Energy scheduling method and device based on traffic flow prediction

Abstract

The invention discloses an energy scheduling method and device based on traffic flow prediction, and the method achieves the dynamic movement of the electric energy of an electric point location with a small power demand to an electric point location with a large power demand through a mobile energy storage unit, assists the stable operation of the electric point location in urgent need of the electric energy, and improves the operation efficiency. And the energy utilization rate of the electric point with less electric demand is greatly improved. In addition, the mobility of the mobile energy storage unit enables the mobile energy storage unit to dynamically realize energy coordination among different electric point locations. Therefore, dynamic energy coordination among a large number of electric point locations can be realized based on a small number of mobile energy storage units, the energy supply cost is greatly reduced, the problem that the fixed energy storage equipment is idle for a long time due to the addition of a large number of fixed energy storage equipment or a small number of fixed energy storage equipment cannot support sudden energy demands is avoided, and the energy supply efficiency is improved. The contradiction between the energy supply cost and the sudden energy demand is solved, and green energy management is realized. Specifically, for the expressway energy system, if the mobile energy storage unit of the scheme is adopted, stable operation of the expressway energy system can be well guaranteed on the basis of no need of arranging a plurality of electric point positions in the expressway traffic flow peak period, the energy utilization rate and the energy supply cost of the high-altitude road energy system are reduced, and the energy utilization rate of the expressway energy system is increased. Dynamic cooperative scheduling of traffic flow and energy supply is realized, the elasticity and reliability of energy scheduling of a highway energy system are improved, and the insufficiency of power grid energy scheduling is effectively made up.

Description

TECHNICAL FIELD

[0001] The present disclosure relates generally to the technical field of energy scheduling, and in particular to an energy scheduling method and device based on traffic flow prediction. BACKGROUND

[0002] With the increase of market share of electric vehicles, energy supply has become a problem to be solved. In the related art, improvements are usually made from the perspective of energy acquisition, such as clean energy conversion in the available space of the highway, including but not limited to photovoltaic power generation, wind power generation, etc. Even if power is generated through multiple energy storage devices, it is easy to cause the problem of insufficient energy reserves due to the increase of traffic flow. In order to cope with sudden energy demand, more energy storage devices are often added to cope with sudden energy demand, but this approach greatly increases the cost of energy supply (such as space cost, device cost, device maintenance cost, etc.), causing great waste of energy when energy demand is low, and reducing energy utilization.

[0003] Through research, in recent years, personnel in the field have mainly tried to realize energy scheduling by moving energy storage units and moving energy storage devices. There are few mobile energy storage scheduling methods that dynamically move the power of power points with less power demand to power points with more power demand to assist the stable operation of power points in urgent need of power. Therefore, there is an urgent need for a dynamic energy coordination method between different power points to realize dynamic energy coordination between a large number of power points, thereby improving the flexibility and reliability of the energy scheduling system of the highway, and effectively making up for the lack of power grid energy scheduling. SUMMARY

[0004] In view of the above-mentioned defects or deficiencies in the prior art, it is desirable to provide an energy scheduling method and device based on traffic flow prediction, which can transfer the available power of power supply points with abundant power to power demand points with insufficient power, thereby achieving supply and demand balance.

[0005] In a first aspect, an energy scheduling method based on traffic flow prediction is provided, comprising:

[0006] For a target time node, predicting the predicted power generation of a plurality of power generation points and the predicted traffic flow of a plurality of charging points;

[0007] Based on the predicted traffic flow of each of the charging points, determining the predicted power demand of each of the charging points;

[0008] Based on the predicted power generation of a plurality of the power generation points and the predicted power demand of a plurality of the charging points, determining at least one power supply point and at least one power demand point, and the available power corresponding to each of the power supply points and the required power corresponding to each of the power demand points;

[0009] determine an energy scheduling strategy based on at least one of the power supply point and the corresponding available power and at least one of the power demand point and the corresponding required power, to transfer the available power of at least one of the power supply point to at least one of the power demand point by the mobile energy storage unit.

[0010] In some embodiments, the determining the energy scheduling strategy based on at least one of the power supply point and the corresponding available power and at least one of the power demand point and the corresponding required power comprises:

[0011] determine at least one group of candidate combinations of the power supply point and the power demand point based on at least one of the power supply point and the corresponding available power and at least one of the power demand point and the corresponding required power;

[0012] generate a candidate scheduling strategy for each group of the candidate combinations of the power supply point and the power demand point;

[0013] select at least one of the candidate scheduling strategies that satisfies a constraint condition corresponding to the energy scheduling strategy as the energy scheduling strategy.

[0014] In some embodiments, the generating the candidate scheduling strategy for each group of the candidate combinations of the power supply point and the power demand point comprises:

[0015] obtain state information of at least one of the mobile energy storage units; the state information comprises at least one of a current position, a current power, a capacity, a remaining capacity, and a charging / discharging efficiency of the mobile energy storage unit;

[0016] generate the candidate scheduling strategy for each group of the candidate combinations of the power supply point and the power demand point based on the state information of at least one of the mobile energy storage units.

[0017] In some embodiments, the energy scheduling strategy comprises at least a transportation route of the mobile energy storage unit.

[0018] In some embodiments, the target time node is a plurality of continuous fixed time intervals, and the method further comprises:

[0019] determine a sequence of energy scheduling strategies corresponding to the plurality of target time nodes based on at least one of the power supply point and the corresponding available power and at least one of the power demand point and the corresponding required power;

[0020] execute the sequence of energy scheduling strategies in sequence, and update the sequence of energy scheduling strategies based on the fixed time interval after each execution of the energy scheduling strategy.

[0021] In some embodiments, the energy scheduling strategy satisfies at least one of the following constraint conditions:

[0022] The required power of at least one of the required power points is reduced to a minimum;

[0023] The transportation cost of the mobile energy storage unit during the transfer process is minimized; and

[0024] The mobile energy storage unit completes the charging operation at the power supply point and the discharging operation at the required power point before the target time.

[0025] In a second aspect, an energy scheduling device based on traffic flow prediction is provided, comprising:

[0026] A prediction module is configured to predict the predicted power generation of a plurality of power generation points and the predicted traffic flow of a plurality of charging points for a target time node.

[0027] A determination module is configured to determine the predicted required power of each charging point based on the traffic flow of each charging point.

[0028] An analysis module is configured to determine at least one power supply point and at least one required power point, and the available power of each power supply point and the required power of each required power point based on the predicted power generation of a plurality of power generation points and the predicted required power of a plurality of charging points.

[0029] A decision module is configured to determine an energy scheduling strategy based on at least one power supply point and its corresponding available power, and at least one required power point and its corresponding required power, so as to transfer the available power of at least one power supply point to at least one required power point through a mobile energy storage unit.

[0030] In a third aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in the embodiments of the present application.

[0031] In a fourth aspect, a computer readable storage medium is provided, which stores a computer program executable by a processor to implement the method described in the embodiments of the present application.

[0032] In a fifth aspect, a computer program product is provided, comprising a computer program, wherein the computer program is executable by a processor to implement the method described in the embodiments of the present application.

[0033] The energy scheduling method and device based on vehicle flow prediction provided by the embodiment of the application, by predicting the predicted power generation and vehicle flow of each power generation point and charging point at a target time node, at least one power supply point and its corresponding available power and at least one power demand point and its corresponding required power are obtained, and then an energy scheduling strategy for a mobile energy storage unit is generated according to the at least one power supply point and its corresponding available power and the at least one power demand point and its corresponding required power, so as to realize the transfer of the available power of the power supply point to the power demand point, thereby effectively reducing the power shortage of the power demand point and meeting the charging demand under the condition of rapid increase of vehicle flow. The electrical energy of the electrical point with less power demand is dynamically moved to the electrical point with more power demand through the mobile energy storage unit, which not only assists the stable operation of the electrical point in urgent need of electrical energy, but also greatly improves the energy utilization rate of the electrical point with less power demand. In addition, the mobility of the mobile energy storage unit enables it to dynamically realize energy coordination between different electrical points. Therefore, based on a small number of mobile energy storage units, dynamic energy coordination between a large number of electrical points can be realized, which greatly reduces the energy supply cost, avoids the problem that a large number of fixed energy storage devices are idle for a long time or a small number of fixed energy storage devices cannot support sudden energy demand, solves the contradiction between energy supply cost and sudden energy demand, and realizes green energy management. Specifically, for the energy system of the expressway, if the mobile energy storage unit of the present application is used, the stable operation of the energy system of the expressway can be well guaranteed during the peak period of vehicle flow on the expressway without the need to set a large number of electrical points, thereby reducing the energy utilization rate and energy supply cost of the energy system of the expressway, realizing dynamic collaborative scheduling of traffic flow and energy supply, improving the flexibility and reliability of energy scheduling of the energy system of the expressway, and effectively making up for the deficiency of power grid energy scheduling.

[0034] Additional aspects and advantages of the application will be set forth in part in the description which follows, and in part will become apparent to those skilled in the art upon examination of the following and / or can be learned by practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0035] Other features, objects, and advantages of the application will become more apparent from the following detailed description of non-limiting embodiments thereof, when read in conjunction with the accompanying drawings:

[0036] Figure 1 A flowchart of an energy scheduling method based on vehicle flow prediction provided by an embodiment of the application is shown;

[0037] Figure 2 A flowchart of an energy scheduling method based on vehicle flow prediction provided by another embodiment of the application is shown;

[0038] Figure 3 A block diagram of an energy scheduling device based on vehicle flow prediction provided by an embodiment of the application is shown;

[0039] Figure 4 A structural schematic diagram of a computer system of an electronic device or a server suitable for implementing the embodiments of the present application is shown. DETAILED DESCRIPTION

[0040] The present application will be further described below in conjunction with the accompanying drawings and embodiments. It can be understood that the specific embodiments described herein are only used to explain the related application, and not to limit the application. In addition, it should be noted that only the parts related to the application are shown in the drawings for ease of description.

[0041] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the accompanying drawings and in conjunction with the embodiments.

[0042] In order to further illustrate the technical solutions provided by the embodiments of the present application, the following will be described in detail in conjunction with the accompanying drawings and specific embodiments. Although the present application provides the method operation instruction steps as shown in the following embodiments or drawings, more or less operation instruction steps can be included in the method based on conventional or non-creative labor. The execution order of the steps is not limited to the execution order provided by the embodiments of the present application in the logical sense. The method can be executed in sequence or in parallel when the actual processing process or the device is executed according to the method order shown in the embodiments or drawings.

[0043] It should be noted that the data obtained or used by the embodiments of the present application needs to be agreed by the user, and the relevant data can be obtained after the user's authorization permission, and the data obtained or used complies with the relevant legal regulations.

[0044] Please refer to Figure 1 , Figure 1 A flowchart of an energy scheduling method based on traffic flow prediction provided by an embodiment of the present application is shown. As Figure 1 shown, the method comprises:

[0045] Step 101, for a target time node, predicting the predicted power generation of a plurality of power generation points and the predicted traffic flow of a plurality of charging points.

[0046] It should be noted that the target time node is a time reference standard for prediction, wherein the target time node is a future time node, and the time length from the current time to the target time node can be adjusted according to the actual example or the historical power supply and demand situation, which is not limited by the present application.

[0047] In some embodiments, the predicted power generation of the plurality of power generation sites can be predicted by a trained prediction model according to weather conditions, solar radiation conditions, and other power generation influencing factors between the current time and the target time node. It should be understood that the embodiments of the present application do not make specific limitations on the power generation method and prediction method of the power generation site.

[0048] In other embodiments, the predicted vehicle flow of the plurality of charging sites can be comprehensively predicted according to historical vehicle flow data, real-time traffic information data, and holiday information of each charging site, and the prediction method is not limited in the present application.

[0049] Step 102, based on the predicted vehicle flow of each charging site, determine the predicted power demand of each charging site.

[0050] That is, after predicting the predicted vehicle flow of the charging site at the target time node, the predicted power demand of the charging site can be further predicted. Specifically, the probability of electric vehicles charging at the charging site can be predicted according to the predicted proportion of electric vehicles in the vehicle flow and the charging probability, and then the predicted power demand of the charging site can be predicted.

[0051] It should be understood that the prediction method of the predicted power demand is not limited in the present application.

[0052] Step 103, based on the predicted power generation of the plurality of power generation sites and the predicted power demand of the plurality of charging sites, determine at least one power supply site and at least one power demand site, and the available power of each power supply site and the required power of each power demand site.

[0053] It should be noted that the power supply site is a site with predicted power generation greater than predicted power demand, including but not limited to a power generation site that only generates power and does not supply power, and a power generation-supply fusion site that both generates power and supplies power. The power demand site is a site with predicted power generation less than predicted power demand, including but not limited to a power supply site that only supplies power and does not generate power, and a power generation-supply fusion site that both generates power and supplies power.

[0054] It should be understood that for the power generation site that only generates power and does not supply power, the predicted power demand of the site is 0, and for the power supply site that only supplies power and does not generate power, the predicted power generation of the site is 0.

[0055] Specifically, for each point, the predicted power generation and the predicted power demand of the point are obtained respectively, and then the size relationship between the predicted power generation and the predicted power demand is judged. If the predicted power generation is greater than the predicted power demand, the point is determined as a power supply point, and the difference between the predicted power generation and the predicted power demand is taken as the available power. If the predicted power generation is less than the predicted power demand, the point is determined as a power demand point, and the difference between the predicted power demand and the predicted power generation is taken as the required power. If the predicted power generation is equal to the predicted power demand, the point is determined as other point, that is, the point does not belong to the power supply point or the power demand point.

[0056] In step 104, based on the at least one power supply point and the corresponding available power thereof and the at least one power demand point and the corresponding required power thereof, an energy scheduling strategy is determined to transfer the available power of the at least one power supply point to the at least one power demand point through a mobile energy storage unit.

[0057] That is, after the available power of the power supply point and the required power of the power demand point are determined, the application further generates an energy scheduling strategy to realize the transfer of the available power of the power supply point to the at least one power demand point through the scheduling of the mobile energy storage unit. The mobile energy storage unit is a movable electric energy storage device, including but not limited to a mobile energy storage vehicle and the like.

[0058] Therefore, the energy scheduling method based on traffic flow prediction provided by the embodiments of the application realizes the transfer of the available power of the power supply point to the power demand point by predicting the predicted power generation and the traffic flow of each power generation point and charging point at the target time node, obtaining the at least one power supply point and the corresponding available power thereof and the at least one power demand point and the corresponding required power thereof, and generating the energy scheduling strategy of the mobile energy storage unit according to the at least one power supply point and the corresponding available power thereof and the at least one power demand point and the corresponding required power thereof. The energy scheduling method effectively reduces the power shortage of the power demand point, meets the charging demand under the traffic flow condition, guarantees the stable operation of the highway energy system through the mobile energy storage unit, realizes the collaborative scheduling of traffic flow and energy supply, improves the flexibility and reliability of the highway energy system, and effectively makes up for the deficiency of the power grid energy scheduling.

[0059] In a feasible embodiment, as shown in FIG. 2, Figure 2 In step 104, based on the at least one power supply point and the corresponding available power thereof and the at least one power demand point and the corresponding required power thereof, the energy scheduling strategy is determined, including:

[0060] In step 201, based on the at least one power supply point and the corresponding available power thereof and the at least one power demand point and the corresponding required power thereof, at least one group of power supply and demand point candidate combinations is determined.

[0061] Optionally, each group of supply-demand point candidate combination includes at least one power supply point and at least one power demand point.

[0062] For example, each group of supply-demand point candidate combination can include one power supply point and one power demand point, i.e., the power supply point provides the power demand point with surplus power, in other words, the available power of the power supply point is transferred to the power demand point through the mobile energy storage unit to reduce the required power of the power demand point, thereby ensuring the power supply demand of the power demand point.

[0063] Alternatively, each group of power supply point candidate combination can include multiple power supply points and one power demand point, i.e., the multiple power supply points in the combination collectively provide the power demand point with surplus power, in other words, the available power of the multiple power supply points is transferred to the power demand point through the mobile energy storage unit. The transfer of the available power of the multiple power supply points can be realized simultaneously through multiple mobile energy storage units, or realized sequentially through one mobile energy storage unit, which is determined by the subsequent candidate scheduling strategy.

[0064] Alternatively, each group of power supply point candidate combination can include one power supply point and multiple power demand points, i.e., the one power supply point in the combination provides the multiple power demand points with surplus power. The transfer of the available power of the one power supply point can be realized simultaneously through multiple mobile energy storage units, or realized sequentially through one mobile energy storage unit, which is determined by the subsequent candidate scheduling strategy.

[0065] Step 202, for each group of supply-demand point candidate combination, a candidate scheduling strategy is generated.

[0066] It should be noted that the energy scheduling strategy at least includes the transportation route of the mobile energy storage unit.

[0067] That is, the candidate scheduling strategy is used to plan the transfer route of the available power of the power supply point in each group of supply-demand point candidate combination to the power demand point by using the mobile energy storage unit.

[0068] In a feasible embodiment, the state information of at least one mobile energy storage unit is acquired, and based on the state information of at least one mobile energy storage unit, a candidate scheduling strategy is generated for each group of supply-demand point candidate combination.

[0069] The state information of the mobile energy storage unit includes at least one of the current position, the current power, the capacity, the remaining capacity, and the charging and discharging efficiency of the mobile energy storage unit.

[0070] Specifically, the transportation route of the mobile energy storage unit from the current position to the power supply point and the power demand point in the supply-demand point candidate combination can be generated according to the current position of the mobile energy storage unit, and the transportation route is taken as the candidate scheduling strategy corresponding to the supply-demand point candidate combination.

[0071] Step 203, taking at least one candidate scheduling strategy satisfying the constraint condition corresponding to the energy scheduling strategy as the energy scheduling strategy.

[0072] That is, after obtaining the candidate scheduling strategy corresponding to each group of supply and demand point position candidate combination, the candidate scheduling strategy is screened by using the constraint condition corresponding to the energy scheduling strategy to obtain the final available energy scheduling strategy.

[0073] In one possible embodiment, the energy scheduling strategy should satisfy at least one of the following constraint conditions:

[0074] (1) the required power of at least one power demand point is reduced to a minimum;

[0075] (2) the transportation cost of the mobile energy storage unit in the transfer process is minimized;

[0076] (3) the mobile energy storage unit completes the charging operation at the power supply point and the discharging operation at the power demand point before the target time.

[0077] As for constraint condition (1), that is, using constraint condition (1) to minimize the required power in the entire power supply system, in other words, to ensure that as many charging points as possible can provide stable power supply.

[0078] As for constraint condition (2), that is, the transportation cost of the mobile energy storage unit for power transfer is minimized. Optionally, the transportation cost is related to the transportation distance and transportation time between the power supply point and the power demand point. Preferably, the smaller the transportation distance between the power supply point and the power demand point, the lower the transportation cost.

[0079] As for constraint condition (3), that is, the power is transferred to the power demand point before the required power at the power demand point is generated, so that the power demand point will not be in a lack of power (power demand) situation.

[0080] In one optional embodiment, the constraint condition can be expressed by the following formula:

[0081]

[0082] wherein Y (t,i,j,k) is the energy scheduling strategy corresponding to the current time t, t is the current time, T is the scheduling time period set, i is the power demand point, C is the power demand point set, a i is the i-th power demand point penalty coefficient, is the actual power demand of the i-th power demand point at the target time node, k is the k-th mobile energy storage unit, K is the mobile energy storage unit set, b kis the transportation cost per unit distance of the kth mobile energy storage unit, j is the power supply point, E is the candidate combination set of power supply points and demand points, d ij is the transportation distance between the power supply point and the demand point, is the movement of the kth mobile energy storage unit from the power supply point to the demand point at time t.

[0083] That is, the combination of the power supply point i, the demand point j and the transportation distance of the mobile energy storage unit k with the minimum penalty and the minimum transportation cost can be determined by using the above constraint formula, and it is taken as the energy scheduling strategy screened.

[0084] It should be understood that the energy scheduling strategy can also include other constraint conditions such as power balance constraint, mobile energy storage unit charging and discharging rate limit constraint, mobile energy storage unit energy storage capacity limit constraint, mobile energy storage unit position-action consistency constraint, mobile path constraint, non-negativity constraint, etc. The specific selection is based on the application scene and actual situation, and the present application does not make specific limitation.

[0085] In a feasible embodiment, in order to further ensure that the energy scheduling strategy can be consistent with the real-time changes of the predicted power generation and the predicted traffic flow, the present application further proposes a step of adjusting the energy scheduling strategy in real time.

[0086] Specifically, based on at least one power supply point and its corresponding available power and at least one demand point and its corresponding required power, an energy scheduling sequence corresponding to a plurality of target time nodes is determined, the energy scheduling strategy sequence is executed in sequence, and after each execution of the energy scheduling strategy, the energy scheduling sequence is updated based on a fixed time interval.

[0087] That is, the corresponding energy scheduling strategy sequence of the continuous plurality of target time nodes of the fixed time interval can be generated at the current time, and the energy scheduling strategy corresponding to the first target time node is executed, that is, the first energy scheduling strategy is executed at the current time to solve the supply and demand balance problem of the first target time node. Then, after indicating the execution of the energy scheduling strategy, the energy scheduling strategy sequence is updated based on the fixed time interval, that is, a new target time node sequence is formed by adding one target time node backward, and an energy scheduling strategy sequence is generated for the target time node sequence, so that the first energy scheduling strategy in the new energy scheduling strategy sequence is executed in real time at the next indication execution. In this way, the first energy scheduling strategy in the new energy scheduling strategy sequence is executed in real time at the next indication execution.

[0088] For example, the energy scheduling strategy sequence can be updated by using the following formula:

[0089]

[0090] wherein P t|t+HThe energy scheduling strategy executed at the current time t, t is the current time, H is the length of the time window composed of multiple target time nodes, J l The optimization target at time t (i.e. the minimum value corresponding to the constraint formula described above), Q l The state information of the mobile energy storage unit at time t, P l The energy scheduling strategy at time t, p is a weight coefficient, used to punish the deviation of the current optimization result from the last round of optimization result, The energy scheduling strategy planned for time t in the last round of energy scheduling strategy sequence.

[0091] In summary, the energy scheduling method based on traffic flow prediction provided by the embodiments of the present application predicts the predicted power generation of each power generation point and the predicted traffic flow of each charging point at the target time node, and then obtains at least one power supply point and its corresponding available power supply and at least one power demand point and its corresponding required power demand, and then generates an energy scheduling strategy for the mobile energy storage unit according to at least one power supply point and its corresponding available power supply and at least one power demand point and its corresponding required power demand, realizes the transfer of the available power supply of the power supply point to the power demand point, effectively reduces the power shortage of the power demand point, meets the charging demand under the traffic flow condition, guarantees the stable operation of the highway energy system through the mobile energy storage unit, realizes the collaborative scheduling of traffic flow and energy supply, improves the flexibility and reliability of the highway energy system, and effectively makes up for the deficiency of the power grid energy scheduling.

[0092] It should be noted that although the operations of the method of the present application are described in a specific order in the accompanying drawings, this does not require or imply that the operations must be performed in that specific order, or that all of the shown operations must be performed to achieve the desired result.

[0093] Figure 3 A block schematic diagram of the energy scheduling device based on traffic flow prediction provided by an embodiment of the present application is shown.

[0094] As Figure 3 shown, the energy scheduling device based on traffic flow prediction 10 comprises:

[0095] A prediction module 11 is configured to predict the predicted power generation of multiple power generation points and the predicted traffic flow of multiple charging points for a target time node;

[0096] A determination module 12 is configured to determine the predicted power demand of each charging point based on the traffic flow of each charging point.

[0097] The analysis module 13 is configured to determine at least one power supply point and at least one power demand point, and the available power of each power supply point and the required power of each power demand point based on the predicted power generation of the plurality of power generation points and the predicted power demand of the plurality of power charging points.

[0098] The decision module 14 is configured to determine an energy scheduling strategy based on the at least one power supply point and the available power thereof, and the at least one power demand point and the required power thereof, so as to transfer the available power of the at least one power supply point to the at least one power demand point by means of the mobile energy storage unit.

[0099] In some embodiments, the decision module 14 is further configured to:

[0100] determine at least one group of power supply and demand point candidate combinations based on the at least one power supply point and the available power thereof, and the at least one power demand point and the required power thereof.

[0101] generate a candidate scheduling strategy for each group of power supply and demand point candidate combinations.

[0102] determine at least one candidate scheduling strategy that satisfies the constraint condition corresponding to the energy scheduling strategy as the energy scheduling strategy.

[0103] In some embodiments, the decision module 14 is further configured to:

[0104] obtain state information of at least one mobile energy storage unit, wherein the state information comprises at least one of a current position, a current power, a capacity, a remaining capacity, and a charging and discharging efficiency of the mobile energy storage unit.

[0105] generate the candidate scheduling strategy for each group of power supply and demand point candidate combinations based on the state information of at least one mobile energy storage unit.

[0106] In some embodiments, the energy scheduling strategy comprises at least a transportation route of the mobile energy storage unit.

[0107] In some embodiments, the target time node is a plurality of consecutive fixed time intervals, and the decision module 14 is further configured to:

[0108] determine a sequence of energy scheduling strategies corresponding to the plurality of target time nodes based on the at least one power supply point and the available power thereof, and the at least one power demand point and the required power thereof.

[0109] execute the sequence of energy scheduling strategies in sequence, and update the sequence of energy scheduling strategies based on the fixed time interval after each execution of the energy scheduling strategy.

[0110] In some embodiments, the energy scheduling strategy satisfies at least one of the following constraints:

[0111] a required power of at least one of the required power points is reduced to a minimum;

[0112] a transportation cost of the mobile energy storage unit in the transferring process is minimized; and

[0113] the mobile energy storage unit completes the charging operation at the power supply point and the discharging operation at the required power point before the target time.

[0114] It should be understood that the modules or modules described in the energy scheduling device 10 based on the traffic flow prediction correspond to the respective steps in the method described with reference to Figure 1 The operations and features described above for the method are equally applicable to the energy scheduling device 10 based on the traffic flow prediction and the modules contained therein, and will not be repeated here. The energy scheduling device 10 based on the traffic flow prediction can be pre-implemented in the browser or other secure application of the electronic device, or can be loaded into the browser or secure application thereof of the electronic device by downloading or the like. The respective modules in the energy scheduling device 10 based on the traffic flow prediction can cooperate with the modules in the electronic device to realize the schemes of the embodiments of the present application.

[0115] In the foregoing detailed description, several modules or units are mentioned. The division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into a plurality of modules or units.

[0116] The following refers to Figure 4 , Figure 4 shows a structural schematic diagram of a computer system of an electronic device or server suitable for implementing the embodiments of the present application,

[0117] As Figure 4 shown, the computer system includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 402 or programs loaded into a random access memory (RAM) 403 from a storage portion 408. In the RAM 403, various programs and data required for operation instructions of the system are also stored. The CPU 401, the ROM 402, and the RAM 403 are connected to each other through a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0118] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, and the like; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), and a speaker; a storage section 408 including a hard disk; and a communication section 409 including a network interface card such as a LAN card or a modem. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, and the like, is installed in the drive 410 as needed, so that a computer program read therefrom can be installed into the storage section 408 as needed.

[0119] In particular, according to the embodiment of the present application, the above reference flow chart Figure 2 The described process can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 409, and / or installed from a removable medium 411. When the computer program is executed by the central processing unit (CPU) 401, the above-mentioned functions defined in the system of the present application are executed.

[0120] It should be noted that the computer-readable medium shown in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this application, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or device. In this application, a computer-readable signal medium can include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. This propagated data signal can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical cable, RF, or any suitable combination thereof.

[0121] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operating instructions of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the aforementioned module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than the order marked in the accompanying drawings. For example, the boxes represented by two connections can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operating instruction, or can be implemented using a combination of dedicated hardware and computer instructions.

[0122] The units or modules described in the embodiments of the present application can be implemented in the form of software or in the form of hardware. The described units or modules can also be arranged in a processor, for example, a processor can be described as including a prediction module, a determination module, an analysis module, and a decision module. Among them, the name of these units or modules does not constitute a limitation on the units or modules themselves in some cases, for example, the prediction module can also be described as "predicting the predicted power generation of the multiple power generation points and the predicted vehicle flow of the multiple charging points for the target time node".

[0123] As another aspect, the present application also provides a computer readable storage medium, which can be included in the electronic device described in the above embodiments, or can exist separately without being assembled into the electronic device. The above computer readable storage medium stores one or more programs, when the programs are used by one or more processors to execute the energy scheduling method based on vehicle flow prediction described in the present application.

[0124] The above description is merely preferred embodiments of the present application and a description of the principles of the technology used. Those skilled in the art should understand that the disclosed range in the present application is not limited to the technical solutions formed by the specific combinations of the above technical features, and also covers other technical solutions formed by any combination of the above technical features or equivalent features without departing from the above disclosed concept. For example, the above features are replaced with the technical features disclosed in the present application (but not limited to) having similar functions to form technical solutions.

Claims

1. An energy scheduling method based on vehicle flow prediction, characterized in that: include: Forecast the power generation of multiple power generation points and the traffic flow of multiple charging points at the target time node; Determining a predicted power requirement for each charging point based on the predicted traffic flow at each charging point; Based on the predicted power generation of the plurality of power generation points and the predicted power demand of the plurality of charging points, determining at least one power supply point and at least one power demand point, as well as the power supply amount corresponding to each power supply point and the power demand corresponding to each power demand point; Based on at least one of the power supply points and its corresponding available power and at least one of the power demand points and its corresponding required power, an energy scheduling strategy is determined to transfer the available power of at least one of the power supply points to at least one of the power demand points through a mobile energy storage unit.

2. The energy scheduling method based on vehicle flow prediction according to claim 1 is characterized in that: The determining of the energy scheduling strategy based on the at least one power supply point and its corresponding available power and the at least one power demand point and its corresponding required power includes: Determining at least one candidate combination of supply and demand points based on at least one power supply point and its corresponding available power and at least one power demand point and its corresponding required power; For each set of candidate supply and demand point combinations, generating a candidate scheduling strategy; At least one of the candidate scheduling strategies that satisfies the constraint conditions corresponding to the energy scheduling strategy is used as the energy scheduling strategy.

3. The energy scheduling method based on vehicle flow prediction according to claim 2 is characterized in that: Generating a candidate scheduling strategy for each candidate combination of supply and demand points includes: Acquiring status information of at least one of the mobile energy storage units; the status information including at least one of the current location, current power, capacity, remaining capacity, and charge / discharge efficiency of the mobile energy storage unit; Based on the status information of at least one of the mobile energy storage units, the candidate scheduling strategy is generated for each group of the candidate supply and demand point combinations.

4. The energy scheduling method based on vehicle flow prediction according to any one of claims 1 to 3, characterized in that: The energy scheduling strategy at least includes the transportation route of the mobile energy storage unit.

5. The energy scheduling method based on vehicle flow prediction according to claim 1 is characterized in that: The target time nodes are a plurality of consecutive nodes at a fixed time interval, and the method further includes: Determining an energy scheduling strategy sequence corresponding to a plurality of target time nodes based on at least one power supply point and its corresponding available power and at least one power demand point and its corresponding required power; The energy scheduling strategy sequence is executed in sequence, and the energy scheduling strategy sequence is updated based on the fixed time interval after each execution of the energy scheduling strategy.

6. The energy scheduling method based on vehicle flow prediction according to claim 1 is characterized in that: The energy scheduling strategy satisfies at least one of the following constraints: The power demand of at least one power demand point is reduced to a minimum; The transportation cost of the mobile energy storage unit during the transfer process is minimized; as well as The mobile energy storage unit completes the charging operation at the power supply point and the discharging operation at the power demand point before the target time.

7. An energy dispatching device based on vehicle flow prediction, characterized in that: include: The prediction module is used to predict the power generation of multiple power generation points and the traffic flow of multiple charging points at the target time node; a determination module, configured to determine a predicted power demand of each charging point based on the traffic flow of each charging point; an analysis module, configured to determine, based on the predicted power generation of the plurality of power generation points and the predicted power demand of the plurality of charging points, at least one power supply point and at least one power demand point, as well as the power supply amount corresponding to each power supply point and the power demand corresponding to each power demand point; The decision module is used to determine an energy scheduling strategy based on at least one power supply point and its corresponding available power and at least one power demand point and its corresponding required power, so as to transfer the available power of at least one power supply point to at least one power demand point through a mobile energy storage unit.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, it implements the energy scheduling method based on vehicle flow prediction as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the energy scheduling method based on vehicle flow prediction as described in any one of claims 1 to 6 is implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the energy scheduling method based on vehicle flow prediction described in any one of claims 1 to 6 is implemented.

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