Energy management method and distributed energy system

Abstract

The invention provides an energy management method and a distributed energy system, and the energy management method comprises the steps: obtaining target prediction data in a current energy scheduling period, the target prediction data comprises demand power prediction data of a household load, and based on the target prediction data, scheduling loss minimization is taken as a target; a target energy distribution strategy of the distributed energy system is determined, a power grid, a power battery and an energy storage battery are controlled to perform energy distribution based on the target energy distribution strategy, the scheduling loss comprises load power supply deviation loss, and the load power supply deviation loss is determined based on power supply power deviation of each household load in the current energy scheduling period; therefore, in the process of supplying power to the household loads through the distributed energy system, the power supply power deviation of each household load can be effectively reduced, and the power supply reliability of each household load can be effectively ensured while the energy utilization efficiency of the distributed energy system is optimized.

Description

Technical Field

[0001] The present application relates to the field of energy management, and particularly to an energy management method and a distributed energy system. Background Art

[0002] With the rapid development of renewable energy and the popularization of electric vehicles, how to efficiently integrate distributed energy resources and achieve the coordinated operation of multi-terminal devices has become a key issue in improving energy utilization efficiency and reducing electricity costs.

[0003] Currently, the energy management strategy for distributed energy systems is relatively single, and it is difficult to optimize the energy utilization efficiency of distributed energy systems while ensuring the power supply reliability for each household load. Summary of the Invention

[0004] To solve the above technical problems, the present application provides an energy management method and a distributed energy system to solve the problem in the prior art that it is difficult to optimize the energy utilization efficiency of distributed energy systems while ensuring the power supply reliability for each household load.

[0005] To achieve the above technical objectives, the embodiments of the present application provide the following technical solutions:

[0006] In a first aspect, an embodiment of the present specification provides an energy management method applied to a distributed energy system for supplying power to household loads. The distributed energy system includes a power grid, a power battery in a vehicle, and a storage battery in a household. The method includes:

[0007] Obtain target prediction data within the current energy scheduling period, where the target prediction data includes predicted demand power data of the household loads;

[0008] Based on the target prediction data, with the goal of minimizing scheduling losses, determine the target energy allocation strategy of the distributed energy system. The scheduling losses include load power supply deviation losses, and the load power supply deviation losses are determined based on the power supply power deviations of each household load within the current energy scheduling period;

[0009] Based on the target energy allocation strategy, control the power grid, the power battery, and the storage battery to perform energy allocation.

[0010] In an embodiment, the determination process of the load power supply deviation losses includes:

[0011] Based on the power supply deviation and power supply priority of each of the household loads within the current energy scheduling period, respectively determine the power supply deviation losses of each of the household loads; wherein, the power supply deviation loss of the household load is positively correlated with the power supply deviation of the household load, and, the power supply deviation loss of the household load is positively correlated with the power supply priority of the household load;

[0012] Based on the sum of the power supply deviation losses of each of the household loads, determine the load power supply deviation loss.

[0013] In one implementation, the target prediction data further includes the electricity price prediction data of the power grid, and, the scheduling loss further includes the electricity cost and / or the life attenuation amount of the target battery, and the target battery includes the energy storage battery and / or the power battery;

[0014] Wherein, the electricity cost is determined based on the electricity price prediction data of the power grid within the current energy scheduling period;

[0015] The life attenuation amount of the target battery is determined based on the change amount of the state of charge of the target battery in each charge and discharge cycle within the current energy scheduling period.

[0016] In one implementation, the method for determining the life attenuation amount of the target battery includes:

[0017] Based on the change amount of the state of charge of the target battery in each charge and discharge cycle, respectively determine the life attenuation amount of the target battery in each charge and discharge cycle;

[0018] Based on the sum of the life attenuation amounts of the target battery in each charge and discharge cycle, determine the life attenuation amount of the target battery within the current energy scheduling period.

[0019] In one implementation, the target prediction data further includes the electricity price prediction data of the power grid;

[0020] Based on the target prediction data, with the goal of minimizing the scheduling loss, determining the target energy allocation strategy of the distributed energy system includes:

[0021] Based on the target prediction data and target constraint conditions, with the goal of minimizing the scheduling loss, determine the target energy allocation strategy of the distributed energy system, and the target constraint conditions include the charging threshold of the energy storage battery, and the charging threshold of the energy storage battery is determined based on the electricity price prediction data of the power grid within the current energy scheduling period.

[0022] In one implementation, the electricity price prediction data of the power grid includes the electricity price prediction values at each moment in the current energy scheduling period;

[0023] The charging threshold of the energy storage battery is negatively correlated with the predicted electricity price value of the power grid.

[0024] In one implementation, the target constraint condition further includes the discharge threshold of the power battery, and the discharge threshold of the power battery is determined based on the health state of the power battery.

[0025] In one implementation, the discharge threshold of the power battery is negatively correlated with the health state of the power battery.

[0026] In one implementation, it further includes:

[0027] In the process of controlling the power grid, the power battery, and the energy storage battery to perform energy distribution based on the target energy distribution strategy, if the power grid fails and the sum of the load power supply limits of the power battery and the energy storage battery is less than the sum of the current demand powers of each household load, then based on the power supply priorities of each household load, a target load is determined from each household load, and the power battery and / or the energy storage battery are controlled to supply power to the target load.

[0028] In a second aspect, an embodiment of the present specification provides a distributed energy system for supplying power to household loads. The distributed energy system includes an energy management module, a power grid, a power battery in a vehicle, and an energy storage battery in a household; the energy management module is configured to execute the energy management method as described in any one of the above.

[0029] In a third aspect, an embodiment of the present specification provides a computer-readable storage medium, on which a computer program is stored. When the computer program is run by a processor, the energy management method as described in any one of the above is implemented.

[0030] In a fourth aspect, an embodiment of the present specification provides a computer program product or a computer program. The computer program product includes a computer program, and the computer program is stored in a computer-readable storage medium; a processor of the computer device reads the computer program from the computer-readable storage medium, and when the processor executes the computer program, the energy management method as described in any one of the above is implemented.

[0031] It can be seen from the above technical scheme that the embodiment of the present application provides an energy management method and a distributed energy system, wherein the distributed energy system is used to supply power to household loads, including a power grid, a power battery in a vehicle, and an energy storage battery in a household. The energy management scheme obtains target prediction data within the current energy scheduling cycle, wherein the target prediction data includes demand power prediction data of household loads, and based on the target prediction data, determines the target energy allocation strategy of the distributed energy system with the goal of minimizing scheduling losses, so as to control the power grid, the power battery, and the energy storage battery for energy distribution based on the target energy allocation strategy, wherein the scheduling loss includes a load power supply deviation loss, and the load power supply deviation loss is determined based on the power supply deviation of each household load within the current energy scheduling cycle. Thus, in the process of supplying power to household loads through the distributed energy system, the power supply deviation of each household load can be effectively reduced, thereby optimizing the energy utilization efficiency of the distributed energy system while effectively ensuring the power supply reliability of each household load. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0033] Figure 1 A schematic diagram of the structure of a distributed energy system provided for the implementation of this specification.

[0034] Figure 2 A schematic flow chart of an energy management method provided for an implementation of this specification. DETAILED DESCRIPTION

[0035] Unless otherwise defined, the technical terms or scientific terms used in the embodiments of this specification shall have the common meanings understood by persons with ordinary skills in the field to which this specification belongs. The words "first", "second" and similar words used in the embodiments of this specification do not indicate any order, quantity or importance, but are only used to avoid confusion of constituent elements.

[0036] Unless otherwise required by the context, throughout the specification, "a plurality of" means "at least two", and "comprising" is construed in an open, inclusive sense, i.e., "including, but not limited to". In the description of the specification, terms such as "one embodiment", "some embodiments", "exemplary embodiments", "examples", "specific examples", or "some examples" are intended to indicate that specific features, structures, materials, or characteristics related to the embodiment or example are included in at least one embodiment or example of this specification. The schematic representations of the above terms do not necessarily refer to the same embodiment or example.

[0037] Next, the technical solutions in the embodiments of this specification will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only some embodiments of this specification, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in this specification without creative efforts belong to the scope protected by this specification.

[0038] Overview

[0039] As described in the background art, with the rapid development of renewable energy and the popularization of electric vehicles, how to efficiently integrate distributed energy resources and achieve the coordinated operation of multi-terminal devices has become a key issue in improving energy utilization efficiency and reducing electricity costs.

[0040] As Figure 1 shown, the distributed energy system may include a power grid 101, a power battery 102 in a vehicle, and a storage battery 103 in a household. Among them, the power grid 101, the power battery 102, and the storage battery 103 can all supply power to the household load 104. In addition, the power grid 101 can also charge the power battery 102 and the storage battery 103, and the storage battery 103 can also charge the power battery 102. For example, the power battery 102 can supply power to the household load 104 through a bidirectional charging and discharging device 105, and the power grid 101 and the storage battery 103 can also charge the power battery 102 through the bidirectional charging and discharging device 105. The bidirectional charging and discharging device 105 can be a bidirectional charging pile or a bidirectional on-vehicle charger, and can be specifically set according to actual needs.

[0041] The distributed energy system may further include an energy management module 106. The energy management module 106 can be respectively connected to the power grid 101, the bidirectional charging and discharging device 105, and the storage battery 103 to control the energy distribution of the power grid 101, the power battery 102, and the storage battery 103.

[0042] In addition, the distributed energy system may further include a user terminal 107, which can be used to display the operating status of the distributed energy system to the user, and can also be used to input user scheduling requirements and user feedback data, etc. The user terminal 107 can be a mobile terminal such as a mobile phone or a tablet computer, or can also be a human-machine interaction unit in the vehicle's center console or the energy management module 106, etc.

[0043] Currently, the energy management strategy for the distributed energy system is relatively single, and it is difficult to optimize the energy utilization efficiency of the distributed energy system while ensuring the power supply reliability for each household load 104.

[0044] To solve the problem that it is difficult to optimize the energy utilization efficiency of the distributed energy system while ensuring the power supply reliability for each household load 104 in the traditional method, in the technical solution of this application, by obtaining the target prediction data in the current energy scheduling period, the target prediction data includes the predicted demand power data of the household load 104, and based on the target prediction data, with the goal of minimizing the scheduling loss, the target energy allocation strategy of the distributed energy system is determined, so as to control the power grid 101, the power battery 102 and the energy storage battery 103 to perform energy allocation based on the target energy allocation strategy. Among them, the scheduling loss includes the load power supply deviation loss, and the load power supply deviation loss is determined based on the power supply power deviation of each household load 104 in the current energy scheduling period. Thus, in the process of supplying power to the household load 104 through the distributed energy system, the power supply power deviation of each household load 104 can be effectively reduced, and further, while optimizing the energy utilization efficiency of the distributed energy system, the power supply reliability for each household load 104 can be effectively ensured.

[0045] Based on the above inventive concept, the energy management method provided by the embodiments of this specification will be described exemplarily below.

[0046] Exemplary Method

[0047] The embodiments of this specification provide an energy management method, which is applied to a distributed energy system for supplying power to a household load 104. The distributed energy system includes a power grid 101, a power battery 102 in a vehicle, and an energy storage battery 103 in a household; as Figure 2 shown, the method includes:

[0048] S201. Obtain the target prediction data in the current energy scheduling period, where the target prediction data includes the predicted demand power data of the household load 104.

[0049] Specifically, the energy management module 106 can control the power grid 101, the power battery 102, and the energy storage battery 103 in the distributed energy system to perform energy distribution according to a preset energy scheduling period. The current energy scheduling period is the energy scheduling period being executed at the current moment.

[0050] The target prediction data may include the predicted demand power data of each household load 104 within the current energy scheduling period. For example, for any household load 104, the predicted demand power data of this household load 104 may include the predicted values of the demand power of this household load 104 at each moment within the current energy scheduling period. In implementation, the predicted demand power data of this household load 104 can be determined based on the start and stop times and working modes of this household load 104 reserved by the user. Additionally, the current demand power of this household load 104, the current date, and the current moment, etc., can be input into a pre-trained demand power prediction model, and the change trend of the demand power of this household load 104 within the current energy scheduling period can be output through this demand power prediction model. The change trend of the demand power of this household load 104 within the current energy scheduling period includes the predicted values of the demand power of this household load 104 at each moment within the current energy scheduling period.

[0051] It can be understood that the target prediction data within the current energy scheduling period may further include the predicted charging demand data of the vehicle. The predicted charging demand data of the vehicle can be the predicted data of the required charging amount of the power battery 102 in the vehicle within the current energy scheduling period. In implementation, based on the vehicle's travel plan (such as destination, driving route, etc.), energy consumption mode, driver's driving habit data, etc., the predicted data of the required electric energy of the vehicle within the current energy scheduling period can be determined, and based on the predicted data of the required electric energy of the vehicle within the current energy scheduling period and the current remaining power of the power battery 102, the required charging amount of the power battery 102 can be determined. Thus, during the process of formulating the target energy distribution strategy, the power grid 101 and / or the energy storage battery 103 can be controlled to charge the power battery 102 according to the predicted charging demand data of the power battery 102.

[0052] In addition, the target prediction data in the current energy scheduling period may also include the predicted electricity price data of the power grid 101 and / or the predicted load data of the power grid 101. Among them, the predicted electricity price data of the power grid 101 may be the predicted data of the change trend of the power grid electricity price in the current energy scheduling period, that is, the predicted electricity price data of the power grid 101 may include the predicted electricity price values at each moment in the current energy scheduling period. In implementation, based on the current electricity price of the power grid 101, the current date, the current moment, etc., through the power grid electricity price prediction model, the change trend of the power grid electricity price in the current energy scheduling period can be predicted. The power grid electricity price prediction model may be a deep learning model or a function expression, etc., and can be specifically set according to actual requirements. Optionally, the power grid electricity price prediction model may be a function expression, specifically as shown in Equation (1):

[0053] E t+1 = m1E t + m2T t + m3S t (1)

[0054] In the formula, E t and E t+1 are the power grid electricity prices at time t and time t + 1 respectively; T t is the change rate of the power grid electricity price at time t, which can be determined according to historical electricity price data; S t is the periodic fluctuation value of the power grid electricity price at time t, such as the holiday fluctuation value, the seasonal fluctuation value, etc., which can be determined according to historical electricity price data; m1, m2, and m3 are all correction coefficients and are constants.

[0055] The predicted load data of the power grid 101 may be the predicted data of the change trend of the power grid load in the current energy scheduling period, that is, the predicted load data of the power grid 101 may include the predicted power grid load values at each moment in the current energy scheduling period. In implementation, based on the current load of the power grid 101, the current date, the current moment, etc., through the pre-trained power grid load prediction model, the change trend of the power grid load in the current energy scheduling period can be predicted. Among them, the current load of the power grid 101 may be the current power consumption load in the area where the home is located, and can be obtained by communicating with the power grid management system, etc. Thus, in the process of formulating the target energy allocation strategy, according to the predicted electricity price data of the power grid 101 and / or the predicted load data of the power grid 101, the power grid 101 can be controlled to charge the power battery 102, charge the energy storage battery 103, and supply power to the home load 104.

[0056] S202. Based on the target prediction data, with the goal of minimizing the scheduling loss, determine the target energy allocation strategy for the distributed energy system. The scheduling loss includes the load power supply deviation loss, which is determined based on the power supply power deviation of each household load 104 within the current energy scheduling period.

[0057] Specifically, the scheduling loss can be used to characterize the comprehensive cost generated during the process of controlling the power grid 101, the power battery 102, and the energy storage battery 103 for energy allocation by the formulated target energy allocation strategy. Among them, the scheduling loss can include the load power supply deviation loss, and the load power supply deviation loss can be used to characterize the overall power supply deviation of each household load 104, that is, it can characterize the reliability of the distributed energy system in supplying power to each household load 104.

[0058] In implementation, the load power supply deviation loss of the current energy scheduling period can be determined based on the power supply power deviation of each household load 104 within the current energy scheduling period. For any household load 104, the power supply power deviation of this household load 104 can be the absolute value of the difference between the power supply power of this household load 104 and the predicted value of the required power of this household load 104. It can be understood that if the absolute value of the difference between the power supply power of this household load 104 and the predicted value of the required power of this household load 104 is different at different moments within the current energy scheduling period, the maximum value among the multiple absolute values of the differences corresponding to this household load 104 can be used as the power supply power deviation of this household load 104. In addition, the average value or median value, etc., of the multiple absolute values of the differences corresponding to this household load 104 can also be used as the power supply power deviation of this household load 104.

[0059] Optionally, the power supply deviation loss of each household load 104 can be determined respectively based on the power supply power deviation of each household load 104 within the current energy scheduling period, and the load power supply deviation loss of the current energy scheduling period can be determined based on the power supply deviation losses of each household load 104. In addition, the load power supply deviation loss of the current energy scheduling period can also be determined based on the power supply power deviation of each household load 104 within the current energy scheduling period and the first target correspondence. The first target correspondence can include the correspondence between the power supply power deviation of each household load 104 and the load power supply deviation loss. This target correspondence can be a mapping table, a function expression, a machine learning model, etc., and can be specifically set according to actual needs.

[0060] It can be understood that the scheduling loss can also include electricity costs, equipment losses of the energy storage battery 103, etc., and can be specifically set according to actual needs.

[0061] Optionally, in the process of determining the target energy allocation strategy of the distributed energy system with the goal of minimizing the scheduling loss based on the target prediction data, a scheduling loss function can be constructed and solved by means of gradient descent or the like to obtain the target energy allocation strategy.

[0062] S203. Based on the target energy allocation strategy, control the power grid 101, the power battery 102, and the energy storage battery 103 to perform energy allocation.

[0063] Specifically, after determining the target energy allocation strategy within the current energy scheduling cycle, the power grid 101, the power battery 102, and the energy storage battery 103 can be controlled to perform energy allocation based on the target energy allocation strategy. For example, the power grid 101 can be controlled to charge the power battery 102, charge the energy storage battery 103, or supply power to the household load 104. The power battery 102 can also be controlled to supply power to the household load 104. The energy storage battery 103 can also be controlled to charge the power battery 102 or supply power to the household load 104, so as to effectively ensure the power supply reliability to each household load 104 while optimizing the energy utilization efficiency of the distributed energy system.

[0064] In a feasible implementation manner, the determination process of the load power supply deviation loss includes:

[0065] Based on the power supply power deviation and power supply priority of each household load 104 within the current energy scheduling cycle, determine the power supply deviation loss of each household load 104 respectively; wherein, the power supply deviation loss of the household load 104 is positively correlated with the power supply power deviation of the household load 104, and the power supply deviation loss of the household load 104 is positively correlated with the power supply priority of the household load 104;

[0066] Based on the sum of the power supply deviation losses of each household load 104, determine the load power supply deviation loss.

[0067] Specifically, the household loads 104 can be divided into multiple levels according to the sensitivity of each household load 104 to power outage, and the sensitivities of different levels of household loads 104 to power outage are different. For any household load 104, the higher the sensitivity of the household load 104 to power outage, the higher the power supply priority of the household load 104.

[0068] Among them, for any household load 104, the power supply deviation loss of the household load 104 can characterize the power supply reliability of the household load 104. The larger the power supply deviation loss of the household load 104, the lower the power supply reliability of the household load 104 is characterized, and the smaller the power supply deviation loss of the household load 104, the higher the power supply reliability of the household load 104 is characterized.

[0069] In implementation, based on the power supply power deviation and power supply priority of the household load 104 within the current energy scheduling period, the power supply deviation loss of the household load 104 can be determined. The power supply deviation loss of the household load 104 can be positively correlated with the power supply power deviation of the household load 104, that is, the greater the power supply power deviation of the household load 104, the greater the power supply deviation loss of the household load 104, indicating that the power supply reliability of the household load 104 is lower; conversely, the smaller the power supply power deviation of the household load 104, the smaller the power supply deviation loss of the household load 104, indicating that the power supply reliability of the household load 104 is higher.

[0070] Meanwhile, the power supply deviation loss of the household load 104 can be positively correlated with the power supply priority of the household load 104, that is, when the power supply power deviation of the household load 104 is fixed, the higher the power supply priority of the household load 104, the greater the power supply deviation loss of the household load 104, indicating that the impact of the power supply power deviation of the household load 104 on the power supply reliability of the household load 104 is greater; conversely, the lower the power supply priority of the household load 104, the smaller the power supply deviation loss of the household load 104, indicating that the impact of the power supply power deviation of the household load 104 on the power supply reliability of the household load 104 is smaller.

[0071] Optionally, based on the power supply power deviation of the household load 104, the power supply priority of the household load 104, and the power supply deviation loss determination model, the power supply deviation loss of the household load 104 can be determined. For example, the power supply deviation loss determination model can be as shown in Equation (2):

[0072]

[0073] In the formula, LP i is the power supply deviation loss of the i-th household load 104, 1 ≤ i ≤ I, and I is the total number of household loads 104; L i is the power supply priority of the i-th household load 104, L i > 0, the higher the power supply priority, the greater L i , the lower the power supply priority, the smaller L i ; k is an adjustment coefficient, and k is a constant greater than 0; P i is the power supply power of the i-th household load 104, P i0 is the predicted value of the demand power of the i-th household load 104, |P i -P i0 | is the power supply power deviation of the i-th household load 104.

[0074] After determining the power supply deviation losses of each household load 104, the power supply deviation losses of each household load 104 can be summed up, and the sum of the power supply deviation losses of each household load 104 is used as the load power supply deviation loss. Thus, in the process of controlling the power grid 101, the power battery 102, and the energy storage battery 103 to perform power distribution according to the target energy distribution strategy, while ensuring that important household loads 104 are powered first, the power supply power deviation of each household load 104 can be effectively reduced, thereby improving the power supply reliability of each household load 104.

[0075] In a feasible implementation manner, the target prediction data further includes the electricity price prediction data of the power grid 101, and the scheduling loss further includes the electricity cost and / or the life attenuation amount of the target battery, and the target battery includes the energy storage battery 103 and / or the power battery 102;

[0076] Wherein, the electricity cost is determined based on the electricity price prediction data of the power grid 101 within the current energy scheduling period;

[0077] The life attenuation amount of the target battery is determined based on the change amount of the state of charge of the target battery in each charge and discharge cycle within the current energy scheduling period.

[0078] Specifically, the target prediction data may further include the electricity price prediction data of the power grid 101 within the current energy scheduling period. At the same time, the scheduling loss may further include the electricity cost.

[0079] Wherein, the electricity cost may include the electricity consumption cost for obtaining electric energy from the power grid 101 within the current energy scheduling period, that is, the total cost of the electric energy output by the power grid 101 within the current energy scheduling period. In implementation, based on the electric energy output by the power grid 101 in each time period and the electricity price prediction value of the power grid 101 in each time period within the current energy scheduling period, the electricity consumption cost for obtaining electric energy from the power grid 101 in each time period can be determined respectively, and the sum of the electricity consumption costs for obtaining electric energy from the power grid 101 in each time period is used as the electricity consumption cost for obtaining electric energy from the power grid 101 within the current energy scheduling period.

[0080] It can be understood that the electricity cost can also include the energy loss cost, which can be determined based on the energy loss of the energy storage battery 103 during the current energy scheduling period. In implementation, the energy storage battery 103 can be charged through the power grid 101, so as to supply power to the household load 104 or charge the power battery 102 through the energy storage battery 103 during peak electricity price periods or power grid 101 failures, etc., to reduce the electricity cost and meet the power demand of the household load 104 and the power battery 102. However, there is a certain amount of energy loss during the transfer of electric energy through the energy storage battery 103. In implementation, the energy loss cost can be determined according to the energy loss amount of the energy storage battery 103 during operation and / or non-operation and the power grid electricity price. For example, when the power grid electricity price is a fixed value, the product of the energy loss amount during the current energy scheduling period and the power grid electricity price can be used as the energy loss cost. When the power grid electricity price is a peak-valley electricity price, the energy loss cost of each time period can be determined respectively according to the electricity price prediction data of the power grid 101 during the current energy scheduling period and the energy loss amount of each time period during the current energy scheduling period, and the sum of the energy loss costs of each time period can be used as the energy loss cost of the distributed energy system during the current energy scheduling period. Thus, in the process of formulating the target energy allocation strategy, by comprehensively considering the energy loss cost and the electricity cost of obtaining electric energy from the power grid 101, the effectiveness of the formulation result of the target energy allocation strategy can be effectively ensured. At the same time, during the process of controlling the power grid 101, the power battery 102 and the energy storage battery 103 to perform energy allocation according to the target energy allocation strategy, the electricity cost can be minimized to the greatest extent.

[0081] Optionally, the energy loss of the energy storage battery 103 can include at least one of the electrochemical loss, energy conversion and transmission loss, and self-discharge loss of the energy storage battery 103 during the current energy scheduling period.

[0082] Among them, the electrochemical loss of the energy storage battery 103 during operation refers to the energy loss caused by internal resistance, incomplete reversibility of electrochemical reactions, etc. during the charging and discharging processes of the energy storage battery 103. For example, during the charging and discharging of a lithium-ion battery, the internal electrolyte conducts ions to generate resistance, causing part of the electrical energy to be converted into heat and lost. In implementation, the electrochemical loss per unit time of the energy storage battery 103 under different operating conditions can be pre-configured. For example, based on the test data or historical operation data of the energy storage battery 103, the difference between the input power during the charging process and the output power during the discharging process of the energy storage battery 103 can be determined, and the electrochemical loss of the energy storage battery 103 per unit time can be determined based on this difference. Additionally, the equivalent circuit model of the energy storage battery 103 can be used to simulate the charging and discharging processes of the energy storage battery 103, and the electrochemical loss of the energy storage battery 103 per unit time can be predicted based on the simulation data. Thus, during the process of formulating the target energy allocation strategy, the operating conditions of the energy storage battery 103 can be determined based on the charge-discharge depth, charge-discharge power, and ambient temperature of the energy storage battery 103, and the electrochemical loss of the energy storage battery 103 during each time period within the current energy scheduling cycle can be determined based on the electrochemical loss per unit time of the energy storage battery 103 under the corresponding operating conditions.

[0083] The energy conversion and transmission loss of the energy storage battery 103 during operation can include the energy loss caused by the conversion of electrical energy and the transmission of energy during the charging and discharging processes of the energy storage battery 103. For example, the energy loss generated by electrical energy conversion components (such as inverters or transformers) and connecting lines during the charging and discharging processes of the energy storage battery 103. For example, when electrical energy conversion components such as inverters convert electrical energy, energy loss is generated due to the on-resistance of switching components, electromagnetic conversion efficiency, etc. Connecting lines such as cables have energy loss during the transmission of electrical energy due to the existence of resistance. In implementation, the energy loss of the connecting line per unit time can be determined based on the preset resistance of the connecting line and the charge-discharge mode of the energy storage battery 103 (such as constant current mode or constant voltage mode), and the energy transmission loss of the connecting line during each time period within the current energy scheduling cycle can be determined based on the energy loss of the connecting line per unit time. Additionally, the energy loss of the electrical energy conversion component per unit time can be determined based on the operating power and electrical energy conversion efficiency of the electrical energy conversion component, and the energy conversion loss of the electrical energy conversion component during each time period within the current energy scheduling cycle can be determined based on the energy loss of the electrical energy conversion component per unit time.

[0084] The self-discharge loss of the energy storage battery 103 during non-operation can include the energy loss caused by the self-discharge of the energy storage battery 103 due to internal chemical reactions, impurities, etc. In implementation, the self-discharge loss per unit time of the energy storage battery 103 under different environmental conditions (such as environmental temperature and humidity) can be pre-configured. For example, the self-discharge loss per unit time of the energy storage battery 103 can be determined based on the test data or historical detection data of the energy storage battery 103. Thus, in the process of formulating the target energy allocation strategy, the self-discharge loss per unit time of the energy storage battery 103 can be determined based on the predicted data of the environmental conditions, and the self-discharge loss amount of the energy storage battery 103 in each time period within the current energy scheduling cycle can be determined according to the self-discharge loss per unit time of the energy storage battery 103.

[0085] It can be understood that for any time period within the current energy scheduling cycle, the electrochemical loss amount, energy transmission loss amount, energy conversion loss amount, and self-discharge loss amount of the energy storage battery 103 in that time period can be summed up, and the summation result can be used as the energy loss amount in that time period, so as to comprehensively and accurately determine the energy loss cost of the energy storage battery 103, and further improve the accuracy of the formulated target energy allocation strategy.

[0086] In addition, the scheduling loss can also include the life attenuation amount of the target battery. The target battery can include the energy storage battery 103, can also include the power battery 102, or can include both the energy storage battery 103 and the power battery 102 at the same time. It can be understood that in the case where the target battery includes both the energy storage battery 103 and the power battery 102 at the same time, the life attenuation amount of the target battery can be the sum of the life attenuation amount of the energy storage battery 103 and the life attenuation amount of the power battery 102.

[0087] For any charge-discharge cycle of the target battery within the current energy scheduling cycle, the change amount of the state of charge of the target battery in that charge-discharge cycle can be the difference between the state of charge of the target battery at the start time of that charge-discharge cycle and the state of charge of the target battery at the end time of that charge-discharge cycle.

[0088] In implementation, the life attenuation amount of the target battery is determined based on the change amount of the state of charge of the target battery in each charge-discharge cycle within the current energy scheduling cycle. For example, based on the change amount of the state of charge of the target battery in each charge-discharge cycle, the life attenuation amount of the target battery in each charge-discharge cycle can be determined respectively, and according to the life attenuation amount of the target battery in each charge-discharge cycle, the life attenuation amount of the target battery within the current energy scheduling cycle can be determined, so as to quickly and accurately determine the life attenuation amount of the target battery within the current energy scheduling cycle.

[0089] Optionally, the load power supply deviation loss, electricity cost, and life attenuation of the target battery within the current energy scheduling period can be weighted and summed to obtain the scheduling loss within the current energy scheduling period. Taking the minimum scheduling loss within the current energy scheduling period as the objective, the energy allocation strategy is optimized to obtain the target energy allocation strategy. Thus, in the process of controlling the power grid 101, power battery 102, and energy storage battery 103 to allocate energy according to the target energy allocation strategy, the power supply reliability of each household load 104 can be ensured while effectively reducing the electricity cost and the life attenuation of the energy storage battery 103 and the power battery 102. Among them, the determination method of the scheduling loss within the current energy scheduling period can be as shown in Equation (3):

[0090] L = α * C + β * LB + γ * LP (3)

[0091] In the formula, L is the scheduling loss within the current energy scheduling period; C is the electricity cost within the current energy scheduling period; LB is the life attenuation of the target battery within the current energy scheduling period; LP is the load power supply deviation loss within the current energy scheduling period; α, β, and γ are the weight coefficients of the electricity cost, the life attenuation of the target battery, and the load power supply deviation loss, respectively.

[0092] In a feasible implementation manner, the determination method of the life attenuation of the target battery includes:

[0093] Based on the change in the state of charge of the target battery in each charge-discharge cycle, the life attenuation of the target battery in each charge-discharge cycle is determined respectively;

[0094] Based on the sum of the life attenuations of the target battery in each charge-discharge cycle, the life attenuation of the target battery within the current energy scheduling period is determined.

[0095] Specifically, for any charge-discharge cycle of the target battery within the current energy scheduling period, the life attenuation of the target battery in this charge-discharge cycle can represent the attenuation of the life of the target battery after this charge-discharge cycle.

[0096] In implementation, based on the change in the state of charge of the target battery in this charge-discharge cycle, the life attenuation of the target battery in this charge-discharge cycle can be determined. For example, based on the change in the state of charge of the target battery in this charge-discharge cycle and the attenuation determination model, the life attenuation of the target battery in this charge-discharge cycle can be determined. For example, the attenuation determination model can be as shown in Equation (4):

[0097] LB j =(SOC start,j -SOC end,j ) 2(4)

[0098] where LB j is the amount of life attenuation of the target battery in the j-th charge-discharge cycle, 1 ≤ j ≤ J, and J is the number of charge-discharge cycles performed by the target battery within the current energy scheduling period; SOC start,j is the state of charge of the target battery at the start time of the j-th charge-discharge cycle, SOC end,j is the state of charge of the target battery at the end time of the j-th charge-discharge cycle, (SOC start,j - SOC end,j ) is the change in the state of charge of the target battery in the j-th charge-discharge cycle.

[0099] After determining the amount of life attenuation of the target battery in each charge-discharge cycle within the current energy scheduling period, the amounts of life attenuation of the target battery in each charge-discharge cycle within the current energy scheduling period can be summed, and the summation result is used as the amount of life attenuation of the target battery within the current energy scheduling period, so that the amount of life attenuation of the target battery within the current energy scheduling period can be determined quickly and accurately. Thus, in the process of controlling the power grid 101, the power battery 102, and the energy storage battery 103 to perform energy distribution according to the target energy distribution strategy, the amount of life attenuation of the power battery 102 and the energy storage battery 103 can be effectively reduced.

[0100] In a feasible implementation manner, the target prediction data further includes the electricity price prediction data of the power grid 101;

[0101] Determining the target energy distribution strategy of the distributed energy system based on the target prediction data with the goal of minimizing the scheduling loss includes:

[0102] Based on the target prediction data and the target constraint conditions, with the goal of minimizing the scheduling loss, determining the target energy distribution strategy of the distributed energy system, where the target constraint conditions include the charging threshold of the energy storage battery 103, and the charging threshold of the energy storage battery 103 is determined based on the electricity price prediction data of the power grid 101 within the current energy scheduling period.

[0103] Specifically, the target prediction data may further include the electricity price prediction data of the power grid 101, and the electricity price prediction data of the power grid 101 may include the predicted electricity price values at each moment within the current energy scheduling period. In implementation, the target energy distribution strategy can be determined based on the target prediction data and the target constraint conditions with the goal of minimizing the scheduling loss.

[0104] Among them, the target constraint condition may include the charging threshold of the energy storage battery 103. The charging threshold of the energy storage battery 103 is the upper limit of the state of charge during the charging process of the energy storage battery 103, that is, the highest power that the energy storage battery 103 is allowed to reach during the charging process. In implementation, the charging threshold of the energy storage battery 103 can be determined based on the electricity price prediction data of the power grid 101 within the current energy scheduling cycle. Optionally, for any moment within the current energy scheduling cycle, the charging threshold of the energy storage battery 103 at that moment can be determined based on the predicted electricity price value of the power grid 101 at that moment. For example, the charging threshold of the energy storage battery 103 at that moment can be determined based on the predicted electricity price value of the power grid 101 at that moment and the preset corresponding relationship between the electricity price of the power grid and the charging threshold of the energy storage battery 103; in addition, the charging threshold of the energy storage battery 103 at that moment can also be determined based on the normalized electricity price at that moment and the preset corresponding relationship between the normalized electricity price and the charging threshold of the energy storage battery 103. The normalized electricity price at that moment can be the ratio of the predicted electricity price value of the power grid 101 at that moment to the highest electricity price of the power grid 101 within the current energy scheduling cycle, and the highest electricity price of the power grid 101 within the current energy scheduling cycle can be determined based on the electricity price prediction data of the power grid 101 within the current energy scheduling cycle.

[0105] Therefore, through the method of the embodiment of the present application, the charging threshold of the energy storage battery 103 can be dynamically updated according to the electricity price prediction data of the power grid 101, and further, in the process of formulating the target energy allocation strategy with the charging threshold of the energy storage battery 103 as the constraint condition, the electricity cost corresponding to the target energy allocation strategy can be further reduced.

[0106] In a feasible implementation manner, the electricity price prediction data of the power grid 101 includes the predicted electricity price values at each moment in the current energy scheduling cycle;

[0107] The charging threshold of the energy storage battery 103 is negatively correlated with the predicted electricity price value of the power grid 101.

[0108] Specifically, for any moment within the current energy scheduling cycle, the charging threshold of the energy storage battery 103 can be negatively correlated with the predicted electricity price value of the power grid 101, that is, the higher the predicted electricity price value of the power grid 101, the lower the charging threshold of the energy storage battery 103, and vice versa, the lower the predicted electricity price value of the power grid 101, the higher the charging threshold of the energy storage battery 103. Thus, when the electricity price is at the valley period, the energy storage battery 103 can be fully utilized to store electric energy, further reducing the electricity cost.

[0109] In a feasible implementation manner, the target constraint condition further includes the discharge threshold of the power battery 102, and the discharge threshold of the power battery 102 is determined based on the health state of the power battery 102.

[0110] Specifically, the target constraint condition may further include the discharge threshold of the power battery 102. The discharge threshold of the power battery 102 is the lower limit of the state of charge during the discharge process of the power battery 102, that is, the minimum value of the power that needs to be reserved during the discharge process of the power battery 102. In implementation, the discharge threshold of the power battery 102 may be determined based on the health state of the power battery 102.

[0111] In implementation, the health state of the power battery 102 may be determined based on the total number of charge-discharge cycles of the power battery 102. For example, for any moment within the current energy scheduling period, based on the number of complete charge-discharge cycles before this moment, and the charge-discharge depth of the charge-discharge cycle being executed at this moment, the health state SOH of the power battery 102 at this moment may be determined. Specifically, it may be as shown in Equation (5):

[0112] SOH = SOH0·e -d(N+ΔN)·T (5)

[0113] In the formula, SOH0 is the initial health state of the power battery 102; d is the aging rate of the power battery 102, which is a constant; N is the number of complete charge-discharge cycles of the power battery 102; △N is the charge-discharge depth of the charge-discharge cycle being executed by the power battery 102; T is the environmental temperature correction coefficient, which may be determined based on the environmental temperature prediction data within the current energy scheduling period. For example, T may be the ratio of the temperature prediction value at this moment to the reference temperature. When T > 1, high temperature will accelerate the aging of the power battery 102, affecting the cycle life of the power battery 102, the self-discharge rate of the power battery 102, and the safety of the power battery 102.

[0114] Among them, for any moment in the current energy scheduling period, the discharge threshold of the power battery 102 at this moment may be determined based on the health state of the power battery 102 at this moment. For example, based on the health state of the power battery 102 at this moment and the preset correspondence between the health state of the power battery 102 and the discharge threshold, the discharge threshold of the power battery 102 at this moment may be determined.

[0115] Thus, through the method of the embodiment of the present application, the discharge threshold of the power battery 102 can be dynamically updated according to the health state of the power battery 102. Furthermore, in the process of using the discharge threshold of the power battery 102 as a constraint condition to formulate the target energy allocation strategy, the life attenuation of the power battery 102 can be further reduced.

[0116] In a feasible implementation manner, the discharge threshold of the power battery 102 is negatively correlated with the health state of the power battery 102.

[0117] Specifically, the discharge threshold of the power battery 102 can be negatively correlated with the state of health of the power battery 102. The lower the state of health of the power battery 102, the higher the discharge threshold of the power battery 102, that is, the more power needs to be reserved during the discharge process of the power battery 102. Conversely, the higher the state of health of the power battery 102, the lower the discharge threshold of the power battery 102, that is, the less power needs to be reserved during the discharge process of the power battery 102, thereby further reducing the life attenuation of the power battery 102.

[0118] Optionally, the target constraint conditions may include the stability constraints of each energy device in the distributed energy system. Each energy device in the distributed energy system may be the power grid 101, the power battery 102, and the energy storage battery 103. For any energy device, the stability constraint of the energy device may include the state switching frequency constraint and / or the load rate constraint of the energy device. The load rate of the energy device may be the ratio of the operating power of the energy device to the rated capacity of the energy device (such as the charging power and the discharging power). In addition, the target constraint conditions may further include the charge and discharge power limits of the power battery 102, the charging threshold of the power battery 102, the cycle life of the power battery 102, the charge and discharge power limits of the energy storage battery 103, the discharge threshold of the energy storage battery 103, the cycle life of the energy storage battery 103, the load limit of the power grid 101, and the user demand constraint, etc. The user demand constraint such as the travel plan input by the user, the charging power preference input by the user, the charging time interval input by the user, etc. Thus, while ensuring the safe and reliable operation of the distributed energy system, the energy scheduling requirements of the user can be effectively met.

[0119] In a feasible implementation manner, it further includes:

[0120] During the process of controlling the power grid 101, the power battery 102, and the energy storage battery 103 to perform energy distribution based on the target energy distribution strategy, if the power grid 101 fails and the sum of the load power supply limits of the power battery 102 and the energy storage battery 103 is less than the sum of the current demand powers of each household load 104, then based on the power supply priority of each household load 104, a target load is determined from each household load 104, and the power battery 102 and / or the energy storage battery 103 is controlled to supply power to the target load.

[0121] Specifically, during the process of controlling the power grid 101, the power battery 102, and the energy storage battery 103 to perform energy distribution based on the target energy distribution strategy, if the power grid 101 fails, that is, the power grid 101 cannot supply electric energy to the power battery 102, the energy storage battery 103, and the household load 104, then the power battery 102 and / or the energy storage battery 103 can be used to supply power to the household load 104.

[0122] The load power supply limit of the power battery 102 can be the upper limit value of the power used by the power battery 102 to supply power to the household load 104. In implementation, based on the maximum power consumption of the power battery 102 for supplying power to the household load 104 and the target power supply duration, the load power supply limit of the power battery 102 can be determined. The maximum power consumption of the power battery 102 for supplying power to the household load 104 can be the difference between the current remaining power of the power battery 102 and the discharge threshold of the power battery 102, or can also be the difference between the current remaining power of the power battery 102 and the vehicle power demand. The vehicle power demand can be the minimum power that needs to be reserved in the power battery 102 according to the vehicle's travel plan. For example, when the current remaining power of the energy storage battery 103 is less than or equal to the discharge threshold of the energy storage battery 103, that is, the energy storage battery 103 cannot supply power to the household load 104, the load power supply limit of the power battery 102 can be determined according to the difference between the current remaining power of the power battery 102 and the discharge threshold of the power battery 102, so as to preferentially meet the power supply requirements of the more important household load 104. When the current remaining power of the energy storage battery 103 is greater than the discharge threshold of the energy storage battery 103, that is, the energy storage battery 103 can normally supply power to the household load 104, the load power supply limit of the power battery 102 can be determined according to the difference between the current remaining power of the power battery 102 and the vehicle power demand, so as to meet the power supply requirements of the household load 104 to the greatest extent while ensuring the vehicle's operation requirements. It can be understood that when the difference between the current remaining power of the power battery 102 and the discharge threshold of the power battery 102 is less than 0, or the difference between the current remaining power of the power battery 102 and the vehicle power demand is less than 0, the load power supply limit of the power battery 102 is 0.

[0123] The load power supply limit of the energy storage battery 103 can be the upper limit value of the power used by the energy storage battery 103 to supply power to the load. In implementation, when the current remaining power of the energy storage battery 103 is greater than the discharge threshold of the energy storage battery 103, based on the maximum output power of the energy storage battery 103 and the target power supply duration, the load power supply limit of the energy storage battery 103 can be determined. The maximum output power of the energy storage battery 103 can be the difference between the current remaining power of the energy storage battery 103 and the discharge threshold of the energy storage battery 103. Among them, when the current remaining power of the energy storage battery 103 is less than the discharge threshold of the energy storage battery 103, the maximum output power of the energy storage battery 103 is 0, that is, the load power supply limit of the energy storage battery 103 is 0.

[0124] In implementation, the sum of the load power supply limit value of the power battery 102 and the load power supply limit value of the energy storage battery 103 can be used as the target power supply limit value, and the target power supply limit value is compared with the sum of the current demand powers of each household load 104. If the target power supply limit value is greater than or equal to the sum of the current demand powers of each household load 104, it indicates that the power supply requirements of all household loads 104 can be met. At this time, the power battery 102 and / or the energy storage battery 103 can be controlled to supply power to each household load 104 according to the current demand power of each household load 104.

[0125] In addition, if the target power supply limit value is less than the sum of the current demand powers of each household load 104, it indicates that the power supply requirements of all household loads 104 cannot be met. At this time, based on the power supply priorities of each household load 104, in the order from high to low of the power supply priorities, the target load among each household load 104 can be determined, and the power battery 102 and / or the energy storage battery 103 can be controlled to supply power to the target load. Among them, if the target power supply limit value is less than or equal to the current demand power of the household load 104 with the highest power supply priority, the household load 104 with the highest power supply priority is used as the target load; if the target power supply limit value is greater than the current demand power of the household load 104 with the highest power supply priority, the magnitude relationship between the target power supply limit value and the sum of the current demand powers of the first g-level household loads 104 can be iteratively determined in the order from high to low of the priorities, 1 < g ≤ G, where G is the total number of levels of the power supply priorities of the household loads 104, until the target power supply limit value is less than or equal to the sum of the current demand powers of the first g-level household loads 104. At this time, if the target power supply limit value is less than the sum of the current demand powers of the first g-level household loads 104, the first (g - 1)-level household loads 104 are used as the target load, and if the target power supply limit value is equal to the sum of the current demand powers of the first g-level household loads 104, the first g-level household loads 104 are used as the target load.

[0126] Among them, in the process of controlling the power battery 102 and / or the energy storage battery 103 to supply power to the household load 104, the energy storage battery 103 can be preferentially used to supply power to the household load 104. When the load power supply limit value of the energy storage battery 103 is 0, the power battery 102 is used to supply power to the household load 104.

[0127] Thus, through the method of the embodiment of the present application, in the case of a power grid 101 failure, the power supply requirements of the household load 104 can be maximally met, and the power supply requirements of the relatively important household load 104 can be preferentially guaranteed, thereby reducing the risk of power outage of the important household load 104.

[0128] Optionally, during the process of controlling the energy distribution among the power grid 101, the power battery 102, and the energy storage battery 103, the operation data of the distributed energy system and / or the user feedback data may also be obtained, and the target energy distribution strategy may be updated according to the operation data of the distributed energy system and / or the user feedback data to meet the power supply requirements in different scenarios.

[0129] Exemplary system

[0130] In an exemplary embodiment of this specification, a distributed energy system is further provided. The distributed energy system is used to supply power to the household load 104. The distributed energy system includes an energy management module 106, a power grid 101, a power battery 102 in a vehicle, and an energy storage battery 103 in a household. The energy management module 106 is configured to execute the energy management method described in any of the above embodiments.

[0131] Exemplary Computer Program Product and Storage Medium

[0132] In addition to the above methods and devices, the energy management method provided in the embodiments of this specification may also be a computer program product, which includes computer program instructions. When the computer program instructions are run by a processor, the processor is caused to execute the steps in the energy management method according to various embodiments of this specification described in the "Exemplary Method" section above.

[0133] The computer program product may be written in any combination of one or more programming languages to write program code for performing the operations of the embodiments of this specification. The programming languages include object-oriented programming languages such as Java, C++, etc., and also include conventional procedural programming languages such as the "C" language or similar programming languages.

[0134] Furthermore, an embodiment of this specification also provides a computer-readable storage medium, on which a computer program is stored. The computer program is executed by a processor to perform the steps in the energy management method according to various embodiments of this specification described in the "Exemplary Method" section above.

[0135] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in this specification can include non-volatile and / or volatile memories. Non-volatile memories can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0136] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0137] The above-described embodiments only represent several implementation manners of this specification. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the solutions provided by the embodiments of this specification. It should be noted that for those of ordinary skill in the art, without departing from the concept of this specification, several modifications and improvements can still be made, and these all belong to the protection scope of this specification. Therefore, the protection scope of the patent of this specification should be subject to the appended claims.

Claims

1. An energy management method, characterized in that, Applied to a distributed energy system for powering household loads, the distributed energy system includes a power grid, a power battery in a vehicle, and an energy storage battery in a household; the method includes: Obtain target prediction data within the current energy scheduling period, where the target prediction data includes predicted demand power data of the household loads; Based on the target prediction data, with the goal of minimizing scheduling losses, determine the target energy allocation strategy of the distributed energy system, where the scheduling losses include load power supply deviation losses, and the load power supply deviation losses are determined based on the power supply power deviations of each household load within the current energy scheduling period; Based on the target energy allocation strategy, control the power grid, the power battery, and the energy storage battery to perform energy allocation.

2. The method according to claim 1, wherein The determination process of the load power supply deviation losses includes: Based on the power supply power deviations and power supply priorities of each household load within the current energy scheduling period, determine the power supply deviation losses of each household load respectively; where the power supply deviation loss of the household load is positively correlated with the power supply power deviation of the household load, and the power supply deviation loss of the household load is positively correlated with the power supply priority of the household load; Based on the sum of the power supply deviation losses of each household load, determine the load power supply deviation losses.

3. The method according to claim 1, characterized in that, The target prediction data further includes predicted electricity price data of the power grid, and the scheduling losses further include electricity consumption costs and / or the amount of life attenuation of the target battery, where the target battery includes the energy storage battery and / or the power battery; Wherein, the electricity consumption cost is determined based on the predicted electricity price data of the power grid within the current energy scheduling period; The amount of life attenuation of the target battery is determined based on the change in the state of charge of the target battery in each charge-discharge cycle within the current energy scheduling period.

4. The method according to claim 3, wherein The method for determining the amount of life attenuation of the target battery includes: Based on the change in the state of charge of the target battery in each charge-discharge cycle, determine the amount of life attenuation of the target battery in each charge-discharge cycle respectively; Based on the sum of the amounts of life attenuation of the target battery in each charge-discharge cycle, determine the amount of life attenuation of the target battery within the current energy scheduling period.

5. The method according to claim 1, wherein The target prediction data further includes predicted electricity price data of the power grid; The determining, based on the target prediction data, with the goal of minimizing scheduling losses, the target energy allocation strategy of the distributed energy system includes: Based on the target prediction data and target constraint conditions, with the goal of minimizing the scheduling losses, determine the target energy allocation strategy of the distributed energy system, where the target constraint conditions include the charging threshold of the energy storage battery, and the charging threshold of the energy storage battery is determined based on the predicted electricity price data of the power grid within the current energy scheduling period.

6. The method according to claim 5, wherein The predicted electricity price data of the power grid includes the predicted electricity price values at each moment within the current energy scheduling period; The charging threshold of the energy storage battery is negatively correlated with the predicted electricity price value of the power grid.

7. The method according to claim 5, wherein The target constraint condition further includes a discharge threshold of the power battery, and the discharge threshold of the power battery is determined based on the state of health of the power battery.

8. The method according to claim 7, wherein The discharge threshold of the power battery is negatively correlated with the state of health of the power battery.

9. The method according to any one of claims 1 to 8, characterized in that It further includes: During the process of controlling the power grid, the power battery and the energy storage battery to perform energy distribution based on the target energy distribution strategy, if the power grid fails and the sum of the load power limit values of the power battery and the energy storage battery is less than the sum of the current demand powers of each household load, then based on the power supply priority of each household load, a target load is determined from each household load, and the power battery and / or the energy storage battery are controlled to supply power to the target load.

10. A distributed energy system, characterized in that, The distributed energy system is used to supply power to household loads, and the distributed energy system includes an energy management module, a power grid, a power battery in a vehicle, and an energy storage battery in a household; the energy management module is configured to execute the energy management method according to any one of claims 1 to 9.

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