Energy storage management method

Abstract

The present invention discloses a method for energy storage management, which belongs to the field of energy storage technology, and includes the following steps: S1, obtaining a load set of a to-be-managed area; S2, determining the load of the to-be-managed area at the current moment based on the load set of the to-be-managed area; S3, determining the energy storage result according to the load of the to-be-managed area at the current moment. The present invention can store excess electric energy during peak periods of electricity demand and release it during low loads, effectively balancing the supply and demand relationship of the power system and flexibly adjusting the energy storage strategy.

Description

Technical Field

[0001] The present invention belongs to the field of energy storage technology, and in particular relates to an energy storage management method. Background Art

[0002] In the context of energy transformation and rapid economic and social development, the stable operation and efficient management of the power system are particularly important. With the large-scale access of renewable energy (such as solar and wind energy), the load characteristics of the power grid have changed significantly, which has put forward higher requirements on the stability and regulation capabilities of the power grid, and it is necessary to introduce more flexible and efficient energy storage energy regulation methods. Energy storage technology can store excess electricity during peak periods of electricity demand and release it during low loads, effectively balancing the supply and demand of the power system. However, the existing energy storage management technology has poor grid load predictability and cannot achieve precise regulation of energy storage energy. Summary of the invention

[0003] In order to solve the above problems, the present invention proposes a storage energy management method.

[0004] The technical solution of the present invention is: a method for energy storage management comprises the following steps:

[0005] S1. Obtain the load set of the area to be managed;

[0006] S2. Based on the load set of the area to be managed, generate a load scaling factor, a first observed load factor, and a second observed load factor, and determine the load of the area to be managed at the current moment;

[0007] S3. Determine the energy storage result according to the load of the area to be managed at the current moment;

[0008] S2 includes the following sub-steps:

[0009] S21. Generate a load scaling factor according to a load set of the area to be managed;

[0010] S22, randomly selecting a node from a plurality of nodes in the area to be managed, and determining a first observed load factor according to a load relationship between the randomly selected node and the remaining nodes;

[0011] S23, randomly selecting a historical moment from the load set, and determining a second observed load factor according to the load amounts of the randomly selected historical moment and the remaining historical moments;

[0012] S24. Determine the load of the area to be managed at the current moment according to the load scaling factor, the first observed load factor and the second observed load factor.

[0013] In the present invention, in S3, a reasonable fluctuation range of the power grid load in the area to be managed is obtained; if the load at the current moment predicted by S2 is less than the reasonable fluctuation range, it is at a load trough and energy should be absorbed; if the load at the current moment predicted by S2 is greater than the reasonable fluctuation range, it is at a load peak and energy should be released, thereby effectively smoothing the fluctuation of the power grid load; if the load at the current moment predicted by S2 is within a reasonable fluctuation range, no operation is required for the time being.

[0014] In the present invention, according to the overall load of the load set of the area to be managed, a scaling factor is determined, which is used to adjust with the first observed load factor and the second observed load factor to predict the overall load of the area to be managed at the current moment. The first observed load factor considers the node distribution of the area to be managed from the perspective of the nodes divided by the grid; the second observed load factor considers the load changes of the area to be managed in the entire historical time from the perspective of the time concept.

[0015] Furthermore, S1 includes the following sub-steps:

[0016] S11, dividing the area to be managed into grids to obtain a number of nodes;

[0017] S12. Obtain the load of each node at different historical moments as a load set.

[0018] Furthermore, in S21, the calculation formula of the load scaling factor a is:

[0019] ; In the formula, represents the load of the qth node at the pth historical moment, P represents the total number of historical moments, Q represents the total number of nodes in the area to be managed, MAX represents the maximum load of the load set, and c represents a constant.

[0020] Further, S22 includes the following sub-steps:

[0021] S221, randomly selecting a node from a plurality of nodes in the area to be managed, and taking the average load of the randomly selected node at all historical moments as a random load attribute;

[0022] S222. Determine the load relationship between the remaining nodes and the randomly selected node according to the random load attribute;

[0023] S223. Calculate a first observed load factor based on the load relationship between the randomly selected node and the remaining nodes.

[0024] The beneficial effect of the above further scheme is: in the present invention, there is a difference in load between the randomly selected node and the remaining nodes, and the difference, that is, the load relationship, needs to be characterized. The first observed load factor is calculated based on the sizes of multiple load relationships, which can reflect the load conditions of different nodes.

[0025] Furthermore, in S222, the load relationship Z between the remaining qth nodes and the randomly selected node is q The expression is:

[0026] ; where NUM1 represents the number of loads of the remaining qth nodes with the same random load attribute among all the loads at all historical moments, NUM0 represents the number of loads of the remaining qth nodes with greater load attributes than the random load attribute among all the loads at all the historical moments, NUM2 represents the number of loads of the remaining qth nodes with less load attributes than the random load attribute among all the loads at all the historical moments, and AVE q represents the mean load of the remaining qth nodes at all historical moments, and ln(·) represents the logarithmic function.

[0027] Furthermore, in S223, the calculation formula of the first observation load factor r1 is:

[0028] ; In the formula, U represents the number of load relationship values ​​greater than 0, Q represents the total number of nodes in the area to be managed, and x represents the random load attribute.

[0029] Furthermore, in S23, the calculation formula of the second observation load factor r2 is:

[0030] ; In the formula, Q represents the total number of nodes in the area to be managed, h1 represents the load of the first node at a randomly selected historical moment, and h q represents the load of the qth node at a randomly selected historical moment, h Q represents the load of the Qth node at a randomly selected historical moment, max(·) represents the maximum value function, MIN represents the minimum load of the load set, and c represents a constant.

[0031] Furthermore, in S24, the calculation formula of the load h of the area to be managed at the current moment is:

[0032] ; In the formula, r1 represents the first observed load factor, r2 represents the second observed load factor, and a represents the load scaling factor.

[0033] The beneficial effects of the present invention are as follows: the present invention can evaluate the load of nodes in the management area at different times, generate a load scaling factor according to the overall load, generate a first observed load factor from the node perspective, generate a second observed load factor from the monitoring moment, and use the three factors to predict the load at the current moment, thereby determining how to handle energy storage. The present invention can store excess electric energy during peak electricity demand periods and release it at low loads, effectively balancing the supply and demand relationship of the power system and flexibly adjusting the energy storage strategy. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 The figure is a flow chart of the energy storage energy management method. DETAILED DESCRIPTION

[0035] The embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0036] like Figure 1 As shown, the present invention provides a method for energy storage management, comprising the following steps:

[0037] S1. Obtain the load set of the area to be managed;

[0038] S2. Based on the load set of the area to be managed, generate a load scaling factor, a first observed load factor, and a second observed load factor, and determine the load of the area to be managed at the current moment;

[0039] S3. Determine the energy storage result according to the load of the area to be managed at the current moment;

[0040] S2 includes the following sub-steps:

[0041] S21. Generate a load scaling factor according to a load set of the area to be managed;

[0042] S22, randomly selecting a node from a plurality of nodes in the area to be managed, and determining a first observed load factor according to a load relationship between the randomly selected node and the remaining nodes;

[0043] S23, randomly selecting a historical moment from the load set, and determining a second observed load factor according to the load amounts of the randomly selected historical moment and the remaining historical moments;

[0044] S24. Determine the load of the area to be managed at the current moment according to the load scaling factor, the first observed load factor and the second observed load factor.

[0045] In the present invention, according to the overall load of the load set of the area to be managed, a scaling factor is determined, which is used to adjust with the first observed load factor and the second observed load factor to predict the overall load of the area to be managed at the current moment. The first observed load factor considers the node distribution of the area to be managed from the perspective of the nodes divided by the grid; the second observed load factor considers the load changes of the area to be managed in the entire historical time from the perspective of the time concept.

[0046] In the present invention, in S3, a reasonable fluctuation range of the power grid load in the area to be managed is obtained; if the load at the current moment predicted by S2 is less than the reasonable fluctuation range, it is at a load trough and energy should be absorbed; if the load at the current moment predicted by S2 is greater than the reasonable fluctuation range, it is at a load peak and energy should be released, thereby effectively smoothing the fluctuation of the power grid load; if the load at the current moment predicted by S2 is within a reasonable fluctuation range, no operation is required for the time being.

[0047] In this embodiment of the present invention, S1 includes the following sub-steps:

[0048] S11, dividing the area to be managed into grids to obtain a number of nodes;

[0049] S12. Obtain the load of each node at different historical moments as a load set.

[0050] In the embodiment of the present invention, in S21, the calculation formula of the load scaling factor a is:

[0051] ; In the formula, represents the load of the qth node at the pth historical moment, P represents the total number of historical moments, Q represents the total number of nodes in the area to be managed, MAX represents the maximum load of the load set, and c represents a constant.

[0052] In this embodiment of the present invention, S22 includes the following sub-steps:

[0053] S221, randomly selecting a node from a plurality of nodes in the area to be managed, and taking the average load of the randomly selected node at all historical moments as a random load attribute;

[0054] S222. Determine the load relationship between the remaining nodes and the randomly selected node according to the random load attribute;

[0055] S223. Calculate a first observed load factor based on the load relationship between the randomly selected node and the remaining nodes.

[0056] In the present invention, there is a difference in load between the randomly selected node and the remaining nodes, and the difference, namely the load relationship, needs to be characterized. The first observed load factor is calculated based on the sizes of multiple load relationships, which can reflect the load conditions of different nodes.

[0057] In the embodiment of the present invention, in S222, the load relationship Z between the remaining qth nodes and the randomly selected node is q The expression is:

[0058] ; where NUM1 represents the number of loads of the remaining qth nodes with the same random load attribute among all the loads at all historical moments, NUM0 represents the number of loads of the remaining qth nodes with greater load attributes than the random load attribute among all the loads at all the historical moments, NUM2 represents the number of loads of the remaining qth nodes with less load attributes than the random load attribute among all the loads at all the historical moments, and AVE q represents the average load of the remaining qth nodes at all historical moments, and ln(·) represents the logarithmic function. q Perform dimensionless processing.

[0059] In the embodiment of the present invention, in S223, the calculation formula of the first observed load factor r1 is:

[0060] ; In the formula, U represents the number of load relationship values ​​greater than 0, Q represents the total number of nodes in the area to be managed, and x represents the random load attribute.

[0061] In the embodiment of the present invention, in S23, the calculation formula of the second observed load factor r2 is:

[0062] ; In the formula, Q represents the total number of nodes in the area to be managed, h1 represents the load of the first node at a randomly selected historical moment, and h q represents the load of the qth node at a randomly selected historical moment, h Q represents the load of the Qth node at a randomly selected historical moment, max(·) represents the maximum value function, MIN represents the minimum load of the load set, and c represents a constant.

[0063] In the embodiment of the present invention, in S24, the calculation formula of the load h of the area to be managed at the current moment is:

[0064] ; In the formula, r1 represents the first observed load factor, r2 represents the second observed load factor, and a represents the load scaling factor.

[0065] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A method for energy storage management, characterized in that: The following steps are involved: S1. Obtain the load set of the area to be managed; S2. Based on the load set of the area to be managed, generate a load scaling factor, a first observed load factor, and a second observed load factor, and determine the load of the area to be managed at the current moment; S3. Determine the energy storage result according to the load of the area to be managed at the current moment; The S2 comprises the following sub-steps: S21. Generate a load scaling factor according to a load set of the area to be managed; S22, randomly selecting a node from a plurality of nodes in the area to be managed, and determining a first observed load factor according to a load relationship between the randomly selected node and the remaining nodes; S23, randomly selecting a historical moment from the load set, and determining a second observed load factor according to the load amounts of the randomly selected historical moment and the remaining historical moments; S24, determining the load of the area to be managed at the current moment according to the load scaling factor, the first observed load factor, and the second observed load factor; In S21, the calculation formula of the load scaling factor a is: ; In the formula, represents the load of the qth node at the pth historical moment, P represents the total number of historical moments, Q represents the total number of nodes in the area to be managed, MAX represents the maximum load of the load set, and c represents a constant.

2. The energy storage management method according to claim 1, characterized in that: The S1 comprises the following sub-steps: S11, dividing the area to be managed into grids to obtain a number of nodes; S12. Obtain the load of each node at different historical moments as a load set.

3. The energy storage management method according to claim 1, characterized in that: The S22 comprises the following sub-steps: S221, randomly selecting a node from a plurality of nodes in the area to be managed, and taking the average load of the randomly selected node at all historical moments as a random load attribute; S222. Determine the load relationship between the remaining nodes and the randomly selected node according to the random load attribute; S223. Calculate a first observed load factor based on the load relationship between the randomly selected node and the remaining nodes.

4. The energy storage management method according to claim 3, characterized in that: In S222, the load relationship Z between the remaining qth nodes and the randomly selected node is q The expression is: ; where NUM1 represents the number of loads of the remaining qth nodes with the same random load attribute among all the loads at all historical moments, NUM0 represents the number of loads of the remaining qth nodes with greater load attributes than the random load attribute among all the loads at all the historical moments, NUM2 represents the number of loads of the remaining qth nodes with less load attributes than the random load attribute among all the loads at all the historical moments, and AVE q represents the mean load of the remaining qth nodes at all historical moments, and ln(·) represents the logarithmic function.

5. The energy storage management method according to claim 3, characterized in that: In S223, the calculation formula of the first observation load factor r1 is: ; In the formula, U represents the number of load relationship values ​​greater than 0, Q represents the total number of nodes in the area to be managed, and X represents the random load attribute.

6. The energy storage management method according to claim 1, characterized in that: In S23, the calculation formula of the second observation load factor r2 is: ; In the formula, Q represents the total number of nodes in the area to be managed, h1 represents the load of the first node at a randomly selected historical moment, and h q represents the load of the qth node at a randomly selected historical moment, h Q represents the load of the Qth node at a randomly selected historical moment, max(·) represents the maximum value function, MIN represents the minimum load of the load set, and c represents a constant.

7. The energy storage management method according to claim 1, characterized in that: In S24, the calculation formula of the load h of the area to be managed at the current moment is: ; In the formula, r1 represents the first observed load factor, r2 represents the second observed load factor, and a represents the load scaling factor.

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