Multi-factor Energy Storage Site Selection and Capacity Determination Optimization Method, System, Terminal and Medium

Abstract

The present invention discloses an optimization method, system, terminal and medium for energy storage site selection and capacity determination considering multiple factors, which relates to the technical field of power systems. The key points of its technical solution are as follows: the load rate of each node in the power system is obtained according to the maximum load and rated capacity during the annual operation of the power system, and the influence of the load rate of each node in the power system on the power supply guarantee of the power supply area is analyzed to obtain the risk power quantity; based on the economic output value data of each region, the output value corresponding to the risk power quantity is analyzed to obtain the risk output value; the risk output value and the total energy storage configuration amount of each region are input into a pre-constructed energy storage site selection and capacity determination model for optimization analysis to obtain the energy storage power and energy storage capacity of each node in the power system. The present invention simultaneously considers two factors, namely regional economic development and improvement of peak shaving and valley filling in the power system. It not only has an improvement and support effect on the power system, but also can overall consider the power supply support of energy storage to local residents and industrial and commercial users.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and more specifically, to an optimization method, system, terminal and medium for energy storage site selection and capacity determination considering multiple factors. Background Art

[0002] With the vigorous development of new energy at home and abroad, the random uncertainty of new energy output has brought serious impacts on the safe and stable operation of power systems. The main impacts include the non-correspondence between the peak and trough of new energy output and the peak and trough of the power consumption side, power imbalance and other problems. Secondly, problems such as difficult prediction, low accuracy and low predictable resolution of new energy output caused by the random uncertainty of new energy output. Finally, in order to promote the consumption of new energy, the output planning of traditional thermal power units has been significantly reduced, resulting in a significant reduction in the system's stable power supply resources, thus causing problems such as power rationing, power shortages and difficult power consumption.

[0003] Existing energy storage planning mainly starts from the perspective of power systems and rarely considers the needs of regional economic development; while the energy storage planning technology carried out from the government perspective mainly considers factors such as land use cost, local economic development, industrial chain construction and comprehensive demonstration applications, lacking an analysis of power systems. However, in the planning and layout of energy storage, it is necessary to not only consider the improvement and support of power systems, but also overall consider the power support of energy storage for local residents and industrial and commercial users, and ensure the maximization of social benefits of user power supply reliability.

[0004] Therefore, how to research and design an energy storage site selection and capacity determination optimization method, system, terminal and medium that can overcome the above defects is an urgent problem for us to solve currently. Summary of the Invention

[0005] To solve the deficiencies in the prior art, the purpose of the present invention is to provide an optimization method, system, terminal and medium for energy storage site selection and capacity determination considering multiple factors, which simultaneously considers two factors: regional economic development and peak shaving and valley filling improvement of power systems. It not only has an improvement and support effect on power systems, but also can overall consider the power support of energy storage for local residents and industrial and commercial users, taking into account the focuses of different planning parties, which is conducive to the implementation and execution of the planning scheme, ensuring user power supply reliability and the maximization of social benefits.

[0006] The above technical purpose of the present invention is achieved through the following technical solutions:

[0007] In the first aspect, an optimization method for energy storage site selection and capacity determination considering multiple factors is provided, including the following steps:

[0008] Obtain the load rate of each node in the power system according to the maximum load and rated capacity of the annual operation in the power system, and analyze the impact of the load rate of each node in the power system on the power supply guarantee of the power supply area to obtain the risk power.

[0009] Analyze the output value corresponding to the risk power based on the economic output value data of each region to obtain the risk output value;

[0010] Input the risk output value and the total energy storage configuration of each region into the pre - constructed energy storage site - selection and capacity - determination model for optimization analysis to obtain the energy storage power and energy storage capacity of each node in the power system.

[0011] Furthermore, the process of obtaining the risk output value is specifically as follows:

[0012] Determine the output value per unit electricity of the area where the over - loaded substation is located based on the economic output value data;

[0013] Determine the risk output value of the corresponding over - loaded substation by multiplying the risk power by the output value per unit electricity.

[0014] Furthermore, the output value per unit electricity is the ratio of the economic output value data to the total electricity consumption of the whole society in the corresponding region.

[0015] Furthermore, the risk power is the power supply corresponding to the nodes in each region exceeding the safe load rate.

[0016] Furthermore, the safe load rate is determined by the product of the percentage of the rated capacity of each substation and the configuration coefficient of the corresponding substation.

[0017] Furthermore, the calculation formula for the energy storage power is specifically as follows:

[0018]

[0019] Wherein, represents the energy storage power that should be configured after the optimization analysis of substation j; f j represents the risk output value of substation j; f i represents the risk output value of substation i; N represents the number of substations; P0 represents the total allowable energy storage power configured in each region.

[0020] Furthermore, the calculation formula for the energy storage capacity is specifically as follows:

[0021]

[0022] Wherein, represents the energy storage capacity that should be configured after the optimization analysis of substation j; t0 represents the maximum charging time allowed for the configured energy storage; represents the energy storage power that should be configured after the optimization analysis of substation j; E es,j,T represents the energy storage capacity demand of substation j on the T - th day, and max(E es,j,T ) represents the maximum value of the energy storage capacity demand of substation j throughout the year.

[0023] In a second aspect, an energy storage site selection and capacity determination optimization system considering multiple factors is provided, including:

[0024] An electricity quantity analysis module, configured to obtain the load rate of each node in the power system according to the maximum load and rated capacity during the annual operation of the power system, analyze the impact of the load rate of each node in the power system on the power supply guarantee of the power supply area, and obtain the risk electricity quantity;

[0025] A production value analysis module, configured to analyze the production value corresponding to the risk electricity quantity based on the economic production value data of each region to obtain the risk production value;

[0026] A capacity determination analysis module, configured to input the risk production value and the total energy storage configuration of each region into a pre-constructed energy storage site selection and capacity determination model for optimization analysis to obtain the energy storage power and energy storage capacity of each node in the power system.

[0027] In a third aspect, a computer terminal is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the energy storage site selection and capacity determination optimization method considering multiple factors as described in any item of the first aspect is implemented.

[0028] In a fourth aspect, a computer-readable medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the energy storage site selection and capacity determination optimization method considering multiple factors as described in any item of the first aspect can be implemented.

[0029] Compared with the prior art, the present invention has the following beneficial effects:

[0030] 1. The energy storage site selection and capacity determination optimization method considering multiple factors proposed by the present invention takes into account both regional economic development and the improvement of peak shaving and valley filling in the power system. It not only has an improvement and support effect on the power system, but also can comprehensively consider the power support of energy storage for local residents and industrial and commercial users, taking into account the focuses of different planning parties, which is conducive to the implementation and execution of the planning scheme, ensuring the reliability of user power consumption and maximizing social benefits;

[0031] 2. The present invention determines the safe load rate based on the product of the percentage of the rated capacity of each substation and the configuration coefficient of the corresponding substation, taking into account the layout of the substation in the entire power system, making the determination of the risk electricity quantity more accurate and reliable. Description of the Drawings

[0032] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, form a part of this application, and do not limit the embodiments of the present invention. In the drawings:

[0033] Figure 1 is the flowchart in the embodiment of the present invention;

[0034] Figure 2 is the flowchart in the embodiment of the present invention;

[0035] Figure 3 is the system block diagram in the embodiment of the present invention. Detailed implementation manners

[0036] To make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with embodiments and drawings. The illustrative embodiments of the present invention and their descriptions are only used to explain the present invention and do not limit the present invention.

[0037] Embodiment 1: An energy storage site selection and capacity determination optimization method considering multiple factors, as Figure 1 shown, includes the following steps:

[0038] Step 1: Obtain the load rate of each node in the power system according to the maximum load and rated capacity during the annual operation of the power system, and analyze the impact of the load rate of each node in the power system on the power supply guarantee of the power supply area to obtain the risk power consumption;

[0039] Step 2: Analyze the output value corresponding to the risk power consumption based on the economic output value data of each region to obtain the risk output value;

[0040] Step 3: Input the risk output value and the total energy storage configuration of each region into a pre-constructed energy storage site selection and capacity determination model for optimization analysis to obtain the energy storage power and energy storage capacity of each node in the power system.

[0041] The process of obtaining the risk output value is specifically as follows: Determine the output value per kilowatt-hour of the region where the overloaded substation is located based on the economic output value data; Determine the risk output value of the corresponding overloaded substation by multiplying the risk power consumption by the output value per kilowatt-hour.

[0042] Specifically, the calculation formula for the risk output value is:

[0043] f i = C GDP,u E risk,i

[0044]

[0045] where f i represents the risk output value of substation i; C GDP,u represents the output value per kilowatt-hour of region u where overloaded substation i is located; E risk,i represents the risk power consumption of substation i; M u represents the economic output value data of region u, such as the total GDP; E u represents the total electricity consumption of region u.

[0046] As shown Figure 2 in the figure, to alleviate the high load rate of overloaded substations in each region, taking each substation in each region as the object, a certain percentage of the rated capacity of each substation is selected as the safe operation threshold. For example, 80% of the rated capacity is used as the safe operation threshold of the substation. Then, for a substation with a rated capacity of 100 MW, the safe operation threshold is 80 MW. Substations with a maximum load rate exceeding the safe operation threshold are selected for energy storage capacity configuration. The maximum load rate of node 3 exceeds 80%, so energy storage needs to be installed to cut this part of the risk power. The risk power is the power supply corresponding to the nodes in each region exceeding the safe load rate.

[0047] In this embodiment, the calculation formula for the energy storage power is specifically as follows:

[0048]

[0049] Among them, represents the energy storage power that should be configured after the optimization analysis of substation j; f j represents the risk output value of substation j; N represents the number of substations; P0 represents the total power allowed for energy storage configuration in each region.

[0050] In addition, the calculation formula for the energy storage capacity is specifically as follows:

[0051]

[0052] Among them, represents the energy storage capacity that should be configured after the optimization analysis of substation j; t0 represents the maximum charging time allowed for the configured energy storage; E es,j,T represents the energy storage capacity demand of substation j on the Tth day, and max(E es,j,T ) represents the maximum value of the energy storage capacity demand of substation j throughout the year.

[0053] In addition, the energy storage power and energy storage capacity need to meet the following conditions:

[0054]

[0055]

[0056] Among them, P es,i,T represents the energy storage power demand of substation i on the Tth day; S i represents the rated capacity of substation i; ρ i,t represents the real-time collected load rate of substation i at time t; ρ0 represents the safe load rate of the substation, and the safe load rate is determined by the product of the percentage of the rated capacity of each substation and the configuration coefficient of the corresponding substation. In this embodiment, it is set to 85%; E es,i,TIt represents the energy storage capacity demand of substation i on the Tth day; Δt is the sampling interval, which can be set to 5 minutes in this embodiment; K is the total sampling time.

[0057] Embodiment 2: An energy storage site selection and capacity determination optimization system considering multiple factors, which is used to implement the energy storage site selection and capacity determination optimization method described in Embodiment 1, as Figure 3 shown, including an electricity analysis module, a production value analysis module, and a capacity determination analysis module.

[0058] The electricity analysis module is used to obtain the load rate of each node in the power system according to the maximum load and rated capacity during the annual operation of the power system, and analyze the impact of the load rate of each node in the power system on the power supply guarantee of the power supply area to obtain the risk electricity quantity;

[0059] The production value analysis module is used to analyze the production value corresponding to the risk electricity quantity based on the economic production value data of each region to obtain the risk production value;

[0060] The capacity determination analysis module is used to input the risk production value and the total energy storage configuration of each region into a pre-constructed energy storage site selection and capacity determination model for optimization analysis to obtain the energy storage power and energy storage capacity of each node in the power system.

[0061] Working principle: The present invention simultaneously considers two factors, namely the regional economic development and the improvement of peak shaving and valley filling in the power system. It not only has an improvement and support effect on the power system, but also can overall consider the power support of energy storage for local residents, industrial and commercial users, taking into account the focus of different planning parties, which is conducive to the implementation and execution of the planning scheme, ensuring the reliability of user power consumption and maximizing social benefits; in addition, the present invention determines the safe load rate based on the product of the percentage of the rated capacity of each substation and the corresponding configuration coefficient of the substation, considering the layout of the substation in the entire power system, making the determination of the risk electricity quantity more accurate and reliable.

[0062] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0063] This application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices produce a means for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or a means for implementing the functions specified in multiple blocks.

[0064] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufactured article including an instruction means that implements the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or a means for implementing the functions specified in multiple blocks.

[0065] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 or a means for implementing the functions specified in multiple blocks.

[0066] The above specific implementation manners further elaborate on the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above are only specific implementation manners of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-factor consideration energy storage site selection and capacity determination optimization method, characterized in that, Including the following steps: Obtain the load rates of each node in the power system based on the maximum load and rated capacity during the annual operation of the power system, analyze the impact of the load rates of each node in the power system on the power supply security of the power supply area, and obtain the risk electricity quantity; Analyze the output value corresponding to the risk electricity quantity based on the economic output value data of each region to obtain the risk output value; Input the risk output value and the total energy storage configuration of each region into the pre-constructed energy storage site selection and capacity determination model for optimization analysis to obtain the energy storage power and energy storage capacity of each node in the power system; the specific calculation formula for the energy storage power is: Among them, represents the energy storage power that should be configured after the optimization analysis of substation j; f j represents the risk output value of substation j; f i represents the risk output value of substation i; N represents the number of substations; P0 represents the total allowable energy storage configuration power in each region; The specific calculation formula for the energy storage capacity is: Among them, represents the energy storage capacity that should be configured after the optimization analysis of substation j; t0 represents the maximum charging time allowed for the configured energy storage; represents the energy storage power that should be configured after the optimization analysis of substation j; E es,j,T represents the energy storage capacity demand of substation j on the T-th day, and max(E es,j,T ) represents the maximum value of the energy storage capacity demand of substation j throughout the year; The specific process of obtaining the risk output value is as follows: Determine the output value per kilowatt-hour of the area where the overloaded substation is located based on the economic output value data; Determine the risk output value of the corresponding overloaded substation by multiplying the risk electricity quantity by the output value per kilowatt-hour; the risk electricity quantity is the power supply electricity corresponding to the nodes in each region exceeding the safe load rate; the safe load rate is determined by the product of the percentage of the rated capacity of each substation and the configuration coefficient of the corresponding substation.

2. The multi-factor consideration energy storage site selection and capacity determination optimization method according to claim 1, characterized in that, The output value per kilowatt-hour is the ratio of the economic output value data to the total electricity consumption of the whole society in the corresponding region.

3. A multi-factor consideration energy storage site selection and capacity determination optimization system, characterized in that, This system is used to implement the multi-factor consideration energy storage site selection and capacity determination optimization method as described in claim 1 or 2, including: An electricity quantity analysis module, which is used to obtain the load rates of each node in the power system based on the maximum load and rated capacity during the annual operation of the power system, and analyze the impact of the load rates of each node in the power system on the power supply security of the power supply area to obtain the risk electricity quantity; An output value analysis module, which is used to analyze the output value corresponding to the risk electricity quantity based on the economic output value data of each region to obtain the risk output value; A capacity determination analysis module, which is used to input the risk output value and the total energy storage configuration of each region into the pre-constructed energy storage site selection and capacity determination model for optimization analysis to obtain the energy storage power and energy storage capacity of each node in the power system.

4. A computer terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the multi-factor consideration energy storage site selection and capacity determination optimization method as described in any one of claims 1-2.

5. A computer-readable medium, on which a computer program is stored, characterized in that, When the computer program is executed by the processor, it can implement the multi-factor consideration energy storage site selection and capacity determination optimization method as described in any one of claims 1-2.

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