Mobile energy storage optimal scheduling method and device considering economy and medium

Abstract

The invention relates to the technical field of energy storage optimization scheduling, in particular to a mobile energy storage optimization scheduling method and device considering economy and a medium, and the method comprises the steps: taking economic optimization as a target, and building a target function of an economy optimization scheduling model by comprehensively considering the grid-connected income, operation cost and loss of a mobile energy storage system; setting spatial constraint and power constraint conditions of mobile energy storage to ensure that the time plan, spatial movement and charging and discharging power logic of the mobile energy storage are correct; and the objective function is optimized and solved based on the set spatial constraint and power constraint conditions, and an optimal scheduling strategy is obtained. Compared with the prior art, the method has the advantages that the optimal scheduling model is given for the economical scheduling of the mobile energy storage, the optimal mobile energy storage scheduling scheme can be obtained, and the operation economy of the mobile energy storage is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage optimization scheduling, and in particular to a mobile energy storage optimization scheduling scheme taking economic efficiency into consideration. Background Art

[0002] Commonly used energy storage includes pumped storage, electrochemical energy storage, etc. These energy storage capacities are large, but their locations are fixed, which is not conducive to flexible adjustment. Therefore, mobile energy storage system (MESS) came into being. It usually uses mobile energy storage vehicles as carriers and carries energy storage equipment. It not only has high flexibility in time and space, but can also be used as an emergency power supply to provide customers with emergency power supply services, effectively supporting energy management optimization, flow distribution improvement, and voltage control of distribution networks. Therefore, the importance of mobile energy storage is increasing. At present, each power company is equipped with a certain number of MESS units, which use vehicles such as cars to realize the mobility of energy storage systems. It has been widely used in some application scenarios of distribution networks, such as seasonal regional load curve adjustment and temporary capacity expansion. With the increasing number of MESS units, their access location and capacity have a deeper impact on the economy and stability of distribution network operation. Therefore, it is necessary to study effective scheduling methods to improve the utilization efficiency of mobile energy storage. Summary of the invention

[0003] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and provide a mobile energy storage optimization scheduling method, device and medium taking economic efficiency into consideration.

[0004] The purpose of the present invention can be achieved by the following technical solutions:

[0005] As a first aspect of the present invention, a method for optimizing and scheduling mobile energy storage considering economic efficiency is provided, the steps comprising:

[0006] Taking economic optimization as the goal, the objective function of the economic optimization scheduling model is constructed by comprehensively considering the grid-connected benefits, operating costs and losses of the mobile energy storage system;

[0007] Set the space constraints and power constraints of mobile energy storage to ensure the correctness of the time plan, space movement, and charging and discharging power logic of mobile energy storage;

[0008] Based on the set space constraints and power constraints, the objective function is optimized and solved to obtain the optimal scheduling strategy. As a preferred technical solution, the objective function is set as follows:

[0009] maxM(x)=K(x)-Y(x)-S(x)

[0010] Among them, M represents the comprehensive economic efficiency of the mobile energy storage system, K(·) represents the grid-connected income, Y(·) represents the operating cost, S(·) represents the loss, and x represents the mobile energy storage scheduling strategy.

[0011] As a preferred technical solution, the grid connection income is expressed as:

[0012]

[0013] Among them, PC tn represents the charging power of mobile energy storage at node n at time t; PR tn represents the discharge power of mobile energy storage at node n at time t; t represents the electricity price at time t.

[0014] As a preferred technical solution, the operating cost Y(·) is expressed as:

[0015] Y(x)=Ty

[0016] Among them, T is the total operation time of mobile energy storage, and y is the unit time usage cost of mobile energy storage.

[0017] As a preferred technical solution, the loss S(·) is expressed as:

[0018]

[0019] Among them, q is the fixed loss cost of the battery in each scheduling cycle; s is the unit charge and discharge capacity loss cost.

[0020] As a preferred technical solution, the mobile energy storage scheduling strategy is expressed as:

[0021] x=[w tn ,e tnm ,α tn ,β tn ,PR tn ,PC tn ]

[0022] Among them, w tn is the position variable, indicating that the MESS is parked at node n at time t; e tnm is the action variable, indicating whether MESS moves from node n to node m at time t; α tn is the action variable, indicating whether MESS reaches node n at time t; β tn is an action variable, indicating whether MESS leaves node n at time t.

[0023] As a preferred technical solution, the space constraints include:

[0024] Mobile energy storage can only be on one node at a time, and cannot stop at a node during movement, as described below:

[0025]

[0026] The mobile energy storage position variable and action variable should change in tandem, as described below:

[0027]

[0028] α tn -β tn =w tn -w (t-1)n

[0029] Wherein, N is the set of nodes in the distribution network system.

[0030] As a preferred technical solution, the power constraint conditions include:

[0031] The charging and discharging power of mobile energy storage at any node and at any time cannot exceed the maximum power, which is described as follows:

[0032] 0≤PR tn ,PC tn ≤P MAX ,n∈N

[0033] The amount of electricity stored in mobile energy storage at any time should be less than the maximum energy storage capacity, as described below:

[0034] 0≤E t ≤E MAX

[0035]

[0036] Among them, P MAX Indicates the maximum charge and discharge power of MESS; E t represents the storage capacity of MESS at time t; E MAX represents the maximum storage capacity of MESS; h represents the charging efficiency of MESS.

[0037] As a second aspect of the present invention, there is provided a mobile energy storage optimization scheduling device taking economy into consideration, comprising a memory, a processor, and a program stored in the memory, wherein when the processor executes the program, the mobile energy storage optimization scheduling method taking economy into consideration as described above is implemented.

[0038] As a third aspect of the present invention, there is provided a storage medium having a program stored thereon, which, when executed, implements the above-mentioned mobile energy storage optimization scheduling method considering economy.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] The present invention proposes a mobile energy storage optimization scheduling method considering economy. The method provides an optimization scheduling model for the economic scheduling of mobile energy storage, including objective function and space and power constraints. The objective function takes economic optimization as the goal, and considers the space-time constraints and power constraints of mobile energy storage at the same time, ensuring the correct logic of the time plan, space movement and charging and discharging power of mobile energy storage, and ensuring that the output plan is actually feasible; and the particle swarm algorithm is used to solve the model to obtain the optimal mobile energy storage scheduling plan, effectively improving the economic efficiency of mobile energy storage operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a flow chart of the mobile energy storage optimization scheduling method considering economy of the present invention;

[0042] Figure 2 Schematic diagram of the spatial constraint model of the present invention; 2a) t = 1, 2; 2b) t = 3; 2c) t = 4;

[0043] Figure 3 A time-sharing electricity price diagram for each point in one of the implementation examples of the present invention;

[0044] Figure 4 This is a diagram showing the optimization of the scheduling strategy results of one of the implementation examples of the present invention;

[0045] Figure 5 This is a comparison chart of optimized scheduling solutions for one of the implementation examples of the present invention. DETAILED DESCRIPTION

[0046] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0047] Example 1

[0048] As a first aspect of the present invention, the present invention proposes a mobile energy storage optimization scheduling method considering economy, the method flow chart is as follows: Figure 1 As shown. An economic optimization scheduling model is constructed, and the time and space constraints and power constraints of mobile energy storage are taken into account, so as to ensure the correct logic of the time plan, spatial movement and charging and discharging power of mobile energy storage, ensure the feasibility of the output plan, and determine the objective function with economic optimization as the goal. The particle swarm algorithm is used to optimize and solve it, and finally output the optimal scheduling strategy.

[0049] 1. Space constraints

[0050] In order to clearly describe the spatial state of MESS, the following variables are introduced:

[0051] (1)e tnm is the action variable, indicating whether MESS moves from node n to node m at time t.

[0052] (2)w tn is a position variable, indicating that the MESS is parked at node n at time t.

[0053] (3)α tn is an action variable, indicating whether MESS reaches node n at time t.

[0054] (4)β tn is an action variable, indicating whether MESS leaves node n at time t.

[0055] The above variables all have values ​​of 0 or 1.

[0056] by Figure 2 For example, at the four moments t=1, t=2, t=3, and t=4, the spatiotemporal information of mobile energy storage is described by the above variables as follows:

[0057] At t=1 and t=2, the MESS stops at node 1 for charging and discharging, and its spatiotemporal information is described as follows:

[0058] e tnm =0,n,m∈[1,2,3,4,5] (1)

[0059] w tn =[w t1 ,w t2 ,w t3 ,w t4 ,w t5 ]=[1,0,0,0,0] (2)

[0060] α tn =0,β tn =0,n∈[1,2,3,4,5] (3)

[0061] At t=3, MESS leaves node 1 and goes to node 3. Its spatiotemporal information is described as follows:

[0062] e t13 =1 (4)

[0063] w tn =[w t1 ,w t2 ,w t3 ,w t4 ,w t5 ]=[0,0,0,0,0] (5)

[0064] α tn =0,β 31 =1,n∈[1,2,3,4,5] (6)

[0065] At t=4, the MESS stops at node 3 for charging and discharging, and its spatiotemporal information is described as follows:

[0066] e tnm =0,n,m∈[1,2,3,4,5] (7)

[0067] w tn =[w t1 ,w t2 ,w t3 ,w t4 ,w t5 ]=[0,0,1,0,0] (8)

[0068] α 43 =1,β tn =0,n∈[1,2,3,4,5] (9)

[0069] The main spatial constraints of MESS include the following:

[0070] 1.1. MESS can only be on one node at a time and cannot stop at a node during movement, as described below:

[0071]

[0072] Wherein, N is the set of nodes in the distribution network system.

[0073] 1.2. The MESS position variables and action variables should change in tandem, as described below:

[0074]

[0075]

[0076] α tn -β tn =w tn -w (t-1)n (13)

[0077] 2. Power Constraints

[0078] In order to clearly describe the power state of MESS, the following variables are introduced:

[0079] (2.1)PR tn It represents the discharge power of MESS at node n at time t.

[0080] (2.2) PC tnIt represents the charging power of MESS at node n at time t.

[0081] (2.3)P MAX Indicates the maximum charge and discharge power of MESS.

[0082] (2.4)E t Represents the storage capacity of MESS at time t.

[0083] (2.5)E MAX Indicates the maximum storage capacity of MESS.

[0084] (2.6)h represents the charging efficiency of MESS.

[0085] The main spatial constraints of MESS include the following:

[0086] 2.1. The charging and discharging power of MESS at any node and at any time cannot exceed the maximum power, as described below:

[0087] 0≤PR tn ,PC tn ≤P MAX ,n∈N (14)

[0088] 2.2. The amount of electricity stored in MESS at any time should be less than the maximum energy storage capacity, as described below:

[0089] 0≤E t ≤E MAX (15)

[0090]

[0091] 3. Objective Function

[0092] The present invention takes economic optimization as the goal, comprehensively considering factors such as the grid connection benefits, operating costs and losses of the MESS system. The specific objective function is set as follows:

[0093] maxM(x)=K(x)-Y(x)-S(x) (17)

[0094] Among them, M represents the comprehensive economic efficiency of the MESS system, K(·) represents the grid-connected income, Y(·) represents the operating cost, S(·) represents the loss, and x represents the MESS scheduling strategy, which are respectively expressed as:

[0095] x=[w tn ,e tnm ,α tm ,β tn ,PR tn ,PC tn ] (18)

[0096]

[0097] Among them, Y is the total operation time of MESS, λ t represents the electricity price at time t.

[0098] Y(x)=Ty (20)

[0099] Where y is the MESS usage cost per unit time.

[0100]

[0101] Among them, q is the fixed loss cost of the battery in each dispatch cycle, and s is the unit charge and discharge capacity loss cost.

[0102] The optimization solution of the objective function can be achieved using iterative algorithms such as particle swarm optimization.

[0103] Example 2

[0104] In this example, a 5-node calculation example is set to verify the effectiveness of the economic optimization scheduling model proposed in the present invention. At the same time, in order to more fully verify the performance of the algorithm, the calculation example is set to adopt the time-of-use electricity price model divided by region, and each location has its own time-of-use electricity price curve, such as Figure 3 As shown: The geographical information between the nodes in the example is shown in Table 1. The charging and discharging efficiency of the MESS unit is set to 95%, and the maximum capacity is 1.5MWh.

[0105] Table 1 Geographic information of the example

[0106] node A B C D E A 0 58.8 17 88 114 B 58.8 0 35 51.2 55.4 C 17 35 0 81.4 65.3 D 88 51.2 81.4 0 97 E 114 55.4 65.3 97 0

[0107] After optimized scheduling, each MESS unit moves and charges and discharges between nodes. The scheduling result of one MESS unit is as follows: Figure 4 As shown: It can be seen from the figure that the MESS unit is discharged at points A and B, and charged at points A, C, D, and E respectively.

[0108] To verify the effectiveness of the method of the present invention, two methods are used for comparison: (1) Common scheduling strategy, that is, at each moment, the node with the better electricity price among the two closest nodes is selected for charging and discharging. (2) Random scheduling, taking the average value of 100 random scheduling results. The comparison results are shown in the figure below:

[0109] It can be seen from the results that the optimized scheduling method of the present invention improves the economy by 18% compared with the ordinary scheduling method and by 46% compared with the random scheduling method, and the optimization effect is quite significant.

[0110] Example 3

[0111] As a second aspect of the present invention, the present application also provides an electronic device, comprising: one or more processors; a memory for storing one or more programs; when the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned mobile energy storage optimization scheduling method considering economy. In addition to the above-mentioned processor, memory and interface, any device with data processing capability in which the device in the embodiment is located may also include other hardware according to the actual function of the device with data processing capability, which will not be described in detail.

[0112] Example 4

[0113] As a third aspect of the present invention, the present application also provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the mobile energy storage optimization scheduling method considering economy as described above. The computer-readable storage medium may be an internal storage unit of any device with data processing capability as described in any of the foregoing embodiments, such as a hard disk or a memory. The computer-readable storage medium may also be an external storage device, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), an SD card, a flash card (Flash Card), etc. equipped on the device. Furthermore, the computer-readable storage medium may also include both an internal storage unit and an external storage device of any device with data processing capability. The computer-readable storage medium is used to store the computer program and other programs and data required by any device with data processing capability, and may also be used to temporarily store data that has been output or is to be output.

[0114] The preferred specific embodiments of the present invention are described in detail above. It should be understood that a person skilled in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, any technical solution that can be obtained by a person skilled in the art through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A mobile energy storage optimization scheduling method considering economic efficiency, characterized in that the steps include: Taking economic optimization as the goal, the objective function of the economic optimization scheduling model is constructed by comprehensively considering the grid-connected benefits, operating costs and losses of the mobile energy storage system; Set the space constraints and power constraints of mobile energy storage to ensure the correctness of the time plan, space movement, and charging and discharging power logic of mobile energy storage; The objective function is optimized based on the set space constraints and power constraints to obtain the optimal scheduling strategy.

2. According to the method of optimizing the dispatching of mobile energy storage considering economic efficiency according to claim 1, it is characterized in that: Said The objective function is set as follows: maxM(x)=K(x)-Y(x)-S(x) Among them, M represents the comprehensive economic efficiency of the mobile energy storage system, K(·) represents the grid-connected income, Y(·) represents the operating cost, S(·) represents the loss, and x represents the mobile energy storage scheduling strategy.

3. A mobile energy storage optimization scheduling method considering economy according to claim 2, characterized in that: The grid connection income is expressed as: Among them, PC tn represents the charging power of mobile energy storage at node n at time t; PR tn represents the discharge power of mobile energy storage at node n at time t; t represents the electricity price at time t.

4. According to the method of optimizing the dispatching of mobile energy storage considering economic efficiency according to claim 2, it is characterized in that: The operating cost Y(·) is expressed as: Y(x)=Ty Among them, Y is the total operation time of mobile energy storage, and y is the unit time usage cost of mobile energy storage.

5. The method for optimizing and dispatching mobile energy storage considering economic efficiency according to claim 2 is characterized in that: The loss S(·) is expressed as: Among them, q is the fixed loss cost of the battery in each scheduling cycle; s is the unit charge and discharge capacity loss cost.

6. The method for optimizing and dispatching mobile energy storage considering economic efficiency according to claim 2 is characterized in that: The mobile energy storage dispatch strategy is expressed as: x=[w tn ,e tnm ,a tn ,b tn ,PR tn ,PC tn ] Among them, w tn is the position variable, indicating that the MESS is parked at node n at time t; e tnm is the action variable, indicating whether MESS moves from node n to node m at time t; α tn is the action variable, indicating whether MESS reaches node n at time t; β tn is an action variable, indicating whether MESS leaves node n at time t.

7. The method for optimizing and dispatching mobile energy storage considering economic efficiency according to claim 1, characterized in that: The space constraints include: Mobile energy storage can only be on one node at a time, and cannot stop at a node during movement, as described below: The mobile energy storage position variable and action variable should change in tandem, as described below: a tn -b tn =w tn -w (t-1)n Wherein, N is the set of nodes in the distribution network system.

8. The method for optimizing and dispatching mobile energy storage considering economic efficiency according to claim 1, characterized in that: The power constraints include: The charging and discharging power of mobile energy storage at any node and at any time cannot exceed the maximum power, which is described as follows: 0≤PR tn ,PC tn ≤P MAX ,n∈N The amount of electricity stored in mobile energy storage at any time should be less than the maximum energy storage capacity, as described below: 0≤E t ≤E MAX Among them, P MAX Indicates the maximum charge and discharge power of MESS; E t represents the storage capacity of MESS at time t; E MAX represents the maximum storage capacity of MESS; h represents the charging efficiency of MESS.

9. A mobile energy storage optimization scheduling device considering economy, comprising a memory, a processor, and a program stored in the memory, characterized in that: When the processor executes the program, the mobile energy storage optimization scheduling method considering economy as described in any one of claims 1-8 is implemented.

10. A storage medium having a program stored thereon, characterized in that: When the program is executed, the mobile energy storage optimization scheduling method considering economy as described in any one of claims 1-8 is implemented.

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