Microgrid energy management method based on economic model prediction and related device

Abstract

The application discloses a micro-grid energy management method and related device based on economic model prediction, and belongs to the technical field of micro-grid energy management. The method comprises the following steps: obtaining a simplified model of a wind power generation subsystem, a photovoltaic power generation subsystem and an energy storage subsystem; establishing a micro-grid system energy management model and determining constraint conditions according to energy flow in micro-grid energy management; designing a target function containing economic performance on the basis of supply and demand balance; solving the optimal working state of each subsystem in the micro-grid based on the simplified model of the wind power generation subsystem, the photovoltaic power generation subsystem and the energy storage subsystem, the micro-grid system energy management model, the minimum target function value and the constraint conditions; and controlling the working state of each subsystem in the micro-grid to be in the optimal working state. The application improves the economy of micro-grid operation.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of micro-grid energy management, and particularly relates to a micro-grid energy management method based on economic model prediction and related devices. BACKGROUND

[0002] A micro-grid is a highly integrated and flexible power supply system, which is composed of power generation units, energy storage devices, loads and intelligent control systems, forming a complete power supply system. This system not only can operate independently of the traditional large power grid, but also can operate in parallel with the main power grid, showing its outstanding flexibility and reliability. However, the traditional micro-grid often faces the challenges of insufficient stability and limited application scenarios in setting and operation. The construction of micro-grid by applying new energy power generation system integrates different types of clean and renewable energy, realizing the diversification and interconnection of energy. In the new energy micro-grid, solar photovoltaic power generation, wind power generation and traditional types of gas turbines can be comprehensively applied for joint power generation and integrated into the public power grid, not only enhancing the stability of the system, but also expanding the application range of the micro-grid, so that it can better meet the power demands of industry, residence and commerce. The energy management of new energy micro-grid is one of the key technologies to ensure the efficient and stable operation of micro-grid.

[0003] Microgrid energy management refers to the real-time monitoring, optimization scheduling and control of power generation, energy storage, load and grid interaction in a microgrid. It involves power generation, distribution, conversion and storage, aiming to achieve efficient use of energy and economic operation of the microgrid. Its main task is to ensure the efficient operation of the power system and the sustainable use of energy. First, by accurately predicting the power demand in the microgrid, the energy management system can reasonably arrange the use of power generation resources and energy storage devices to meet the changing load demand. Second, the energy management system needs to optimize the scheduling of various power generation units, including solar, wind and traditional generators, to maximize cost-effectiveness and minimize environmental impact. In addition, energy storage management is a key part of microgrid energy management, which involves determining the charging and discharging time of the energy storage subsystem to balance supply and demand fluctuations and improve energy efficiency. Electricity price management is also important, and the energy management system must be able to respond to price changes by implementing demand response strategies to flexibly adjust load or energy storage operations to reduce electricity costs. System protection and safety are also important tasks of energy management, which ensures the stable operation of the microgrid in the face of various faults and abnormal conditions, and ensures the reliability of power supply. Finally, the microgrid energy management system also needs to have the ability to operate in isolation, which can maintain power supply independently when disconnected from the main grid, enhancing the autonomy and anti-interference capability of the system. Through the coordinated execution of these comprehensive tasks, microgrid energy management provides strong support for clean, efficient and reliable power supply. Through advanced energy management technology, microgrid can achieve optimal allocation and dynamic balance of energy, thereby improving energy efficiency, reducing energy waste, and ensuring the continuity and reliability of power supply. With the continuous progress of new energy technology and the improvement of intelligent level, new energy microgrid is expected to play a more important role in the future energy field and contribute to clean, efficient and sustainable energy supply. However, most of the current research on microgrid energy management does not consider adjusting the output power of renewable energy according to load demand, which is prone to wind and light abandonment. SUMMARY

[0004] The purpose of the present application is to provide a microgrid energy management method based on economic model prediction and related devices, which can adjust the output of renewable energy in the microgrid according to the load demand, to solve the technical problem of wind and light abandonment in microgrid energy management.

[0005] In order to achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0006] In a first aspect, the present application provides a microgrid energy management method based on economic model prediction, comprising:

[0007] According to the working principle of the wind power electronic system, the photovoltaic power electronic system and the energy storage subsystem, a simplified model of the wind power electronic system, a simplified model of the photovoltaic power electronic system and a simplified model of the energy storage subsystem are established;

[0008] According to the energy flow in the energy management of the micro-grid, a micro-grid system energy management model is established, and constraint conditions are determined;

[0009] On the basis of supply and demand balance, a target function containing economic performance is designed;

[0010] Based on the simplified model of the wind power electronic system, the simplified model of the photovoltaic power electronic system, the simplified model of the energy storage subsystem and the micro-grid system energy management model, the optimal working state of each subsystem in the micro-grid is solved by taking the minimum value of the target function as the goal and combining the constraint conditions;

[0011] The working state of each subsystem in the micro-grid is controlled at the optimal working state.

[0012] Further, the simplified model of the wind power electronic system is:

[0013]

[0014] Wherein, is the state vector of the wind power electronic system; and are the quadrature current and direct current of the terminal current of the multi-stage permanent magnet synchronous wind turbine in the rotor reference frame, respectively; is the electrical angular velocity; is the synchronous motor resistance; is the magnetic flux connected to the stator winding; is the DC bus voltage; is the control signal of the wind power electronic system, used to adjust the duty cycle of the DC / DC converter ; is the moment of inertia of the wind wheel; is the stator inductance; is the mechanical torque of the wind power subsystem; is the pole pair number of the wind turbine.

[0015] Further, the simplified model of the photovoltaic power electronic system is:

[0016]

[0017] Wherein, is the state vector of the photovoltaic power electronic system; is the port voltage of the photovoltaic array; is the current injected into the DC bus of the photovoltaic power electronic system; and respectively the output current and voltage of a photovoltaic panel; the capacitance and inductance of the converter; the DC bus voltage; the control signal; the output current of a photovoltaic subsystem; the number of photovoltaic panels in parallel in a photovoltaic array; the number of photovoltaic cells in series for each parallel photovoltaic panel; the photogenerated current at a reference irradiance; the reverse saturation current of a photovoltaic cell; and respectively the output current and voltage of a photovoltaic panel; the elementary charge; the Boltzmann constant; is the absolute temperature of a photovoltaic cell; is the bias coefficient of the diode; is the series resistance.

[0018] Further, the simplified model of the energy storage subsystem is:

[0019]

[0020] where, is the terminal voltage of the battery; is the output current of the battery, and are the quadrature and direct currents of the terminal current of a multi-stage permanent magnet synchronous wind generator in the rotor reference frame; is the current injected into the DC bus by the photovoltaic subsystem; is the current demanded by the load; is the DC bus voltage; is the control signal of the wind subsystem; is the voltage source; is the equivalent series resistance of the battery; is the equivalent capacitance of the battery.

[0021] Further, the expression of the energy management model of the microgrid system is:

[0022]

[0023] where, is the remaining energy of the energy storage subsystem at time is the remaining energy of the energy storage subsystem at time is the charging / discharging power of the energy storage subsystem at time t, for the sampling interval, the difference between supply and demand of electricity at time t, representing the output power of the wind turbine generator set at time t, representing the output power of the wind turbine generator set at time t, representing the output power of the photovoltaic generator set at time t, representing the output power of the photovoltaic generator set at time t, representing the charging / discharging power of the energy storage subsystem at time t, representing the charging / discharging power of the energy storage subsystem at time t, the power exchanged with the distribution network by the microgrid system at time t, the power exchanged with the distribution network by the microgrid system at time t, representing the load demand at time t.

[0024] Further, the objective function is:

[0025]

[0026]

[0027] wherein, representing the optimal solution of the microgrid at time t, representing the optimal solution of the microgrid at time t, representing the optimal solution of the microgrid at time t, representing the sum of the cost of various energies, representing the cost function of the imbalance between supply and demand, representing the fluctuation cost of energy exchange with the distribution network, representing the cost of the battery; is the power generation cost coefficient of the wind turbine generator system, is the power generation cost coefficient of the photovoltaic generator system, is the marginal benefit / cost coefficient of the discharging / charging of the battery, is the marginal cost / benefit coefficient of the purchase / sale of electricity to the distribution network, is a constant coefficient, representing the output power of the wind turbine generator set at time t, representing the output power of the wind turbine generator set at time t, representing the output power of the photovoltaic generator set at time t, representing the output power of the photovoltaic generator set at time t, representing the charging / discharging power of the energy storage subsystem at time t, representing the charging / discharging power of the energy storage subsystem at time t, the power exchanged with the distribution network by the microgrid system at time t, the power exchanged with the distribution network by the microgrid system at time t, is the charging / discharging power of the energy storage subsystem at time t minus the charging / discharging power of the energy storage subsystem at time t, is the charging / discharging power of the energy storage subsystem at time t minus the power exchanged with the distribution network by the microgrid system at​​ the power of the micro-grid system interacting with the power distribution network at the power of the micro-grid system interacting with the power distribution network at

[0028] Further, the constraint condition is:

[0029]

[0030] wherein, represents the output power of the wind turbine generator set at represents the output power of the photovoltaic generator set at represents the charging / discharging power of the energy storage subsystem at the power of the micro-grid system interacting with the power distribution network at represents the remaining capacity of the energy storage subsystem at represents the minimum value of the output power of the wind turbine generator set; represents the maximum value of the output power of the wind turbine generator set; represents the minimum value of the output power of the photovoltaic generator set; represents the maximum value of the output power of the photovoltaic generator set; represents the maximum value of the battery charging / discharging power; represents the maximum value of the power sold / bought by the micro-grid to the power distribution network; represents the maximum value of the battery capacity; represents the minimum value of the battery capacity.

[0031] In a second aspect, the present application provides a micro-grid energy management device based on a distributed economic model prediction, comprising:

[0032] a subsystem model establishing module, configured to establish a simplified model of the wind turbine subsystem, a simplified model of the photovoltaic subsystem and a simplified model of the energy storage subsystem according to the working principles of the wind turbine subsystem, the photovoltaic subsystem and the energy storage subsystem;

[0033] an energy management model establishing module, configured to establish an energy management model of the micro-grid system and determine a constraint condition according to the energy flow during the energy management of the micro-grid;

[0034] a target function designing module, configured to design a target function containing economic performance on the basis of supply-demand balance;

[0035] ​​​​​An optimal state solving module is configured to solve the optimal working state of each subsystem in the micro-grid based on the simplified model of the wind power generation subsystem, the simplified model of the photovoltaic power generation subsystem, the simplified model of the energy subsystem, and the micro-grid system energy management model, with the minimum target function value as the target and in combination with the constraint conditions.

[0036] A control module is configured to control the working state of each subsystem in the micro-grid to be at the optimal working state.

[0037] In a third aspect, the present application provides an electronic device, comprising:

[0038] at least one processor; and

[0039] a memory in communication connection with the at least one processor; wherein

[0040] the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the micro-grid energy management method based on economic model prediction according to any one of the first aspect of the present application.

[0041] In a fourth aspect, the present application provides a computer readable storage medium storing a computer program, and the computer program is executed by a processor to implement the micro-grid energy management method based on economic model prediction according to any one of the first aspect of the present application.

[0042] Compared with the prior art, the present application has at least the following beneficial technical effects:

[0043] (1) The micro-grid is a flexible power supply system integrating power generation, energy storage, load and intelligent control, and can operate independently or in parallel with the main grid, with high flexibility and reliability. In the face of the limitations of traditional micro-grid stability and application range, the new energy micro-grid integrates solar photovoltaic, wind power and gas turbine and other clean energy, enhances the system stability and broadens the application scenarios. New energy micro-grid energy management is the key to its efficient and stable operation, involving real-time, optimized dispatching of power generation, distribution and storage, and ensuring efficient energy use and economic operation. The scheme optimizes and coordinates the operation of each subsystem of the micro-grid, realizes the optimization of economic performance on the basis of maintaining the balance between supply and demand of the system.

[0044] (2) The present application predicts the new energy power generation, the charging and discharging power of the energy storage subsystem and the power of energy interaction between the micro-grid system and the power distribution network within a period of time, optimizes the dispatching of power generation resources and the management of energy storage based on the prediction results, maximally utilizes renewable energy, reduces the phenomenon of curtailment of wind and light, improves the utilization rate and economic benefit of renewable energy, supports stable power supply of the micro-grid under various conditions and provides support for clean, efficient and sustainable energy supply. With the development of new energy technology, new energy micro-grid will play a more key role in the future energy field.

[0045] (3) The present application relates to power generation, distribution and storage and the like, the sum of the cost of various energies, the cost of supply-demand imbalance, the fluctuation cost of energy interaction with the power distribution network and the fluctuation cost of battery use are involved in the objective function, by finding the optimal solution of the objective function, the optimal allocation of energy is realized, when the load demand is large, wind power generation is given priority, photovoltaic power generation is considered secondly, if wind power generation and photovoltaic power generation are insufficient, then the storage battery is supplemented, if the load demand still cannot be met, then power is purchased from the main grid; when the load demand is small, under the condition of meeting the load demand, the excess power is considered to charge the storage battery and sell power to the main grid. The present application guarantees the dynamic balance of supply and demand, improves the energy utilization efficiency, reduces energy waste and guarantees the continuity and reliability of power supply. With the continuous progress of new energy technology and the improvement of intelligent level, new energy micro-grid is expected to play a more important role in the future energy field and make contributions to clean, efficient and sustainable energy supply. BRIEF DESCRIPTION OF DRAWINGS

[0046] Figure 1 A micro-grid energy management method flow chart based on a distributed economic model predictive control is provided for the present application;

[0047] Figure 2 A wind speed, light intensity and temperature change curve diagram within one day for the area where the micro-grid system is located;

[0048] Figure 3 A load demand and renewable energy output power change curve diagram within one day for the micro-grid system;

[0049] Figure 4 A state of charge change curve diagram within one day for the energy storage subsystem;

[0050] Figure 5 A curve diagram of charging and discharging power of the energy storage subsystem and energy interaction power between the micro-grid and the power distribution network within one day for the micro-grid system;

[0051] Figure 6 A power supply and load demand change curve diagram within one day for the micro-grid system;

[0052] Figure 7 A structural block diagram of the micro-grid energy management system based on the distributed economic model prediction is provided for an embodiment of the present application.

[0053] Figure 8 A block diagram of the electronic device for an embodiment of the present application. DETAILED DESCRIPTION

[0054] In order for those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts should fall within the protection scope of the present application.

[0055] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" 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 have to be limited to only those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to the process, method, product, or device.

[0056] Micro-grid energy management refers to the process of real-time monitoring, optimal scheduling and control of power generation, energy storage, load and grid interaction in a micro-grid.

[0057] The present application will be described in further detail below with reference to the drawings:

[0058] Embodiment 1

[0059] Reference Figure 1 The present embodiment provides a micro-grid energy management method based on a distributed economic model prediction, comprising the following steps:

[0060] Step 1: According to the working principle of the micro-grid, the working principles of the wind power generation subsystem, the photovoltaic power generation subsystem and the energy storage subsystem are analyzed respectively, and simplified models of the wind power generation subsystem, the photovoltaic power generation subsystem and the energy storage subsystem are established according to their dynamic characteristics.

[0061] The mathematical model of the wind power generation subsystem is as follows:

[0062] The mathematical model of the wind power generation subsystem is as follows:

[0063] where, is the state vector of the wind power electronic system, which and the output power of the wind power generator group P w The relationship between them is: ; and are the quadrature current and direct current of the multi-stage permanent magnet synchronous wind power generator in the rotor reference frame, respectively; is the electrical angular velocity; is the synchronous motor resistance; is the magnetic flux of the stator winding connection; is the DC bus voltage; is the control signal (switching control) of the wind power electronic system, which is used to adjust the duty ratio of the DC / DC converter ; is the moment of inertia of the wind wheel; is the stator inductance; is the mechanical torque of the wind power subsystem; is the number of pole pairs of the wind turbine.

[0064] The mathematical model of the photovoltaic power electronic system is as follows:

[0065]

[0066] where, is the state vector of the photovoltaic power electronic system, which and the output power of the photovoltaic power generator group have the relationship: ; is the port voltage of the photovoltaic array; is the current injected into the DC bus of the photovoltaic power electronic system; and are the capacitance and inductance of the converter in the photovoltaic power electronic system, respectively; is the control signal (switching control); represents the output current of the photovoltaic subsystem; represents the number of photovoltaic panels in parallel in the photovoltaic array; represents the number of photovoltaic cells in series for each parallel photovoltaic panel; represents the photo-generated current under the reference light intensity; represents the reverse saturation current of the photovoltaic cell; and are the output current and voltage of the photovoltaic panel, respectively; represents the electronic charge constant; represents the Boltzmann constant; It is the absolute temperature of the photovoltaic cell; for The bias coefficient of the junction; It is a series resistor.

[0067] The mathematical model of the energy storage subsystem is shown below:

[0068]

[0069] in, The battery's terminal voltage and its charging and discharging power of the energy storage subsystem. Is there a relationship between them?

[0070] ; This refers to the output current of the battery. Injecting current into the DC bus of the photovoltaic power generation system; The current required by the load; It is a voltage source; This is the equivalent series resistance of the battery; This is the equivalent capacitance of the battery.

[0071] Step 2: Based on the models of each subsystem established in Step 1, establish a microgrid system energy management model by analyzing the energy flow during microgrid energy management, and determine the constraints.

[0072] The energy management model expression for a microgrid system is:

[0073]

[0074] in, for The remaining power of the energy storage subsystem at all times. for The charging / discharging power of the energy storage subsystem at any given time. The sampling interval is... for The difference between power supply and demand at any given moment requires It is 0. Representative at The output power of the wind turbine generator at any given time. Representative at The output power of the photovoltaic generator set at all times Representative at The charging and discharging power of the energy storage subsystem at all times. For microgrid systems in The power that constantly interacts with the energy distribution network. Representative at The load demand at any given moment. At that time, the energy storage subsystem is in a charging state. At that time, the energy storage subsystem is in a discharging state; when At that time, the microgrid sells electricity to the distribution network. At that time, the microgrid purchases electricity from the distribution network.

[0075] The constraints are as follows:

[0076]

[0077] in, Representative at The output power of the wind turbine generator at any given time. Representative at The output power of the photovoltaic generator set at all times Representative at The charging and discharging power of the energy storage subsystem at all times. For microgrid systems in The power that constantly interacts with the energy distribution network. Representative at The remaining power of the energy storage subsystem at any given time. When At that time, the energy storage subsystem is in a charging state. At that time, the energy storage subsystem is in a discharging state; when At that time, the microgrid sells electricity to the distribution network. At that time, the microgrid purchases electricity from the distribution network. This represents the minimum output power of the wind turbine generator set; This represents the maximum output power of the wind turbine generator set; This represents the minimum output power of the photovoltaic generator set; This represents the maximum output power of the photovoltaic generator set; This represents the maximum value of the battery's charging / discharging power. This represents the maximum power that the microgrid sells / buys from the distribution network; This represents the maximum value of the battery capacity. This represents the minimum battery capacity.

[0078] Step 3: Based on the microgrid system energy management model established in Step 2, and considering the supply-demand balance, design an objective function that includes economic performance, based on the principle of economic model predictive control. The microgrid's first... The objective function of each subsystem is:

[0079]

[0080] in, Representing microgrid systems The optimal solution at time k, including the wind power, photovoltaic power, charging / discharging power of the energy storage battery, and the power exchanged with the grid, For predicting the time domain;

[0081] Wherein, The sum of the costs of various energies, The cost function representing the imbalance between supply and demand, The fluctuation cost representing the energy exchange with the distribution network, And The two can avoid the reduction of power quality and the influence on system stability caused by the imbalance between supply and demand, The fluctuation cost representing the use of the battery, which can optimize the use of the battery to prolong the cycle life of the battery, and the expansion formula is as follows:

[0082]

[0083] Wherein, The wind power generation cost coefficient of the wind power generation system, The photovoltaic power generation cost coefficient of the photovoltaic power generation system, The marginal benefit / cost coefficient of the discharge / charge of the battery, The marginal cost / benefit coefficient of purchasing / selling power to the distribution network, The constant coefficient, The The charging / discharging power of the energy storage subsystem at time k minus The charging / discharging power of the energy storage subsystem at time k, The power exchanged with the distribution network by the micro-grid system at time k minus the power exchanged with the distribution network by the micro-grid system at time k.

[0084] Step 4: Combine the simplified model of each subsystem, the energy management model of the micro-grid system, and solve the optimal solution of the micro-grid system at time k with the minimum target function value as the goal and the constraint condition. ;

[0085] Step 5, according to step 3 and the principle of distributed model predictive control, load demand and optimal solution , control the power generation state of the wind turbine, the power generation state of the photovoltaic generator, the charging / discharging state of the energy storage subsystem, and the state of the micro-grid purchasing / selling power from the distribution network. In the condition of ensuring the stability of the micro-grid system, the energy management of the micro-grid is realized.

[0086] Simulation example

[0087] ​​​The micro-grid system in the embodiment is simulated by using the method, the parameters of the system are shown in Table 1 and Table 2, the wind speed, the light intensity and the temperature in the region where the micro-grid is located within one day are shown in Table 3. Figure 2

[0088] Table 1 Controller parameters in the embodiment

[0089]

[0090] Table 2 Parameters in constraint conditions in the embodiment

[0091]

[0092] Distributed control is the most widely used control method in the field of electric power, and has the advantages of flexible control structure, small calculation burden, strong model applicability, etc., and has unique advantages in solving the control problem of distributed power generation system. Since the predictive control has the ability to solve the complex problems of constraint, pure time delay, nonlinearity and multiple input and output, and has strong robustness. The goal of micro-grid energy management is to realize efficient use of energy, while ensuring the economy and reliability of the system. Therefore, the distributed economic model predictive control has broad application prospects in solving the problem of micro-grid energy management.

[0093] In order to verify that the distributed economic model predictive control method can realize the goal of micro-grid energy management, the simulation experiment is carried out in the embodiment. In the simulation process, the sampling time is set to , and the prediction time domain is .

[0094] Fig. 1 shows the control of the output power of the wind turbine generator set and the photovoltaic generator set based on the distributed economic model predictive control; Fig. 2 shows the change curve of the state of charge of the energy storage subsystem within one day; Fig. 3 shows the curve of the charging and discharging power of the energy storage subsystem and the energy exchange power between the micro-grid and the distribution network within one day; Fig. 4 shows the change curve of the power supply and the load demand of the micro-grid system within one day based on the distributed economic model predictive control. Figure 3 Figure 4 Fig. 1 shows the control of the output power of the wind turbine generator set and the photovoltaic generator set based on the distributed economic model predictive control; Fig. 2 shows the change curve of the state of charge of the energy storage subsystem within one day; Fig. 3 shows the curve of the charging and discharging power of the energy storage subsystem and the energy exchange power between the micro-grid and the distribution network within one day; Fig. 4 shows the change curve of the power supply and the load demand of the micro-grid system within one day based on the distributed economic model predictive control. Figure 5 Figure 6 Fig. 1 shows the control of the output power of the wind turbine generator set and the photovoltaic generator set based on the distributed economic model predictive control; Fig. 2 shows the change curve of the state of charge of the energy storage subsystem within one day; Fig. 3 shows the curve of the charging and discharging power of the energy storage subsystem and the energy exchange power between the micro-grid and the distribution network within one day; Fig. 4 shows the change curve of the power supply and the load demand of the micro-grid system within one day based on the distributed economic model predictive control. Figure 6 ​​​It can be seen that the distributed economic model predictive control can reduce energy fluctuation, improve power supply quality and realize peak clipping and valley filling while meeting the actual operation constraints of each unit. Therefore, the distributed economic model predictive control algorithm can realize fast regulation of system frequency stability while greatly reducing the calculation burden under the condition of meeting various constraints of the system, thereby proving the applicability and superiority of the distributed economic model predictive control algorithm in solving the control problem of microgrid energy management.

[0095] The following is a system embodiment of the present application, which can be used to execute the method embodiments of the present application. For details not disclosed in the device embodiments, please refer to the method embodiments of the present application.

[0096] Embodiment 2

[0097] Please refer to Figure 7 The embodiment provides a microgrid energy management system based on distributed economic model prediction, which comprises:

[0098] A subsystem model establishing module is configured to establish a simplified model of a wind power subsystem, a simplified model of a photovoltaic power subsystem and a simplified model of an energy storage subsystem according to the working principles of the wind power subsystem, the photovoltaic power subsystem and the energy storage subsystem;

[0099] An energy management model establishing module is configured to establish a microgrid system energy management model and determine constraint conditions according to energy flow during microgrid energy management;

[0100] A target function designing module is configured to design a target function containing economic performance on the basis of supply-demand balance;

[0101] An optimal state solving module is configured to solve optimal working states of each subsystem in the microgrid based on the simplified model of the wind power subsystem, the simplified model of the photovoltaic power subsystem, the simplified model of the energy storage subsystem and the microgrid system energy management model, with the minimum target function value as the target and in combination with the constraint conditions;

[0102] A control module is configured to control the working states of each subsystem in the microgrid to be at the optimal working states.

[0103] All related contents of each step involved in the foregoing embodiment of the microgrid energy management method based on distributed economic model prediction can be cited to the function description of the function module corresponding to the microgrid energy management system based on distributed economic model prediction in the embodiment of the present application, which will not be repeated here.

[0104] Embodiment 3

[0105] Refer to Figure 8The embodiment provides an electronic device, which comprises a processor and a memory, the processor is connected with the memory through a bus; the memory is used for storing a computer program, the computer program comprises program instructions, and the processor is used for executing the program instructions stored in the computer storage medium. The processor can be a central processing unit (CPU), and can also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components and the like, which are the computing core and control core of the terminal, are suitable for implementing one or more instructions, and are particularly suitable for loading and executing one or more instructions in the computer storage medium to realize a corresponding method flow or a corresponding function; the processor in the embodiment of the application can be used for the operation of the micro-grid energy management method based on the distributed economic model prediction. The bus can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus and the like. For the convenience of representation, Figure 8 Only one line is used in the figure, but it does not mean that there is only one bus or only one type of bus.

[0106] Embodiment 4

[0107] The embodiment provides a storage medium, specifically, a computer readable storage medium (Memory), which is a memory device in an electronic device and is used for storing programs and data. It can be understood that the computer readable storage medium herein can include a built-in storage medium in the electronic device, and of course can include an extended storage medium supported by the electronic device. The computer readable storage medium provides a storage space, and the storage space stores an operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and the instructions can be one or more computer programs (including program codes). It should be noted that the computer readable storage medium herein can be a high-speed RAM memory or a non-volatile memory such as at least one disk memory. One or more instructions stored in the computer readable storage medium can be loaded and executed by the processor to realize the corresponding steps of the micro-grid energy management method based on the distributed economic model prediction in the above embodiment.

[0108] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage, etc.) containing computer-usable program code.

[0109] The present application is described with reference to flowcharts and / or block diagrams according to the methods, devices (systems), and computer program products of the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams can be implemented by computer program instructions. These computer program instructions can be provided to a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, so that the instructions executed by the computer or other programmable data processing devices produce a device that implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1 an apparatus that carries out the functions specified in one or more flows and / or blocks.

[0110] These computer program instructions can also be stored in a computer readable memory that can guide the computer or other programmable data processing devices to work in a specific way, so that the instructions stored in the computer readable memory produce a manufactured product including instruction apparatus, which implements the functions specified in the flowcharts and / or block diagrams. Figure 1 one or more flows and / or blocks Figure 1the function specified in the one or more blocks.

[0111] These computer program instructions can also be loaded into computer or other programmable data processing devices, so that a series of operation steps are performed on the computer or other programmable data processing devices to generate computer-implemented processes, thus the instructions executed on the computer or other programmable data processing devices provide a process for implementing the flow Figure 1 the flow or flows and / or blocks Figure 1 the steps of the function specified in the one or more blocks.

[0112] In the description of the present specification, the description of the terms "one embodiment", "some embodiments", "an example", "a specific example", or "some examples" and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, the person skilled in the art can combine and combine the different embodiments or examples described in the present specification and the features of the different embodiments or examples without contradiction.

[0113] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present application and not to limit it, although the present application has been described in detail with reference to the above embodiments, those skilled in the art should understand: the specific embodiments of the present application can still be modified or replaced by the equivalent, without departing from the spirit and scope of the present application, any modification or equivalent replacement of the present application, which should be covered in the protection scope of the claims of the present application.

Claims

1. A microgrid energy management method based on economic model prediction, characterized in that, The method comprises the following steps: a simplified model of a wind power generation subsystem, a simplified model of a photovoltaic power generation subsystem and a simplified model of an energy storage subsystem are established according to working principles of the wind power generation subsystem, the photovoltaic power generation subsystem and the energy storage subsystem; an energy management model of the micro-grid system is established according to energy flow during energy management of the micro-grid, and constraint conditions are determined; a target function containing economic performance is designed on the basis of supply-demand balance; optimal working states of each subsystem in the micro-grid are solved based on the simplified model of the wind power generation subsystem, the simplified model of the photovoltaic power generation subsystem, the simplified model of the energy storage subsystem and the energy management model of the micro-grid system, with the minimum value of the target function as the target, and in combination with the constraint conditions; working states of each subsystem in the micro-grid are controlled to be in the optimal working states; the expression of the energy management model of the micro-grid system is: in, for The remaining power of the energy storage subsystem at all times. for The remaining power of the energy storage subsystem at all times. for The charging / discharging power of the energy storage subsystem at any given time. The sampling interval is... for The difference between electricity supply and demand at any given moment. Representative at The output power of the wind turbine generator at any given time. Representative at The output power of the photovoltaic generator set at all times Representative at The charging and discharging power of the energy storage subsystem at all times. For microgrid systems in The power that constantly interacts with the energy distribution network. Representative at The load demand at any given moment; the target function is: wherein, represents the optimal solution of the microgrid at time, is the prediction horizon, represents the sum of the cost of various energy sources, represents the cost function of the supply-demand imbalance, represents the fluctuation cost of energy interaction with the distribution network, represents the cost of the battery; is the power generation cost coefficient of the wind turbine system, is the power generation cost coefficient of the photovoltaic system, is the marginal benefit / cost coefficient of the discharge / charge of the battery, is the marginal cost / benefit coefficient of the purchase / sale of power to the distribution network, is a constant coefficient, represents the output power of the wind turbine at time, represents the output power of the photovoltaic turbine at time, represents the charge / discharge power of the energy storage subsystem at time, is the power of the microgrid system at time energy interaction with the distribution network, is the charge / discharge power of the energy storage subsystem at time minus the charge / discharge power of the energy storage subsystem at time, is the power of the microgrid system at time energy interaction with the distribution network minus the power of the microgrid system at time energy interaction with the distribution network; the constraint conditions are: wherein, represents the output power of the wind turbine generator at the time t, represents the output power of the wind turbine generator at the time t, represents the output power of the photovoltaic generator at the time t, represents the output power of the photovoltaic generator at the time t, represents the charge / discharge power of the energy storage subsystem at the time t, represents the charge / discharge power of the energy storage subsystem at the time t, is the power exchanged by the microgrid system with the distribution grid at the time t, is the power exchanged by the microgrid system with the distribution grid at the time t, represents the remaining amount of energy of the energy storage subsystem at the time t; represents the minimum value of the wind turbine generator output power; represents the maximum value of the wind turbine generator output power; represents the minimum value of the photovoltaic generator output power; represents the maximum value of the photovoltaic generator output power; represents the maximum value of the battery charge / discharge power; represents the maximum value of the battery charge / discharge power; represents the maximum value of the power sold / bought by the microgrid to / from the distribution grid; represents the maximum value of the battery capacity; represents the minimum value of the battery capacity.

2. The microgrid energy management method based on economic model prediction of claim 1, wherein, the simplified model of the wind power generation subsystem is: wherein, is the state vector of the wind power electronic system; and are the quadrature and direct currents of the multi-stage permanent magnet synchronous wind generator terminal currents in the rotor reference frame, respectively; is the electrical angular velocity; is the synchronous machine resistance; is the stator winding linked flux; is the DC bus voltage; is the control signal of the wind power electronic system, used to regulate the duty cycle of the DC / DC converter ; is the moment of inertia of the wind wheel; is the stator inductance; is the mechanical torque of the wind power subsystem; is the number of pole pairs of the wind turbine. 3.The microgrid energy management method based on economic model prediction of claim 1, wherein, the simplified model of the photovoltaic power generation subsystem is: in, Let this be the state vector of the photovoltaic power generation system; This refers to the port voltage of the photovoltaic array; Injecting current into the DC bus of the photovoltaic power generation system; and In photovoltaic power generation systems The capacitors and inductors of the converter; This is the DC bus voltage; For control signals; This represents the output current of the photovoltaic subsystem; This represents the number of photovoltaic panels connected in parallel in the photovoltaic array; This represents the number of photovoltaic cells connected in series in each parallel photovoltaic panel; Represents the photocurrent under reference illumination intensity; Represents the reverse saturation current of a photovoltaic cell; and These are the output current and voltage of the photovoltaic panel, respectively. Represents the electron charge constant; Represents the Boltzmann constant; It is the absolute temperature of the photovoltaic cell; for The bias coefficient of the junction; It is a series resistor. 4.The microgrid energy management method based on economic model prediction of claim 1, wherein, the simplified model of the energy storage subsystem is: wherein, terminal voltage of the battery; output current of the battery, and are the direct and quadrature currents of the multi-stage permanent magnet synchronous wind generator in the rotor reference frame, respectively; is the current injected into the DC bus of the PV power system; is the current demanded by the load; is the DC bus voltage; is the control signal of the wind power system; is the voltage source; is the equivalent series resistance of the battery; is the equivalent capacitance of the battery.

5. A microgrid energy management system based on distributed economic model prediction, for implementing the method of claim 1, characterized in that, The method comprises the following steps: a subsystem model establishing module is configured to establish the simplified model of the wind power generation subsystem, the simplified model of the photovoltaic power generation subsystem and the simplified model of the energy storage subsystem according to working principles of the wind power generation subsystem, the photovoltaic power generation subsystem and the energy storage subsystem; an energy management model establishing module is configured to establish the energy management model of the micro-grid system according to energy flow during energy management of the micro-grid, and determine the constraint conditions; a target function designing module is configured to design the target function containing economic performance on the basis of supply-demand balance; an optimal state solving module is configured to solve the optimal working states of each subsystem in the micro-grid based on the simplified model of the wind power generation subsystem, the simplified model of the photovoltaic power generation subsystem, the simplified model of the energy storage subsystem and the energy management model of the micro-grid system, with the minimum value of the target function as the target, and in combination with the constraint conditions; a control module is configured to control the working states of each subsystem in the micro-grid to be in the optimal working states.

6. An electronic device, comprising: The method comprises the following steps: at least one processor; and a memory in communication connection with the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the micro-grid energy management method based on economic model prediction according to any one of claims 1 to 4.

7. A computer readable storage medium storing a computer program, characterized in that, The computer program is executed by the processor to implement the micro-grid energy management method based on economic model prediction according to any one of claims 1 to 4.

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