Energy scheduling method and distributed energy system

Abstract

The invention provides an energy scheduling method and a distributed energy system, and the method comprises the steps: obtaining target prediction data in a current energy scheduling period, and determining a target scheduling strategy of the distributed energy system based on the target prediction data and a target constraint condition of energy scheduling by taking the minimization of power consumption cost as a scheduling target; and controlling the photovoltaic power generation system, the power grid, the power battery and the energy storage battery to carry out energy distribution based on the target scheduling strategy, and the target constraint condition comprises the operation stability constraint of the distributed energy system, so that in the process of controlling the photovoltaic power generation system, the power grid, the power battery and the energy storage battery to carry out energy distribution according to the target scheduling strategy, the energy distribution efficiency is improved. The power utilization cost can be effectively reduced while safe and reliable operation of each terminal device in the distributed energy system can be ensured, so that the energy utilization efficiency of the distributed energy system can be effectively improved.

Description

Technical Field

[0001] This application relates to the field of energy scheduling, and in particular, to an energy scheduling 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 scheduling strategies for distributed energy systems are relatively single, and it is difficult to optimize the energy utilization efficiency of distributed energy systems while ensuring the safe and reliable operation of each terminal device in the distributed energy system. Summary of the Invention

[0004] To solve the above technical problems, this application provides an energy scheduling 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 the distributed energy system while ensuring the safe and reliable operation of each terminal device in the distributed energy system.

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

[0006] In a first aspect, an embodiment of this specification provides an energy scheduling method, which is applied to a distributed energy system. The distributed energy system includes a photovoltaic power generation system, 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. The target prediction data includes at least part of the data among the predicted power generation data of the photovoltaic power generation system, the predicted household load data, the predicted charging demand data of the vehicle, and the predicted power price and / or load data of the power grid.

[0008] Based on the target prediction data and the target constraint conditions of energy scheduling, with the goal of minimizing the electricity cost as the scheduling target, determine the target scheduling strategy of the distributed energy system. The target constraint conditions include the operation stability constraint of the distributed energy system.

[0009] Based on the target scheduling strategy, control the photovoltaic power generation system, the power grid, the power battery, and the storage battery to perform energy distribution.

[0010] In an embodiment, the operation stability constraint of the distributed energy system includes the stability constraint of the target device in the distributed energy system.

[0011] Among them, the stability constraint of the target device includes the state transition constraint and / or the load rate constraint of the target device. The state transition constraint is used to restrict the state transition frequency of the target device, and the load rate constraint is used to restrict the operating power of the target device.

[0012] In one implementation, the target constraint condition further includes a user constraint;

[0013] Among them, the user constraint includes the required charge amount and the required charge time window of the power battery determined based on the vehicle travel plan input by the user.

[0014] In one implementation, the electricity cost includes the energy loss cost of the distributed energy system;

[0015] Among them, the energy loss cost of the distributed energy system is determined based on the energy loss of the energy storage battery during the current energy scheduling period.

[0016] In one implementation, the energy loss of the energy storage battery includes at least one of the electrochemical loss, the energy conversion and transmission loss, and the self-discharge loss of the energy storage battery during the current energy scheduling period.

[0017] In one implementation, the electricity cost further includes the grid energy cost and / or the equipment loss cost of the energy storage battery;

[0018] Among them, the grid energy cost is determined based on the electricity cost of obtaining electric energy from the grid during the current energy scheduling period, and the revenue from the power generation and distribution system, the power battery, and the energy storage battery feeding back electric energy to the grid;

[0019] The equipment loss cost of the energy storage battery is determined based on the cycle attenuation cost and / or the fault maintenance cost of the energy storage battery during the current energy scheduling period.

[0020] In one implementation, the method for determining the household load prediction data includes:

[0021] Based on the environmental data during the current energy scheduling period, the operating duration of the household load during the current energy scheduling period, and the operating duration of the household load within a preset time window during the current energy scheduling period, determine the electricity load compensation amount of the household load;

[0022] Based on the electricity load compensation amount of the household load, compensate the preset electricity load of the household load to obtain the electricity load prediction data of the household load during the current energy scheduling period.

[0023] Determine the household load prediction data based on the sum of the electricity load prediction data of each of the household loads within the current energy scheduling cycle.

[0024] In one embodiment, it further includes:

[0025] During the process of controlling the energy distribution of the photovoltaic power generation system, the power grid, the power battery of the vehicle, and the energy storage battery, obtain the operating state of the distributed energy system and / or user feedback data;

[0026] Based on the operating state of the distributed energy system and / or the user feedback data, update the target scheduling strategy.

[0027] In one embodiment, it further includes:

[0028] Based on the household load prediction data within the current energy scheduling cycle, determine the fluctuation state of the household load, where the household load prediction data includes the change trend of the sum of the electricity load prediction data of each household load;

[0029] When the fluctuation state of the household load indicates that the household load meets the flattening condition, based on the electricity load prediction data of each household load and the power supply priority of each household load, determine the target load among each household load;

[0030] Generate the recommended data corresponding to the target load, where the recommended data is used to remind the user to reduce the electricity load of the target load.

[0031] In a second aspect, an embodiment of this specification provides a distributed energy system, including an energy scheduling module, a photovoltaic power generation system, a power grid, a power battery in a vehicle, and an energy storage battery in a household;

[0032] The energy scheduling module is respectively connected to the photovoltaic power generation system, the power grid, the vehicle, and the energy storage battery, and is configured to execute the energy scheduling method as described in any one of the above.

[0033] In a third aspect, an embodiment of this 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 scheduling method as described in any one of the above is implemented.

[0034] In a fourth aspect, an embodiment of this 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 scheduling method as described in any one of the above is implemented.

[0035] As can be seen from the above technical solution, the embodiments of the present application provide an energy scheduling method and a distributed energy system. The distributed energy system includes a photovoltaic power generation system, a power grid, a power battery in a vehicle, and a storage battery in a household. The method obtains target prediction data within the current energy scheduling period. The target prediction data includes at least part of the power generation power prediction data of the photovoltaic power generation system, the household load prediction data, the charging demand prediction data of the vehicle, and the prediction data of the electricity price and / or load of the power grid. Based on the target prediction data and the target constraint conditions of energy scheduling, with the goal of minimizing the electricity cost, the target scheduling strategy of the distributed energy system is determined. Based on the target scheduling strategy, the photovoltaic power generation system, the power grid, the power battery, and the storage battery are controlled to perform energy distribution. Among them, the target constraint conditions include the operation stability constraint of the distributed energy system. Therefore, in the process of controlling the photovoltaic power generation system, the power grid, the power battery, and the storage battery to perform energy distribution according to the target scheduling strategy, it is possible to effectively reduce the electricity cost while ensuring the safe and reliable operation of each terminal device in the distributed energy system, and thus effectively improve the energy utilization efficiency of the distributed energy system. Description of the Drawings

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only the embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained according to the provided drawings.

[0037] Figure 1 It is a schematic structural diagram of a distributed energy system provided for the embodiments of this specification.

[0038] Figure 2 It is a schematic flowchart of an energy scheduling method provided for the embodiments of this specification. Detailed Embodiments

[0039] Unless otherwise defined, the technical terms or scientific terms used in the embodiments of this specification should have the ordinary meanings understood by those of ordinary skill in the art to which this specification belongs. The "first", "second" and similar terms used in the embodiments of this specification do not represent any order, quantity or importance, but are only used to avoid confusion of components.

[0040] 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 the present specification. The schematic representations of the above terms do not necessarily refer to the same embodiment or example.

[0041] Next, the technical solutions in the embodiments of the present specification will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present specification. Obviously, the described embodiments are only a part of the embodiments of the present specification, rather than all of the embodiments. Based on the embodiments in the present specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present specification.

[0042] Overview

[0043] 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.

[0044] Among them, as Figure 1As shown in the figure, a distributed energy system may include various energy devices such as a photovoltaic power generation system 101, a power grid 102, a power battery 103 in a vehicle, and an energy storage battery 104 in a household. The photovoltaic power generation system 101 may include a photovoltaic module 1011 and a power conversion module 1012. The photovoltaic module 1011 is used to convert solar energy into direct current electrical energy, and the power conversion module 1012 is used to convert the direct current electrical energy output by the photovoltaic module 1011 into alternating current electrical energy for storage in the energy storage battery 104 and / or for powering household loads 105. At the same time, electrical energy can also be fed back to the power grid 102. The power grid 102 can charge the power battery 103 and the energy storage battery 104, and can also power the household loads 105. The vehicle can be an electric vehicle or a hybrid electric vehicle. A power battery 103 and a bidirectional charging and discharging device 106 (such as a bidirectional on-board charger) can be provided in the vehicle. The power battery 103 in the vehicle can interact electrical energy with the power grid 102 and the energy storage battery 104 in the household through the bidirectional charging and discharging device 106. For example, the power battery 103 can be charged through the power grid 102 and / or the energy storage battery 104, or the electrical energy in the power battery 103 can be fed back to the power grid 102 or transmitted to the energy storage battery 104. In addition, the power battery 103 can also power the household loads 105 through the bidirectional charging and discharging device 106. The energy storage battery 104 in the household can be used to power the household loads 105 with the stored electrical energy and to charge the power battery 103, and can also feed back electrical energy to the power grid 102.

[0045] The distributed energy system may further include an energy scheduling module 107. The energy scheduling module 107 can be respectively connected to the power conversion module 1012 in the photovoltaic power generation system 101, the power grid 102, the bidirectional charging and discharging device 106 in the vehicle, and the energy storage battery 104 to perform energy scheduling on the photovoltaic power generation system 101, the power grid 102, the power battery 103, and the energy storage battery 104. Each energy device in the distributed energy system can perform data interaction with the energy scheduling module 107 through a communication network. The communication network can support Internet of Things protocols such as Wi-Fi, 5G, Zigbee, etc.

[0046] The distributed energy system may further include a user terminal 108. The user terminal 108 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 108 can be a mobile terminal such as a mobile phone or a tablet computer, and can also be a vehicle's center console or a human-machine interaction unit in the energy scheduling module 107, etc.

[0047] Currently, the energy scheduling 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 safe and reliable operation of each terminal device in the distributed energy system.

[0048] To solve the problem that it is difficult to optimize the energy utilization efficiency of a distributed energy system while ensuring the safe and reliable operation of each terminal device in the distributed energy system in the traditional method, in the technical solution of this application, an energy scheduling method is provided, and this energy scheduling method can be executed by the energy scheduling module 107 in the distributed energy system. Among them, this energy scheduling method obtains target prediction data within the current energy scheduling period, and the target prediction data includes at least partial data among the power generation power prediction data of the photovoltaic power generation system 101, the household load prediction data, the charging demand prediction data of the vehicle, and the prediction data of the electricity price and / or load of the power grid 102. And based on the target prediction data and the target constraint conditions of energy scheduling, with the goal of minimizing the electricity cost, the target scheduling strategy of the distributed energy system is determined, so as to control the energy distribution of the photovoltaic power generation system 101, the power grid 102, the power battery 103, and the energy storage battery 104 based on the target scheduling strategy. Among them, the target constraint conditions include the operation stability constraint of the distributed energy system. Therefore, in the process of controlling the energy distribution of the photovoltaic power generation system 101, the power grid 102, the power battery 103, and the energy storage battery 104 according to the target scheduling strategy, it is possible to effectively reduce the electricity cost while ensuring the safe and reliable operation of each terminal device in the distributed energy system, and thus effectively improve the energy utilization efficiency of the distributed energy system.

[0049] Based on the above inventive concept, the energy scheduling method provided by the embodiments of this specification will be described exemplarily below.

[0050] Exemplary method

[0051] The embodiments of this specification provide an energy scheduling method, which is applied to a distributed energy system, and the distributed energy system includes a photovoltaic power generation system 101, a power grid 102, a power battery 103 in a vehicle, and an energy storage battery 104 in a household; as Figure 2 shown, the method includes:

[0052] S201. Obtain target prediction data within the current energy scheduling period, where the target prediction data includes at least partial data among the power generation power prediction data of the photovoltaic power generation system 101, the household load prediction data, the charging demand prediction data of the vehicle, and the prediction data of the electricity price and / or load of the power grid 102.

[0053] Specifically, the energy scheduling module 107 can perform energy scheduling on the photovoltaic power generation system 101, the power grid 102, the power battery 103, and the energy storage battery 104 in the distributed energy system according to a preset energy scheduling period. The current energy scheduling period is the energy scheduling period currently being executed at the current moment.

[0054] The target prediction data may include at least some of the power generation prediction data of the photovoltaic power generation system 101, the household load prediction data, the charging demand prediction data of the vehicle, and the electricity price prediction data and / or load prediction data of the power grid 102.

[0055] Among them, the power generation prediction data of the photovoltaic power generation system 101 may include the prediction data of the change trend of the power generation power of the photovoltaic power generation system 101 within the current energy scheduling cycle. In implementation, the power generation prediction data of the photovoltaic power generation system 101 may be determined based on the environmental prediction data within the current energy scheduling cycle. The environmental prediction data within the current energy scheduling cycle may include light intensity prediction data, environmental temperature prediction data, etc. The environmental prediction data of the area where the photovoltaic power generation system 101 is located may be obtained by communicating with the weather prediction system. Optionally, for any moment within the current energy scheduling cycle, the power generation power of the photovoltaic power generation system 101 at this moment may be as shown in Equation (1):

[0056]

[0057] In the formula, P pv is the power generation power of the photovoltaic power generation system 101; η is the energy conversion efficiency of the photovoltaic module 1011; A is the area of the photovoltaic panels in the photovoltaic module 1011; I STC is the predicted value of the light intensity at this moment; γ is the temperature coefficient, which represents the change amount of the energy conversion efficiency of the photovoltaic module 1011 when the environmental temperature changes by 1°C; T is the predicted value of the environmental temperature.

[0058] The household load prediction data may include the change trend of the sum of the electricity load prediction data of each household load 105 within the current energy scheduling cycle. For example, for any household load 105, the change trend of the electricity load prediction data of this household load 105 within the current energy scheduling cycle may be determined based on the start and stop times of this household load 105 reserved by the user, environmental data, etc. The change trend of the electricity load prediction data of this household load 105 within the current energy scheduling cycle may also be predicted based on the current electricity load of this household load 105, the current date, the current time, etc., through a pre-trained household load prediction model, and may be specifically set according to actual needs.

[0059] The charging demand prediction data of the vehicle may be the prediction data of the required charging amount of the power battery 103 in the vehicle within the current energy scheduling cycle; in implementation, the prediction data of the required electric energy of the vehicle within the current energy scheduling cycle may be determined based on the travel plan of the vehicle (such as the destination, driving route, etc.), the energy consumption mode, the driving habit data of the driver, etc., and the required charging amount of the power battery 103 may be determined based on the prediction data of the required electric energy of the vehicle within the current energy scheduling cycle and the current remaining power of the power battery 103.

[0060] The electricity price prediction data of the power grid 102 can be the prediction data of the change trend of the electricity price of the power grid within the current energy scheduling period. In implementation, based on the current electricity price of the power grid 102, the current date, the current time, etc., through a pre-trained power grid electricity price prediction model, the change trend of the electricity price of the power grid within the current energy scheduling period can be predicted.

[0061] The load prediction data of the power grid 102 can be the prediction data of the change trend of the power grid load within the current energy scheduling period. In implementation, based on the current load of the power grid 102, the current date, the current time, etc., through a pre-trained power grid load prediction model, the change trend of the power grid load within the current energy scheduling period can be predicted. Among them, the current load of the power grid 102 can be the current power consumption load in the area where the household is located, and can be obtained by communicating with the power grid management system, etc.

[0062] S202. Based on the target prediction data and the target constraint conditions of energy scheduling, with the goal of minimizing the electricity consumption cost, determine the target scheduling strategy of the distributed energy system; the target constraint conditions include the operation stability constraint of the distributed energy system.

[0063] Specifically, the energy scheduling module 107 can determine the target scheduling strategy of the distributed energy system based on the target prediction data within the current energy scheduling period and the target constraint conditions of energy scheduling, with the goal of minimizing the electricity consumption cost. Among them, the determination method of the electricity consumption cost can be set according to actual needs. For example, it can include the cost required to obtain electric energy from the power grid 102, and can also include the income obtained from feeding back electric energy to the power grid 102. It can also include the cost corresponding to the energy loss generated during the energy scheduling process of the distributed energy system, and can also include the equipment loss cost of the energy storage battery 104, etc. It can be understood that in the case where the electricity consumption cost includes multiple costs, the target scheduling strategy can be determined with the goal of minimizing the sum of multiple costs.

[0064] The target constraint conditions can include the operation stability constraint of the distributed energy system. The operation stability constraint can include the operation stability constraints of each energy device in the distributed energy system (such as the photovoltaic power generation system 101, the power grid 102, the power battery 103, and the energy storage battery 104). For example, it can include the stability constraint of the change of the device state, and can also include the stability constraint of the device load, etc. Thus, when the target scheduling strategy meets the target constraint conditions, it can effectively ensure the safe and reliable operation of each energy device in the distributed energy system during the energy scheduling process.

[0065] In addition, the target constraint conditions may further include constraints on the operating parameters of the power battery 103. For example, the lower limit and upper limit of the remaining power of the power battery 103, and the limit value of the charging and discharging power of the power battery 103. Thus, it can be ensured that during the charging and discharging process of the power battery 103, its remaining power does not exceed the upper limit of its remaining power and is not lower than the lower limit of its remaining power. At the same time, the charging and discharging power of the power battery 103 is limited within the allowable range, so as to effectively ensure the safe and reliable operation of the power battery 103 during the energy scheduling process.

[0066] The target constraint conditions may further include constraints on the operating parameters of the energy storage battery 104. For example, the lower limit and upper limit of the remaining power of the energy storage battery 104, the limit value of the charging and discharging power of the energy storage battery 104, and the cycle life of the energy storage battery 104. Thus, it can be ensured that during the charging and discharging process of the energy storage battery 104, its remaining power does not exceed the upper limit of its remaining power and is not lower than the lower limit of its remaining power, the charging and discharging power of the energy storage battery 104 is limited within the allowable range, and the number of charge-discharge cycles of the energy storage battery 104 is limited within its cycle life, so as to effectively ensure the safe and reliable operation of the energy storage battery 104 during the energy scheduling process.

[0067] The target constraint conditions may further include constraints on the operating parameters of the power conversion module 1012 in the photovoltaic power generation system 101. For example, the maximum input-output power, conversion efficiency, working temperature threshold, etc. of the power conversion module 1012. Thus, during the operation of the power conversion module 1012, it can be ensured that it operates within the allowable power range, avoiding situations such as overload or reduced efficiency, so as to effectively ensure the safe and reliable operation of the power conversion module 1012 during the energy scheduling process.

[0068] The target constraint conditions may further include user demand constraints. For example, the travel plan input by the user, the charging power preference input by the user, the charging time interval input by the user, etc., to meet the user's energy scheduling needs and improve the flexibility during the energy scheduling process.

[0069] The target constraint conditions may further include the operating parameter constraints of the power grid 102. For example, the operating parameter constraints of the power grid 102 may include grid access specifications (such as voltage, frequency, harmonics, etc.). Thus, when the power battery 103, the power conversion module 1012, and the energy storage battery 104 feed back power to the power grid 102, they can follow the grid access specifications of the power grid 102, so that the fed-back power meets the grid standards and avoids causing adverse effects on the power grid 102, thereby effectively ensuring the safe and reliable operation of the power grid 102 during the energy scheduling process. In addition, the operating parameter constraints of the power grid 102 may further include grid load limits, etc., that is, fully considering the load-carrying capacity of the power grid 102, avoiding excessive increase in the grid load during peak grid load periods, making the power consumption demand of the system match the power supply capacity of the power grid 102, ensuring that the power grid 102 operates within a safe and stable load range, and thus effectively ensuring the safe and reliable operation of the power grid 102 during the energy scheduling process.

[0070] In implementation, for any moment t within the current energy scheduling period, the target scheduling strategy at this moment may include the electric power P obtained from the power grid 102 at this moment grid,t , the power generation power P of the photovoltaic power generation system 101 pv,t , the discharge power of the power battery 103 , the discharge power of the energy storage battery 104 , the power consumption power P of the household load 105 load,t , the charging power of the power battery 103 , and the charging power of the energy storage battery 104 For example, the target scheduling strategy at this moment may be as shown in Equation (2):

[0071]

[0072] In the formula, P total,t is the total power balance at moment t, that is, the dynamic balance between the input power and the consumed power of the distributed energy system. When P total,t is 0, it indicates that the power supply and demand are balanced, that is, there is no power waste and no power supply shortage, which can effectively ensure the stability of the coordinated operation of multiple-terminal devices such as vehicles, the photovoltaic power generation system 101, the energy storage battery 104, and the household load 105. It can be understood that the target scheduling strategy at this moment may further include the power of the photovoltaic power generation system 101, the power battery 103, and the energy storage battery 104 feeding back energy to the power grid 102, and this part of the energy is not reflected in Equation (2).

[0073] S203. Based on the target scheduling strategy, control the photovoltaic power generation system 101, the power grid 102, the power battery 103, and the energy storage battery 104 to perform energy distribution.

[0074] Specifically, control instructions for the photovoltaic power generation system 101, the power grid 102, the power battery 103, and the energy storage battery 104 can be generated based on the target scheduling strategy, so as to control the photovoltaic power generation system 101, the power grid 102, the power battery 103, and the energy storage battery 104 to perform energy distribution through the corresponding control instructions, thereby being able to effectively reduce the electricity cost while ensuring the safe and reliable operation of each terminal device in the distributed energy system, and further being able to effectively improve the energy utilization efficiency of the distributed energy system.

[0075] In a feasible implementation manner, the operation stability constraint of the distributed energy system includes the stability constraint of the target device in the distributed energy system;

[0076] Among them, the stability constraint of the target device includes the state switching constraint and / or the load rate constraint of the target device. The state switching constraint is used to restrict the state switching frequency of the target device, and the load rate constraint is used to restrict the operating power of the target device.

[0077] Specifically, the operation stability constraint of the distributed energy system can include the stability constraint of the target device in the distributed energy system. The target device can include the energy devices in the distributed energy system. For example, the target device can include some or all of the devices such as the power conversion module 1012, the power battery 103, the energy storage battery 104, and the power grid 102.

[0078] For any target device, the stability constraint of the target device is used to ensure the stability of the target device during operation, so as to reduce the risk of the target device failing or its lifespan decaying due to abnormal scheduling.

[0079] Among them, the stability constraint of the target device can include the state switching constraint of the target device. The state switching constraint can be used to restrict the state switching frequency of the target device. For example, the state switching constraint can be that the state switching frequency is less than or equal to 3 times per hour. The state switching can be the switching between the charging state and the discharging state, can also be the switching of the working mode, and can also be the switching between the on state and the off state, etc., which can be specifically set according to actual requirements. Thus, during the energy scheduling process, by imposing state switching constraints on the target device, it is possible to effectively avoid the target device failing or its lifespan decaying due to frequent state switching.

[0080] In addition, the stability constraint of the target device may further include the load rate constraint of the target device. The load rate constraint can be used to restrict the load rate of the target device during operation. The load rate of the target device during operation can be the ratio of the operating power of the target device to the rated capacity of the target device. The operating power can be the charging power, discharging power, input power, output power, etc. For example, the load rate constraint can be that the load rate is within the range of 10% to 90%, so as to avoid equipment failures or life attenuation caused by long-term overload (such as, load rate > 100%) or long-term low load (such as, load rate < 10%) of each energy device.

[0081] In a feasible implementation manner, the target constraint conditions further include user constraints;

[0082] Among them, the user constraints include the required charging amount and the required charging time window of the power battery 103 determined based on the vehicle travel plan input by the user.

[0083] Specifically, the user constraint can be the constraint condition in the process of formulating the target scheduling strategy determined according to the energy scheduling demand information input by the user. In implementation, the user can input the vehicle travel plan through the user terminal 108. The vehicle travel plan can include the destination, departure time, passing locations, etc.

[0084] In implementation, the energy scheduling module 107 can determine the target driving route of the vehicle based on the vehicle travel plan input by the user, and determine the required electric energy of the vehicle during the operation according to the vehicle travel plan, the driving habits of the driver, the road condition prediction results and other data, that is, the electric energy that the vehicle needs to consume. Thus, based on the required electric energy and the current remaining power of the power battery 103, the required charging amount of the power battery 103 can be determined. For example, when the required electric energy is greater than or equal to the upper limit value of the remaining power of the power battery 103, the difference between the upper limit value of the remaining power of the power battery 103 and the current remaining power can be used as the required charging amount of the power battery 103. When the required electric energy is less than the upper limit value of the remaining power of the power battery 103, the difference between the required electric energy of the power battery 103 and the current remaining power can be used as the required charging amount of the power battery 103. When the required electric energy is less than the current remaining power of the power battery 103, the required charging amount of the power battery 103 can be 0, so as to be able to meet the energy demand of the vehicle during travel to the greatest extent.

[0085] In addition, the energy scheduling module 107 can also determine the required charging time window of the power battery 103 based on the vehicle travel plan input by the user. The required charging time window of the power battery 103 can be before the departure time in the vehicle travel plan, so as to be able to effectively meet the energy demand of the vehicle during travel.

[0086] In a feasible implementation, the electricity cost includes the energy loss cost of the distributed energy system;

[0087] Wherein, the energy loss cost of the distributed energy system is determined based on the energy loss of the energy storage battery 104 during the current energy scheduling period.

[0088] Specifically, the electricity cost may include the energy loss cost of the distributed energy system, and this energy loss cost can be determined based on the energy loss of the energy storage battery 104 during the current energy scheduling period.

[0089] In implementation, the energy storage battery 104 can be charged through the power grid 102, the photovoltaic power generation system 101, and the power battery 103, so as to supply power to the household load 105 or charge the power battery 103 through the energy storage battery 104 during peak electricity price periods or power grid 102 failures, etc., to reduce the electricity cost and meet the electrical energy demands of the household load 105 and the power battery 103. However, there is a certain amount of energy loss during the transfer of electrical energy through the energy storage battery 104. In implementation, the energy loss cost can be determined according to the energy loss amount of the energy storage battery 104 during the operation process and / or non-operation process 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 102 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.

[0090] Thus, in the process of formulating the target scheduling strategy, by comprehensively considering the energy loss cost of the distributed energy system, the effectiveness of the formulation result of the target scheduling strategy can be effectively ensured. At the same time, in the process of controlling the energy distribution of the photovoltaic power generation system 101, the power grid 102, the power battery 103, and the energy storage battery 104 according to the target scheduling strategy, the electricity cost can be minimized.

[0091] In a feasible implementation, the energy loss of the energy storage battery 104 includes at least one of the electrochemical loss, energy conversion and transmission loss, and self-discharge loss of the energy storage battery 104 during the current energy scheduling period.

[0092] Specifically, the energy loss of the energy storage battery 104 may include the electrochemical loss during the operation of the energy storage battery 104 within the current energy scheduling cycle. The electrochemical loss during the operation of the energy storage battery 104 refers to the energy loss caused by the internal resistance, incomplete reversibility of the electrochemical reaction, etc. during the charging and discharging processes of the energy storage battery 104. For example, during the charging and discharging processes 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 104 under different operating conditions can be pre-configured. For example, based on the test data or historical operating data of the energy storage battery 104, the difference between the amount of electricity input during the charging process and the amount of electricity output during the discharging process of the energy storage battery 104 can be determined, and the electrochemical loss per unit time of the energy storage battery 104 can be determined based on this difference. Additionally, an equivalent circuit model of the energy storage battery 104 can be used to simulate the charging and discharging processes of the energy storage battery 104, and the electrochemical loss per unit time of the energy storage battery 104 can be predicted based on the simulation data. Thus, during the process of formulating the target scheduling strategy, based on the charge and discharge depth, charge and discharge power, and ambient temperature of the energy storage battery 104, etc., the operating conditions of the energy storage battery 104 can be determined, and based on the electrochemical loss per unit time of the energy storage battery 104 under the corresponding operating conditions, the electrochemical loss amount of the energy storage battery 104 in each time period within the current energy scheduling cycle can be determined.

[0093] The energy loss of the energy storage battery 104 may also include the energy conversion and transmission loss during the operation of the energy storage battery 104 within the current energy scheduling cycle. The energy conversion and transmission loss may 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 104. For example, the energy loss generated by the electrical energy conversion components (such as inverters or transformers) and the connection lines, etc. during the charging and discharging processes of the energy storage battery 104. For example, during the conversion of electrical energy by electrical energy conversion components such as inverters, energy loss is generated due to the on-resistance of the switching components, electromagnetic conversion efficiency, etc. The connection lines such as cables have energy loss during the transmission of electrical energy due to the existence of resistance. In implementation, based on the preset resistance of the connection line and the charge and discharge mode of the energy storage battery 104 (such as constant current mode or constant voltage mode), the energy loss per unit time of the connection line can be determined, and based on the energy loss per unit time of the connection line, the energy transmission loss amount of the connection line in each time period within the current energy scheduling cycle can be determined. Additionally, based on the operating power and electrical energy conversion efficiency of the electrical energy conversion component, the energy loss per unit time of the electrical energy conversion component can be determined, and based on the energy loss per unit time of the electrical energy conversion component, the energy conversion loss amount of the electrical energy conversion component in each time period within the current energy scheduling cycle can be determined.

[0094] In addition, the energy loss of the energy storage battery 104 may further include the self-discharge loss of the energy storage battery 104 during non-operation within the current energy scheduling period. The self-discharge loss may include the energy loss caused by the self-discharge of the energy storage battery 104 due to internal chemical reactions, impurities, etc. During implementation, the self-discharge loss per unit time of the energy storage battery 104 under different environmental conditions (such as environmental temperature and humidity) may be pre-configured. For example, the self-discharge loss per unit time of the energy storage battery 104 may be determined based on the test data or historical detection data of the energy storage battery 104. Thus, during the process of formulating the target scheduling strategy, the self-discharge loss per unit time of the energy storage battery 104 may be determined based on the predicted data of the environmental conditions, and the self-discharge loss amount of the energy storage battery 104 during each time period within the current energy scheduling period may be determined according to the self-discharge loss per unit time of the energy storage battery 104.

[0095] Thus, through the method of the embodiment of the present application, the energy loss of the energy storage battery 104 can be comprehensively and effectively determined. Furthermore, during the process of determining the energy loss cost of the distributed energy system based on the energy loss of the energy storage battery 104, the accuracy of the determination result of the energy loss cost can be further improved.

[0096] In a feasible implementation manner, the electricity consumption cost further includes the grid energy cost and / or the equipment loss cost of the energy storage battery 104;

[0097] Wherein, the grid energy cost is determined based on the electricity consumption cost for obtaining electric energy from the grid 102 within the current energy scheduling period, and the revenue from the photovoltaic power generation system 101, the power battery 103, and the energy storage battery 104 for feeding back electric energy to the grid 102;

[0098] The equipment loss cost of the energy storage battery 104 is determined based on the cycle attenuation cost and / or the fault maintenance cost of the energy storage battery 104 within the current energy scheduling period.

[0099] Specifically, the electricity consumption cost may further include the grid energy cost, and the grid energy cost may be determined based on the electricity consumption cost for obtaining electric energy from the grid 102 within the current energy scheduling period and the revenue from the photovoltaic power generation system 101, the power battery 103, and the energy storage battery 104 for feeding back electric energy to the grid 102.

[0100] Among them, the electricity consumption cost for obtaining electric energy from the power grid 102 during the current energy scheduling period, that is, the total cost of the electric energy output by the power grid 102 during the current energy scheduling period. In implementation, based on the electric energy output by the power grid 102 in each time period and the electricity price prediction results of the power grid 102 in each time period during the current energy scheduling period, the electricity consumption cost for obtaining electric energy from the power grid 102 in each time period can be determined respectively, and the sum of the electricity consumption costs for obtaining electric energy from the power grid 102 in each time period is used as the electricity consumption cost for obtaining electric energy from the power grid 102 during the current energy scheduling period.

[0101] The revenue from the power generation and storage system 101, the power battery 103, and the energy storage battery 104 feeding back electric energy to the power grid 102 during the current energy scheduling period, that is, the revenue obtained when the power generation and storage system 101, the power battery 103, and the energy storage battery 104 feed back electric energy to the power grid 102 during the current energy scheduling period. In implementation, based on the electric energy fed back by the power generation and storage system 101, the power battery 103, and the energy storage battery 104 to the power grid 102 in each time period and the electricity price prediction results of the power grid 102 in each time period, the revenue from feeding back electric energy to the power grid 102 in each time period can be determined respectively, and the sum of the revenues from feeding back electric energy to the power grid 102 in each time period is used as the revenue from feeding back electric energy to the power grid 102 during the current energy scheduling period.

[0102] In implementation, the difference between the electricity consumption cost for obtaining electric energy from the power grid 102 and the revenue from feeding back electric energy to the power grid 102 during the current energy scheduling period can be used as the power grid energy cost during the current energy scheduling period, so as to effectively ensure the accuracy of the determination result of the power grid energy cost.

[0103] In addition, the electricity cost may further include the equipment loss cost of the energy storage battery 104. The equipment loss cost of the energy storage battery 104 may also be determined according to the cycle attenuation cost and / or the fault maintenance cost of the energy storage battery 104 within the current energy scheduling period. Among them, in the case of using the energy storage battery 104 for electric energy transfer, while energy loss occurs, it will also cause equipment loss to the energy storage battery 104. For example, it may cause faults and / or lifespan attenuation of the energy storage battery 104 and related equipment (such as, power conversion components, connection lines, etc.). In implementation, the cycle attenuation cost of the energy storage battery 104 within the current energy scheduling period may be determined based on the number of cycles of the energy storage battery 104 within the current energy scheduling period and the preset lifespan of the energy storage battery 104. In addition, the preset lifespan of the energy storage battery 104 may be divided into multiple operation stages, and according to historical data or test data, etc., the preset failure rate of the energy storage battery 104 in different operation stages may be configured. Thus, the fault maintenance cost of the energy storage battery 104 within the current energy scheduling period may be determined according to the current operation stage of the energy storage battery 104 and the preset failure rate in the current operation stage, so as to effectively ensure the accuracy of the determination result of the equipment loss cost of the energy storage battery 104.

[0104] Optionally, the sum of the grid energy cost, the equipment loss cost of the energy storage battery 104, and the energy loss cost of the distributed energy system may be used as the electricity cost, and a target scheduling strategy may be formulated with the minimum electricity cost as the goal. Thus, in the process of controlling the energy distribution of the photovoltaic power generation system 101, the power grid 102, the power battery 103, and the energy storage battery 104 according to the target scheduling strategy, the overall electricity cost of the distributed energy system can be effectively reduced.

[0105] In a feasible implementation manner, the method for determining the household load prediction data includes:

[0106] Based on the environmental data within the current energy scheduling period, the operation duration of the household load 105 within the current energy scheduling period, and the operation duration of the household load 105 within the preset time window within the current energy scheduling period, determine the electricity load compensation amount of the household load 105;

[0107] Based on the electricity load compensation amount of the household load 105, compensate the preset electricity load of the household load 105 to obtain the electricity load prediction data of the household load 105 within the current energy scheduling period;

[0108] Based on the sum of the electricity load prediction data of each household load 105 within the current energy scheduling period, determine the household load prediction data.

[0109] Specifically, the household load prediction data within the current energy scheduling period may include the change trend of the sum of the electricity load prediction data of each household load 105 within the current energy scheduling period. That is, the household load prediction data within the current energy scheduling period includes the sum of the electricity load prediction data of each household load 105 at each moment within the current energy scheduling period. It can be understood that the household load 105 here refers to the load that needs to operate within the current energy scheduling period in the household.

[0110] Among them, for any moment t within the current energy scheduling period, the electricity load prediction data of each household load 105 at this moment can be obtained respectively, and the sum of the electricity load prediction data of each household load 105 at this moment is used as the household load prediction data at this moment. Thus, the household load prediction data at each moment within the current energy scheduling period can be obtained, that is, the household load prediction data within the current energy scheduling period is obtained.

[0111] In implementation, for any household load 105, based on the environmental data within the current energy scheduling period, the operation duration of this household load 105 within the current energy scheduling period, and the operation duration of this household load 105 within the preset time window within the current energy scheduling period, the electricity load compensation amount of this household load 105 at moment t can be determined, and based on the electricity load compensation amount of this household load 105 at moment t, the preset electricity load of this household load 105 is compensated to obtain the electricity load prediction data of this household load 105 at moment t.

[0112] Among them, the environmental data within the current energy scheduling period may include the prediction data of the change trend of the environmental temperature within the current energy scheduling period; the operation duration of this household load 105 within the current energy scheduling period can be the operation duration of this household load 105 within the current energy scheduling period up to moment t. For example, the operation duration of this household load 105 within the current energy scheduling period can be determined according to the scheduled start time of this household load 105; the preset time window within the current energy scheduling period can be the time window during holidays within the current energy scheduling period, and the operation duration of this household load 105 within the preset time window within the current energy scheduling period can be the operation duration of this household load 105 within the preset time window within the current energy scheduling period up to moment t.

[0113] In the process of determining the power consumption load compensation amount of the household load 105 at time t, the first power consumption load compensation amount can be determined according to the predicted environmental temperature data at time t, the second power consumption load compensation amount can be determined according to the running duration of the household load 105 within the current energy scheduling period, and the third power consumption load compensation amount can be determined according to the running duration of the household load 105 within the preset time window in the current energy scheduling period. Then, based on the sum of the first power consumption load compensation amount, the second power consumption load compensation amount, and the third power consumption load compensation amount, the power consumption load compensation amount of the household load 105 is determined. Additionally, the predicted environmental temperature data at time t, the running duration of the household load 105 within the current energy scheduling period, and the running duration of the household load 105 within the preset time window in the current energy scheduling period can be input into a pre-trained prediction model, and the power consumption load compensation amount of the household load 105 at time t can be output through this prediction model.

[0114] In addition, the preset power consumption load of the household load 105 can be the baseline load of the household load 105. For example, it is the lowest power consumption load of the household load 105 in the corresponding operating mode. In implementation, the sum of the preset power consumption load of the household load 105 and the power consumption load compensation amount of the household load 105 at time t can be used as the power consumption load prediction data of the household load 105 at time t.

[0115] Optionally, the determination method of the power consumption load prediction data of the household load 105 at time t can be as shown in Equation (3):

[0116] P load(t) = β0 + β1 × T(t) + β2 × H1(t) + β3 × H2(t) + ε (3)

[0117] In the formula, P load(t) is the power consumption load prediction data of the household load 105 at time t; β0 is the preset power consumption load of the household load 105; T(t) is the predicted environmental temperature data at time t; H1(t) is the running duration of the household load 105 within the current energy scheduling period up to time t; H2(t) is the running duration of the household load 105 within the preset time window in the current energy scheduling period up to time t; β1, β2, and β3 are the temperature coefficient, time coefficient, and holiday coefficient respectively, all of which are constants; ε is the error term, which is a constant.

[0118] Therefore, through the method of the embodiments of the present application, the accuracy of the household load prediction data can be effectively improved. Furthermore, in the process of controlling the energy distribution of the photovoltaic power generation system 101, the power grid 102, the power battery 103, and the energy storage battery 104 according to the target scheduling strategy, the power demand of each household load 105 can be effectively guaranteed.

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

[0120] During the process of controlling the energy distribution of the photovoltaic power generation system 101, the power grid 102, the power battery 103, and the energy storage battery 104, obtain the operating state of the distributed energy system and / or user feedback data;

[0121] Based on the operating state of the distributed energy system and / or the user feedback data, update the target scheduling strategy.

[0122] Specifically, during the process of controlling the energy distribution of the photovoltaic power generation system 101, the power grid 102, the power battery 103, and the energy storage battery 104 by the energy scheduling module 107, the operating state of the distributed energy system can also be obtained. The operating state of the distributed energy system can include the operating states of various energy devices in the distributed energy system, and can also include the power supply state of the distributed energy system. For any energy device, the operating state of the energy device can include the number of faults of the energy device per unit time, the operating efficiency of the energy device, etc. The power supply state of the distributed energy system can be used to characterize whether the distributed energy system can meet the power supply demands and electricity costs of the household load 105 and the vehicle during the scheduling process.

[0123] In addition, the user can also input user feedback data to the energy scheduling module 107 through the user terminal 108. For example, the evaluation of the energy scheduling result, the adjustment requirements for the energy scheduling strategy, etc.

[0124] In implementation, the energy scheduling module 107 can update the target scheduling strategy based on the operating data of the distributed energy system and / or the user feedback data. At the same time, the relevant parameters and algorithms in the process of formulating the target scheduling strategy can also be optimized to further improve the energy scheduling accuracy of the distributed energy system.

[0125] In a feasible implementation manner, it further includes:

[0126] Based on the household load prediction data within the current energy scheduling period, determine the fluctuation state of the household load. The household load prediction data includes the change trend of the sum of the electricity load prediction data of each household load 105;

[0127] When the fluctuation state of the household load indicates that the household load meets the smoothing condition, based on the electricity load prediction data of each household load 105 and the power supply priorities of each household load 105, determine the target load among each household load 105;

[0128] Generate recommended data corresponding to the target load, where the recommended data is used to remind the user to reduce the power consumption load of the target load.

[0129] Specifically, the household load prediction data may include the change trend of the sum of the power consumption load prediction data of each household load 105. That is, the household load prediction data within the current energy scheduling period includes the household load at each moment within the current energy scheduling period. That is, the household load prediction data within the current energy scheduling period includes the change trend of the household load within the current energy scheduling period, and for any moment, the household load at that moment is the sum of the power consumption load prediction data of each household load 105 at that moment.

[0130] After obtaining the household load prediction data within the current energy scheduling period, the energy scheduling module 107 may further determine the fluctuation state of the household load based on the household load prediction data within the current energy scheduling period.

[0131] Among them, based on the household load prediction data within the current energy scheduling period, the household load peak value and the household load valley value within the current energy scheduling period may be determined to determine the fluctuation state of the household load according to the load peak-valley difference within the current energy scheduling period. The load peak-valley difference is the difference between the household load peak value and the household load valley value within the current energy scheduling period. For example, when the load peak-valley difference is greater than a predetermined load threshold, it is determined that the fluctuation of the household load is large, that is, the fluctuation state of the household load indicates that the household load meets the flattening condition. When the load peak-valley difference is less than or equal to the predetermined load threshold, it is determined that the fluctuation of the household load is acceptable, that is, the fluctuation state of the household load indicates that the household load does not meet the flattening condition.

[0132] In addition, the fluctuation state of the household load may also be determined according to the load peak-valley difference rate within the current energy scheduling period. The load peak-valley difference rate is the ratio of the load peak-valley difference within the current energy scheduling period to the household load peak value within the current energy scheduling period. For example, when the load peak-valley difference rate within the current energy scheduling period is greater than a predetermined peak-valley difference rate threshold, it is determined that the fluctuation of the household load is large, that is, the fluctuation state of the household load indicates that the household load meets the flattening condition. When the load peak-valley difference rate within the current energy scheduling period is less than or equal to the predetermined peak-valley difference rate threshold, it is determined that the fluctuation of the household load is acceptable, that is, the fluctuation state of the household load indicates that the household load does not meet the flattening condition.

[0133] It is also possible to determine the change trend of the load volatility within the current energy scheduling period based on the household loads at adjacent times within the current energy scheduling period, and determine the fluctuation state of the household load according to the change trend of the load volatility within the current energy scheduling period. For example, when the load volatility at at least one time within the current energy scheduling period is greater than the volatility threshold, it is determined that the household load fluctuates greatly, that is, the fluctuation state of the household load indicates that the household load meets the flattening condition. When the load volatility at each time within the current energy scheduling period is less than or equal to the volatility threshold, it is determined that the fluctuation of the household load is acceptable, that is, the fluctuation state of the household load indicates that the household load does not meet the flattening condition.

[0134] In implementation, when the fluctuation state of the household load indicates that the household load meets the flattening condition, the target load in each household load 105 can be determined based on the power consumption load prediction data of each household load 105 and the power supply priority of each household load 105 within the current energy scheduling period, and the recommended data corresponding to the target load is generated. Among them, the recommended data can be sent to the user terminal 108 to remind the user to reduce the power consumption load of the target load, so that in the process of energy scheduling of distributed energy, the impact on energy equipment such as the power grid 102 caused by large fluctuations in household load can be effectively avoided.

[0135] Exemplary System

[0136] In an exemplary embodiment of the present specification, a distributed energy system is further provided, including an energy scheduling module 107, a photovoltaic power generation system 101, a power grid 102, a power battery 103 in a vehicle, and a storage battery 104 in a household;

[0137] The energy scheduling module 107 is respectively connected to the photovoltaic power generation system 101, the power grid 102, the vehicle, and the energy storage battery 104, and is configured to execute the energy scheduling method according to any one of the above embodiments.

[0138] Exemplary Device

[0139] In an exemplary embodiment of the present specification, an electronic device is further provided. The electronic device includes at least one processor and at least one memory. A computer program is stored in the memory, and when the computer program is executed by the processor, the energy scheduling method according to any one of the above embodiments is implemented.

[0140] Exemplary computer program product and storage medium

[0141] In addition to the above methods and devices, the energy scheduling method provided by 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 scheduling method according to various embodiments of this specification described in the "Exemplary Method" section above of this specification.

[0142] 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.

[0143] In addition, the embodiments of this specification also provide 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 scheduling method according to various embodiments of this specification described in the "Exemplary Method" section above of this specification.

[0144] Those of ordinary skill in the art can understand that all or part of the processes of implementing the methods in 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 may 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 may include non-volatile and / or volatile memories. Non-volatile memories may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memories may 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), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0145] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise 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 to be within the scope described in this specification.

[0146] The above-described embodiments only represent several implementation manners of this specification. Their descriptions are relatively specific and detailed, but they should not be construed as limiting 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 shall be subject to the appended claims.

Claims

1. An energy scheduling method, characterized in that, Applied to a distributed energy system, the distributed energy system includes a photovoltaic power generation system, a power grid, a power battery in a vehicle, and a energy storage battery in a household; the method includes: Obtaining target prediction data within a current energy scheduling period, the target prediction data including at least partial data among the predicted power generation data of the photovoltaic power generation system, the predicted household load data, the predicted charging demand data of the vehicle, and the predicted power price and / or load data of the power grid. Based on the target prediction data and the target constraint conditions of energy scheduling, with minimizing the electricity cost as the scheduling target, determining the target scheduling strategy of the distributed energy system; the target constraint conditions include the operation stability constraint of the distributed energy system. Based on the target scheduling strategy, controlling the photovoltaic power generation system, the power grid, the power battery, and the energy storage battery to perform energy distribution.

2. The method according to claim 1, characterized in that, The operation stability constraint of the distributed energy system includes the stability constraint of the target equipment in the distributed energy system. Wherein, the stability constraint of the target equipment includes the state switching constraint and / or the load rate constraint of the target equipment, the state switching constraint is used to restrict the state switching frequency of the target equipment, and the load rate constraint is used to restrict the operating power of the target equipment.

3. The method according to claim 1, characterized in that, The target constraint conditions further include user constraints. Wherein, the user constraints include the required charging amount and the required charging time window of the power battery determined based on the vehicle travel plan input by the user.

4. The method according to claim 1, wherein The electricity cost includes the energy loss cost of the distributed energy system. Wherein, the energy loss cost of the distributed energy system is determined based on the energy loss of the energy storage battery within the current energy scheduling period.

5. The method according to claim 4, characterized in that, The energy loss of the energy storage battery includes at least one of the electrochemical loss, the energy conversion and transmission loss, and the self-discharge loss of the energy storage battery within the current energy scheduling period.

6. The method according to claim 4, wherein The electricity cost further includes the grid energy cost and / or the equipment loss cost of the energy storage battery. Wherein, the grid energy cost is determined based on the electricity cost for obtaining electric energy from the grid within the current energy scheduling period, and the revenue from the photovoltaic power generation system, the power battery, and the energy storage battery for feeding back electric energy to the grid. The equipment loss cost of the energy storage battery is determined based on the cycle attenuation cost and / or the fault maintenance cost of the energy storage battery within the current energy scheduling period.

7. The method according to claim 1, wherein The method for determining the predicted household load data includes: Based on the environmental data within the current energy scheduling period, the operating duration of the household load within the current energy scheduling period, and the operating duration of the household load within a preset time window within the current energy scheduling period, determining the electricity load compensation amount of the household load. Based on the electricity load compensation amount of the household load, compensating the preset electricity load of the household load to obtain the predicted electricity load data of the household load within the current energy scheduling period. Based on the sum of the predicted electricity load data of each household load within the current energy scheduling period, determining the predicted household load data.

8. The method according to any one of claims 1 to 7, characterized in that, Further includes: In the process of controlling the energy distribution of the photovoltaic power generation system, the power grid, the power battery of the vehicle, and the energy storage battery of the home, obtain the operating status of the distributed energy system and / or user feedback data; Based on the operating status of the distributed energy system and / or the user feedback data, update the target scheduling strategy.

9. The method according to any one of claims 1 to 7, characterized in that, It further includes: Based on the household load prediction data within the current energy scheduling period, determine the fluctuation state of the household load, where the household load prediction data includes the change trend of the sum of the predicted power consumption data of each household load; When the fluctuation state of the household load indicates that the household load meets the flattening condition, based on the predicted power consumption data of each household load and the power supply priority of each household load, determine the target load among each household load; Generate recommended data corresponding to the target load, where the recommended data is used to remind the user to reduce the power consumption load of the target load.

10. A distributed energy system, characterized in that, It includes an energy scheduling module, a photovoltaic power generation system, a power grid, a power battery in a vehicle, and an energy storage battery in a home; The energy scheduling module is respectively connected to the photovoltaic power generation system, the power grid, the vehicle, and the energy storage battery, and is configured to execute the energy scheduling method according to any one of claims 1 to 9.

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