Micro-grid optimization scheduling method, system and device and readable storage medium

Abstract

The invention provides a micro-grid optimization scheduling method, system and device and a readable storage medium, and the method comprises the steps: constructing a micro-grid system which is used for collecting the operation data of a distributed power generation device and energy storage equipment in real time, and adjusting the load of a user side through executing a scheduling strategy; constructing a micro-grid optimization scheduling model, setting an objective function of the model, setting constraint conditions of the model, and calculating an optimal operation strategy of the micro-grid system by solving the micro-grid optimization scheduling model; constructing a QUBO model, and associating the QUBO model with the micro-grid optimization scheduling model to determine an optimal operation strategy of the micro-grid system by calculating an optimal solution of the QUBO model; based on the QUBO model, performing conversion processing on the objective function and the constraint condition of the micro-grid optimization scheduling model to determine the objective function of the QUBO model; and obtaining operation data of the micro-grid system in real time, and generating an optimal operation strategy through the micro-grid optimization scheduling model.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid dispatching, and more specifically to a microgrid optimization dispatching method, system, device and readable storage medium. Background Art

[0002] In the context of global green development, with the large-scale grid connection of renewable energy, traditional power systems are gradually evolving towards distributed and intelligent directions. Microgrids, as a new form of power system, can effectively cope with the instability of renewable energy generation, meet local load demands and operate in coordination with the main grid by integrating distributed generation, energy storage systems and intelligent control. However, due to the multi-variable and multi-constrained characteristics of microgrid operation, traditional scheduling optimization methods are difficult to cope with, and it is difficult to arrange and schedule the optimized operation of microgrids.

[0003] At present, the optimization of microgrid operation strategy has always been the technical direction of research and practice in new power systems. It belongs to the optimization problem. In practical applications, rolling optimization methods, linear programming methods, dynamic programming methods, etc. are often used. With the large-scale promotion of AI methods, various heuristic algorithms such as genetic algorithms, particle swarm algorithms and ant colony algorithms have been applied. However, these methods have good performance when solving small-scale problems or problems in typical scenarios. However, when there are more and more randomly connected distributed power sources in microgrids, mobile loads such as electric vehicle charging and other power loads increase, the connection topology of microgrids changes all the time, and the operation in microgrids becomes more complex and random. The economic operation optimization of the entire system becomes an optimization calculation that integrates large-scale, multi-data, variable increase, constraint changes and real-time characteristics. The above optimization methods gradually show limitations in optimization. Summary of the invention

[0004] In view of the above problems, the purpose of the present invention is to provide a microgrid optimization scheduling method, system, device and readable storage medium, which can optimize the microgrid operation strategy based on optical quantum computing and effectively improve the economic operation efficiency of the microgrid.

[0005] In order to achieve the above object, the present invention is implemented through the following technical solutions: In a first aspect, the present invention discloses a microgrid optimization scheduling method, comprising: Build a microgrid system to collect real-time operating data of distributed power generation devices and energy storage equipment, and adjust user-side loads by executing scheduling strategies; Construct a microgrid optimization dispatch model, set the objective function of the model, and set the constraints of the model to calculate the optimal operation strategy of the microgrid system by solving the microgrid optimization dispatch model; Construct the QUBO model and associate it with the microgrid optimization dispatch model to determine the optimal operation strategy of the microgrid system by calculating the optimal solution of the QUBO model; Based on the QUBO model, the objective function and constraint conditions of the microgrid optimization dispatch model are transformed to determine the objective function of the QUBO model. Acquire the operation data of the microgrid system in real time, and generate the optimal operation strategy of the microgrid system through the microgrid optimization scheduling model.

[0006] Further, the microgrid system includes: a distributed power generation device, an energy storage device and a user load collection unit; Distributed power generation devices include photovoltaic power generation devices and wind power generation devices; The energy storage device is connected to the microgrid through an energy storage converter for regulating and storing electric energy; the energy storage device uses a battery; User load collection unit, used to collect user side load; User-side loads include non-adjustable loads and adjustable loads; The non-adjustable load is the basic power demand; the adjustable load includes the power load of electric vehicles and smart home devices.

[0007] Furthermore, the microgrid optimization dispatching model is constructed, the objective function of the model is set, and the constraint conditions of the model are set, including: In a 24-hour dispatch cycle, the microgrid operation cycle is divided into 16 equivalent periods of 1.5 hours each. With the goal of maximizing the revenue of microgrid operators, a microgrid optimization dispatch model based on subsidy-based demand response is constructed. The objective function of the microgrid optimization scheduling model is as follows:

[0008]

[0009]

[0010]

[0011] in, The benefits of microgrids providing electricity to users, is the subsidy cost for microgrid operators to encourage users to respond to demand, The battery loss cost caused by multiple charging and discharging of the battery; Time-of-use electricity price, To motivate compensation prices, It is the battery charging and discharging loss; is the total load after demand response adjustment at time t; are the charging and discharging power of the battery respectively; Power balance constraints, energy storage system constraints and demand response constraints are set for the microgrid optimal scheduling model. Further, the power balance constraint includes:

[0012]

[0013]

[0014] The energy storage system constraints include energy storage charging and discharging power constraints and energy storage capacity constraints; The energy storage charging and discharging power constraints include:

[0015]

[0016]

[0017] The energy storage capacity constraints include:

[0018]

[0019] in, , is the upper limit of charge and discharge power; , is the state variable, When it is 1, the battery is in charging state. When it is 1, the battery is in discharge state; , are the upper and lower limits of energy storage capacity, , is the charge and discharge efficiency of the battery; The demand response constraints include:

[0020]

[0021]

[0022] in, Power caps for users participating in demand response; , is the state variable, When it is 1, the user increases the load. When it is 1, the user reduces the load.

[0023] Further, the QUBO model includes:

[0024] in, is the binary variable to be determined, and its value is , is the objective function, is the coefficient of the quadratic term, which is a known quantity; Convert the QUBO model into the following linear algebra form:

[0025] in, is a binary vector, for matrix, The goal is to find Smallest or Largest ,Right now: .

[0026] Furthermore, based on the QUBO model, the objective function and constraint conditions of the microgrid optimization scheduling model are converted to determine the objective function of the QUBO model, including: The objective function of the microgrid optimization scheduling model is converted into the following minimum value form:

[0027] The charging and discharging power of the battery and the related regulating power are mapped to the following combination of quantum bits:

[0028]

[0029]

[0030]

[0031] in, is the qubit of the integer part of the decision variable, is the quantum bit number of the fractional part of the decision variable; If the decimal places of the decision variable only occupy 2 qubits; = , = , = , = ; Substituting the quantum bit combination into the converted target function, it can be converted into Function term of the model .

[0032] Furthermore, based on the QUBO model, the objective function and constraint conditions of the microgrid optimization scheduling model are converted and processed respectively to determine the objective function of the QUBO model, and further include: when , , , Respectively represent When the quantum bits of all decision variables in a time period are equal, the constraints of the microgrid optimization scheduling model are converted into QUBO model penalty terms: Generate QUBO model objective function:

[0033] in, It is the penalty term of the QUBO model converted by the battery charging and discharging state constraint and the user demand response state constraint. is the penalty term of the QUBO model converted by the balance constraint that the output power in all time periods is equal to the input power, and is the penalty term of the QUBO model converted through the battery charging and discharging power constraint, and is the penalty term of the QUBO model converted by the battery capacity constraint, and is the penalty term of the QUBO model converted by the demand response power constraint.

[0034] In a second aspect, the present invention further discloses a microgrid optimization dispatching system, comprising: System building module, used to build a microgrid system, collect the operating data of distributed generation devices and energy storage equipment in real time, and adjust the user-side load by executing the dispatch strategy; The first model building module is used to build a microgrid optimization scheduling model, set the objective function of the model, and set the constraints of the model to calculate the optimal operation strategy of the microgrid system by solving the microgrid optimization scheduling model; The second model building module is used to build a QUBO model and associate it with the microgrid optimization scheduling model to determine the optimal operation strategy of the microgrid system by calculating the optimal solution of the QUBO model; A conversion processing module is used to convert the objective function and constraint conditions of the microgrid optimization scheduling model based on the QUBO model to determine the objective function of the QUBO model; The strategy generation module is used to obtain the operating data of the microgrid system in real time and generate the optimal operating strategy of the microgrid system through the microgrid optimization scheduling model.

[0035] In a third aspect, the present invention further discloses a microgrid optimization dispatching device, comprising: A memory, used for storing a microgrid optimization scheduling program; A processor is used to implement the steps of the microgrid optimization scheduling method as described in any one of the above items when executing the microgrid optimization scheduling program.

[0036] In a fourth aspect, the present invention further discloses a readable storage medium, on which a microgrid optimization scheduling program is stored. When the microgrid optimization scheduling program is executed by a processor, the steps of the microgrid optimization scheduling method as described in any one of the above items are implemented.

[0037] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention discloses a microgrid optimization scheduling method, system, device and readable storage medium. First, based on a microgrid system model including distributed power generation, energy storage equipment and user load, an optimization scheduling model is established with the goal of maximizing the economic benefits of microgrid operators. The model dynamically balances energy supply and demand by stimulating user demand response and scheduling energy storage systems, thereby achieving the goal of economic operation and efficiency maximization of the microgrid. Then, in the calculation of the optimal operation strategy, the optimization problem is converted into a quadratic unconstrained binary optimization (QUBO) model, and the optical quantum computing method is used to efficiently solve it.

[0038] The present invention solves the problem of microgrid optimization scheduling based on subsidy-based demand response by introducing a coherent optical quantum computing method, which not only ensures the effectiveness of the optimization calculation of complex operating conditions in the microgrid, but also ensures real-time performance. The present invention applies the quantum bit sharing mechanism in computational solutions, which significantly reduces the number of quantum bits required and improves the utilization efficiency of quantum computing resources. It not only reduces computing resources, but also takes computing performance into consideration at the same time, realizes complex optimization calculations of microgrids, and effectively reduces the consumption of computing resources.

[0039] It can be seen that compared with the prior art, the present invention has outstanding substantive features and significant progress, and the beneficial effects of its implementation are also obvious. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0041] Figure 1 It is a method flow chart of a specific implementation mode of the present invention.

[0042] Figure 2 It is a system structure diagram of a specific implementation mode of the present invention. DETAILED DESCRIPTION

[0043] In order to enable those skilled in the art to better understand the scheme of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0044] See also Figure 1 As shown, this embodiment provides a microgrid optimization scheduling method, comprising the following steps: S1: Build a microgrid system to collect the operating data of distributed power generation devices and energy storage equipment in real time, and adjust the user-side load by executing scheduling strategies.

[0045] In a specific implementation, the constructed microgrid system consists of distributed power generation devices (photovoltaic power generation and wind power generation), energy storage equipment (batteries), user loads, etc. The energy storage equipment is connected to the microgrid through an energy storage converter, and assumes the function of regulating and storing electric energy. The user-side load includes daily electricity demand for households, industry and commerce, etc., which is divided into non-adjustable loads and adjustable loads. Among them, the non-adjustable load is the basic power demand and has rigid characteristics; the adjustable load includes electric vehicles, smart home devices, etc., which can respond to the imbalance of power supply and demand through reasonable scheduling strategies.

[0046] S2: Construct a microgrid optimization scheduling model, set the objective function of the model, and set the constraints of the model to calculate the optimal operation strategy of the microgrid system by solving the microgrid optimization scheduling model.

[0047] In a specific implementation, the method divides the microgrid operation cycle into 16 equivalent time periods (1.5 hours for one period). Within a 24-hour scheduling cycle, the microgrid manages its internal adjustable load and energy storage system according to the predicted values ​​of renewable energy generation and load.

[0048] Furthermore, with the goal of maximizing the profits of microgrid operators, a microgrid optimization scheduling model based on subsidy-based demand response is constructed. By solving the optimization model, a dynamic balance of energy supply and demand is achieved. While meeting the electricity needs of users, the overall operating costs are minimized to the greatest extent, thereby formulating the best operating strategy for the microgrid.

[0049] The objective function of the microgrid optimization dispatch model is as follows:

[0050]

[0051]

[0052]

[0053] in, The benefits of microgrids providing electricity to users, is the subsidy cost for microgrid operators to encourage users to respond to demand, The battery loss cost caused by multiple charging and discharging of the battery; Time-of-use electricity price, To motivate compensation prices, It is the battery charging and discharging loss; is the total load after demand response adjustment at time t; are the charging and discharging power of the battery respectively.

[0054] At this point, it is necessary to set constraints for the microgrid optimization dispatch model. The setting of constraints is determined by the composition of each power source and load characteristics in the microgrid, and the constraints of voltage and flow in the internal topology connection method refer to the basic requirements of the power grid. Constraints include power balance constraints, energy storage system constraints, and demand response constraints. Among them, energy storage system constraints include energy storage charging and discharging power constraints and energy storage capacity constraints.

[0055] The constraints are as follows: (1) Power balance constraints:

[0056]

[0057] Where: , are photovoltaic and wind power generation power, is the original load; , They are the load powers adjusted upward (transferred in) and downward (transferred out) by demand response; , is a Boolean variable.

[0058] (2) Energy storage system constraints: Energy storage charging and discharging power constraints:

[0059]

[0060]

[0061] Energy storage capacity constraints:

[0062]

[0063] in, , is the upper limit of charge and discharge power; , is the state variable, When it is 1, the battery is in charging state. When it is 1, the battery is in a discharging state; the energy storage system cannot be charged and discharged at the same time; , are the upper and lower limits of energy storage capacity, , It is the charge and discharge efficiency of the battery.

[0064] (3) Demand response constraints

[0065]

[0066]

[0067] in, Power caps for users participating in demand response; , is the state variable, When it is 1, the user increases the load. When it is 1, the user reduces the load; the user cannot transfer in and out the load at the same time.

[0068] S3: Construct the QUBO model and associate it with the microgrid optimization scheduling model to determine the optimal operation strategy of the microgrid system by calculating the optimal solution of the QUBO model.

[0069] It should be noted that the method of calculating using the characteristics of photons can better control the coherence and low noise characteristics of quantum bits, providing an ideal quantum computing solution for solving complex problems. Therefore, in the process of solving the microgrid combinatorial optimization problem of this method, the coherent optical quantum computer usually maps the problem into the Ising model or the quantum quadratic unconstrained binary optimization (QUBO) model for processing. The Ising model is a spin system model, whose goal is to find the lowest energy state of the system by adjusting the spin direction, and the QUBO model is the binary representation of the model. The coherent optical quantum computer encodes the variables and constraints of the optimization problem into the photon quantum state, so that the photon pulses interact and evolve in the ring cavity, and gradually approach the lowest energy state of the system. Finally, by detecting the quantum information of the output state and decoding it, the optimal solution of the corresponding optimization problem can be obtained, thereby achieving efficient combinatorial optimization solution.

[0070] In a specific implementation, the QUBO model can be constructed in the following manner: The mathematical form of the quadratic unconstrained binary optimization problem (QUBO) is expressed as:

[0071] in, is the binary variable to be determined, and its value is , is the objective function, is the coefficient of the quadratic term, which is a known quantity. Correspondingly, it can be written in linear algebra as:

[0072] in, is a binary vector, for matrix, The goal is to find Smallest or Largest ,Right now:

[0073] Based on this, the process of solving the QUBO model by coherent optical quantum computers is to convert the CIM input, CIM return process.

[0074] S4: Based on the QUBO model, the objective function and constraint conditions of the microgrid optimization scheduling model are converted respectively to determine the objective function of the QUBO model.

[0075] In a specific implementation, the working principle of the coherent optical quantum computer is based on the process of energy minimization, that is, finding the lowest energy state of the system (that is, the optimal solution). Therefore, it is necessary to convert the objective function into the form of seeking the minimum value:

[0076] To transform the target function, we only need to map each continuous adjustment power into a set of quantum bits, the combination of which represents the possible values ​​of the continuous variable:

[0077]

[0078]

[0079]

[0080] in, is the qubit of the integer part of the decision variable, is the qubit of the decimal part of the decision variable. Due to the limitation of the computational scale of quantum computers, it is assumed that the decimal part of the decision variable only occupies 2 qubits. = , = , = , = .

[0081] Substituting these quantum bit combinations into the optimization objective function (15), it can be converted into Function term of the model .

[0082] In a specific implementation, the specific process of converting the constraint conditions of the microgrid optimization scheduling model is as follows: 1. Conversion of battery and demand response state constraints: , , , Respectively represent The quantum bits of all decision variables in each time period, then the battery charging and discharging state constraints and the user demand response state constraints can be converted into the following QUBO model penalty terms:

[0083] 2. Conversion of full-time power balance constraints: For the balance constraint that the output power is equal to the input power in all time periods, it can be converted into the penalty term of the following QUBO model:

[0084] 3. Conversion of battery charging and discharging power constraints: Take the battery charging power constraint (7) as an example: when the battery is not in the charging state, the charging power is equal to 0. , are all equal to 0. The above constraints are equivalent to the following two constraints:

[0085]

[0086] Introducing a penalty factor Convert the above constraints into QUBO model penalty terms:

[0087] Similarly, the penalty term of the battery discharge power constraint (8) can be obtained:

[0088] 4. Conversion of battery capacity constraints: For the battery capacity constraint (10), a set of slack variables is introduced and expressed in binary. Take the right side of the equation as an example:

[0089]

[0090] in, is a binary variable representing the integer part of the slack variable, is a two-level variable that represents the decimal part of the slack variable; Control the accuracy of this set of conversions. When the number of quantum bits is sufficient, any real number can be represented in theory. However, due to the size limitation of quantum computers, only 1 quantum bit is given to the decimal part. It refers to the number of quantum bits required to make the constructed numerical combination just meet or include the constraint range.

[0091] Therefore, the penalty term of the QUBO model with battery capacity constraint in period t can be expressed as:

[0092]

[0093] in,

[0094] 5. Conversion of demand response power constraints: The same method as the battery charging and discharging power inequality constraint mentioned above is used to introduce quantum bits and penalty coefficients to obtain the QUBO model penalty items for users to increase and decrease load power respectively:

[0095] Similarly, the penalty term for battery discharge power constraint can be obtained:

[0096] Finally, through the above transformation, the objective function of the QUBO model for optimizing the microgrid operation strategy is obtained: .

[0097] S5: Obtain the operating data of the microgrid system in real time, and generate the optimal operating strategy of the microgrid system through the microgrid optimization scheduling model.

[0098] In a specific implementation, according to the above-mentioned coherent optical quantum computer solution principle and QUBO model conversion method, the operation data of the microgrid system is acquired and input in real time. After the coherent optical quantum computer solution principle and QUBO model conversion, the optimal solution is solved and output to the micro-electric operation scheduling system as an operation plan for optimized operation.

[0099] Due to the limited computing scale of coherent optical quantum computers, the number of quantum bits needs to be reduced as much as possible to better find the optimal solution. This method uses a new conversion method to convert the inequality constraints of battery and demand response power into the penalty term of the QUBO model. Different from the common conversion method of adding slack variables, this method uses a set of quantum bits to simultaneously represent two constraints. Take the battery charging and discharging power constraint as an example: and They cannot be 1 at the same time, so we can use only one set of quantum bits. To indicate the upper limit of charging and discharging power, and To control the charge and discharge state. By using this unified variable, the number of quantum bits can be effectively reduced without affecting the description of the system's charge and discharge strategy.

[0100] At the same time, Kaiwu is used to generate the QUBO matrix corresponding to the model. To achieve more efficient calculations, the elements in the matrix are appropriately restricted in bit width, and the matrix precision is adjusted through Kaiwu's built-in functions. After the precision is reduced to 14, the matrix dimension is 881 qubits. Finally, its parameter matrix is ​​injected into CIM. Through the iterative evolution of the quantum state and the feedback loop, the system is gradually optimized to minimize the Hamiltonian, and finally the phase state of the qubit is measured and mapped to a binary solution.

[0101] It can be seen that this method not only reduces the consumption of computing resources, but also ensures the effectiveness of the constraints.

[0102] See also Figure 2 As shown, the present invention also discloses a microgrid optimization scheduling system, including: a system construction module 1, a first model construction module 2, a second model construction module 3, a conversion processing module 4 and a strategy generation module 5. System construction module 1 is used to construct a microgrid system, which is used to collect the operating data of distributed power generation devices and energy storage devices in real time, and adjust the user-side load by executing the dispatching strategy; The first model building module 2 is used to build a microgrid optimization scheduling model, set the objective function of the model, and set the constraints of the model to calculate the optimal operation strategy of the microgrid system by solving the microgrid optimization scheduling model.

[0103] The second model building module 3 is used to build the QUBO model and associate it with the microgrid optimization scheduling model to determine the best operation strategy of the microgrid system by calculating the optimal solution of the QUBO model.

[0104] The conversion processing module 4 is used to convert the objective function and constraint conditions of the microgrid optimization scheduling model based on the QUBO model to determine the objective function of the QUBO model.

[0105] The strategy generation module 5 is used to obtain the operation data of the microgrid system in real time and generate the best operation strategy of the microgrid system through the microgrid optimization scheduling model.

[0106] The specific implementation of the microgrid optimization scheduling system of this embodiment is basically the same as the specific implementation of the microgrid optimization scheduling method described above, and will not be repeated here.

[0107] The present invention also discloses a microgrid optimization scheduling device, comprising a processor and a memory; wherein, when the processor executes the microgrid optimization scheduling upgrade program stored in the memory, the steps of the microgrid optimization scheduling method as described in any one of the above items are implemented.

[0108] Furthermore, the microgrid optimization and dispatching device in this embodiment may also include: The input interface is used to obtain the microgrid optimization scheduling program imported from the outside, and save the obtained microgrid optimization scheduling program to the memory, and can also be used to obtain various instructions and parameters transmitted by external terminal devices, and transmit them to the processor, so that the processor can use the above various instructions and parameters to carry out corresponding processing. In this embodiment, the input interface can specifically include but is not limited to a USB interface, a serial interface, a voice input interface, a fingerprint input interface, a hard disk reading interface, etc.

[0109] The output interface is used to output various data generated by the processor to the terminal device connected thereto, so that other terminal devices connected to the output interface can obtain various data generated by the processor. In this embodiment, the output interface may specifically include but is not limited to a USB interface, a serial interface, etc.

[0110] The communication unit is used to establish a remote communication connection between the microgrid optimization and scheduling device and the external server so that the microgrid optimization and scheduling device can mount the image file to the external server. In this embodiment, the communication unit may specifically include but is not limited to a remote communication unit based on wireless communication technology or wired communication technology.

[0111] The keyboard is used to obtain various parameter data or instructions input by the user by tapping the keycaps in real time.

[0112] The display is used to display the relevant information of the server power supply line short circuit locating process in real time.

[0113] The mouse can be used to assist users in inputting data and simplify user operations.

[0114] The present invention also discloses a readable storage medium, wherein the readable storage medium includes a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable hard disk, a CD-ROM or any other form of storage medium known in the technical field. The readable storage medium stores a microgrid optimization scheduling program, and when the microgrid optimization scheduling program is executed by a processor, the steps of the microgrid optimization scheduling method described in any one of the above items are implemented.

[0115] In summary, the present invention optimizes the microgrid operation strategy based on photonic quantum computing, which can effectively improve the economic operation efficiency of the microgrid.

[0116] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. As for the method disclosed in the embodiment, since it corresponds to the system disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0117] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0118] In the several embodiments provided by the present invention, it should be understood that the disclosed systems, systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of systems or units, which can be electrical, mechanical or other forms.

[0119] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0120] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, or each module may exist physically separately, or two or more modules may be integrated into one unit.

[0121] Similarly, each processing unit in each embodiment of the present invention may be integrated into one functional module, or each processing unit may exist physically, or two or more processing units may be integrated into one functional module.

[0122] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0123] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0124] The above is a detailed introduction to the microgrid optimization scheduling method, system, device and readable storage medium provided by the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications also fall within the scope of protection of the present invention.

Claims

1. A microgrid optimization scheduling method, characterized in that: include: Build a microgrid system to collect real-time operating data of distributed generation devices and energy storage equipment, and adjust user-side loads by executing dispatch strategies; Construct a microgrid optimization dispatch model, set the objective function of the model, and set the constraints of the model to calculate the optimal operation strategy of the microgrid system by solving the microgrid optimization dispatch model; Construct the QUBO model and associate it with the microgrid optimization dispatch model to determine the optimal operation strategy of the microgrid system by calculating the optimal solution of the QUBO model; Based on the QUBO model, the objective function and constraint conditions of the microgrid optimization dispatch model are transformed to determine the objective function of the QUBO model. Acquire the operation data of the microgrid system in real time, and generate the optimal operation strategy of the microgrid system through the microgrid optimization scheduling model.

2. The microgrid optimization scheduling method according to claim 1, characterized in that: The microgrid system includes: a distributed power generation device, an energy storage device and a user load collection unit; Distributed power generation devices include photovoltaic power generation devices and wind power generation devices; The energy storage device is connected to the microgrid through an energy storage converter for regulating and storing electric energy; the energy storage device uses a battery; User load collection unit, used to collect user side load; User-side loads include non-adjustable loads and adjustable loads; The non-adjustable load is the basic power demand; the adjustable load includes the power load of electric vehicles and smart home devices.

3. The microgrid optimization scheduling method according to claim 2 is characterized in that: The microgrid optimization dispatch model is constructed, the objective function of the model is set, and the constraint conditions of the model are set, including: In a 24-hour dispatch cycle, the microgrid operation cycle is divided into 16 equivalent periods of 1.5 hours each. With the goal of maximizing the revenue of microgrid operators, a microgrid optimization dispatch model based on subsidy-based demand response is constructed. The objective function of the microgrid optimization scheduling model is as follows: in, The benefits of microgrids providing electricity to users, is the subsidy cost for microgrid operators to encourage users to respond to demand, The battery loss cost caused by multiple charging and discharging of the battery; Time-of-use electricity price, To motivate compensation prices, It is the battery charging and discharging loss; is the total load after demand response adjustment at time t; are the charging and discharging power of the battery respectively; Power balance constraints, energy storage system constraints and demand response constraints are set for the microgrid optimal scheduling model.

4. The microgrid optimization scheduling method according to claim 3 is characterized in that: The power balance constraints include: The energy storage system constraints include energy storage charging and discharging power constraints and energy storage capacity constraints; The energy storage charging and discharging power constraints include: The energy storage capacity constraints include: in, , is the upper limit of charge and discharge power; , is the state variable, When it is 1, the battery is in charging state. When it is 1, the battery is in discharge state; , are the upper and lower limits of energy storage capacity, , is the charge and discharge efficiency of the battery; The demand response constraints include: in, Power caps for users participating in demand response; , is the state variable, When it is 1, the user increases the load. When it is 1, the user reduces the load.

5. The microgrid optimization scheduling method according to claim 4, characterized in that: The QUBO model includes: in, is the binary variable to be determined, and its value is , is the objective function, is the coefficient of the quadratic term, which is a known quantity; Convert the QUBO model into the following linear algebra form: in, is a binary vector, for matrix, The goal is to find Smallest or Largest ,Right now: 。 6. The microgrid optimization scheduling method according to claim 5, characterized in that: Based on the QUBO model, the objective function and constraint conditions of the microgrid optimization scheduling model are converted and processed respectively to determine the objective function of the QUBO model, including: The objective function of the microgrid optimization scheduling model is converted into the following minimum value form: The charging and discharging power of the battery and the related regulating power are mapped to the following combination of quantum bits: in, is the qubit of the integer part of the decision variable, is the quantum bit number of the fractional part of the decision variable; If the decimal places of the decision variable only occupy 2 qubits; = , = , = , = ; Substituting the quantum bit combination into the converted target function, it can be converted into Function term of the model .

7. The microgrid optimization scheduling method according to claim 6, characterized in that: Based on the QUBO model, the objective function and constraint conditions of the microgrid optimization scheduling model are converted and processed respectively to determine the objective function of the QUBO model, and further include: when , , , Respectively represent When the quantum bits of all decision variables in a time period are equal, the constraints of the microgrid optimization scheduling model are converted into QUBO model penalty terms: Generate QUBO model objective function: in, It is the penalty term of the QUBO model converted by the battery charging and discharging state constraint and the user demand response state constraint. is the penalty term of the QUBO model converted by the balance constraint that the output power in all time periods is equal to the input power, and is the penalty term of the QUBO model converted through the battery charging and discharging power constraint, and is the penalty term of the QUBO model converted by the battery capacity constraint, and is the penalty term of the QUBO model converted by the demand response power constraint.

8. A microgrid optimization dispatching system, characterized in that: The system adopts the microgrid optimization scheduling method according to any one of claims 1 to 7; The system comprises: System building module, used to build a microgrid system, collect the operating data of distributed generation devices and energy storage equipment in real time, and adjust the user-side load by executing the dispatch strategy; The first model building module is used to build a microgrid optimization scheduling model, set the objective function of the model, and set the constraints of the model to calculate the optimal operation strategy of the microgrid system by solving the microgrid optimization scheduling model; The second model building module is used to build a QUBO model and associate it with the microgrid optimization scheduling model to determine the optimal operation strategy of the microgrid system by calculating the optimal solution of the QUBO model; A conversion processing module is used to convert the objective function and constraint conditions of the microgrid optimization scheduling model based on the QUBO model to determine the objective function of the QUBO model; The strategy generation module is used to obtain the operating data of the microgrid system in real time and generate the optimal operating strategy of the microgrid system through the microgrid optimization scheduling model.

9. A microgrid optimization dispatching device, characterized in that: include: A memory, used for storing a microgrid optimization scheduling program; A processor is used to implement the steps of the microgrid optimization scheduling method as described in any one of claims 1 to 7 when executing the microgrid optimization scheduling program.

10. A readable storage medium, characterized in that: The readable storage medium stores a microgrid optimization scheduling program, and when the microgrid optimization scheduling program is executed by the processor, the steps of the microgrid optimization scheduling method according to any one of claims 1 to 7 are implemented.

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