Micro-grid energy management method based on economic model prediction and related device

Abstract

The invention discloses a micro-grid energy management method based on economic model prediction and a related device, and belongs to the technical field of micro-grid energy management, and the method comprises the steps: simplifying models of a wind power generation subsystem, a photovoltaic power generation subsystem and an energy storage subsystem; establishing a micro-grid system energy management model according to energy flow during micro-grid energy management, and determining constraint conditions; on the basis of supply and demand balance, designing a target function including economic performance; based on simplified models of a wind power generation subsystem, a photovoltaic power generation subsystem and an energy storage subsystem and a micro-grid system energy management model, the optimal working state of each subsystem in the micro-grid is solved by taking the minimum target function value as the target and combining constraint conditions; and controlling the working state of each subsystem in the micro-grid to be in the optimal working state. According to the invention, the operation economy of the micro-grid is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of microgrid energy management, and in particular relates to a microgrid energy management method based on economic model prediction and related devices. Background Art

[0002] Microgrid is a highly integrated and flexible power supply system, which consists of power generation units, energy storage equipment, loads and intelligent control systems to form a complete power supply system. This system can not only operate independently of traditional large-scale power grids, but also be connected to the main grid, showing its excellent flexibility and reliability. However, traditional microgrids often face challenges of insufficient stability and limited application scenarios during their setup and operation. The application of new energy power generation systems to build microgrids achieves energy diversification and interconnection by integrating different types of clean and renewable energy. In new energy microgrids, solar photovoltaic power generation, wind power generation and traditional gas turbines can be used to generate electricity together and incorporated into the public power grid, which not only enhances the stability of the system, but also expands the scope of application of microgrids, enabling them to better meet various power needs such as industry, residential and commercial. Energy management of new energy microgrids is one of the key technologies to ensure efficient and stable operation of microgrids.

[0003] Microgrid energy management refers to the process of real-time monitoring, optimization and control of power generation, energy storage, load and grid interaction in microgrids. It involves multiple aspects such as power generation, distribution, conversion and storage, with the aim of achieving efficient use of energy and economic operation of microgrids. Its main task is to ensure efficient operation of the power system and 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 equipment to meet the changing load demand. Secondly, 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 link in microgrid energy management. It involves determining the charging and discharging timing of the energy storage subsystem to balance supply and demand fluctuations and improve energy utilization efficiency. Electricity price management is equally important. The energy management system must be able to respond to changes in electricity prices and flexibly adjust load or energy storage operations to reduce electricity costs by implementing strategies such as demand response. System protection and safety are also important tasks of energy management, which ensure that the microgrid can operate stably in the face of various faults and abnormal situations and guarantee the reliability of power supply. Finally, the microgrid energy management system must also have the ability to operate in an isolated island, and can independently maintain power supply when disconnected from the main grid, enhancing the autonomy and anti-interference ability of the system. Through the coordinated execution of these comprehensive tasks, microgrid energy management provides strong support for achieving clean, efficient and reliable power supply. Through advanced energy management technology, microgrids can achieve optimal configuration and dynamic balance of energy, thereby improving energy utilization efficiency, reducing energy waste, and ensuring the continuity and reliability of power supply. With the continuous advancement of new energy technology and the improvement of intelligence level, new energy microgrids are expected to play a more important role in the future energy field and contribute to the realization of clean, efficient and sustainable energy supply. However, most of the current research on microgrid energy management issues does not consider adjusting the output power of renewable energy according to load demand, which is prone to wind and solar abandonment. Summary of the invention

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

[0005] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides a microgrid energy management method based on economic model prediction, comprising: According to the working principles of the wind power generation subsystem, the photovoltaic generation subsystem and the energy storage subsystem, a simplified model of the wind power generation subsystem, a simplified model of the photovoltaic generation subsystem and a simplified model of the energy storage subsystem are established; According to the energy flow during microgrid energy management, the energy management model of the microgrid system is established and the constraint conditions are determined; Based on the balance between supply and demand, design an objective function that includes economic performance; Based on the simplified model of wind power generation subsystem, photovoltaic power generation subsystem, energy storage subsystem and microgrid system energy management model, the optimal working state of each subsystem in the microgrid is solved with the minimum objective function value combined with the constraints. Control the working state of each subsystem in the microgrid to be in the optimal working state.

[0006] Furthermore, the simplified model of the wind turbine subsystem is:

[0007] in, is the state vector of the wind electronic system; and They are the orthogonal current and DC current of the terminal current of the multi-stage permanent magnet synchronous wind turbine generator in the rotor reference frame; is the electrical angular velocity; is the synchronous motor resistance; is the magnetic flux connected to the stator winding; is the DC bus voltage; It is the control signal of the wind 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 subsystem; is the pole pair number of the fan.

[0008] Furthermore, the simplified model of the photovoltaic power generation system is:

[0009] in, is the state vector of the photovoltaic power generation system; is the terminal voltage of the PV array; The current injected into the DC bus for the photovoltaic power generation system; and They are respectively The capacitor and inductor of the converter; is the DC bus voltage; is the control signal; Represents the output current of the photovoltaic subsystem; Represents the number of PV panels connected in parallel in the PV array; Represents the number of photovoltaic cells connected in series in each parallel photovoltaic panel; represents the photocurrent under 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 electron charge constant; represents the Boltzmann constant; is the absolute temperature of the photovoltaic cell; for The bias coefficient of the junction; is the series resistance.

[0010] Furthermore, the simplified model of the energy storage subsystem is:

[0011] in, Battery terminal voltage; is the output current of the battery, and They are the orthogonal current and DC current of the terminal current of the multi-stage permanent magnet synchronous wind turbine generator in the rotor reference frame; The current injected into the DC bus for the photovoltaic power generation system; is the current required by the load; is the DC bus voltage; It is the control signal of the wind electronic system; is a voltage source; is the equivalent series resistance of the battery; is the equivalent capacitance of the battery.

[0012] Furthermore, the energy management model of the microgrid system is expressed as:

[0013] in, for The remaining power of the energy storage subsystem at any moment, for The remaining power of the energy storage subsystem at any moment, for The charging / discharging power of the energy storage subsystem at the moment, is the sampling interval, for The difference between electricity supply and demand at any given moment, Representatives in The output power of the wind turbine at any moment, Representatives in The output power of the photovoltaic generator set at the moment, Representatives in The charging and discharging power of the energy storage subsystem at each moment, For microgrid systems The power that interacts with the distribution network energy at all times, Representatives in Load demand at any time.

[0014] Furthermore, the objective function is:

[0015]

[0016] in, Representing microgrids The best solution at any time, For the prediction time domain, Represents the sum of the costs of various energy sources, represents the cost function of the supply-demand imbalance, represents the fluctuating cost of energy interaction with the distribution grid, Represents the cost of the battery; is the power generation cost coefficient of the wind power generation subsystem, is the power generation cost coefficient of the photovoltaic power generation system, is the marginal benefit / cost coefficient of battery discharge / charging, is the marginal cost / benefit coefficient of buying / selling electricity to the distribution network, is a constant coefficient, Representatives in The output power of the wind turbine at any moment, Representatives in The output power of the photovoltaic generator set at the moment, Representatives in The charging and discharging power of the energy storage subsystem at each moment, For microgrid systems The power that interacts with the distribution network energy at all times, for The charging and discharging power of the energy storage subsystem at the moment minus The charging and discharging power of the energy storage subsystem at each moment, For microgrid systems The power that interacts with the distribution network at all times minus the power of the microgrid system The power that interacts with the distribution grid energy at all times.

[0017] Furthermore, the constraints are:

[0018] in, Representatives in The output power of the wind turbine at any moment, Representatives in The output power of the photovoltaic generator set at the moment, Representatives in The charging and discharging power of the energy storage subsystem at each moment, For microgrid systems The power that interacts with the distribution network energy at all times, Representatives in The remaining power of the energy storage subsystem at all times; Represents the minimum output power of the wind turbine generator set; Represents the maximum output power of the wind turbine generator set; Represents the minimum output power of the photovoltaic generator set; Represents the maximum output power of the photovoltaic generator set; Represents the maximum value of battery charging / discharging power; Represents the maximum value of power sold / purchased by the microgrid to the distribution grid; Represents the maximum value of the battery capacity; Represents the minimum value of the battery capacity.

[0019] In a second aspect, the present invention provides a microgrid energy management device based on distributed economic model prediction, comprising: A subsystem model building module is used to build a simplified model of the wind power generation subsystem, a simplified model of the photovoltaic generation subsystem and a simplified model of the energy storage subsystem according to the working principles of the wind power generation subsystem, the photovoltaic generation subsystem and the energy storage subsystem; The energy management model building module is used to build the energy management model of the microgrid system according to the energy flow during the microgrid energy management, and determine the constraint conditions; The objective function design module is used to design an objective function including economic performance based on the balance between supply and demand; The optimal state solving module is used to solve the optimal working state of each subsystem in the microgrid 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 energy management model of the microgrid system, with the minimum objective function value as the goal and combined with the constraint conditions; The control module is used to control the working state of each subsystem in the microgrid to be in the optimal working state.

[0020] In a third aspect, the present invention provides an electronic device, comprising: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the microgrid energy management method based on economic model prediction as described in any one of the first aspects of the present invention.

[0021] In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the microgrid energy management method based on economic model prediction as described in any one of the first aspects of the present invention.

[0022] Compared with the prior art, the present invention has at least the following beneficial technical effects: (1) A microgrid is a flexible power supply system that integrates power generation, energy storage, load, and intelligent control. It can operate independently or in parallel with the main power grid and has high flexibility and reliability. Faced with the limitations of the stability and application scope of traditional microgrids, new energy microgrids have enhanced system stability and broadened application scenarios by integrating clean energy such as solar photovoltaics, wind power generation, and gas turbines. Energy management of new energy microgrids is the key to their efficient and stable operation, involving real-time, optimized scheduling of power generation, distribution, and storage to ensure efficient energy utilization and economic operation. This solution optimizes economic performance while maintaining system supply and demand balance by optimizing and coordinating the operation of various subsystems of the microgrid.

[0023] (2) The present invention predicts the amount of renewable energy power generation, the charging and discharging power of the energy storage subsystem, and the power of energy interaction between the microgrid system and the distribution network over a period of time, and optimizes the scheduling of power generation resources and energy storage management based on the prediction results, maximizes the use of renewable energy, reduces the phenomenon of wind and solar power abandonment, improves the utilization rate and economic benefits of renewable energy, supports microgrids to provide stable power supply under various conditions, and provides support for clean, efficient, and sustainable energy supply. With the development of new energy technologies, new energy microgrids will play a more critical role in the future energy field.

[0024] (3) The present invention involves multiple aspects such as the generation, distribution and storage of electricity. The objective function involves the sum of the costs of various energy sources, the cost of supply and demand imbalance, the fluctuating cost of energy interaction with the distribution network, and the fluctuating cost of battery use. By finding the optimal solution that meets the objective function, the optimal configuration of energy is achieved. When the load demand is large, wind power generation is given priority, followed by photovoltaic power generation. If wind power generation and photovoltaic power generation are insufficient, batteries are used to supplement them. If the load demand is still not met, electricity is purchased from the main grid. When the load demand is small, if the load demand is met, the excess power is considered to charge the battery and sell electricity to the main grid. The present invention ensures the dynamic balance of supply and demand, improves energy utilization efficiency, reduces energy waste, and ensures the continuity and reliability of power supply. With the continuous advancement of new energy technology and the improvement of intelligence level, new energy microgrids are expected to play a more important role in the future energy field and contribute to the realization of clean, efficient and sustainable energy supply. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 A flow chart of a microgrid energy management method based on distributed economic model predictive control provided by the present invention; Figure 2 It is a curve chart showing the changes in wind speed, light intensity and temperature in the area where the microgrid system is located within a day; Figure 3 It is a curve diagram of load demand and output power variation of renewable energy in a microgrid system within a day; Figure 4 It is a curve diagram of the change of the state of charge of the energy storage subsystem within one day; Figure 5 It is a curve chart of the charging and discharging power of the energy storage subsystem of the microgrid system within one day and the energy interaction power between the microgrid and the distribution network; Figure 6 It is a curve diagram of the change of power supply and load demand of the microgrid system within one day; Figure 7 A structural block diagram of a microgrid energy management system based on distributed economic model prediction provided by an embodiment of the present invention; Figure 8 A block diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0026] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] Microgrid 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 microgrid.

[0029] The present invention is further described in detail below in conjunction with the accompanying drawings: Example 1 Reference Figure 1 This embodiment provides a microgrid energy management method based on distributed economic model prediction, comprising the following steps: Step 1: According to the working principle of the microgrid, the working principles of the wind power generation subsystem, photovoltaic power generation subsystem and energy storage subsystem are analyzed respectively, and simplified models of the wind power generation system, photovoltaic power generation system and energy storage subsystem are established according to their dynamic characteristics.

[0030] The mathematical model of the wind turbine subsystem is as follows:

[0031] in, is the state vector of the wind electronic system, and its output power of the wind turbine generator set P w The relationship between them is: ; and They are the orthogonal current and DC current of the terminal current of the multi-stage permanent magnet synchronous wind turbine generator in the rotor reference frame; is the electrical angular velocity; is the synchronous motor resistance; is the magnetic flux connected to the stator winding; is the DC bus voltage; It is the control signal (switch control) of the wind 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 subsystem; is the pole pair number of the fan.

[0032] The mathematical model of the photovoltaic power generation system is as follows:

[0033] in, is the state vector of the photovoltaic power generation system, and its output power Is there a relationship between: ; is the terminal voltage of the PV array; The current injected into the DC bus for the photovoltaic power generation system; and They are respectively The capacitor and inductor of the converter; is the control signal (switch control); Represents the output current of the photovoltaic subsystem; Represents the number of PV panels connected in parallel in the PV array; Represents the number of photovoltaic cells connected in series in each parallel photovoltaic panel; represents the photocurrent under 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 electron charge constant; represents the Boltzmann constant; is the absolute temperature of the photovoltaic cell; for The bias coefficient of the junction; is the series resistance.

[0034] The mathematical model of the energy storage subsystem is as follows:

[0035] in, The terminal voltage of the battery and its charging and discharging power of the energy storage subsystem Is there a relationship between: ; is the output current of the battery; The current injected into the DC bus for the photovoltaic power generation system; is the current required by the load; is a voltage source; is the equivalent series resistance of the battery; is the equivalent capacitance of the battery.

[0036] Step 2: Based on the models of each subsystem established in step 1, the energy management model of the microgrid system is established by analyzing the energy flow during microgrid energy management, and the constraints are determined.

[0037] The energy management model expression of the microgrid system is:

[0038] in, for The remaining power of the energy storage subsystem at any moment, for The charging / discharging power of the energy storage subsystem at the moment, is the sampling interval, for The difference between power supply and demand at the moment requires is 0. Representatives in The output power of the wind turbine at any moment, Representatives in The output power of the photovoltaic generator set at the moment, Representatives in The charging and discharging power of the energy storage subsystem at each moment, For microgrid systems The power that interacts with the distribution network energy at all times, Representatives in The load demand at the moment. When When , the energy storage subsystem is in the discharge state; when When When the microgrid is powered on, it purchases electricity from the distribution grid.

[0039] The constraints are as follows:

[0040] in, Representatives The output power of the wind turbine at any moment, Representatives The output power of the photovoltaic generator set at the moment, Representatives The charging and discharging power of the energy storage subsystem at each moment, For microgrid systems The power that interacts with the distribution network energy at all times, Representatives The remaining power of the energy storage subsystem at that moment. When When , the energy storage subsystem is in the discharge state; when When When the microgrid is powered on, it purchases electricity from the distribution grid. Represents the minimum output power of the wind turbine generator set; Represents the maximum output power of the wind turbine generator set; Represents the minimum output power of the photovoltaic generator set; Represents the maximum output power of the photovoltaic generator set; Represents the maximum value of battery charging / discharging power; Represents the maximum value of power sold / purchased by the microgrid to the distribution grid; Represents the maximum value of the battery capacity; Represents the minimum value of the battery capacity.

[0041] Step 3: Based on the energy management model of the microgrid system established in step 2, and based on the economic model predictive control principle, an objective function including economic performance is designed on the basis of considering the balance between supply and demand. The objective function of each subsystem is:

[0042] in, Representing microgrid system The optimal solution at the time includes the wind power generation power, photovoltaic power generation power, energy storage battery charging and discharging power and grid interaction power at the time k. For the prediction time domain; in, Represents the sum of the costs of various energy sources, represents the cost function of the supply-demand imbalance, represents the fluctuating cost of energy interaction with the distribution grid, and These two measures can avoid the degradation of power quality and the impact on system stability caused by imbalance between supply and demand. Represents the fluctuating cost of battery use. The use of batteries can be optimized to extend the battery cycle life. The expansion formulas are as follows:

[0043] in, is the power generation cost coefficient of the wind power generation subsystem, is the power generation cost coefficient of the photovoltaic power generation system, is the marginal benefit / cost coefficient of battery discharge / charging, is the marginal cost / benefit coefficient of buying / selling electricity to the distribution network, is a constant coefficient, for The charging and discharging power of the energy storage subsystem at the moment minus The charging and discharging power of the energy storage subsystem at each moment, For microgrid systems The power that interacts with the distribution network at all times minus the power of the microgrid system The power that interacts with the distribution grid energy at all times.

[0044] Step 4: Combine the simplified models of each subsystem and the energy management model of the microgrid system, take the minimum value of the objective function as the goal, and solve the microgrid system in combination with the constraints. The best solution at any time ; Step 5: According to step 3 and the distributed model predictive control principle, load demand and optimal solution , control the power generation status of wind turbines, photovoltaic generators, the charging and discharging status of energy storage subsystems, and the state of microgrids buying / selling electricity from distribution networks. Under the condition of ensuring the stability of the microgrid system, the energy management of the microgrid is realized.

[0045] Simulation Example The microgrid system in this embodiment is simulated by the method of the present invention. The system parameters are shown in Table 1 and Table 2. The wind speed, light intensity and temperature in the area where the microgrid is located in one day are shown in Table 1 and Table 2. Figure 2 shown.

[0046] Table 1 Controller parameters in the embodiment

[0047] Table 2 Parameters in the constraints of the embodiment

[0048] Distributed control is the most widely used control method in the power field. It has the advantages of flexible control structure, small computational burden, strong model applicability, etc., and has unique advantages in solving the control problems of distributed power generation systems. Because predictive control has the ability to solve complex problems such as constraints, pure time delay, nonlinearity, and multiple inputs and multiple outputs, and has strong robustness. The goal of microgrid energy management is to achieve efficient use of energy while ensuring the economy and reliability of the system. Therefore, distributed economic model predictive control has broad application prospects in solving the problem of microgrid energy management.

[0049] In order to verify that the distributed economic model predictive control method can achieve the goal of microgrid energy management, the present invention conducted a simulation experiment. During the simulation process, the sampling time is set , prediction time domain .

[0050] Attached Figure 3 The output power control of wind turbines and photovoltaic generators is given based on the distributed economic model predictive control; Figure 4 The curve diagram of the charge state change of the energy storage subsystem within one day is given; Figure 5 The curve diagram of the charging and discharging power of the energy storage subsystem of the microgrid system within one day and the energy interaction power between the microgrid and the distribution network is given; Figure 6 Based on the distributed economic model predictive control, the power supply and load demand curve of the microgrid system in one day is given. The analysis shows that under the condition of system load and renewable energy fluctuations, the energy management of the system under the distributed economic model predictive control can meet the load demand. Figure 6 It can be seen that the use of distributed economic model predictive control can reduce energy fluctuations, improve power supply quality, and achieve peak shaving and valley filling while meeting the actual operating constraints of each unit. Therefore, the distributed model predictive control algorithm can achieve rapid adjustment of system frequency stability while meeting various system constraints, while greatly reducing the computational burden, thus proving the applicability and superiority of the distributed economic model predictive control algorithm in solving the control problem of microgrid energy management.

[0051] The following is a system embodiment of the present invention, which can be used to implement the method embodiment of the present invention. For details not disclosed in the device embodiment, please refer to the method embodiment of the present invention.

[0052] Example 2 See also Figure 7 This embodiment provides a microgrid energy management system based on distributed economic model prediction, including: A subsystem model building module is used to build a simplified model of the wind power generation subsystem, a simplified model of the photovoltaic generation subsystem and a simplified model of the energy storage subsystem according to the working principles of the wind power generation subsystem, the photovoltaic generation subsystem and the energy storage subsystem; The energy management model building module is used to build the energy management model of the microgrid system according to the energy flow during the microgrid energy management, and determine the constraint conditions; The objective function design module is used to design an objective function including economic performance based on the balance between supply and demand; The optimal state solving module is used to solve the optimal working state of each subsystem in the microgrid 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 energy management model of the microgrid system, with the minimum objective function value as the goal and combined with the constraint conditions; The control module is used to control the working state of each subsystem in the microgrid to be in the optimal working state.

[0053] All relevant contents of each step involved in the aforementioned embodiment of the microgrid energy management method based on distributed economic model prediction can be referred to the functional description of the functional modules corresponding to the microgrid energy management system based on distributed economic model prediction in the embodiment of the present invention, and will not be repeated here.

[0054] Example 3 Reference Figure 8 , This embodiment provides an electronic device, which includes a processor and a memory, and the processor and the memory are connected through a bus; the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or 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, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions in a computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of a microgrid energy management method based on distributed economic model prediction. The bus can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 8 The fact that only one line is used in the diagram does not mean that there is only one bus or only one type of bus.

[0055] Example 4 This embodiment provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in an electronic device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in electronic devices and, of course, extended storage media supported by electronic devices. The computer-readable storage medium provides a storage space that stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by a processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the microgrid energy management method based on distributed economic model prediction in the above embodiment.

[0056] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0057] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0058] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1A function specified in one or more boxes.

[0059] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0060] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, without contradiction.

[0061] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A microgrid energy management method based on economic model prediction, characterized in that: include: According to the working principles of the wind power generation subsystem, the photovoltaic generation subsystem and the energy storage subsystem, a simplified model of the wind power generation subsystem, a simplified model of the photovoltaic generation subsystem and a simplified model of the energy storage subsystem are established; According to the energy flow during microgrid energy management, the energy management model of the microgrid system is established and the constraint conditions are determined; Based on the balance between supply and demand, design an objective function that includes economic performance; Based on the simplified model of wind power generation subsystem, photovoltaic power generation subsystem, energy storage subsystem and microgrid system energy management model, the optimal working state of each subsystem in the microgrid is solved with the minimum objective function value combined with the constraints. Control the working state of each subsystem in the microgrid to be in the optimal working state.

2. The microgrid energy management method based on economic model prediction according to claim 1 is characterized in that: The simplified model of the wind power subsystem is: in, is the state vector of the wind electronic system; and They are the orthogonal current and DC current of the terminal current of the multi-stage permanent magnet synchronous wind turbine generator in the rotor reference frame; is the electrical angular velocity; is the synchronous motor resistance; is the magnetic flux connected to the stator winding; is the DC bus voltage; It is the control signal of the wind 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 subsystem; is the pole pair number of the fan.

3. The microgrid energy management method based on economic model prediction according to claim 1 is characterized in that: The simplified model of the photovoltaic power generation system is: in, is the state vector of the photovoltaic power generation system; is the terminal voltage of the PV array; The current injected into the DC bus for the photovoltaic power generation system; and They are respectively The capacitor and inductor of the converter; is the DC bus voltage; is the control signal; Represents the output current of the photovoltaic subsystem; Represents the number of PV panels connected in parallel in the PV array; Represents the number of photovoltaic cells connected in series in each parallel photovoltaic panel; represents the photocurrent under 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 electron charge constant; represents the Boltzmann constant; is the absolute temperature of the photovoltaic cell; for The bias coefficient of the junction; is the series resistance.

4. The microgrid energy management method based on economic model prediction according to claim 1 is characterized in that: The simplified model of the energy storage subsystem is: in, Battery terminal voltage; is the output current of the battery, and They are the orthogonal current and DC current of the terminal current of the multi-stage permanent magnet synchronous wind turbine generator in the rotor reference frame; The current injected into the DC bus for the photovoltaic power generation system; is the current required by the load; is the DC bus voltage; It is the control signal of the wind electronic system; is a voltage source; is the equivalent series resistance of the battery; is the equivalent capacitance of the battery.

5. The microgrid energy management method based on economic model prediction according to claim 1 is characterized in that: The energy management model expression of the microgrid system is: in, for The remaining power of the energy storage subsystem at any moment, for The remaining power of the energy storage subsystem at any moment, for The charging / discharging power of the energy storage subsystem at the moment, is the sampling interval, for The difference between electricity supply and demand at any given moment, Representatives in The output power of the wind turbine at any moment, Representatives in The output power of the photovoltaic generator set at the moment, Representatives in The charging and discharging power of the energy storage subsystem at each moment, For microgrid systems The power that interacts with the distribution network energy at all times, Representatives in Load demand at any time.

6. The microgrid energy management method based on economic model prediction according to claim 1, characterized in that: The objective function is: in, Representing microgrids The best solution at any time, For the prediction time domain, Represents the sum of the costs of various energy sources, represents the cost function of the supply-demand imbalance, represents the fluctuating cost of energy interaction with the distribution grid, Represents the cost of the battery; is the power generation cost coefficient of the wind power generation subsystem, is the power generation cost coefficient of the photovoltaic power generation system, is the marginal benefit / cost coefficient of battery discharge / charging, is the marginal cost / benefit coefficient of buying / selling electricity to the distribution network, is a constant coefficient, Representatives in The output power of the wind turbine at any moment, Representatives in The output power of the photovoltaic generator set at the moment, Representatives in The charging and discharging power of the energy storage subsystem at each moment, For microgrid systems The power that interacts with the distribution network energy at all times, for The charging and discharging power of the energy storage subsystem at the moment minus The charging and discharging power of the energy storage subsystem at each moment, For microgrid systems The power that interacts with the distribution network at all times minus the power of the microgrid system The power that interacts with the distribution grid energy at all times.

7. The microgrid energy management method based on economic model prediction according to claim 1, characterized in that: The constraints are: in, Representatives in The output power of the wind turbine at any moment, Representatives in The output power of the photovoltaic generator set at the moment, Representatives in The charging and discharging power of the energy storage subsystem at each moment, For microgrid systems The power that interacts with the distribution network energy at all times, Representatives in The remaining power of the energy storage subsystem at all times; Represents the minimum output power of the wind turbine generator set; Represents the maximum output power of the wind turbine generator set; Represents the minimum output power of the photovoltaic generator set; Represents the maximum output power of the photovoltaic generator set; Represents the maximum value of battery charging / discharging power; Represents the maximum value of power sold / purchased by the microgrid to the distribution grid; Represents the maximum value of the battery capacity; Represents the minimum value of the battery capacity.

8. A microgrid energy management system based on distributed economic model prediction, characterized in that: include: A subsystem model building module is used to build a simplified model of the wind power generation subsystem, a simplified model of the photovoltaic generation subsystem and a simplified model of the energy storage subsystem according to the working principles of the wind power generation subsystem, the photovoltaic generation subsystem and the energy storage subsystem; The energy management model building module is used to build the energy management model of the microgrid system according to the energy flow during the microgrid energy management, and determine the constraint conditions; The objective function design module is used to design an objective function including economic performance based on the balance between supply and demand; The optimal state solving module is used to solve the optimal working state of each subsystem in the microgrid 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 energy management model of the microgrid system, with the minimum objective function value as the goal and combined with the constraint conditions; The control module is used to control the working state of each subsystem in the microgrid to be in the optimal working state.

9. An electronic device, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed 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 microgrid energy management method based on economic model prediction as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the microgrid energy management method based on economic model prediction according to any one of claims 1 to 7 is implemented.

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