Hybrid power supply system energy optimization scheduling method

Abstract

The invention discloses an energy optimization scheduling method for a hybrid power supply system, and the method comprises the steps: analyzing the constraint factors of a flexible load, setting the constraint conditions of a linear integer inequality, building a flexible load working model, building a load response model through the influence factors, such as real-time electricity price, and deducing a general modeling method according to the special conditions. A similar method for searching key parameter influence factors is provided for general data modeling, so that basic operation steps are derived, principal components can be quickly searched for a large amount of data, and a control target is analyzed by controlling the principal components; through organic combination of a genetic algorithm (GA), cyclic automatic optimization planning, piecewise function judgment and other methods, a demand response model which most meets target requirements is designed, and basic theoretical knowledge is combined with actual modeling application.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid dispatching, and in particular to an energy optimization dispatching method for a hybrid power supply system. Background Art

[0002] According to data, in recent years, in order to solve the problem of distributed power access, coordinate the contradiction between large power grids and distributed power sources, and fully tap the value and benefits that distributed power generation brings to power grids and users, the concept of microgrid has emerged. Microgrid technology is regarded as an effective way to solve the current energy utilization problem and has become a hot topic of research at home and abroad. A microgrid is a controllable system composed of multiple distributed power sources, energy storage devices, local cold and heat loads, and monitoring and protection devices. It has flexible operation modes and dispatchability. It can be connected to the large power grid through a single interface, or it can be stably and autonomously operated in an isolated island with energy storage units. The capacity of a microgrid is generally from several kilowatts to several megawatts. It is usually connected to a low-voltage or medium- and high-voltage distribution network and is an important part of the smart grid.

[0003] Microgrids can increase the reliability of the power system, which is conducive to improving the disaster resistance of the power system, thereby reducing the power outage losses to a certain extent, and under certain conditions, they can also provide power for the black start of the large power grid; microgrids can improve the quality of electric energy, which is conducive to improving the service level of power grid companies; microgrids can realize peak load shaving and valley filling, reducing or delaying the investment in new transmission projects and large-scale power plant systems, thereby reducing electricity prices and bringing economic benefits to users to the greatest extent. Therefore, in-depth research on microgrids has important theoretical and engineering value.

[0004] Chinese patent document CN107671414A discloses a "microgrid optimization scheduling method". It includes establishing a microgrid model; constructing a first-stage optimization scheduling model based on the power purchase and sales status of the microgrid to the upstream power grid; using the microgrid operating cost and the amount of new energy discarded as the objective function, and using energy storage, new energy output, demand response and energy interaction between the microgrid and the power grid as constraints to construct a second-stage optimization scheduling model; solving the model to obtain the final microgrid optimization scheduling result. The present invention constructs a microgrid model composed of wind power, photovoltaics, energy storage, transferable loads and interruptible loads, and then establishes a fuzzy set based on KL divergence. Finally, a min-max-min three-layer two-stage distributed robust optimization model is proposed, and an algorithm based on C&CG is used to solve it. The above technical solution lacks scheduling optimization for hybrid power supply systems, and it is difficult to meet the current needs of microgrid optimization scheduling. Summary of the invention

[0005] The present invention mainly solves the technical problem that the original technical solution lacks scheduling optimization for hybrid power supply systems and is difficult to meet the current microgrid optimization scheduling needs. It provides an energy optimization scheduling method for a hybrid power supply system, analyzes the constraints of flexible loads, sets linear integer inequality constraints, establishes a flexible load working model, and establishes a load response model based on influencing factors such as real-time electricity prices. The general modeling method is derived from this special case, and a similar method for finding key parameter influencing factors is provided for general data modeling. The basic operating steps are derived from this, and the main components can be quickly found when facing a large amount of data. The control target is analyzed by controlling the main components. By organically combining methods such as genetic algorithms (GA), cyclic automatic optimization planning, and piecewise function judgment, the demand response model that best meets the target requirements is designed, and basic theoretical knowledge is combined with practical modeling applications.

[0006] The above technical problem of the present invention is mainly solved by the following technical solution: The present invention comprises the following steps: S1 sets the time interval to collect power grid information; S2 sets the rigid load requirements at different times; S3 establishes a supply and demand balance scheduling model based on energy storage equipment; S4 introduces flexible load and battery status as controllable factors for constraints.

[0007] Preferably, the step S1 of collecting grid information includes collecting the status of distributed energy and real-time electricity prices, and initializing the charge state of the battery.

[0008] Preferably, the step S2 sets the demands of the rigid load at different times, giving priority to satisfying the electricity demand of the rigid load. When the supply is lower than the demand, the large power grid also directly supplies power to the rigid load or the battery directly discharges the rigid load.

[0009] As a preferred method, a mathematical model is established, and the power consumption time range of equipment a [α a , β a ], power consumption time d a , power demand in, For device a in h a Working status at the moment, P a is the rated power of the device.

[0010] Preferably, the step S3 establishes a supply and demand balance scheduling model based on energy storage equipment, specifically including: in, Indicates the current power of the distributed power source. Indicates the current load power. Indicates the current rated power of the distributed power source. Indicates all fixed loads at the current moment. Indicates the current activity load, SOC h Indicates the current state of charge.

[0011] As a preference, SOC h ∈[SOC min , SOC max ] Among them, lithium batteries are used as energy storage devices to limit their state of charge [SOC min , SOC max ] charge and discharge, and use the variable Record the charge and discharge status of lithium batteries. When it is greater than 0, it indicates that the lithium battery is in charging state. When it is less than 0, it indicates that the lithium battery is in a charging state, and its absolute value indicates the current charge and discharge capacity of the lithium battery.

[0012] Preferably, the step S4 establishes a working model for the flexible load: Among them, h a is the time point at which the device may work. The time when the equipment starts working. is the time when the equipment ends working, a is the equipment number, m is the number of non-interruptible loads, n is the number of interruptible loads, For device a in h a Working status at the moment, P a is the rated power of the equipment, d a is the working time, E a is the total power requirement of the device.

[0013] As a preference, when When , it indicates that the device is in deactivated state.

[0014] Preferably, a load control program is set in the smart meter, and the real-time electricity price is used as the regulating lever for the system energy scheduling. The power consumption period of the household flexible load is reasonably planned, the opening and closing of the equipment is automatically set, and the load response at the next moment is allocated according to the control variables at this moment.

[0015] Preferably, when establishing the supply and demand balance scheduling model in step S3, the electricity price is used as a factor to improve social benefits, and the product of the real-time electricity price and the difference between supply and demand is minimized, thereby improving the overall economic benefits while improving energy utilization.

[0016] The beneficial effects of the present invention are: 1. By considering adding small-scale DG power generation to household users, DG energy supply is given priority. First, the rigid load electricity demand is met unconditionally. Secondly, for flexible loads, under the condition of meeting their electricity constraints, the DG power supply and real-time electricity price within the load working period are analyzed, and their electricity working period is reasonably planned to obtain the load control optimization model.

[0017] 2. Consider the application of energy storage devices in smart home energy systems, establish appropriate energy scheduling strategies through bidirectional energy storage devices such as batteries, automatically control the influencing factors of each key parameter, dynamically adjust the charging and discharging time, and formulate automatic adjustment strategies in advance.

[0018] 3. By comparing the experimental schemes with and without load control, energy storage control and other strategies, the role of the energy optimization scheduling algorithm proposed in this paper in improving DG utilization rate, power supply and demand matching, and overall electricity purchase costs is verified. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flow chart for determining a fitness function according to the present invention.

[0020] Figure 2 It is a flow chart of success determination of a fitness function of the present invention.

[0021] Figure 3 It is a flow chart of a fitness function determination failure of the present invention.

[0022] Figure 4 It is a flow chart of the present invention for determining according to the real-time electricity price change.

[0023] Figure 5 This is a model diagram of a household hybrid power supply system in a real-time electricity price environment of the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the technical solution of the present invention is further described in detail below through embodiments and in combination with the accompanying drawings. It should be understood that the specific implementation method described here is only an optimal embodiment of the present invention, which is only used to explain the present invention and does not limit the scope of protection of the present invention. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0025] Before discussing the exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flow charts. Although the flow charts describe the operations (or steps) as sequential processes, many of the operations (or steps) therein can be implemented in parallel, concurrently, or simultaneously. In addition, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but can also have additional steps not included in the drawings; the process can correspond to a method, function, procedure, subroutine, subprogram, etc.

[0026] First, the current status of research on energy optimization and dispatching of microgrids. The research fields of microgrids mainly include the following eight aspects: research on the interaction mechanism between microgrids and large power grids under high penetration, optimization planning of new distribution systems including microgrids, research on the mechanism of distributed energy storage on the safe and stable operation of microgrids, microgrid grid-connected control machine, coordinated control of multiple distributed power sources in microgrids, protection principles and technologies of microgrids and distribution systems including microgrids, comprehensive simulation of microgrid systems with distributed power generation functions, power quality analysis and control of microgrids and distribution systems including microgrids, and economic operation theories and energy optimization management methods of microgrids. Compared with traditional power systems, the ability of microgrids to withstand disturbances is relatively weak, especially with the access of distributed energy sources with strong randomness such as wind power generation and photovoltaic power generation. The safety of the system faces higher risks. Therefore, effective economic dispatch of microgrids is an important content that needs to be studied.

[0027] The operation of distributed energy (DG) such as small wind turbines and rooftop photovoltaics on the home user side provides energy support for the power demand of electrical equipment; the intermittent and volatile nature of distributed power generation encourages users to make full use of the power resources provided by DG; the adjustability of flexible loads and the controllability of energy storage units provide feasibility for energy allocation and scheduling. However, since DG is easily affected by environmental factors such as wind speed and light intensity, its power supply is volatile, random and intermittent at different times. There are certain limitations in directly using the current power generation of DG to supply power to the load. Therefore, the comprehensive optimization of the allocation of DG capacity in the home hybrid power supply system has become the focus of demand response research. This part of the research work uses smart meters to achieve two-way information interaction between real-time electricity prices and load electricity consumption based on the day-ahead forecast of real-time electricity prices, analyzes the relationship between electricity load and electricity prices in home systems, establishes a load response model based on modeling optimization algorithms such as genetic algorithms, formulates a flexible load electricity consumption period adjustment strategy with real-time electricity prices as the adjustment lever, and adds energy storage devices such as lithium batteries on the user side. Through the coordination of energy storage equipment, an energy optimization scheduling mode that minimizes the total electricity price and maximizes the utility is constructed to achieve economical operation of the smart grid system.

[0028] The technical solution of the present invention is further specifically described below through embodiments and in conjunction with the accompanying drawings.

[0029] Embodiment: A hybrid power supply system energy optimization scheduling method of this embodiment, such as Figures 1 to 4 As shown, the following steps are included: S1 sets the time interval to collect grid information, including the state of distributed energy and real-time electricity prices, and initializes the state of charge of the battery.

[0030] S2 sets the demand of rigid load at different times. Set the demand of rigid load at different times to give priority to meeting the power demand of rigid load. When supply is lower than demand, the large power grid also directly supplies power to the rigid load or the battery directly discharges the rigid load.

[0031] Establish a mathematical model, the power consumption time range of equipment a [α a , β a ], power consumption time d a , power demand in, For device a in h a Working status at the moment, P a is the rated power of the device.

[0032] S3 establishes a supply and demand balance scheduling model based on energy storage equipment. Specifically includes: in, Indicates the current power of the distributed power source. Indicates the current load power. Indicates the current rated power of the distributed power source. Indicates all fixed loads at the current moment. Indicates the current activity load, SOC h Indicates the current state of charge.

[0033] SOC h ∈[SOC min , SOC max ] Among them, lithium batteries are used as energy storage devices to limit their state of charge [SOC min , SOC max ] charge and discharge, and use the variable Record the charge and discharge status of lithium batteries. When it is greater than 0, it indicates that the lithium battery is in charging state. When it is less than 0, it indicates that the lithium battery is in a charging state, and its absolute value indicates the current charge and discharge capacity of the lithium battery.

[0034] When establishing a supply and demand balance scheduling model, electricity price is used as a factor to improve social benefits. By minimizing the product of real-time electricity price and the difference between supply and demand, the overall economic benefits can be improved while improving energy utilization.

[0035] S4 introduces flexible load and battery status as controllable factors for constraints. A working model is established for the flexible load: Among them, h a is the time point at which the device may work. The time when the equipment starts working. is the time when the equipment ends working, a is the equipment number, m is the number of non-interruptible loads, n is the number of interruptible loads, For device a in h a Working status at the moment, P a is the rated power of the equipment, d a is the working time, E a is the total power requirement of the equipment. When , it indicates that the device is in deactivated state.

[0036] The operation of distributed energy (DG) such as small wind turbines and rooftop photovoltaics on the home user side provides energy support for the power demand of electrical equipment; the intermittent and volatile nature of distributed power generation encourages users to make full use of the power resources provided by DG; the adjustability of flexible loads and the controllability of energy storage units provide feasibility for energy allocation and scheduling. However, since DG is easily affected by environmental factors such as wind speed and light intensity, its power supply is volatile, random and intermittent at different times. There are certain limitations in directly using the current power generation of DG to supply power to the load. Therefore, the comprehensive optimization of the allocation of DG capacity in the home hybrid power supply system has become the focus of demand response research.

[0037] A simulation of a home hybrid power supply system under real-time electricity price environment Figure 5As shown in the figure, a load control program is set in the smart meter, and the real-time electricity price is used as the adjustment lever of the system energy scheduling. The electricity consumption period of the home flexible load is reasonably planned, the opening and closing of the equipment is automatically set, and the load response at the next moment is allocated according to the control variables at this moment. The home hybrid power supply system studied includes small wind turbines and solar power generation. Lithium batteries are used as energy storage devices. Their charging and discharging behaviors can be flexibly adjusted through appropriate charging and discharging strategies. They can be charged during the remaining DG time or the period with low RTP, and discharged during the period when DG is short or RTP is high. The real-time electricity price used changes every half an hour, and is predicted by the electricity price and load information in the history. As shown in the figure, this is a model of the home hybrid power supply system under the real-time electricity price environment, in which DG, energy storage equipment, and power load can be connected to the energy controller through the home area network (HAN), and the working period of the power equipment and the charging and discharging behavior of the energy storage equipment can be controlled in real time through a specific energy scheduling algorithm.

[0038] Specifically, the theoretical significance of this part of the research is as follows: ① By analyzing the constraints of flexible loads, setting linear integer inequality constraints, and establishing a flexible load working model. ② Establishing a load response model based on influencing factors such as real-time electricity prices, and deriving general modeling methods from special cases, providing a similar method for finding key parameter influencing factors for general data modeling, and thus deriving basic operating steps, which can quickly find the main components when facing a large amount of data, and then analyze the control objectives by controlling the main components. ③ By organically combining methods such as genetic algorithms (GA), cyclic automatic optimization planning, and piecewise function judgment, the demand response model that best meets the target requirements is designed, combining basic theoretical knowledge with practical modeling applications.

[0039] At the same time, the application value of this part of the research can be reflected in: ① By considering adding small DG power generation to household users, giving priority to DG energy supply, first of all, the rigid load electricity demand is unconditionally met, and secondly, for flexible loads, under the condition of meeting their electricity constraints, the DG power supply and real-time electricity price are analyzed within the load working period, and their electricity working period is reasonably planned to obtain the load control optimization model. ② Considering the application of energy storage equipment in smart home energy systems, through bidirectional energy storage equipment such as batteries, a suitable energy scheduling strategy is established to automatically control the number of influencing factors of each key parameter, dynamically adjust the charging and discharging time, and formulate automatic adjustment strategies in advance. ③ By comparing the experimental schemes with or without load control, energy storage control and other strategies, the role of the energy optimization scheduling algorithm proposed in this paper in improving DG utilization rate, power supply and demand matching, and overall electricity purchase costs is verified.

[0040] When establishing the constraint model of energy storage equipment on the power grid, in order to simplify the complexity of the problem, only the battery is charged when the power grid is sufficient, and when it is insufficient, it is discharged to its own user load as a household uninterruptible power supply, while ignoring the feedback effect of the battery on the power grid. However, this does not mean that the battery can only be used as a single individual to provide charging and discharging services to a single user. Several batteries can be used as a small system to coordinate the power supply to the power load within the system.

[0041] In this study, the focus is on adjusting the power consumption period and power consumption of users' flexible loads based on the reference quantity of real-time electricity prices in an environment of two-way interaction of smart grid information. From the perspective of power supply, energy storage devices are a coordinated supplement to DG power supply. By dynamically replanning the DG power supply status, the power matching of self-made energy supply and equipment energy consumption is balanced to improve the power generation efficiency of household self-provided power supply; from the perspective of power consumption, user demand is a manageable resource, and electricity consumption is an economic choice. Planning the load power consumption period based on the restrictive relationship between real-time electricity prices, power supply, and power demand can improve the economic benefits of the home hybrid power supply system. In the above way, the social and economic benefits brought by the balance of supply and demand are retained, and the user's utility maximization and cost minimization are met. In addition, some improvements have been made to the existing energy scheduling methods in terms of modeling methods, analysis perspectives, and application strategies. The main innovations of this part of the research are as follows: A multi-objective genetic algorithm integer programming problem is established with the dual goals of reducing electricity costs and seeking a balance between electricity supply and demand. According to the different working characteristics of interruptible loads and non-interruptible loads, the crossover mutation strategy is executed in segments to speed up the calculation speed and simplify the complexity of the algorithm interaction, while ensuring that the supply and demand differences of the entire equipment group are minimized, thereby improving the accuracy and efficiency of the algorithm.

[0042] When establishing user utility maximization, the power consumption habits of specific equipment are taken into consideration, flexible load and battery status are used as controllable factors, and the constraints of total power consumption demand and latest power consumption time of different equipment are introduced. The charging and discharging characteristics of the bidirectional energy storage load are fully utilized to optimize the overall energy distribution.

[0043] When establishing a supply and demand balance scheduling model, electricity prices are considered as a factor to improve social benefits. By minimizing the product of real-time electricity prices and the difference between supply and demand, the overall economic benefits can be improved while improving energy utilization.

[0044] Three energy scheduling methods with different focuses: The first one: load control model based on DG. Users with their own power sources such as photovoltaic power generation can give priority to using their own power generation, without having to purchase electricity from the power grid and then pay for electricity. Due to the intermittent power generation and large fluctuations in energy supply of distributed power sources (DGs), excess power will cause energy waste, and insufficient power cannot meet load demand. Therefore, it is crucial to make full use of the power resources provided by DGs and establish a dynamic balance between power supply and user demand. Flexible load allows smart meters to independently choose power consumption time according to electricity prices in different time periods, control load consumption, and thus establish a load balancing mechanism. Depending on the different objects of action, there are different energy scheduling optimization goals: the user side focuses on saving electricity costs while meeting user needs, and the grid side seeks to reduce the peak-valley power difference.

[0045] The second type: RTP-based load dispatch model. By executing the DG-based load control model, DG energy can be used to the maximum extent without energy storage. However, when the overall power consumption is greater than the power supply, no matter how the flexible load power consumption period is arranged at some times, there will always be a situation where supply is lower than demand. In this case, the model can no longer continue to allocate the flexible load power consumption arrangement. However, since the home hybrid power supply system is connected to the large power grid, there are real-time changes in electricity price information. How to use RTP as an adjustment lever based on the real-time electricity price information of the power market to shift the working hours of part of the load to reduce the cost of purchasing electricity from the power grid is the key issue that needs to be solved here. It should be noted that the load dispatch strategy based on RTP does not simply migrate the load to the time when the electricity price is lower. Since the amount of electricity purchased at different times is a function related to time, and the real-time electricity price is related to time, the electricity demand is related to the real-time electricity price. Multiplying the real-time electricity price factor makes the entire target planning no longer a linear equation, nor is it just a single search for the overall balance of supply and demand. Instead, it uses price leverage to correct the supply and demand situation at low electricity prices while giving priority to meeting the supply and demand balance when the electricity price is higher. Giving priority to meeting the supply and demand matching at high electricity prices and using the excess electricity in other time periods to make up for the current demand can reduce the load demand for power supply from the power grid at high electricity prices, reduce the cost of electricity, and better reflect economic benefits. It can be seen that the load dispatch strategy based on RTP seeks the overall optimization of the product of the amount of electricity purchased and the electricity price.

[0046] The third type: energy optimization scheduling model based on energy storage equipment. With the goal of matching DG power generation, the load power consumption curve is adjusted through real-time electricity prices. On the basis of meeting the supply and demand balance as much as possible [i], the excess power is stored by energy storage devices such as batteries, and the insufficient power is discharged by lithium batteries for a short period of time. If it still cannot meet the user's power demand, it will purchase electricity from the smart grid at low electricity prices to form an energy scheduling plan. In this energy scheduling optimization process, electric vehicles are used as energy storage devices in the power grid system. The on-board energy storage device is used to exchange energy with the power grid. An optimization plan including energy storage factors is established to perform multi-objective dynamic planning scheduling of the microgrid. That is, after fully utilizing the power provided by DG power generation, a load migration strategy is adopted. Finally, a segmented judgment method is used to search for the optimal "DG-battery-load" energy optimization mode. Through the charging and discharging actions of the energy storage device, the DG power supply is dynamically adjusted to match the planned load power demand, thereby maximizing the DG utilization rate.

[0047] A person of ordinary skill in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium may include: a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, etc.

[0048] The specific embodiments described herein are merely examples of the spirit of the present invention. The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that technicians in the technical field to which the present invention belongs can make various modifications or supplements to the described specific embodiments or replace them in a similar manner, but will not deviate from the spirit of the present invention or exceed the scope defined in the attached claims. For ordinary technicians in this field, multiple deformations and improvements can also be made without departing from the concept of the present invention. Therefore, the scope of protection of the patent of the present invention shall be based on the attached claims.

Claims

1. A hybrid power supply system energy optimization scheduling method, characterized in that: The following steps are involved: S1 sets the time interval to collect power grid information; S2 sets the rigid load requirements at different times; S3 establishes a supply and demand balance scheduling model based on energy storage equipment; S4 introduces flexible load and battery status as controllable factors for constraints.

2. The method for optimizing energy scheduling of a hybrid power supply system according to claim 1, characterized in that: The step S1 collects grid information including collecting the status of distributed energy and real-time electricity prices, and initializes the charge status of the battery.

3. The method for optimizing energy scheduling of a hybrid power supply system according to claim 1, characterized in that: The step S2 sets the demand of the rigid load at different times, giving priority to meeting the power demand of the rigid load. When the supply is lower than the demand, the large power grid also directly supplies power to the rigid load or the battery directly discharges the rigid load.

4. A hybrid power supply system energy optimization scheduling method according to claim 1 or 2, characterized in that: When the supply and demand balance scheduling model is established in step S3, the electricity price is used as a factor to improve social benefits. By minimizing the product of the real-time electricity price and the difference between supply and demand, the overall economic benefits are improved while improving energy utilization.

5. The method for optimizing energy scheduling of a hybrid power supply system according to claim 1, characterized in that: Establish a mathematical model, the power consumption time range of equipment a [α a , β a ], power consumption time d a , power demand in, For device a in h a Working status at the moment, P a is the rated power of the device.

6. The method for optimizing energy scheduling of a hybrid power supply system according to claim 1, characterized in that: The step S3 establishes a supply and demand balance scheduling model based on energy storage equipment, specifically including: in, Indicates the current power of distributed power source. Indicates the current load power. Indicates the current rated power of the distributed power source. Indicates all fixed loads at the current moment. Indicates the current activity load, SOC h Indicates the current state of charge.

7. The method for optimizing energy scheduling of a hybrid power supply system according to claim 5, characterized in that: SOCIETY h ∈[SOC min , SOC max ] Among them, lithium batteries are used as energy storage devices to limit their state of charge [SOC min , SOC max ] charge and discharge, and use the variable Record the charge and discharge status of lithium batteries. When it is greater than 0, it indicates that the lithium battery is in charging state. When it is less than 0, it indicates that the lithium battery is in a charging state, and its absolute value indicates the current charge and discharge capacity of the lithium battery.

8. The method for optimizing energy scheduling of a hybrid power supply system according to claim 1, characterized in that: The step S4 establishes a working model for the flexible load: Among them, h a is the time point at which the device may work. The time when the equipment starts working. is the time when the equipment ends working, a is the equipment number, m is the number of non-interruptible loads, n is the number of interruptible loads, For device a in h a Working status at the moment, P a is the rated power of the equipment, d a is the working time, E a is the total power requirement of the device.

9. The method for optimizing energy scheduling of a hybrid power supply system according to claim 8, characterized in that: when When , it indicates that the device is in deactivated state.

10. A hybrid power supply system energy optimization scheduling method according to claim 1 or 8, characterized in that: Set up a load control program in the smart meter, use real-time electricity prices as the adjustment lever for system energy scheduling, reasonably plan the power consumption period of home flexible loads, automatically set the opening and closing of equipment, and allocate the load response at the next moment according to the control variables at this moment.

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