A method, system, device and medium for optimizing and managing the energy of a microgrid

Abstract

A method, system, device and medium for microgrid energy optimization management proposed by an embodiment of the present application relate to the technical field of power system automation. The method includes: dividing the management stage to obtain a day-ahead robust energy optimization management stage and an intra-day real-time rolling energy optimization management stage; establishing a day-ahead robust energy optimization management model and an intra-day real-time rolling energy optimization management model; obtaining first operation data of the microgrid; solving the day-ahead robust energy optimization management model according to the first operation data to obtain a day-ahead robust energy optimization management strategy; obtaining second operation data of the microgrid; solving the intra-day real-time rolling energy optimization management model according to the day-ahead robust energy optimization management strategy and the second operation data to obtain a target microgrid energy optimization management strategy. The embodiment of the present application improves the reliability of energy optimization management.

Description

Technical Field

[0001] The present application relates to the technical field of power system automation, and particularly to a method, system, device and medium for microgrid energy optimization management. Background Art

[0002] As a distributed power system for achieving power supply-demand balance, the reliable and efficient operation of a microgrid depends on appropriate energy management methods. However, due to the high volatility and uncertainty of renewable energy generation and power end-user consumption, the reasonable optimization of microgrid energy management has become a very challenging task. Against this background, related technologies have proposed various solutions to the problem of microgrid energy optimization management. Among them, the stochastic optimization energy management method based on probability distribution theory has been widely adopted due to its strong universality. However, the stochastic optimization energy management method focuses on using the renewable energy generation and power end-user consumption data of historical typical days for microgrid energy optimization management, but these historical data deviate from reality and are prone to reducing the reliability of energy optimization management strategies. Therefore, how to provide a method for microgrid energy optimization management that can improve the reliability of energy optimization management has become an urgent technical problem to be solved. Summary of the Invention

[0003] The main objective of the embodiments of the present application is to propose a method, system, device and medium for microgrid energy optimization management, which can improve the reliability of energy optimization management.

[0004] To achieve the above objective, a first aspect of the embodiments of the present application proposes a method for microgrid energy optimization management, which is applied to a microgrid. The microgrid includes a plurality of execution devices, and the method includes:

[0005] Obtain a management stage, divide the management stage to obtain a day-ahead robust energy optimization management stage and an intra-day real-time rolling energy optimization management stage;

[0006] For the day-ahead robust energy optimization management stage, establish a day-ahead robust energy optimization management model;

[0007] For the intra-day real-time rolling energy optimization management stage, establish an intra-day real-time rolling energy optimization management model;

[0008] Obtain first operation data of the microgrid, where the first operation data includes historical data of the execution devices and day-ahead prediction data of the execution devices;

[0009] Solve the day-ahead robust energy optimization management model according to the first operation data to obtain a day-ahead robust energy optimization management strategy;

[0010] Obtain the second operating data of the microgrid, where the second operating data includes the intraday real-time data of the execution device;

[0011] Solve the intraday real-time stage rolling energy optimization management model according to the day-ahead robust energy optimization management strategy and the second operating data to obtain the target microgrid energy optimization management strategy.

[0012] In some embodiments, the solving the intraday real-time stage rolling energy optimization management model according to the day-ahead robust energy optimization management strategy and the second operating data to obtain the target microgrid energy optimization management strategy includes:

[0013] Obtain the rolling optimization time domain, and determine the solving period according to the rolling optimization time domain;

[0014] Obtain the starting moment of the current solution. When the starting moment is within the solving period, solve the intraday real-time stage rolling energy optimization management model according to the day-ahead robust energy optimization management strategy and the second operating data to obtain the target microgrid energy optimization management strategy.

[0015] In some embodiments, the day-ahead robust energy optimization management model includes multiple variables. Before solving the day-ahead robust energy optimization management model according to the first operating data to obtain the day-ahead robust energy optimization management strategy, the method further includes:

[0016] Update the day-ahead robust energy optimization management model, specifically including:

[0017] Perform type detection on each of the variables to obtain type detection results; the type detection results include a first result, and the first result is used to characterize that the type of the variable is an uncertain type;

[0018] For each of the variables, if the type detection result is the first result, perform boundary value transformation on the variable to obtain the transformed variable;

[0019] Obtain the updated day-ahead robust energy optimization management model based on the transformed variables.

[0020] In some embodiments, the execution device includes a photovoltaic power generation device, an energy storage device, an elastic load, and an inelastic load.

[0021] In some embodiments, the establishing the day-ahead robust energy optimization management model for the day-ahead robust energy optimization management stage includes:

[0022] Obtain the day-ahead data of the day-ahead robust energy optimization management stage, where the day-ahead data includes: the day-ahead equipment cost of the microgrid, the day-ahead photovoltaic power generation data of the photovoltaic power generation equipment, the day-ahead inelastic operation data of the inelastic load, the day-ahead elastic operation data of the elastic load, and the day-ahead energy storage operation data of the energy storage equipment;

[0023] Construct a day-ahead energy management cost minimization model for the microgrid based on the day-ahead equipment cost;

[0024] Construct a model based on the day-ahead photovoltaic power generation data to obtain a day-ahead photovoltaic power output model;

[0025] Construct a model based on the day-ahead inelastic operation data to obtain a day-ahead inelastic load demand model;

[0026] Construct a model based on the day-ahead elastic operation data to obtain a day-ahead elastic load demand model;

[0027] Construct a model based on the day-ahead energy storage operation data to obtain a day-ahead energy storage system operation model;

[0028] Construct a day-ahead reliable operation constraint condition model for the microgrid according to the preset constraint conditions;

[0029] Merge the day-ahead energy management cost minimization model for the microgrid, the day-ahead photovoltaic power output model, the day-ahead inelastic load demand model, the day-ahead elastic load demand model, the day-ahead energy storage system operation model, and the day-ahead reliable operation constraint condition model for the microgrid to obtain the day-ahead robust energy optimization management model.

[0030] In some embodiments, before obtaining the elastic operation data of the elastic load, the method further includes:

[0031] Receive an operation instruction for the elastic load;

[0032] Adjust the magnitude of the electrical load of the elastic load based on the operation instruction.

[0033] In some embodiments, for the intra-day real-time rolling energy optimization management stage, establishing an intra-day real-time rolling energy optimization management model includes:

[0034] Obtain the third operation data of the microgrid, where the third operation data includes: the start time of the rolling optimization time domain, the preset solution period of the rolling optimization time domain, the intra-day power purchase data from the main grid, the intra-day inelastic load data, the intra-day elastic load data, the intra-day energy storage system operation data, and the intra-day photovoltaic power generation data;

[0035] Construct a model according to the aforementioned day-ahead robust energy optimization management strategy, the starting time of the rolling optimization time domain, and the preset solution period of the rolling optimization time domain, to obtain an intra-day real-time rolling energy management deviation minimization model;

[0036] Construct a model according to the intra-day power purchase data from the main grid, the intra-day inelastic load data, the intra-day elastic load data, the intra-day energy storage system operation data, and the intra-day photovoltaic power generation data, to obtain an intra-day real-time power balance constraint condition model;

[0037] Construct a model according to the intra-day photovoltaic power generation data, to obtain an intra-day real-time photovoltaic output model;

[0038] Construct a model according to the intra-day inelastic load data, to obtain an intra-day real-time inelastic load model;

[0039] Construct a model according to the intra-day energy storage operation data, to obtain an intra-day energy storage system real-time operation model;

[0040] Construct a model according to the intra-day power purchase data from the main grid, to obtain an intra-day real-time power purchase model from the main grid;

[0041] Merge the intra-day real-time rolling energy management deviation minimization model, the intra-day real-time power balance constraint condition model, the intra-day real-time photovoltaic output model, the intra-day real-time inelastic load model, the intra-day energy storage system real-time operation model, and the intra-day real-time power purchase model from the main grid, to obtain the intra-day real-time rolling energy optimization management model.

[0042] The second aspect of the embodiments of the present application proposes a microgrid energy optimization management system, which is applied to a microgrid. The microgrid includes a plurality of execution devices, including:

[0043] A stage division module, configured to obtain a management stage, perform stage division on the management stage, and obtain a day-ahead robust energy optimization management stage and an intra-day real-time rolling energy optimization management stage;

[0044] A day-ahead model creation module, configured to establish a day-ahead robust energy optimization management model for the day-ahead robust energy optimization management stage;

[0045] An intra-day model creation module, configured to establish an intra-day real-time rolling energy optimization management model for the intra-day real-time rolling energy optimization management stage;

[0046] A first operation data acquisition module, configured to acquire first operation data of the microgrid. The first operation data includes historical data of the execution device and day-ahead prediction data of the execution device;

[0047] An initial solution module, configured to solve the day-ahead robust energy optimization management model according to the first operation data, so as to obtain a day-ahead robust energy optimization management strategy;

[0048] A second operation data acquisition module, configured to acquire second operation data of the microgrid, where the second operation data includes intra-day real-time data of the execution device;

[0049] A target solution module, configured to solve the intra-day real-time stage rolling energy optimization management model according to the day-ahead robust energy optimization management strategy and the second operation data, so as to obtain a target microgrid energy optimization management strategy.

[0050] A third aspect of the embodiments of the present application provides a computer device, where the computer device includes a memory and a processor. Among them, a computer program is stored in the memory, and when the computer program is executed by the processor, the processor is configured to execute a method for microgrid energy optimization management according to any one of the embodiments of the first aspect of the present application.

[0051] A fourth aspect of the embodiments of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the computer program is executed by a computer, the computer is configured to execute a method for microgrid energy optimization management according to any one of the embodiments of the first aspect of the present application.

[0052] A method, system, device, and medium for microgrid energy optimization management provided by the embodiments of the present application divide the entire management stage of the microgrid into a day-ahead robust energy optimization management stage and an intra-day real-time rolling energy optimization management stage, and respectively construct models to obtain a day-ahead robust energy optimization management model and an intra-day real-time rolling energy optimization management model. First, solve the day-ahead robust energy optimization management model to obtain a day-ahead robust energy optimization management strategy, and then solve the intra-day real-time rolling energy optimization management model according to the day-ahead robust energy optimization management strategy and the second operation data to obtain a target microgrid energy optimization management strategy. The embodiments of the present application improve the reliability of microgrid management through the division of management stages, the separate solution of management models, and specifically, the joint determination of the target microgrid energy optimization management strategy according to the day-ahead robust energy optimization management strategy and the second operation data. Description of the Drawings

[0053] Figure 1 is a flowchart of a method for microgrid energy optimization management provided by an embodiment of the present application;

[0054] Figure 2 is Figure 1 a flowchart of step S102 in

[0055] Figure 3 It is a flowchart of a method for optimizing and managing microgrid energy provided by another embodiment of the present application;

[0056] Figure 4 is Figure 1 a flowchart of step S105 in

[0057] Figure 5 a block diagram of the module structure of a system for optimizing and managing microgrid energy provided by an embodiment of the present application;

[0058] Figure 6 a schematic diagram of the hardware structure of a computer device provided by an embodiment of the present application. Detailed implementation manners

[0059] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0060] It should be noted that although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different module division in the device or a different order in the flowchart. Terms such as "first" and "second" in the specification, claims and the above-mentioned drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0062] First, several terms involved in the present application are analyzed:

[0063] Artificial intelligence (AI): It is a new technical science that studies, develops theories, methods, technologies and application systems for simulating, extending and expanding human intelligence; artificial intelligence is a branch of computer science. Artificial intelligence attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a way similar to human intelligence. The research in this field includes robots, speech recognition, image recognition, natural language processing and expert systems, etc. Artificial intelligence can simulate the information process of human consciousness and thinking. Artificial intelligence also uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results of theories, methods, technologies and application systems.

[0064] Embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Among them, Artificial Intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results.

[0065] A method for optimizing and managing microgrid energy provided by embodiments of this application can be applied to artificial intelligence. The basic technologies of artificial intelligence generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, and mechatronics. The software technologies of artificial intelligence mainly include several major directions such as computer vision technology, robotics technology, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0066] As a distributed power system that can make full use of local renewable energy to achieve a balance between power supply and demand, the reliable and efficient operation of a microgrid depends on appropriate energy management methods. However, due to the high volatility and uncertainty of renewable energy generation and power consumption of end-users, it has become a very challenging task to reasonably optimize the energy management of the microgrid. In this context, a variety of solution technologies have been proposed for the problem of microgrid energy optimization management. Among them, the stochastic optimization energy management method based on probability distribution theory and the robust optimization energy management method based on robust optimization theory have been widely adopted due to their strong universality. However, in practical applications, it has been found that there are drawbacks in using the above methods for energy optimization management. The specific reasons include: the stochastic optimization energy management method focuses on using the renewable energy generation and power consumption data of historical typical days for microgrid energy optimization management, and there is a deviation between the prediction data and the actual data related to the day-ahead and real-time operation stages of the microgrid, which leads to a reduction in the reliability of the energy optimization management strategy. The robust optimization energy management method usually obtains the optimization strategy based on the worst-case scenario assumption, which usually results in the energy optimization management strategy being too conservative, so that the economic performance of the microgrid in the actual operation scenario is significantly reduced.

[0067] Based on this, to break through the limitations of stochastic optimization and robust optimization, the embodiments of the present application introduce the tube model predictive control theory. By splitting the original optimization problem into two sub-optimization problems, namely offline robust optimization and online real-time feedback optimization, while avoiding the over-conservatism of the optimization strategy, the economy of the optimization strategy is significantly improved. In this regard, the embodiments of the present application disclose a method for microgrid energy optimization management, specifically a method for microgrid energy optimization management based on the tube model predictive control theory. Applying this method to address the microgrid energy optimization management problem can obtain a microgrid energy optimization management strategy that takes into account robustness, economy, and reliability. In addition, the embodiments of the present application also provide a system, a computer device, and a computer storage medium for microgrid energy optimization management, which are used to execute a method for microgrid energy optimization management.

[0068] The method for microgrid energy optimization management provided by the embodiments of the present application is applied to the server side and can also be software running on the server side. In some embodiments, the server side can be configured as an independent physical server, or can be configured as a server cluster or distributed system composed of multiple physical servers, or can also be configured as a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application implementing the above method, etc., but is not limited to the above forms.

[0069] The embodiments of the present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor systems, microprocessor-based systems, set-top boxes, programmable consumer computer devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in a distributed computing environment, where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0070] The method for microgrid energy optimization management provided by the embodiments of the present application will be specifically described through the following embodiments.

[0071] Refer to Figure 1, A method for optimizing and managing the energy of a microgrid according to an embodiment of the present application is applied to a microgrid. The microgrid includes a plurality of execution devices. The method includes, but is not limited to, steps S101 to S107.

[0072] Step S101: Obtain the management stage, divide the management stage, and obtain the day-ahead robust energy optimization management stage and the intra-day real-time rolling energy optimization management stage.

[0073] Step S102: For the day-ahead robust energy optimization management stage, establish a day-ahead robust energy optimization management model.

[0074] Step S103: Obtain the first operation data of the microgrid. The first operation data includes the historical data of the execution devices and the day-ahead prediction data of the execution devices.

[0075] Step S104: Solve the day-ahead robust energy optimization management model according to the first operation data to obtain the day-ahead robust energy optimization management strategy.

[0076] Step S105: For the intra-day real-time rolling energy optimization management stage, establish an intra-day real-time rolling energy optimization management model.

[0077] Step S106: Obtain the second operation data of the microgrid. The second operation data includes the intra-day real-time data of the execution devices.

[0078] Step S107: Solve the intra-day real-time stage rolling energy optimization management model according to the day-ahead robust energy optimization management strategy and the second operation data to obtain the target microgrid energy optimization management strategy.

[0079] Steps S101 to S107 illustrated in the embodiments of the present application divide the entire management stage of the microgrid into an offline stage (day-ahead robust energy optimization management stage) and an online real-time stage (intra-day real-time rolling energy optimization management stage), and respectively construct models to obtain an offline model (day-ahead robust energy optimization management model) and an online real-time model (intra-day real-time rolling energy optimization management model). First, solve the offline model to obtain the offline management strategy (day-ahead robust energy optimization management strategy), and then solve the online real-time model according to the offline management strategy and real-time data (second operation data) to obtain the online real-time management strategy (target microgrid energy optimization management strategy). By dividing the management stage, separately solving the management models, and specifically determining the online real-time management strategy according to the offline management strategy and real-time data, the embodiments of the present application improve the accuracy and real-time performance of the online real-time management strategy, and also improve the reliability of the microgrid management.

[0080] In step S101 of some embodiments, the management phase specifically refers to the time span for optimization management. For example, the 24 hours from the day before to 24 hours within the day is called a management phase. Another example is that the 48 hours from the day before to 24 hours within the day is called a management phase. The division of the management phase is mainly for offline and online division. For example, if the 24 hours from the day before to 24 hours within the day is called a management phase, then the 24 hours from the day before is classified as the offline phase, and the 24 hours within the day is the online real-time phase. If the 48 hours from the day before to 24 hours within the day is called a management phase, then the 48 hours from the day before is classified as the offline phase, and the 24 hours within the day is the online real-time phase.

[0081] It should be noted that the microgrid includes multiple execution devices, and the execution devices include photovoltaic power generation devices, energy storage devices, flexible loads that can participate in demand-side response, and inelastic loads that cannot participate in demand-side response.

[0082] In step S102 of some embodiments, for the day-ahead robust energy optimization management phase, a day-ahead robust energy optimization management model is established. Specifically, establishing the day-ahead robust energy optimization management model includes a microgrid energy management cost minimization model, a photovoltaic power output model, an inelastic load demand model, a flexible load demand-side response model, an energy storage system operation model, and a microgrid reliable operation constraint condition model.

[0083] In some embodiments, referring to Figure 2 , step S102 specifically includes but is not limited to steps S201 to S206.

[0084] Step S201, obtain the day-ahead data of the day-ahead robust energy optimization management phase. The day-ahead data includes: the day-ahead equipment cost of the microgrid, the day-ahead photovoltaic power generation data of the photovoltaic power generation device, the day-ahead inelastic operation data of the inelastic load, the day-ahead flexible operation data of the flexible load, and the day-ahead energy storage operation data of the energy storage device.

[0085] Step S202, construct a model based on the day-ahead equipment cost to obtain a microgrid day-ahead energy management cost minimization model.

[0086] Specifically, the equipment cost includes the power purchase cost from the main grid, the compensation cost for flexible loads participating in demand-side response, and the depreciation cost of the energy storage system. The microgrid energy management cost minimization model includes: the power purchase cost model of the microgrid from the main grid, the compensation cost model for flexible loads participating in demand-side response, and the depreciation cost model of the energy storage system, as shown in formulas (1) to (4).

[0087] C = C u + C r + C b (1)

[0088]

[0089]

[0090]

[0091] Among them, C, C u , C r , C b are respectively the energy management cost of the microgrid, the electricity purchase cost from the main grid, the compensation cost for the flexible load to participate in the demand response, and the depreciation cost of the energy storage system; t is the decision-making period; T is the set of all decision-making periods; ε u is the time-of-use electricity price of the main grid; p u,t is the electricity purchase quantity of the microgrid from the main grid at time t; is the start time of the demand response; ΔT r is the duration of the demand response; ε r is the compensation cost coefficient for the flexible load to participate in the demand response; Δp r,t is the total amount of flexible load participating in the demand response at time t; ε b is the depreciation cost coefficient of the energy storage system; is the charging electricity quantity of the energy storage system at time t; is the discharging electricity quantity of the energy storage system at time t; Δt is the duration of time t, with the unit of 1h.

[0092] Step S203, construct a model based on the day-ahead photovoltaic power generation data to obtain a day-ahead photovoltaic power output model.

[0093] Specifically, the photovoltaic power generation data includes actual photovoltaic power output data, predicted photovoltaic power output data, expected photovoltaic power output data, and prediction errors. The predicted photovoltaic power output data includes the minimum predicted photovoltaic power output and the maximum predicted photovoltaic power output. The photovoltaic power output model includes a photovoltaic power output upper and lower limit constraint model and a photovoltaic power output prediction model, as shown in formulas (5) and (6) respectively.

[0094] p pv,min ≤p pv,t ≤p pv,max (5)

[0095]

[0096] Among them, p pv,t is the actual photovoltaic power output data at time t; p pv,min is the minimum predicted photovoltaic power output predicted according to meteorological data; p pv,max is the maximum predicted photovoltaic power output predicted according to meteorological data; is the expected photovoltaic power output data predicted day-ahead; is the error of the day-ahead prediction, and the error range is

[0097] Step S204: Construct a model based on the inelastic operation data to obtain a day-ahead inelastic load demand model.

[0098] Specifically, the inelastic operation data includes inelastic load actual demand data, inelastic load forecast data, inelastic load expected demand data, and inelastic load forecast error data. The inelastic load forecast data includes the minimum value and the maximum value of the inelastic load forecast. The inelastic load demand model includes an inelastic load demand upper and lower bound constraint model and an inelastic load forecast model, as shown in formulas (7) and (8) respectively.

[0099] p ie,min ≤ p ie,t ≤ p ie,max (7)

[0100]

[0101] where p ie,t is the inelastic load actual demand data at time t; p ie,min and p ie,max are the minimum value and the maximum value of the day-ahead forecast of p ie,t respectively; is the day-ahead forecasted inelastic load demand expected data; p ie,t is the error of the day-ahead prediction, and the error range is

[0102]

[0103] Before step 205, a microgrid energy optimization management method further includes but is not limited to:

[0104] Receiving an operation instruction for the flexible load;

[0105] Adjusting the magnitude of the electric power load of the flexible load based on the operation instruction.

[0106] Specifically, the flexible loads within the microgrid (such as air conditioner loads, water heater loads, washing machine loads, etc.) can adjust the magnitude of the electric power load according to the requirements of the operation instruction, so as to update the flexible operation data in real time and improve the timeliness of the data.

[0107] Step S205: Construct a model based on the flexible operation data to obtain a day-ahead flexible load demand model.

[0108] Specifically, the flexible operation data includes the actual data of the power regulation amount, the maximum value of the power regulation amount, the minimum value of the power regulation amount, the satisfaction index, the actual duration of the power regulation, the minimum value of the power regulation duration, and the maximum value of the power regulation duration. The flexible load demand-side response model includes a model for the upper and lower limit constraints of the power demand of the flexible load, a model for the total amount constraint of the power quantity response, a model for the satisfaction constraint, and a model for the response duration constraint, as shown in formulas (9)-(12) respectively.

[0109] Δp r,min ≤Δp r,t ≤Δp r,max (9)

[0110] ∑ t∈T Δp r,t =0 (10)

[0111]

[0112] ΔT r,min ≤T r ≤ΔT r,max (12)

[0113] Among them, Δp r,t is the power regulation amount of the flexible load participating in the demand-side response at time t; Δp r,min and Δp r,max are the minimum value and the maximum value of Δp r,t respectively; is the satisfaction index of the flexible load participating in the demand-side response; T r is the power regulation duration of the flexible load participating in the demand-side response; ΔT r,min and ΔT r,max are the minimum value and the maximum value of T r respectively.

[0114] Step S206, construct a model based on the energy storage operation data to obtain the day-ahead energy storage system operation model.

[0115] Specifically, the energy storage operation data includes charging power data, discharging power data, state of charge data, charging efficiency coefficient data, discharging power coefficient data, and electricity capacity data. The energy storage system operation model includes a model for the upper and lower limit constraints of the charging power of the energy storage system, a model for the upper and lower limit constraints of the discharging power, and a model for the state of charge constraint, as shown in formulas (13)-(15) respectively.

[0116]

[0117]

[0118]

[0119] Among them, is the maximum charging power of the energy storage system; is the maximum discharging power of the energy storage system; SOC t-1 is the state of charge of the energy storage system in the (t - 1) period; SOC min and SOC max are the minimum and maximum values of SOC t-1 ; and are the charging and discharging efficiency coefficients of the energy storage system respectively; B b is the electricity capacity of the energy storage system; To ensure that the energy storage system does not charge and discharge simultaneously,

[0120] Step S207, construct a day-ahead reliable operation constraint condition model for the microgrid according to the preset constraint conditions.

[0121] Specifically, the day-ahead reliable operation constraint condition model for the microgrid includes a microgrid operation power balance constraint condition model, a power upper and lower limit constraint condition model for the microgrid to purchase electricity from the main grid, and a standby power constraint condition model for coping with emergencies, as shown in formulas (16)-(18) respectively.

[0122]

[0123] 0 ≤ p u,t ≤ p u,max (17)

[0124]

[0125] Among them, p u,max is the maximum value of p u,t ; l is the standby coefficient.

[0126] Step S208, merge the day-ahead energy management cost minimization model, the day-ahead photovoltaic output model, the day-ahead inelastic load demand model, the day-ahead elastic load demand model, the day-ahead energy storage system operation model, and the day-ahead reliable operation constraint condition model for the microgrid to obtain a day-ahead robust energy optimization management model.

[0127] It should be noted that based on the above steps S201 to S208, a day-ahead robust energy optimization management model has been obtained, but there are uncertain variables in this model, which will affect the stability of the subsequent model solution. Therefore, in some embodiments, referring to Figure 3 , the embodiments of the present application update the day-ahead robust energy optimization management model, specifically including but not limited to steps S301 to S303:

[0128] Step S301: Perform type detection on each variable to obtain a type detection result. The type detection result includes a first result, which is used to represent that the type of the variable is an uncertain type.

[0129] Step S302: For each variable, if the type detection result is the first result, perform boundary value conversion on the variable to obtain a converted variable.

[0130] Step S303: Obtain an updated day-ahead robust energy optimization management model based on the converted variable.

[0131] It should be noted that the type detection result includes a second result, which is used to represent that the type of the variable is a definite type.

[0132] Steps S301 to S303 shown in the embodiments of this application are to make the day-ahead energy optimization management strategy robust. The present invention introduces robust optimization theory to convert formulas (6), (8), (16), and (18) that reflect the strong uncertainty of photovoltaic output and inelastic load demand into deterministic robust equivalent equations, as shown in formula (19).

[0133]

[0134] Among them, δ t and γ t are the Lagrange coefficients at time period t.

[0135] More specifically, it can be seen from formula (19) that formula (19) is a new constraint without uncertain variables because the uncertainty variables p pv,t and p ie,t in the original constraint condition models (6), (8), (16), and (18) have been replaced by deterministic boundary parameters and . When solving the day-ahead robust energy optimization management model of the microgrid, the original constraint condition models (6), (8), (16), and (18) need to be replaced by the robust constraint condition model (19). Thus, the complete form of the day-ahead robust energy optimization management model in the offline stage consists of formulas (1)-(5), (7), (9)-(15), (17), and (19).

[0136] In step S103 of some embodiments, obtain the first operation data of the microgrid. The first operation data includes historical data of execution devices and day-ahead prediction data. Specifically, the historical data includes historical data of load, historical data of photovoltaic power generation, and historical state data of the energy storage system. The day-ahead prediction data includes day-ahead prediction data of load, day-ahead prediction data of photovoltaic power generation, and day-ahead prediction state data of the energy storage system.

[0137] In step S104 of some embodiments, the day-ahead robust energy optimization management model is solved according to the first operation data to obtain a day-ahead robust energy optimization management strategy.

[0138] In one example, based on the first operation data, an appropriate solution algorithm is used to solve the day-ahead robust energy optimization management model, and a day-ahead robust energy optimization management strategy that can cope with uncertainties can be obtained.

[0139] In step S105 of some embodiments, a rolling energy optimization management model for the real-time operation of the microgrid is established, which specifically includes:

[0140] Obtain the third operation data of the microgrid, where the third operation data includes: the starting time of the rolling optimization time domain, the preset solution period of the rolling optimization time domain, the data of the power purchased from the main grid during the day, the inelastic load data during the day, the elastic load data during the day, the operation data of the energy storage system during the day, and the photovoltaic power generation data during the day;

[0141] According to the day-ahead robust energy optimization management strategy, the starting time of the rolling optimization time domain, and the preset solution period of the rolling optimization time domain, a model is constructed to obtain a model for minimizing the deviation of the real-time rolling energy management during the day;

[0142] According to the data of the power purchased from the main grid during the day, the inelastic load data during the day, the elastic load data during the day, the operation data of the energy storage system during the day, and the photovoltaic power generation data during the day, a model is constructed to obtain a model for the real-time power balance constraint conditions during the day;

[0143] According to the photovoltaic power generation data during the day, a model is constructed to obtain a model for the real-time photovoltaic output during the day;

[0144] According to the inelastic load data during the day, a model is constructed to obtain a model for the real-time inelastic load during the day;

[0145] According to the operation data of the energy storage during the day, a model is constructed to obtain a model for the real-time operation of the energy storage system during the day;

[0146] According to the data of the power purchased from the main grid during the day, a model is constructed to obtain a model for the real-time power purchase from the main grid during the day;

[0147] According to the model for minimizing the deviation of the real-time rolling energy management during the day, the model for the real-time power balance constraint conditions during the day, the model for the real-time photovoltaic output during the day, the model for the real-time inelastic load during the day, the model for the real-time operation of the energy storage system during the day, and the model for the real-time power purchase from the main grid during the day, they are merged to obtain a rolling energy optimization management model for the real-time operation of the microgrid.

[0148] Among them, in one example, the model for minimizing the deviation of the real-time rolling energy management during the day is as shown in formula (20).

[0149]

[0150] Among them, C A is the actual energy optimization management cost of the microgrid; is the actual power purchased by the microgrid from the main grid during the intraday stage at time t; is the robust optimal value of the microgrid's power purchase from the main grid obtained in the day-ahead stage; is the actual state of charge of the energy storage system during the intraday stage at time t; is the predicted state of charge of the energy storage system obtained in the day-ahead stage; t st is the starting time of the current rolling optimization time domain; T tmpc is the preset solution period of the rolling optimization time domain.

[0151] Furthermore, the intraday real-time power balance constraint condition model is shown in Formula (21). The intraday real-time inelastic load model is shown in Formula (21). The intraday real-time photovoltaic output model is shown in Formula (22). The intraday real-time operation model of the energy storage system is shown in Formulas (23) to (27). The intraday real-time power purchase model from the main grid is shown in Formula (28).

[0152]

[0153]

[0154]

[0155]

[0156]

[0157]

[0158]

[0159]

[0160] Among them, the superscript A represents the decision variable in the intraday real-time stage.

[0161] In step S106 of some embodiments, the second operation data of the microgrid is obtained, and the second operation data includes the intraday real-time data of the execution device. The intraday real-time data of the execution device includes: the intraday real-time data of the load, the intraday real-time power generation data of the photovoltaic power generation, and the intraday real-time operation state data of the energy storage system.

[0162] In step S107 of some embodiments, the intraday real-time stage rolling energy optimization management model is solved according to the day-ahead robust energy optimization management strategy and the second operation data to obtain the target microgrid energy optimization management strategy.

[0163] It can be understood that during the model solving process of the embodiments of the present application, the optimization time domain in the model is rolling and progressive, that is, the single calculation result of the model is only applicable to a specific period within a day, ensuring the real-time performance and reliability of the energy optimization management strategy.

[0164] In some embodiments, referring to Figure 4 , step S107 specifically includes but is not limited to steps S401 to S402:

[0165] Step S401, obtain the rolling optimization time domain, and determine the solution period according to the rolling optimization time domain;

[0166] Step S402, obtain the starting moment of the current solution. When the starting moment is within the solution period, solve the rolling energy optimization management model for the intraday real-time stage according to the day-ahead robust energy optimization management strategy and the second operation data to obtain the target microgrid energy optimization management strategy.

[0167] More specifically, when the starting moment is within the solution period, solve the day-ahead robust energy optimization management model based on the day-ahead robust energy optimization management strategy, the intraday real-time data of the load, the intraday real-time power generation data of the photovoltaic power generation, and the intraday real-time operation state data of the energy storage system to obtain the target microgrid energy optimization management strategy.

[0168] In an example, the day-ahead robust energy optimization management stage mainly calculates the microgrid robust energy optimization management strategy by using the predicted data of the previous 24 hours; the rolling energy optimization management stage for the intraday real-time stage generates the target microgrid energy optimization management strategy based on the robust energy optimization management strategy generated in advance and the real-time data.

[0169] In another example, the day-ahead robust energy optimization management strategy is used as the input parameter of the rolling energy optimization management model for the intraday real-time stage. Based on the day-ahead robust energy optimization management strategy, the real-time data of the photovoltaic output and the load demand, and by using an appropriate solution algorithm to solve the rolling energy optimization management model for the intraday real-time stage, the target microgrid energy optimization management strategy that takes into account robustness, economy, and reliability can be obtained.

[0170] In the embodiment of the present application, a two-stage microgrid energy optimization management architecture is constructed according to the tube model predictive control theory. This architecture includes a day-ahead robust energy optimization management stage and an intra-day real-time rolling energy optimization management stage. For the day-ahead robust energy optimization management stage of the microgrid, a day-ahead robust energy optimization management model of the microgrid is constructed, which includes a microgrid energy management cost minimization model, a photovoltaic output model, a demand-side response model, an energy storage system energy management model, and a microgrid reliable operation constraint condition model. For the intra-day real-time rolling energy optimization management stage, based on the calculation results of the day-ahead robust energy optimization management model and the actual operation data of the microgrid during the day, an intra-day real-time rolling energy optimization management model of the microgrid is constructed, which includes an intra-day real-time rolling energy optimization management deviation minimization model and a microgrid real-time reliable operation constraint condition model. According to the mathematical analysis model established based on the two-stage energy optimization management architecture of the microgrid, substituting actual data for solution can obtain an objective microgrid energy optimization management strategy that takes into account robustness, economy, and reliability.

[0171] Please refer to Figure 5 , the embodiment of the present application also provides a microgrid energy optimization management system, which can implement the above microgrid energy optimization management method. Figure 5 FIG. is the block diagram of the module structure of the microgrid energy optimization management system provided by the embodiment of the present application. The system includes: a stage division module 501, a day-ahead model creation module 502, a first operation data acquisition module 503, an initial solution module 504, an intra-day model creation module 505, a second operation data acquisition module 506, and a target solution module 507. Among them, the stage division module 501 is used to obtain the management stage, divide the management stage, and obtain the day-ahead robust energy optimization management stage and the intra-day real-time rolling energy optimization management stage; the day-ahead model creation module 502 is used to establish a day-ahead robust energy optimization management model for the day-ahead robust energy optimization management stage; the first operation data acquisition module 503 is used to obtain the first operation data of the microgrid, and the first operation data includes the historical data of the execution device and the day-ahead prediction data of the execution device; the initial solution module 504 is used to solve the day-ahead robust energy optimization management model according to the first operation data to obtain a day-ahead robust energy optimization management strategy; the intra-day model creation module 505 is used to establish an intra-day real-time rolling energy optimization management model for the intra-day real-time rolling energy optimization management stage; the second operation data acquisition module 506 is used to obtain the second operation data of the microgrid, and the second operation data includes the intra-day real-time data of the execution device; the target solution module 507 is used to solve the intra-day real-time rolling energy optimization management model according to the day-ahead robust energy optimization management strategy and the second operation data to obtain an objective microgrid energy optimization management strategy.

[0172] In an embodiment of the present application, a system for optimizing and managing the energy of a microgrid divides the entire management stage of the microgrid into an offline stage (day-ahead robust energy optimization management stage) and an online real-time stage (intraday real-time rolling energy optimization management stage). Model construction is carried out separately to obtain an offline model (day-ahead robust energy optimization management model). First, the offline model is solved to obtain an offline management strategy (day-ahead robust energy optimization management strategy). An online real-time model (intraday real-time rolling energy optimization management model) is constructed, and then the online real-time model is solved according to the offline management strategy and real-time data (second operation data) to obtain an online real-time management strategy (target microgrid energy optimization management strategy). By dividing the management stage, separately solving the management models, and specifically determining the online real-time management strategy based on the offline management strategy and real-time data, the accuracy and real-time performance of the online real-time management strategy are improved, and the reliability of microgrid management is also improved.

[0173] The system for optimizing and managing the energy of a microgrid according to an embodiment of the present application is used to execute the method for optimizing and managing the energy of a microgrid in the above embodiment. The specific processing process is the same as that of the method for optimizing and managing the energy of a microgrid in the above embodiment, and will not be elaborated here one by one.

[0174] An embodiment of the present application further provides a computer device, including a memory and a processor. Among them, a computer program is stored in the memory, and when the computer program is executed by the processor, the processor is used to execute the method for optimizing and managing the energy of a microgrid according to any one of the embodiments of the present application.

[0175] The following will be combined with Figure 6 The hardware structure of the computer device will be described in detail. The computer device includes: a processor 601, a memory 602, an input / output interface 603, a communication interface 604, and a bus 605.

[0176] The processor 601 can be implemented in a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, etc., and is used to execute relevant programs to implement the technical solutions provided by the embodiments of the present application;

[0177] The memory 602 can be implemented in the form of a ROM (Read Only Memory), a static storage device, a dynamic storage device, or a RAM (Random Access Memory), etc. The memory 602 can store an operating system and other application programs. When implementing the technical solutions provided in the embodiments of this specification through software or firmware, the relevant program codes are stored in the memory 602 and are called by the processor 601 to execute a method for optimizing and managing the energy of a microgrid in an embodiment of this application;

[0178] The input / output interface 603 is used to implement information input and output;

[0179] The communication interface 604 is used to implement communication and interaction between this device and other devices. Communication can be achieved through a wired method (such as USB, network cable, etc.) or through a wireless method (such as a mobile network, WIFI, Bluetooth, etc.); and the bus 605 transmits information between various components of the device (such as the processor 601, the memory 602, the input / output interface 603, and the communication interface 604);

[0180] Among them, the processor 601, the memory 602, the input / output interface 603, and the communication interface 604 achieve communication connections with each other inside the device through the bus 605.

[0181] The embodiments of this application also provide a computer-readable storage medium. The computer-readable storage medium stores a computer program. When the computer program is executed by a computer, the computer is used to execute a method for optimizing and managing the energy of a microgrid as described in any one of the embodiments of this application.

[0182] As a non-transitory computer-readable storage medium, the memory can be used to store non-transitory software programs and non-transitory computer-executable programs. In addition, the memory can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one disk storage device, a flash memory device, or other non-transitory solid-state storage devices. In some embodiments, the memory can optionally include a memory that is remotely provided relative to the processor, and these remote memories can be connected to the processor through a network. Examples of the above networks include, but are not limited to, the Internet, an enterprise intranet, a local area network, a mobile communication network, and combinations thereof.

[0183] The embodiments described in the embodiments of this application are for more clearly explaining the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. Those skilled in the art can know that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are equally applicable to similar technical problems.

[0184] Those skilled in the art can understand that Figures 1 to 4 the technical solutions shown in

[0185] do not constitute a limitation to the embodiments of the present application, and may include more or fewer steps than those illustrated, or combine certain steps, or different steps.

[0186] Those of ordinary skill in the art can understand that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, and their appropriate combinations.

[0187] The terms "first", "second", "third", "fourth", etc. (if any) in the description of the present application and the above accompanying drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged under appropriate circumstances, so that the embodiments of the present application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units does not necessarily need to be limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or are inherent to these processes, methods, products, or devices.

[0188] It should be understood that in the present application, "at least one (item)" means one or more, and "a plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that there can be three relationships. For example, "A and / or B" can mean: only A exists, only B exists, and both A and B exist at the same time. Among them, A and B can be singular or plural. The character " / " generally means that the associated objects before and after are in an "or" relationship. "At least one (one) of the following" or its similar expression means any combination of these items, including any combination of single items (ones) or plural items (ones). For example, at least one (one) of a, b, or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0189] In several embodiments provided by the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0190] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0191] In addition, in each embodiment of the present application, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0192] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store programs.

[0193] The preferred embodiments of the embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the rights of the embodiments of the present application. Any modifications, equivalent replacements, and improvements made by those skilled in the art without departing from the scope and essence of the embodiments of the present application shall be within the scope of the rights of the embodiments of the present application.

Claims

1. A method for optimizing and managing the energy of a microgrid, characterized in that, Applied to a microgrid, the microgrid includes a plurality of execution devices, and the execution devices include a photovoltaic power generation device, an energy storage device, an elastic load, and an inelastic load. The method includes: Obtain a management stage, divide the management stage to obtain a day-ahead robust energy optimization management stage and an intra-day real-time rolling energy optimization management stage; For the day-ahead robust energy optimization management stage, establish a day-ahead robust energy optimization management model, including: obtain the day-ahead data of the day-ahead robust energy optimization management stage, and the day-ahead data includes: the day-ahead equipment cost of the microgrid, the day-ahead photovoltaic power generation data of the photovoltaic power generation device, the day-ahead inelastic operation data of the inelastic load, the day-ahead elastic operation data of the elastic load, and the day-ahead energy storage operation data of the energy storage device; construct a microgrid day-ahead energy management cost minimization model according to the day-ahead equipment cost; construct a day-ahead photovoltaic output model according to the day-ahead photovoltaic power generation data; construct a day-ahead inelastic load demand model according to the day-ahead inelastic operation data; construct a day-ahead elastic load demand model according to the day-ahead elastic operation data; construct a day-ahead energy storage system operation model according to the day-ahead energy storage operation data; construct a microgrid day-ahead reliable operation constraint condition model according to the preset constraint conditions; merge the microgrid day-ahead energy management cost minimization model, the day-ahead photovoltaic output model, the day-ahead inelastic load demand model, the day-ahead elastic load demand model, the day-ahead energy storage system operation model, and the microgrid day-ahead reliable operation constraint condition model to obtain the day-ahead robust energy optimization management model; Obtain the first operation data of the microgrid, and the first operation data includes the historical data of the execution device and the day-ahead prediction data of the execution device; Solve the day-ahead robust energy optimization management model according to the first operation data to obtain a day-ahead robust energy optimization management strategy; For the intra-day real-time rolling energy optimization management stage, an intra-day real-time stage rolling energy optimization management model is established, including: obtaining the third operation data of the microgrid, where the third operation data includes: the starting moment of the rolling optimization time domain, the preset solution period of the rolling optimization time domain, the intra-day power purchase data from the main grid, the intra-day inelastic load data, the intra-day elastic load data, the intra-day energy storage system operation data, and the intra-day photovoltaic power generation data; constructing a model according to the day-ahead robust energy optimization management strategy, the starting moment of the rolling optimization time domain, and the preset solution period of the rolling optimization time domain to obtain an intra-day real-time rolling energy management deviation minimization model; constructing a model according to the intra-day power purchase data from the main grid, the intra-day inelastic load data, the intra-day elastic load data, the intra-day energy storage system operation data, and the intra-day photovoltaic power generation data to obtain an intra-day real-time power balance constraint condition model; constructing a model according to the intra-day photovoltaic power generation data to obtain an intra-day real-time photovoltaic output model; constructing a model according to the intra-day inelastic load data to obtain an intra-day real-time inelastic load model; constructing a model according to the intra-day energy storage operation data to obtain an intra-day energy storage system real-time operation model; constructing a model according to the intra-day power purchase data from the main grid to obtain an intra-day real-time power purchase model from the main grid; and merging the intra-day real-time rolling energy management deviation minimization model, the intra-day real-time power balance constraint condition model, the intra-day real-time photovoltaic output model, the intra-day real-time inelastic load model, the intra-day energy storage system real-time operation model, and the intra-day real-time power purchase model from the main grid to obtain the intra-day real-time stage rolling energy optimization management model; Obtain the second operation data of the microgrid, where the second operation data includes the intra-day real-time data of the execution device; Solve the intra-day real-time stage rolling energy optimization management model according to the day-ahead robust energy optimization management strategy and the second operation data to obtain the target microgrid energy optimization management strategy.

2. The method according to claim 1, wherein The solving the intra-day real-time stage rolling energy optimization management model according to the day-ahead robust energy optimization management strategy and the second operation data to obtain the target microgrid energy optimization management strategy includes: Obtain the rolling optimization time domain and determine the solution period according to the rolling optimization time domain; Obtain the starting moment of the current solution. When the starting moment is within the solution period, solve the intra-day real-time stage rolling energy optimization management model according to the day-ahead robust energy optimization management strategy and the second operation data to obtain the target microgrid energy optimization management strategy.

3. The method according to claim 1, wherein The day-ahead robust energy optimization management model includes multiple variables. Before solving the day-ahead robust energy optimization management model according to the first operation data to obtain the day-ahead robust energy optimization management strategy, the method further includes: Updating the day-ahead robust energy optimization management model, specifically including: Perform type detection on each of the variables to obtain a type detection result; the type detection result includes a first result, and the first result is used to characterize that the type of the variable is an uncertain type; For each of the variables, if the type detection result is the first result, perform boundary value conversion on the variable to obtain the converted variable; Obtain the updated daily robust energy optimization management model based on the converted variable.

4. The method according to any one of claims 1 to 3, characterized in that Before obtaining the elastic operation data of the elastic load, the method further includes: Receive an operation instruction for the elastic load; Adjust the magnitude of the power load of the elastic load based on the operation instruction.

5. A system for optimizing the energy management of a microgrid, characterized in that, Applied to a microgrid, the microgrid includes a plurality of execution devices, and the execution devices include a photovoltaic power generation device, an energy storage device, an elastic load, and an inelastic load, including: A stage division module, configured to obtain a management stage, divide the management stage to obtain a daily robust energy optimization management stage and an intraday real-time rolling energy optimization management stage; A daily model creation module, configured to establish a daily robust energy optimization management model for the daily robust energy optimization management stage, including: obtaining the daily data of the daily robust energy optimization management stage, where the daily data includes: the daily equipment cost of the microgrid, the daily photovoltaic power generation data of the photovoltaic power generation device, the daily inelastic operation data of the inelastic load, the daily elastic operation data of the elastic load, the daily energy storage operation data of the energy storage device; constructing a microgrid daily energy management cost minimization model according to the daily equipment cost; constructing a daily photovoltaic power output model according to the daily photovoltaic power generation data; constructing a daily inelastic load demand model according to the daily inelastic operation data; constructing a daily elastic load demand model according to the daily elastic operation data; constructing a daily energy storage system operation model according to the daily energy storage operation data; constructing a microgrid daily reliable operation constraint condition model according to preset constraint conditions; and merging the microgrid daily energy management cost minimization model, the daily photovoltaic power output model, the daily inelastic load demand model, the daily elastic load demand model, the daily energy storage system operation model, and the microgrid daily reliable operation constraint condition model to obtain the daily robust energy optimization management model; A first operation data acquisition module, configured to acquire first operation data of the microgrid, where the first operation data includes historical data of the execution device and daily prediction data of the execution device; An initial solution module, configured to solve the daily robust energy optimization management model according to the first operation data to obtain a daily robust energy optimization management strategy; An intraday model creation module, which is used to establish an intraday real-time stage rolling energy optimization management model for the intraday real-time rolling energy optimization management stage, including: obtaining the third operation data of the microgrid, where the third operation data includes: the starting moment of the rolling optimization time domain, the preset solution period of the rolling optimization time domain, the intraday power purchase data from the main grid, the intraday inelastic load data, the intraday elastic load data, the intraday energy storage system operation data, and the intraday photovoltaic power generation data; constructing a model according to the day-ahead robust energy optimization management strategy, the starting moment of the rolling optimization time domain, and the preset solution period of the rolling optimization time domain to obtain an intraday real-time rolling energy management deviation minimization model; constructing a model according to the intraday power purchase data from the main grid, the intraday inelastic load data, the intraday elastic load data, the intraday energy storage system operation data, and the intraday photovoltaic power generation data to obtain an intraday real-time power balance constraint condition model; constructing a model according to the intraday photovoltaic power generation data to obtain an intraday real-time photovoltaic output model; constructing a model according to the intraday inelastic load data to obtain an intraday real-time inelastic load model; constructing a model according to the intraday energy storage operation data to obtain an intraday energy storage system real-time operation model; constructing a model according to the intraday power purchase data from the main grid to obtain an intraday real-time power purchase model from the main grid; merging the intraday real-time rolling energy management deviation minimization model, the intraday real-time power balance constraint condition model, the intraday real-time photovoltaic output model, the intraday real-time inelastic load model, the intraday energy storage system real-time operation model, and the intraday real-time power purchase model from the main grid to obtain the intraday real-time stage rolling energy optimization management model; A second operation data acquisition module, which is used to acquire the second operation data of the microgrid, where the second operation data includes the intraday real-time data of the execution device; A target solution module, which is used to solve the intraday real-time stage rolling energy optimization management model according to the day-ahead robust energy optimization management strategy and the second operation data to obtain a target microgrid energy optimization management strategy.

6. A computer device, characterized in that, The computer device includes a memory and a processor, where a computer program is stored in the memory, and when the computer program is executed by the processor, the processor is used to execute: The method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and when the computer program is executed by a computer, the computer is used to execute: The method according to any one of claims 1 to 4.

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