is predicted based on the historical electricity price data to obtain a reference predicted electricity price; the end time of the preset time period is earlier than the start time of the day-ahead stage; the end time of the day-ahead stage is earlier than the start time of the intraday stage; a reference day-ahead market bidding model is determined based on the m sub-models and the reference predicted electricity price; the reference day-ahead market bidding model is determined based on the m sub-models and the reference predicted electricity price The bidding model includes a first objective function and a first constraint; the first objective function is processed for electricity price uncertainty to obtain a second objective function; the first constraint is processed for source-load uncertainty to obtain a second constraint; a target day-ahead market bidding model is determined based on the second objective function and the second constraint; a day-ahead clearing result is determined based on the target day-ahead market bidding model; the day-ahead clearing result is calculated based on a preset intraday deviation penalty cost mechanism to obtain an intraday deviation penalty cost; a model is constructed based on the intraday deviation penalty cost and a preset cost correction scheme to obtain an intraday real-time scheduling model; the day-ahead clearing result is adjusted based on the intraday real-time scheduling model to obtain an intraday scheduling result. By processing electricity price uncertainty and source-load uncertainty, the accuracy of the day-ahead clearing result is improved, and it is optimized in real time through the intraday deviation penalty cost mechanism, thereby improving the accuracy and real-time performance of resource scheduling.

[0048] For easier understanding, see Figure 1 , Figure 1 This is a diagram of an application scenario for resource scheduling of a multi-resource aggregate, provided in an embodiment of the present application. A multi-resource aggregate represents an aggregate that integrates multiple different types of power resources (such as distributed power sources, energy storage, and adjustable loads). The resource scheduling system is responsible for coordinating and allocating the various resources of the multi-resource aggregate to achieve optimal resource utilization. The multi-resource aggregate provides the resource scheduling system with dispatchable resource information, such as resource status, capacity, and power generation or consumption capabilities. The power market provides the resource scheduling system with market-related information, such as electricity price fluctuations and trading rules. Based on this resource and market information, the resource scheduling system can formulate a reasonable resource scheduling strategy and, based on this resource scheduling strategy, send scheduling instructions to the multi-resource aggregate to guide the operation of its resources, such as charging and discharging energy storage, adjusting the power generation of distributed power sources, and starting and stopping adjustable loads. This achieves optimal allocation and efficient utilization of power resources, improving the economic efficiency and stability of overall power operations.

[0049] For easier understanding, see Figure 2 , Figure 2 This is a structural diagram of a multi-resource aggregate provided in an embodiment of the present application, wherein the multi-resource aggregate includes photovoltaic resources, wind power resources, distributed generator sets, interruptible loads, transferable loads, and energy storage resources.

[0050] Photovoltaic resources can be obtained through photovoltaic power generation technology. Photovoltaic power generation technology refers to an energy technology that uses solar cells to directly convert solar radiation into electrical energy. It is clean, green, and highly reliable. The output characteristics of photovoltaic power generation are closely related to environmental factors. Its output power model is as follows:

[0051]

[0052] in, Indicates photovoltaic output power; 、 、 represents the maximum output power, light intensity, and ambient temperature under standard test conditions; G and T represent the light intensity and temperature in the current environment; k represents the temperature coefficient, typically set to -0.45, but no specific limit is given here. It should be noted that the light intensity in the current environment will vary with temperature.

[0053] Wind power resources can be obtained through wind power generation technology. Wind power generation technology refers to an energy technology that uses wind power to drive the rotation of wind turbines, converting wind energy into mechanical energy, and then converting 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 the output power of wind power generation and wind speed is shown below:

[0054]

[0055]

[0056]

[0057] in, Indicates the output power of the wind turbine; Indicates the rated power of the wind turbine; 、 、 、 Indicates the cut-in wind speed, cut-out wind speed, rated wind speed and actual wind speed of the wind turbine.

[0058] Among them, distributed generator sets refer to power generation equipment that can adjust output power at any time according to power demand. Such power generation equipment includes but is not limited to diesel generators and coal-fired units, which are not specifically limited here. 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, and 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 base load power supply. Among them, the power generation costs of distributed generator sets are as follows:

[0059]

[0060] in, 、 Indicates the operating cost and actual output of distributed generator sets; 、 Represents the operating cost coefficient of distributed generator sets.

[0061] The constraints satisfied by the distributed generator set include the unit output constraint and the ramp constraint, as shown below:

[0062]

[0063]

[0064] in, 、 Indicates the upper and lower limits of the unit output; 、 Indicates the upper and lower limits of the unit's climbing; t indicates the time point corresponding to moment t.

[0065] Interruptible loads refer to power loads that can be temporarily interrupted during power system operation based on grid demand or emergency situations. Typically, this type of load is contracted by users (such as industrial users and commercial facilities), who agree, 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 and enhance system stability during peak load periods or when grid failures occur. Managing interruptible loads helps reduce power system operating costs and improve power supply reliability. The cost of invoking interruptible loads is shown below:

[0066]

[0067]

[0068] in, represents the call cost of the interruptible load user; It represents the unit compensation cost for load interruption of interruptible load users; Indicates the load interruption amount of interruptible load users; They represent the maximum interruption limit of load respectively.

[0069] Among them, 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 period or area with lower grid load during the peak period of electricity demand, thereby optimizing the operation of the grid. Transferable loads are 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 in electricity can be reduced, the economy and stability of the power system can be improved, and excessive loads during peak periods can be avoided. Among them, the call cost of transferable load users is as follows:

[0070]

[0071]

[0072]

[0073]

[0074] in, represents the call cost of the transferable load user; It represents the unit compensation cost for load transfer of transferable load users; 、 Indicates the load increase and decrease of transferable load users, which must be the same throughout the entire dispatch cycle; Indicates the maximum load transfer upper limit.

[0075] Energy storage resources can be obtained through energy storage devices. Energy storage devices, such as battery energy storage systems and pumped hydropower storage, can store electricity when there is surplus electricity. They can release electricity during power shortages, thus regulating power supply and demand, smoothing power fluctuations, and improving the stability and reliability of the power system. The operating costs and constraints of the energy storage devices are as follows:

[0076]

[0077]

[0078]

[0079]

[0080]

[0081]

[0082] in, represents the operating cost of the energy storage equipment; 、 Indicates the charging power and discharging power of the energy storage device; Indicates the unit operating cost of energy storage equipment; The upper limit of charge and discharge of energy storage equipment; Indicates the energy storage capacity of the energy storage device; 、 Indicates the upper and lower limits of the energy storage capacity of the energy storage device; The charging and discharging efficiency of the energy storage device; Indicates the energy storage capacity of the energy storage device at the initial moment; Represents the energy storage capacity of the energy storage device at time T.

[0083] It can be seen that by integrating different types of resources into a multi-resource aggregate and conducting unified coordination and management, it is easier to participate in electricity market transactions and grid operation and dispatching.

[0084] For easier understanding, see Figure 3 , Figure 3 This is a system architecture diagram of a resource scheduling system provided in an embodiment of the present application. The resource scheduling system includes an information collection module, a scheduling analysis module, and a strategy output module. The information collection module is used to collect information on the power generation capacity (e.g., photovoltaic and wind power power forecasts), energy storage capacity status, and adjustable load regulation potential of resources within a multi-resource aggregate, and to obtain real-time information on electricity prices, market supply and demand trends, and trading rules in the power market. The scheduling analysis module is used to analyze the information collected by the information collection module and, in combination with power system operating constraints such as power balance constraints and equipment operating parameter constraints, construct a resource scheduling model. For example, the objective function may be cost minimization or profit maximization, taking into account resource generation costs, power purchase costs, and power sales revenue, while also incorporating operational constraints of various resources (e.g., output limits of distributed generators and power storage charge and discharge limits). The resource scheduling model is then solved using a pre-defined optimization algorithm (e.g., linear programming, integer programming, or heuristic algorithms) to obtain a preliminary resource scheduling strategy, including the generation, consumption, and energy storage charge and discharge arrangements for each resource at different time periods. Then, based on actual conditions and pre-set mechanisms, the resource scheduling strategy is evaluated and optimized to arrive at the final resource scheduling strategy. The strategy output module outputs the formulated scheduling strategy to the multi-resource aggregation for execution. During execution, it continuously monitors resource operating status, power market changes, and other conditions. If actual conditions deviate from expectations, timely feedback is provided and the scheduling strategy is readjusted to ensure that resource scheduling remains optimal or near optimal.

[0085] It can be seen that by analyzing the electricity price fluctuations in the power market and combining the cost characteristics of various resources in the multi-resource aggregate to schedule resources, it is convenient to improve the overall resource utilization efficiency and reduce the resource scheduling cost.

[0086] The following combination Figure 4 The electronic device in the embodiment of the present application is described. Figure 4 is a structural diagram of an electronic device provided in an embodiment of the present application, such as Figure 4 As shown, the electronic device includes one or more processors, a memory, a communication interface and one or more programs, and the processor is communicatively connected with the memory and the communication interface via an internal communication bus.

[0087] Among them, the processor is mainly used for:

[0088] Obtain m sub-models, where the m sub-models are obtained by training m MRA models using m resource data in the day-ahead phase; each resource data corresponds to a resource; and m is an integer greater than 1;

[0089] Obtaining historical electricity price data within a preset time period, and predicting the electricity price for the intraday phase based on the historical electricity price data to obtain a reference predicted electricity price; the end time of the preset time period is earlier than the start time of the day-ahead phase; and the end time of the day-ahead phase is earlier than the start time of the intraday phase;

[0090] Determining a reference day-ahead market bidding model based on the m sub-models and the reference predicted electricity price; the reference day-ahead market bidding model includes a first objective function and a first constraint condition;

[0091] Performing electricity price uncertainty processing on the first objective function to obtain a second objective function;

[0092] Performing source-load uncertainty processing on the first constraint condition to obtain a second constraint condition;

[0093] determining a target day-ahead market bidding model according to the second objective function and the second constraint condition;

[0094] Determine the day-ahead clearing result based on the target day-ahead market bidding model;

[0095] Calculate the day-ahead clearing result according to the preset intraday deviation penalty cost mechanism to obtain the intraday deviation penalty cost;

[0096] Based on the intraday deviation penalty cost and according to a preset cost correction scheme, a model is constructed to obtain an intraday real-time scheduling model;

[0097] The day-ahead clearing result is adjusted according to the intraday real-time scheduling model to obtain an intraday scheduling result.

[0098] The one or more programs are stored in the above-mentioned memory and are configured to be executed by the above-mentioned processor, and the one or more programs include instructions for executing any step in the above-mentioned method embodiment.

[0099] The processor may be a central processing unit, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field programmable gate array or other programmable logic device, a transistor logic device, a hardware component, or any combination thereof, without specific limitation herein. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors. The communication unit may be a communication interface, a transceiver, a transceiver circuit, etc., and the storage unit may be a memory. The memory may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memory.

[0100] It is understood that the electronic device may include more or fewer structural elements than those in the above structural block diagram, for example, including a power module, physical buttons, Wi-Fi module, speaker, Bluetooth module, sensor, display module, etc., which are not limited here. It is understood that the electronic device may be equipped with Figure 3 The system architecture described.

[0101] After understanding the software and hardware architecture of this application, Figure 5 An optimization scheduling method based on a multi-resource aggregate in an embodiment of the present application is described. Figure 5 : This is a flow chart of an optimization scheduling method based on a multi-resource aggregate provided in an embodiment of the present application, which specifically includes the following steps:

[0102] Step S501: Obtain m sub-models.

[0103] The m sub-models are obtained by training m MRA models with m resource data in the day-ahead stage; each resource data corresponds to one resource; and m is an integer greater than 1.

[0104] The specific steps of obtaining m sub-models include:

[0105] A1. Obtain reference resource data; the reference resource data is any one of the m resource data;

[0106] A2. Determine the reference resource type corresponding to the reference resource data; the reference resource type includes any one of the following: photovoltaic, wind power, distributed generator set, interruptible load, transferable load, and energy storage;

[0107] A3. Determine reference type parameters according to the reference resource type;

[0108] A4. Determine a reference objective function and reference constraint conditions according to the reference type parameters;

[0109] A5. Determine a reference sub-model according to the reference objective function and the reference constraint condition; the reference sub-model is a sub-model corresponding to the reference resource data among the m sub-models.

[0110] In a specific embodiment, first, one resource data item is selected from m resource data items as reference resource data, and then the reference resource type corresponding to the reference resource data item is determined. Different resource types correspond to different operating characteristics and influencing factors, and the reference resource type includes any of the following: photovoltaic, wind power, distributed generators, interruptible loads, transferable loads, and energy storage. Then, corresponding reference type parameters are determined based on the reference resource type. For example, when the reference resource type is photovoltaic, the reference type parameters include but are not limited to light intensity, temperature, and photovoltaic panel efficiency. When the reference resource type is energy storage, the reference type parameters include but are not limited to energy storage capacity, charge and discharge efficiency, and charge and discharge power limits. These parameters are not specifically limited here.

[0111] Next, the corresponding reference objective function and reference constraint conditions are determined based on the reference type parameters. For example, when the reference resource type is energy storage, a reference objective function can be constructed based on the reference type parameters corresponding to the energy storage. The reference objective function can be a function related to the energy storage operating cost to minimize the charging and discharging costs under the preset charging and discharging requirements. The operating parameter restrictions corresponding to the reference type parameters can be determined based on the actual operating restrictions of the energy storage equipment, and then the reference constraint conditions are determined based on the operating parameter restrictions. The reference constraint conditions include but are not limited to the upper limit of charging and discharging of the energy storage equipment and the upper and lower limits of the energy storage capacity, which are not specifically limited here.

[0112] Finally, the reference objective function and reference constraints are combined to construct a reference sub-model for the reference resource data. This sub-model can predict or guide the resource's operational strategy by finding the optimal solution to the reference objective function under the reference constraints based on the relevant input parameters.

[0113] It can be seen that by analyzing the characteristics and operating rules of different resources, a detailed and accurate model basis can be provided for the subsequent overall scheduling decision-making of multiple resource aggregates, which helps to improve the accuracy and scientificity of resource scheduling and better cope with various complex situations in the power market.

[0114] Step S502 : acquiring historical electricity price data within a preset time period, and predicting the electricity price within a day based on the historical electricity price data to obtain a reference predicted electricity price.

[0115] The end time of the preset time period is earlier than the start time of the day-ahead phase; and the end time of the day-ahead phase is earlier than the start time of the intraday phase.

[0116] Specifically, the preset time period includes, but is not limited to, one day, one week, or one month, and is not specifically limited here. Historical electricity price data is analyzed using a preset forecasting method, i.e., based on trends, periodicity, and other characteristics of the historical electricity price data, combined with current electricity market information and influencing factors, to produce a forecast result for the electricity price within the daily period, i.e., a reference forecast electricity price. The forecasting method may be time series analysis or a machine learning algorithm, and is not specifically limited here.

[0117] Step S503 : determining a reference day-ahead market bidding model according to the m sub-models and the reference predicted electricity price.

[0118] The reference day-ahead market bidding model includes a first objective function and a first constraint condition; and determining the reference day-ahead market bidding model based on the m sub-models and the reference predicted electricity price specifically comprises the following steps:

[0119] B1. Construct an objective function for the reference day-ahead market bidding model to obtain the first objective function. The first objective function is as follows:

[0120]

[0121] in, represents the total market return on the day before; represents the reference predicted electricity price; represents the day-ahead transaction volume; , The electricity sold the day before. represents the amount of electricity purchased on the day before; represents the power generation cost of distributed generators; represents the call cost of the interruptible load user; represents the call cost of the transferable load user; represents the operating cost of the energy storage equipment;

[0122] B2. Obtaining the sub-models whose resource types are photovoltaic and wind power among the m sub-models, and obtaining a photovoltaic power model and a wind power model;

[0123] B3. Predicting the power generation power in the intraday stage according to the photovoltaic power model and the wind power model to obtain a photovoltaic power prediction value and a wind power prediction value;

[0124] B4. Obtaining the constraint conditions corresponding to each of the m sub-models to obtain m constraint conditions;

[0125] B5. Determine a first power balance condition corresponding to the reference day-ahead market bidding model based on the photovoltaic power forecast value and the wind power forecast value;

[0126] B6. Determine the first constraint condition based on the m constraint conditions, the first power balance condition, and a preset market power purchase and sales constraint condition;

[0127] B7. Determine the reference day-ahead market bidding model based on the first objective function and the first constraint condition.

[0128] In a specific embodiment, first, the objective function of the reference day-ahead market bidding model is constructed based on the reference predicted electricity price, the day-ahead transaction volume, and the sum of the costs of various resources to obtain a first objective function. Then, the sub-models of the m sub-models with photovoltaic and wind power as resource types are obtained to obtain photovoltaic power models and wind power models. The generated power during the intraday period is then predicted based on the photovoltaic power models and wind power models to obtain photovoltaic power forecast values ​​and wind power forecast values.

[0129] Next, the constraints corresponding to each of the m sub-models are obtained, resulting in m constraints. For example, the upper and lower limits of the power generation of distributed generators, the charge and discharge power limits and capacity limits of energy storage devices, etc. The first power balance condition corresponding to the reference day-ahead market bidding model is then determined based on the photovoltaic power forecast and wind power forecast. The first power balance condition is as follows:

[0130]

[0131] in, Represents the predicted value of photovoltaic power; Indicates the predicted value of wind power; Indicates the load forecast value.

[0132] Among them, the multi-resource aggregate should meet the transaction constraints of the electricity market, that is, it cannot purchase and sell electricity at the same time. Indicates that multiple resource aggregates sell electricity in the day-ahead market. It means no electricity is sold. Indicates that multiple resource aggregates purchase electricity in the day-ahead market. Therefore, when the product of the two is 0, it proves that at least one of them is 0. Then the preset market purchase and sale constraints are as follows:

[0133]

[0134] Finally, the m constraints, the first power balance condition, and the market power purchase and sales constraints are integrated to obtain the first constraint. By combining the first objective function with the first constraint, a reference day-ahead market bidding model is constructed. This reference day-ahead market bidding model is used to make market bidding decisions during the day-ahead phase. Under the premise of satisfying the first constraint, the first objective function is optimized to determine the optimal resource scheduling and trading strategy, thereby improving resource utilization efficiency and economic benefits.

[0135] It can be seen that building a reference day-ahead market bidding model by comprehensively considering multiple factors and constraints can help improve the accuracy and rationality of decision-making, reduce market risks, improve resource utilization efficiency, and promote the healthy development of the power market.

[0136] Step S504 : performing electricity price uncertainty processing on the first objective function to obtain a second objective function.

[0137] The step of performing electricity price uncertainty processing on the first objective function to obtain the second objective function specifically includes:

[0138] C1. Analyze the historical electricity price data to obtain a probability distribution of electricity prices;

[0139] C2. Divide the electricity price probability distribution into N equal parts to obtain N intervals; the length of each interval is 1 / N; N is an integer greater than 1;

[0140] C3. Randomly draw N random numbers from each of the N intervals according to a preset random drawing formula; the random drawing formula is as follows:

[0141]

[0142] in, represents a random number drawn from the i-th interval among the N intervals; r represents any random number in [0,1];

[0143] C4. Calculate according to a preset electricity price scenario value calculation formula to obtain N electricity price scenario values; the electricity price scenario value calculation formula is as follows:

[0144]

[0145] in, represents the i-th electricity price scenario value among the N electricity price scenario values; Represents a probability distribution function corresponding to the historical electricity price data;

[0146] C5. Calculating the N electricity price scenario values ​​according to a preset clustering algorithm to obtain S electricity price scenario values; S is an integer greater than 1 and less than N;

[0147] C6. Adjust the first objective function according to the S electricity price scenario values ​​to obtain the second objective function.

[0148] In a specific embodiment, historical electricity price data is first analyzed in depth. Using statistical and probability calculation methods, the probability of electricity prices occurring within different value ranges is determined, thereby obtaining a probability distribution for electricity prices. The electricity price probability distribution is then divided into N equal intervals, each with a length of 1 / N. Discretizing the probability space of electricity prices facilitates subsequent random sampling and scenario value calculations. Then, a random sampling formula is used to randomly sample each of the N intervals, obtaining N random numbers. By simulating the random selection of electricity prices within different intervals, consideration is increased for price uncertainty.

[0149] Next, according to the calculation formula for the electricity price scenario value, N electricity price scenario values ​​are obtained, each scenario value represents a different possible electricity price situation. Then, according to a pre-set clustering algorithm, the N electricity price scenario values ​​are calculated to obtain S electricity price scenario values. Among them, the clustering algorithm can be a K-means clustering algorithm, which randomly extracts S initial electricity price scenario values ​​from the N electricity price scenario values ​​and sets them as S initial cluster centers. Then, according to the Euclidean distance formula, the distance between each electricity price scenario value and the S initial cluster centers is calculated. Then, each electricity price scenario value is assigned to the initial cluster center with the smallest distance. Then, the mean corresponding to each cluster center is calculated and used as the updated cluster center. Finally, the steps of assigning electricity price scenario values ​​and updating cluster centers are repeated until the S cluster centers no longer change, thereby obtaining the S electricity price scenario values ​​corresponding to the final S cluster centers. By using this clustering algorithm, similar electricity price scenario values ​​can be classified into one category, thereby reducing the number of scenario values, retaining representative electricity price scenarios, and simplifying subsequent analysis and calculation.

[0150] Then, the first objective function is adjusted according to the S electricity price scenario values ​​to obtain the second objective function. The second objective function is as follows:

[0151]

[0152] Where S represents the number of electricity price scenario values; represents the scenario probability corresponding to the s-th electricity price scenario value; 、 、 、 They respectively represent the reference predicted electricity price, day-ahead electricity sales, day-ahead electricity purchases, power generation costs of distributed generators, call costs of interruptible load users, call costs of transferable load users, and operating costs of energy storage equipment corresponding to the s-th electricity price scenario value.

[0153] This means that through a series of operations, including analysis of historical electricity price data, random sampling, and scenario value calculation, the uncertainty of electricity prices is fully considered. Simulating multiple possible electricity price scenarios makes decision-making more robust and reduces the risks associated with electricity price fluctuations.

[0154] Step S505: performing source-load uncertainty processing on the first constraint condition to obtain a second constraint condition.

[0155] For easier understanding, see Figure 6 , Figure 6 : This is a flow chart of a source-load uncertainty processing method provided by an embodiment of the present application. The source-load uncertainty processing is performed on the first constraint condition to obtain the second constraint condition. The specific steps include:

[0156] D1. Calculate the net load according to a preset net load calculation formula to obtain a reference net load; the net load calculation formula is as follows:

[0157]

[0158] in, represents the reference payload; Indicates load power; Indicates photovoltaic power; represents wind power;

[0159] D2. Calculate the photovoltaic power forecast value and the wind power forecast value according to the net load calculation formula to obtain a net load forecast value;

[0160] D3. Determine a first target formula based on the reference net load and the predicted net load value; the first target formula is as follows:

[0161]

[0162] in, represents the predicted net load value; Represents the random error corresponding to the net load forecast value, where the error mean is 0 and the variance is ;

[0163] D4. Adjust the first power balance condition according to the first target formula to obtain an opportunity constraint condition;

[0164] D5. Determine the second constraint condition based on the m constraints, the opportunity constraint condition, and the market power purchase and sale constraint condition.

[0165] In a specific embodiment, the net load is first calculated according to a pre-set net load calculation formula to obtain a reference net load. Net load represents the difference between load power and renewable energy output. Renewable energy output can be photovoltaic power or wind power, but this is not specifically limited here. Then, the predicted photovoltaic power and wind power values ​​are substituted into the net load calculation formula to obtain a predicted net load value. A first target formula is then determined based on the reference net load and the predicted net load value.

[0166] Next, the first power balance condition is adjusted according to the first target formula to obtain the opportunity constraint condition, wherein the opportunity constraint condition is as follows:

[0167]

[0168] in, It represents the confidence probability level and can be set according to a preset reference range, which can be 90%-99% and is not specifically limited here. It should be noted that this chance constraint is difficult to solve directly and can be converted into a deterministic form for calculation. The deterministic form of this chance constraint is as follows:

[0169]

[0170] in, Finally, the m constraints, opportunity constraints, and market power purchase and sales constraints are integrated to obtain the second constraint.

[0171] It can be seen that by predicting the net load, the uncertainty of new energy power generation such as photovoltaic and wind power and load is fully considered, making the constraints more in line with the actual operation of the power system and improving the reliability and robustness of the decision.

[0172] Step S506: determining a target day-ahead market bidding model according to the second objective function and the second constraint condition.

[0173] Specifically, the target day-ahead market bidding model is determined based on the second objective function and the second constraint. The second objective function takes into account electricity price uncertainty. By processing historical electricity price data to obtain multiple price scenario values ​​and adjusting accordingly, it can more accurately reflect market returns under different electricity price scenarios. The second constraint condition addresses source-load uncertainty in the first constraint condition, integrating the constraints of each resource sub-model, opportunity constraints after considering net load uncertainty, and market power purchase and sales constraints, comprehensively covering various restrictions in power system operation.

[0174] Step S507: determining a day-ahead clearing result according to the target day-ahead market bidding model.

[0175] Specifically, the target day-ahead market bidding model can be solved according to a preset optimization algorithm to obtain the optimal values ​​of the decision variables. The optimization algorithm includes, but is not limited to, linear programming, integer programming, genetic algorithm, and particle swarm optimization. The decision variables include, but are not limited to, the power generation of distributed generators, the adjustment amount of interruptible and transferable loads, the charge and discharge power of energy storage equipment, and the power purchase and sales in the day-ahead market, which are not specifically limited here. The day-ahead clearing results include the generation-side clearing results, the load-side clearing results, the energy storage clearing results, and the market transaction clearing results. The generation-side clearing results represent the power generation of various types of power generation resources at different time periods in the day-ahead plan, the load-side clearing results represent the specific adjustment plan for interruptible and transferable loads, the energy storage clearing results represent the charge and discharge power and power changes of energy storage equipment at different time periods, and the market transaction clearing results represent the power purchase and sales in the day-ahead market.

[0176] Step S508 : calculating the day-ahead clearing result according to a preset intraday deviation penalty cost mechanism to obtain an intraday deviation penalty cost.

[0177] The step of calculating the day-ahead clearing result according to the preset intraday deviation penalty cost mechanism to obtain the intraday deviation penalty cost specifically includes:

[0178] E1. Calculate the positive deviation power and negative deviation power according to the preset deviation power calculation formula;

[0179] The calculation formula of the deviation power is as follows:

[0180]

[0181]

[0182] in, Indicates the positive deviation quantity; Indicates the negative deviation quantity; and They represent the actual amount of electricity purchased by the multi-resource aggregate in the day-ahead market and the actual amount of electricity purchased in the intraday market respectively; and They represent the actual winning bid electricity sales of the multi-resource aggregate in the day-ahead market and the actual electricity sales in the intraday market respectively; and represent the actual winning transaction volume of the multi-resource aggregate in the day-ahead market and the actual transaction volume in the intraday market respectively;

[0183] E2. Calculate the intraday deviation penalty cost according to a preset intraday deviation penalty cost calculation formula to obtain the intraday deviation penalty cost;

[0184] The intraday deviation penalty cost calculation formula is as follows:

[0185]

[0186] in, represents the intraday deviation penalty cost; Indicates the intraday real-time market price; represents the day-ahead clearing price corresponding to the day-ahead clearing result; represents the deviation of the bid quantity of the multi-resource aggregate in the intraday market and the day-ahead market; ,when When it is greater than or equal to 0, is 1; when When it is less than 0, is 0.

[0187] In a specific embodiment, first, a calculation is performed according to a preset deviation power calculation formula to obtain positive deviation power and negative deviation power. The deviation power calculation formula is as follows:

[0188]

[0189]

[0190] It should be noted that when the actual amount of electricity purchased by a multi-resource aggregate exceeds the amount of electricity purchased at the winning bid, or the actual amount of electricity sold is lower than the amount of electricity sold at the winning bid, the aggregate must pay the corresponding deviation cost according to the real-time electricity price; when the actual amount of electricity purchased by a multi-resource aggregate is lower than the amount of electricity purchased at the winning bid, or the actual amount of electricity sold exceeds the amount of electricity sold at the winning bid, the aggregate can obtain corresponding benefits according to the difference between the real-time electricity price and the day-ahead electricity price. , .

[0191] Next, the positive deviation electricity and negative deviation electricity are substituted into the intraday deviation penalty cost calculation formula to calculate the intraday deviation penalty cost.

[0192] The calculation formula for intraday deviation penalty cost is as follows:

[0193]

[0194] It should be noted that when u is 1, If it is greater than or equal to 0, it means that the deviation power is positive, and the multi-resource aggregate needs to pay the deviation cost, that is, ; When u is 0, If it is less than 0, it means that the deviation power is negative, then the multi-resource aggregate can obtain part of the benefits, that is, .

[0195] It can be seen that by calculating the positive and negative deviation electricity volume and the intraday deviation penalty cost, the economic impact of the deviation of electricity volume in the day-ahead and intraday market transactions of the multi-resource aggregate can be quantified, which is convenient for more accurate prediction of power generation and consumption, reducing electricity volume deviation and penalty cost. At the same time, it also helps to maintain the order of electricity market transactions and reasonably reflect the impact of market price fluctuations on transaction results.

[0196] Step S509 : constructing a model based on the intraday deviation penalty cost and a preset cost correction scheme to obtain an intraday real-time scheduling model.

[0197] For easier understanding, see Figure 7 , Figure 7 This is a flow chart of constructing an intraday real-time scheduling model provided by an embodiment of the present application. The model is constructed based on the intraday deviation penalty cost and according to a preset cost correction scheme to obtain an intraday real-time scheduling model. The specific steps include:

[0198] F1. Calculate the energy storage cost change and the distributed generator cost change according to a preset cost correction calculation formula. The cost correction calculation formula is as follows:

[0199]

[0200] in, represents the change in energy storage cost; represents the unit operating cost of energy storage; Indicates the energy storage charging power during the day-ahead period; Indicates the energy storage discharge power during the day-ahead period; Indicates the energy storage charging power during the intra-day stage; Indicates the energy storage discharge power during the intra-day stage;

[0201]

[0202] in, represents the cost change of the distributed generator set; Indicates the unit operating cost coefficient; represents the output of the distributed generator set during the day-ahead period; Indicates the output of the distributed generator set during the intraday period;

[0203] F2. Determine a third objective function based on the energy storage cost change and the distributed generator cost change; the third objective function is as follows:

[0204]

[0205] in, represents the total cost of the intraday period;

[0206] F3. Determine a second power balance condition corresponding to the third objective function;

[0207] F4. Determine a third constraint condition based on the m constraint conditions, the second power balance condition, and the market power purchase and sale constraint condition;

[0208] F5. Determine the intraday real-time scheduling model according to the third objective function and the third constraint condition.

[0209] In a specific embodiment, a calculation is first performed according to a preset cost correction formula to obtain the energy storage cost change and the distributed generator cost change. A third objective function is then determined based on the energy storage cost change and the distributed generator cost change. A second power balance condition corresponding to the third objective function is then determined, where the second power balance condition is as follows:

[0210]

[0211] in, represents the photovoltaic power during the intraday phase; Indicates the wind power new energy in the intraday stage; Indicates the load power during the intraday phase; , , It represents the corresponding amount of demand in the day-ahead phase, which remains unchanged in the intraday phase.

[0212] Next, the m constraints, the second power balance condition, and the market power purchase and sales constraint are integrated to obtain the third constraint. Finally, the intraday real-time scheduling model is determined based on the third objective function and the third constraint.

[0213] It can be seen that by optimizing the third objective function and constructing a real-time scheduling model within the day, the optimal output of distributed generators and the charging and discharging strategies of energy storage can be determined during the day, thereby achieving optimal resource allocation of multi-resource aggregates in real-time operation within the day, reducing total costs, and improving overall economy and reliability.

[0214] Step S510: adjusting the day-ahead clearing result according to the intraday real-time scheduling model to obtain an intraday scheduling result.

[0215] Specifically, the intraday real-time dispatch model allows for optimization and adjustment of the day-ahead clearing results based on real-time power system status and power market information, resulting in intraday dispatch results. By implementing these intraday dispatch results, we can better adapt to the actual operation of the power system, reduce the risk of increased costs due to forecast deviations and actual operational changes, and improve the stability and economic efficiency of power system operations. This also facilitates more rational utilization of various power resources, improves resource utilization efficiency, ensures a balance between power supply and demand, and provides more practical operational guidance for power market participants and system operators.

[0216] The above mainly introduces the solution of the embodiment of the present application from the perspective of the execution process of the method side. It is understandable that in order to realize the above functions, the electronic device includes a hardware structure and / or software module corresponding to each function. Those skilled in the art should easily realize that, in combination with the units and algorithm steps of each example described in the embodiment provided in this article, the present application can be implemented in the form of hardware or a combination of hardware and computer software.

[0217] The embodiment of the present application can divide the functional units of the electronic device according to the above method example. For example, each functional unit can be divided according to each function, or two or more functions can be integrated into one processing unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. It should be noted that the division of units in the embodiment of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation.

[0218] In the case of dividing each functional module into corresponding functional modules, Figure 8 This is a block diagram of the functional modules of an optimization and scheduling device based on a multi-resource aggregate provided in an embodiment of the present application. The optimization and scheduling device based on a multi-resource aggregate 800 includes a first acquisition module 810, a second acquisition module 820, a first determination module 830, a processing module 840, a second determination module 850, a calculation module 860, a construction module 870, and an adjustment module 880, wherein:

[0219] The first acquisition module 810 is used to acquire m sub-models, where the m sub-models are obtained by training m MRA models using m resource data in the day-ahead phase; each resource data corresponds to a resource; and m is an integer greater than 1.

[0220] The second acquisition module 820 is configured to acquire historical electricity price data within a preset time period, and predict the electricity price for the intraday phase based on the historical electricity price data to obtain a reference predicted electricity price; the end time of the preset time period is earlier than the start time of the day-ahead phase; and the end time of the day-ahead phase is earlier than the start time of the intraday phase;

[0221] The first determination module 830 is configured to determine a reference day-ahead market bidding model based on the m sub-models and the reference predicted electricity price; the reference day-ahead market bidding model includes a first objective function and a first constraint condition;

[0222] The processing module 840 is configured to process the first objective function for uncertainty of electricity price to obtain a second objective function; and process the first constraint condition for uncertainty of source and load to obtain a second constraint condition;

[0223] The second determining module 850 is configured to determine a target day-ahead market bidding model based on the second objective function and the second constraint condition; and determine a day-ahead clearing result based on the target day-ahead market bidding model.

[0224] The calculation module 860 is configured to calculate the day-ahead clearing result according to a preset intraday deviation penalty cost mechanism to obtain an intraday deviation penalty cost;

[0225] The construction module 870 is used to construct a model based on the intraday deviation penalty cost and a preset cost correction scheme to obtain an intraday real-time scheduling model;

[0226] The adjustment module 880 is used to adjust the day-ahead clearing result according to the intraday real-time scheduling model to obtain an intraday scheduling result;

[0227] Optionally, in terms of acquiring m sub-models, the first acquiring module 810 is specifically configured to:

[0228] Acquire reference resource data; the reference resource data is any one of the m resource data;

[0229] Determine a reference resource type corresponding to the reference resource data; the reference resource type includes any one of the following: photovoltaic, wind power, distributed generator set, interruptible load, transferable load, and energy storage;

[0230] Determining a reference type parameter according to the reference resource type;

[0231] Determining a reference objective function and a reference constraint condition according to the reference type parameter;

[0232] A reference sub-model is determined according to the reference objective function and the reference constraint condition; the reference sub-model is a sub-model corresponding to the reference resource data in the m sub-models.

[0233] Optionally, in determining the reference day-ahead market bidding model according to the m sub-models and the reference predicted electricity price, the first determining module 830 is specifically configured to:

[0234] The objective function of the reference day-ahead market bidding model is constructed to obtain the first objective function; the first objective function is as follows:

[0235]

[0236] in, represents the total market return on the day before; represents the reference predicted electricity price; represents the day-ahead transaction volume; , The electricity sold the day before. represents the amount of electricity purchased on the day before; represents the power generation cost of distributed generators; represents the call cost of the interruptible load user; represents the call cost of the transferable load user; represents the operating cost of the energy storage equipment;

[0237] Obtaining the sub-models whose resource types are the photovoltaic and wind power in the m sub-models, and obtaining a photovoltaic power model and a wind power model;

[0238] Predicting the generated power in the intraday phase according to the photovoltaic power model and the wind power model to obtain a photovoltaic power prediction value and a wind power prediction value;

[0239] Obtaining the constraint condition corresponding to each sub-model in the m sub-models to obtain m constraint conditions;

[0240] Determining a first power balance condition corresponding to the reference day-ahead market bidding model according to the photovoltaic power forecast value and the wind power forecast value;

[0241] Determining the first constraint condition according to the m constraint conditions, the first power balance condition and the preset market power purchase and sale constraint condition;

[0242] The reference day-ahead market bidding model is determined according to the first objective function and the first constraint condition.

[0243] Optionally, in the aspect of performing electricity price uncertainty processing on the first objective function to obtain the second objective function, the processing module 840 is specifically configured to:

[0244] Analyzing the historical electricity price data to obtain an electricity price probability distribution;

[0245] Divide the electricity price probability distribution into N equal parts to obtain N intervals; the length of each interval is 1 / N; N is an integer greater than 1;

[0246] Each of the N intervals is randomly sampled according to a preset random sampling formula to obtain N random numbers; the random sampling formula is as follows:

[0247]

[0248] in, represents a random number drawn from the i-th interval among the N intervals; r represents any random number in [0,1];

[0249] Calculate according to the preset electricity price scenario value calculation formula to obtain N electricity price scenario values; the electricity price scenario value calculation formula is as follows:

[0250]

[0251] in, represents the i-th electricity price scenario value among the N electricity price scenario values; Represents the probability distribution function corresponding to the historical electricity price data;

[0252] Calculating the N electricity price scenario values ​​according to a preset clustering algorithm to obtain S electricity price scenario values; S is an integer greater than 1 and less than N;

[0253] The first objective function is adjusted according to the S electricity price scenario values ​​to obtain the second objective function.

[0254] Optionally, in performing source-load uncertainty processing on the first constraint condition to obtain the second constraint condition, the processing module 840 is further specifically configured to:

[0255] The net load is calculated according to a preset net load calculation formula to obtain a reference net load; the net load calculation formula is as follows:

[0256]

[0257] in, represents the reference payload; Indicates load power; Indicates photovoltaic power; represents wind power;

[0258] Calculating the photovoltaic power prediction value and the wind power prediction value according to the net load calculation formula to obtain a net load prediction value;

[0259] A first target formula is determined according to the reference net load and the net load forecast value; the first target formula is as follows:

[0260]

[0261] in, represents the predicted net load value; Represents the random error corresponding to the net load forecast value, where the error mean is 0 and the variance is ;

[0262] Adjusting the first power balance condition according to the first target formula to obtain an opportunity constraint condition;

[0263] The second constraint condition is determined according to the m constraint conditions, the opportunity constraint condition and the market power purchase and sale constraint condition.

[0264] Optionally, in calculating the day-ahead clearing result according to the preset intraday deviation penalty cost mechanism to obtain the intraday deviation penalty cost, the calculation module 860 is specifically configured to:

[0265] Calculate according to the preset deviation power calculation formula to obtain positive deviation power and negative deviation power;

[0266] The calculation formula of the deviation power is as follows:

[0267]

[0268]

[0269] in, Indicates the positive deviation quantity; Indicates the negative deviation quantity; and They represent the actual amount of electricity purchased by the multi-resource aggregate in the day-ahead market and the actual amount of electricity purchased in the intraday market respectively; and They represent the actual winning bid electricity sales of the multi-resource aggregate in the day-ahead market and the actual electricity sales in the intraday market respectively; and represent the actual winning transaction volume of the multi-resource aggregate in the day-ahead market and the actual transaction volume in the intraday market respectively;

[0270] Calculate the intraday deviation penalty cost according to the preset intraday deviation penalty cost calculation formula to obtain the intraday deviation penalty cost;

[0271] The intraday deviation penalty cost calculation formula is as follows:

[0272]

[0273] in, represents the intraday deviation penalty cost; Indicates the intraday real-time market price; represents the day-ahead clearing price corresponding to the day-ahead clearing result; represents the deviation of the bid quantity of the multi-resource aggregate in the intraday market and the day-ahead market; ,when When it is greater than or equal to 0, is 1; when When it is less than 0, is 0.

[0274] Optionally, in the aspect of constructing a model based on the intraday deviation penalty cost and according to a preset cost correction scheme to obtain an intraday real-time scheduling model, the construction module 870 is specifically configured to:

[0275] The change in energy storage cost and the change in distributed generator cost are calculated according to a preset cost correction calculation formula; the cost correction calculation formula is as follows:

[0276]

[0277] in, represents the change in energy storage cost; represents the unit operating cost of energy storage; Indicates the energy storage charging power during the day-ahead period; Indicates the energy storage discharge power during the day-ahead period; Indicates the energy storage charging power during the intra-day stage; Indicates the energy storage discharge power during the intra-day stage;

[0278]

[0279] in, represents the cost change of the distributed generator set; Indicates the unit operating cost coefficient; represents the output of the distributed generator set during the day-ahead period; Indicates the output of the distributed generator set during the intraday period;

[0280] A third objective function is determined based on the energy storage cost change and the distributed generator cost change; the third objective function is as follows:

[0281]

[0282] in, represents the total cost of the intraday period;

[0283] Determining a second power balance condition corresponding to the third objective function;

[0284] Determining a third constraint condition based on the m constraint conditions, the second power balance condition, and the market power purchase and sale constraint condition;

[0285] The intraday real-time scheduling model is determined according to the third objective function and the third constraint condition.

[0286] It can be seen that by processing the uncertainty of electricity prices and source-load uncertainties, it is convenient to improve the accuracy of the day-ahead clearing results, and to optimize them in real time through the intraday deviation penalty cost mechanism, thereby improving the accuracy and real-time performance of resource scheduling.

[0287] It should be noted that the specific implementation of each operation can adopt the corresponding description of the method embodiment shown above. The optimization scheduling device 800 based on the multi-resource aggregate can be used to execute the above method embodiment of this application, which will not be repeated here.

[0288] 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.

[0289] 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.

[0290] It should be noted that, for the above-mentioned various embodiments, for the sake of simplicity of description, they are all expressed as a series of action combinations. Those skilled in the art should know that this application is not limited by the order of the actions described, because some steps in the embodiments of the present application can be performed in other orders or simultaneously. In addition, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions, steps, modules or units involved are not necessarily required by the embodiments of the present application.

[0291] In the above embodiments, the description of each embodiment in the embodiments of the present application has different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant description of other embodiments. 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.

[0292] 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 via 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. This computer program product comprises one or more computer instructions. When these computer program instructions are loaded and executed on a computer, they fully or partially produce the processes or functions described in the embodiments of the present application. 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 wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital video disc (DVD)), or a semiconductor medium (eg, a solid state disk (SSD)).

[0293] 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.

[0294] 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. An optimization scheduling method based on a multi-resource aggregate, characterized in that: The method comprises: Obtain m sub-models, where the m sub-models are obtained by training m MRA models using m resource data in the day-ahead phase; each resource data corresponds to a resource; and m is an integer greater than 1; Obtaining historical electricity price data within a preset time period, and predicting the electricity price for the intraday phase based on the historical electricity price data to obtain a reference predicted electricity price; the end time of the preset time period is earlier than the start time of the day-ahead phase; and the end time of the day-ahead phase is earlier than the start time of the intraday phase; Determining a reference day-ahead market bidding model based on the m sub-models and the reference predicted electricity price; the reference day-ahead market bidding model includes a first objective function and a first constraint condition; Performing electricity price uncertainty processing on the first objective function to obtain a second objective function; Performing source-load uncertainty processing on the first constraint condition to obtain a second constraint condition; determining a target day-ahead market bidding model according to the second objective function and the second constraint condition; Determine the day-ahead clearing result based on the target day-ahead market bidding model; Calculate the day-ahead clearing result according to the preset intraday deviation penalty cost mechanism to obtain the intraday deviation penalty cost; Based on the intraday deviation penalty cost and according to a preset cost correction scheme, a model is constructed to obtain an intraday real-time scheduling model; Adjusting the day-ahead clearing result according to the intraday real-time scheduling model to obtain an intraday scheduling result; The step of determining a reference day-ahead market bidding model based on the m sub-models and the reference predicted electricity price includes: The objective function of the reference day-ahead market bidding model is constructed to obtain the first objective function; the first objective function is as follows: in, represents the total market return on the day before; represents the reference predicted electricity price; represents the day-ahead transaction volume; , The electricity sold the day before. represents the amount of electricity purchased on the day before; represents the power generation cost of distributed generators; represents the call cost of the interruptible load user; represents the call cost of the transferable load user; represents the operating cost of the energy storage equipment; Obtaining sub-models whose resource types are photovoltaic and wind power among the m sub-models, and obtaining a photovoltaic power model and a wind power model; Predicting the generated power in the intraday phase according to the photovoltaic power model and the wind power model to obtain a photovoltaic power prediction value and a wind power prediction value; Obtaining the constraint condition corresponding to each sub-model in the m sub-models to obtain m constraint conditions; Determining a first power balance condition corresponding to the reference day-ahead market bidding model according to the photovoltaic power forecast value and the wind power forecast value; Determining the first constraint condition according to the m constraint conditions, the first power balance condition and the preset market power purchase and sale constraint condition; determining the reference day-ahead market bidding model according to the first objective function and the first constraint condition; The performing source-load uncertainty processing on the first constraint condition to obtain the second constraint condition includes: The net load is calculated according to a preset net load calculation formula to obtain a reference net load; the net load calculation formula is as follows: in, represents the reference payload; Indicates load power; Indicates photovoltaic power; represents wind power; Calculating the photovoltaic power prediction value and the wind power prediction value according to the net load calculation formula to obtain a net load prediction value; A first target formula is determined according to the reference net load and the net load forecast value; the first target formula is as follows: in, represents the predicted net load value; Represents the random error corresponding to the net load forecast value, where the error mean is 0 and the variance is ; Adjusting the first power balance condition according to the first target formula to obtain an opportunity constraint condition; The second constraint condition is determined according to the m constraint conditions, the opportunity constraint condition and the market power purchase and sale constraint condition.

2. The method according to claim 1, wherein The obtaining of m sub-models includes: Acquire reference resource data; the reference resource data is any one of the m resource data; Determine a reference resource type corresponding to the reference resource data; the reference resource type includes any one of the following: photovoltaic, wind power, distributed generator set, interruptible load, transferable load, and energy storage; Determining a reference type parameter according to the reference resource type; Determining a reference objective function and a reference constraint condition according to the reference type parameter; A reference sub-model is determined according to the reference objective function and the reference constraint condition; the reference sub-model is a sub-model corresponding to the reference resource data in the m sub-models.

3. The method according to claim 1, wherein The performing electricity price uncertainty processing on the first objective function to obtain a second objective function includes: Analyzing the historical electricity price data to obtain an electricity price probability distribution; Divide the electricity price probability distribution into N equal parts to obtain N intervals; the length of each interval is 1 / N; N is an integer greater than 1; Each of the N intervals is randomly sampled according to a preset random sampling formula to obtain N random numbers; the random sampling formula is as follows: in, represents a random number drawn from the i-th interval among the N intervals; r represents any random number in [0,1]; Calculate according to the preset electricity price scenario value calculation formula to obtain N electricity price scenario values; the electricity price scenario value calculation formula is as follows: in, represents the i-th electricity price scenario value among the N electricity price scenario values; Represents the probability distribution function corresponding to the historical electricity price data; Calculating the N electricity price scenario values ​​according to a preset clustering algorithm to obtain S electricity price scenario values; S is an integer greater than 1 and less than N; The first objective function is adjusted according to the S electricity price scenario values ​​to obtain the second objective function.

4. The method according to any one of claims 1 to 3, wherein The day-ahead clearing result is calculated according to the preset intraday deviation penalty cost mechanism to obtain the intraday deviation penalty cost, including: Calculate according to the preset deviation power calculation formula to obtain positive deviation power and negative deviation power; The calculation formula of the deviation power is as follows: in, Indicates the positive deviation quantity; Indicates the negative deviation quantity; and They represent the actual amount of electricity purchased by the multi-resource aggregate in the day-ahead market and the actual amount of electricity purchased in the intraday market respectively; and They represent the actual winning bid electricity sales of the multi-resource aggregate in the day-ahead market and the actual electricity sales in the intraday market respectively; and represent the actual winning transaction volume of the multi-resource aggregate in the day-ahead market and the actual transaction volume in the intraday market respectively; Calculate the intraday deviation penalty cost according to the preset intraday deviation penalty cost calculation formula to obtain the intraday deviation penalty cost; The intraday deviation penalty cost calculation formula is as follows: in, represents the intraday deviation penalty cost; Indicates the intraday real-time market price; represents the day-ahead clearing price corresponding to the day-ahead clearing result; represents the deviation of the bid quantity of the multi-resource aggregate in the intraday market and the day-ahead market; ,when When it is greater than or equal to 0, is 1; when When it is less than 0, is 0.

5. The method according to claim 4, wherein The model is constructed based on the intraday deviation penalty cost and according to a preset cost correction scheme to obtain an intraday real-time scheduling model, including: The change in energy storage cost and the change in distributed generator cost are calculated according to a preset cost correction calculation formula; the cost correction calculation formula is as follows: in, represents the change in energy storage cost; represents the unit operating cost of energy storage; Indicates the energy storage charging power during the day-ahead period; Indicates the energy storage discharge power during the day-ahead period; Indicates the energy storage charging power during the intra-day stage; Indicates the energy storage discharge power during the intra-day stage; in, represents the cost change of the distributed generator set; Indicates the unit operating cost coefficient; represents the output of the distributed generator set during the day-ahead period; Indicates the output of the distributed generator set during the intraday period; A third objective function is determined based on the energy storage cost change and the distributed generator cost change; the third objective function is as follows: in, represents the total cost of the intraday period; Determining a second power balance condition corresponding to the third objective function; Determining a third constraint condition based on the m constraint conditions, the second power balance condition, and the market power purchase and sale constraint condition; The intraday real-time scheduling model is determined according to the third objective function and the third constraint condition.

6. An optimization scheduling device based on a multi-resource aggregate, used to execute the method according to any one of claims 1 to 5, characterized in that: The apparatus includes a first acquisition module, a second acquisition module, a first determination module, a processing module, a second determination module, a calculation module, a construction module, and an adjustment module, wherein: The first acquisition module is used to acquire m sub-models, where the m sub-models are obtained by training m MRA models using m resource data in the day-ahead phase; each resource data corresponds to a resource; and m is an integer greater than 1; The second acquisition module is configured to acquire historical electricity price data within a preset time period, and predict the electricity price for the intraday phase based on the historical electricity price data to obtain a reference predicted electricity price; the end time of the preset time period is earlier than the start time of the day-ahead phase; and the end time of the day-ahead phase is earlier than the start time of the intraday phase; The first determination module is configured to determine a reference day-ahead market bidding model based on the m sub-models and the reference predicted electricity price; the reference day-ahead market bidding model includes a first objective function and a first constraint condition; The processing module is configured to perform electricity price uncertainty processing on the first objective function to obtain a second objective function; and perform source-load uncertainty processing on the first constraint condition to obtain a second constraint condition; The second determining module is configured to determine a target day-ahead market bidding model based on the second objective function and the second constraint condition; and determine a day-ahead clearing result based on the target day-ahead market bidding model; The calculation module is configured to calculate the day-ahead clearing result according to a preset intraday deviation penalty cost mechanism to obtain an intraday deviation penalty cost; The construction module is used to construct a model based on the intraday deviation penalty cost and a preset cost correction scheme to obtain an intraday real-time scheduling model; The adjustment module is used to adjust the day-ahead clearing result according to the intraday real-time scheduling model to obtain an intraday scheduling result; The first determining module is specifically configured to: The objective function of the reference day-ahead market bidding model is constructed to obtain the first objective function; the first objective function is as follows: in, represents the total market return on the day before; represents the reference predicted electricity price; represents the day-ahead transaction volume; , The electricity sold the day before. represents the amount of electricity purchased on the day before; represents the power generation cost of distributed generators; represents the call cost of the interruptible load user; represents the call cost of the transferable load user; represents the operating cost of the energy storage equipment; Obtaining the sub-models whose resource types are the photovoltaic and wind power in the m sub-models, and obtaining a photovoltaic power model and a wind power model; Predicting the generated power in the intraday phase according to the photovoltaic power model and the wind power model to obtain a photovoltaic power prediction value and a wind power prediction value; Obtaining the constraint condition corresponding to each sub-model in the m sub-models to obtain m constraint conditions; Determining a first power balance condition corresponding to the reference day-ahead market bidding model according to the photovoltaic power forecast value and the wind power forecast value; Determining the first constraint condition according to the m constraint conditions, the first power balance condition and the preset market power purchase and sale constraint condition; The reference day-ahead market bidding model is determined according to the first objective function and the first constraint condition.

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 The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions. When the program instructions are executed by a processor, the processor is caused to perform the method according to any one of claims 1 to 5.

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