Optimization method and system for pumped storage locating and sizing of micro-grid small hydropower station

Abstract

The invention discloses an optimization method and system for pumping and storage location and sizing of micro-grid small hydropower stations, and the method comprises the steps: obtaining new energy power generation data in a micro-grid, calculating a new energy power generation proportion according to the new energy power generation data, and delimiting the location priorities corresponding to different intervals of the new energy power generation proportion; constructing a cost analysis system according to the site selection related cost; a site selection comprehensive evaluation model is established according to the site selection priority and the cost analysis system, and the optimal site selection is screened out; analyzing a power load demand in the micro-grid and a new energy power generation fluctuation characteristic, and determining a basic capacity demand of small hydropower pumping storage; according to preset constraint conditions, cost minimization and new energy consumption maximization serve as targets, a constant volume optimization model is constructed, the constant volume optimization model is solved through a preset solving algorithm, and the optimal energy storage capacity and the optimal installed power are obtained. The method can improve the economy and adaptability of a micro-grid small hydropower pumping and storage project, promotes the efficient utilization of new energy, and improves the stability of a micro-grid.

Description

Technical Field

[0001] This invention belongs to the field of microgrid energy management technology, and in particular relates to an optimization method and system for the site selection and capacity determination of small hydropower pumped storage in microgrids. Background Technology

[0002] Microgrids, as a new type of power system, integrate various distributed energy sources, with the proportion of renewable energy continuously increasing. Small pumped-storage hydropower can play a role in energy storage and peak shaving within microgrids, but improper site selection and capacity determination can lead to excessive costs and poor renewable energy integration. Current technologies for small pumped-storage hydropower in microgrids do not adequately consider the proportion and cost of renewable energy generation, and the algorithms used for capacity determination are insufficient in terms of accuracy and efficiency, failing to meet the requirements for efficient microgrid operation. Therefore, there is an urgent need for a method and system for site selection and capacity determination of small pumped-storage hydropower in microgrids that prioritizes the proportion of renewable energy generation and various costs, and employs a superior algorithm. Summary of the Invention

[0003] This invention provides an optimization method and system for the site selection and capacity determination of pumped storage hydropower in microgrids, which solves the technical problem that the algorithm used for capacity determination is insufficient in terms of solution accuracy and efficiency, making it difficult to meet the requirements of efficient operation of microgrids.

[0004] In a first aspect, the present invention provides an optimization method for the site selection and capacity determination of pumped storage hydropower in microgrids, comprising: Acquire renewable energy power generation data within the microgrid, calculate the renewable energy power generation ratio based on the renewable energy power generation data, and define the site selection priority corresponding to different intervals of renewable energy power generation ratio; Construct a cost analysis system based on site selection-related costs; Based on the site selection priority and the cost analysis system, a comprehensive site selection evaluation model is established to select the optimal site. Analyze the power load demand and the fluctuation characteristics of new energy power generation in the microgrid to determine the basic capacity requirement of pumped storage for small hydropower. Based on preset constraints, and with the goals of minimizing costs and maximizing renewable energy consumption, a fixed-capacity optimization model is constructed. The model is then solved using a preset solution algorithm to obtain the optimal energy storage capacity and installed power.

[0005] Secondly, the present invention provides an optimization system for the site selection and capacity determination of small hydropower pumped storage in microgrids, comprising: The acquisition module is configured to acquire new energy power generation data within the microgrid, calculate the proportion of new energy power generation based on the new energy power generation data, and define the site selection priority corresponding to different intervals of the proportion of new energy power generation. The module is configured to build a cost analysis system based on site selection-related costs. The screening module is configured to establish a comprehensive site selection evaluation model based on the site selection priority and the cost analysis system, and to screen out the optimal site. The module is configured to analyze the power load demand and the fluctuation characteristics of new energy power generation in the microgrid, and to determine the basic capacity requirements of small hydropower pumped storage. The solution module is configured to construct a fixed-capacity optimization model based on preset constraints, with the objectives of minimizing costs and maximizing renewable energy consumption. The fixed-capacity optimization model is then solved using a preset solution algorithm to obtain the optimal energy storage capacity and installed power.

[0006] Thirdly, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the microgrid small hydropower pumped storage site selection and capacity determination optimization method according to any embodiment of the present invention.

[0007] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the steps of the optimization method for the location and capacity determination of small hydropower pumped storage in a microgrid according to any embodiment of the present invention.

[0008] The optimization method and system for site selection and capacity determination of pumped storage hydropower in microgrids disclosed in this application take the proportion of renewable energy generation and various costs (power plant construction cost, waterway construction cost, transmission line cost, and operation and maintenance cost) as the main factors when selecting a site. The site selection model is constructed in combination with the characteristics of the microgrid. The capacity determination process comprehensively considers the power supply and demand, renewable energy consumption and cost-effectiveness within the microgrid. The algorithm uses the jackal optimization algorithm and the projection iterative optimization algorithm to solve the problem. This method can improve the economy and adaptability of pumped storage hydropower projects in microgrids, promote the efficient utilization of renewable energy, and enhance the stability of the microgrid. Attached Figure Description

[0009] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 A flowchart illustrating an optimization method for the site selection and capacity determination of pumped storage hydropower in a microgrid, as provided in an embodiment of the present invention; Figure 2 This is a structural block diagram of an optimized system for the site selection and capacity determination of pumped storage hydropower in a microgrid, provided in an embodiment of the present invention. Figure 3This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0012] Please see Figure 1 The flowchart illustrates an optimization method for the site selection and capacity determination of pumped storage hydropower in a microgrid, as described in this application.

[0013] like Figure 1 As shown, the optimization method for the site selection and capacity determination of pumped storage hydropower in microgrids specifically includes the following steps: Step S101: Obtain new energy power generation data within the microgrid, calculate the proportion of new energy power generation based on the new energy power generation data, and delineate the site selection priorities corresponding to different intervals of the proportion of new energy power generation.

[0014] In this step, data on the power generation of new energy sources such as wind and solar power within the microgrid, as well as the total power generation data of the microgrid, are collected to calculate the proportion of new energy power generation. , The proportion of new energy power generation; Annual wind power generation; Annual solar power generation; This represents the total power generation of the microgrid. The proportion of renewable energy power generation is categorized as high (…). ),middle( ),Low( The three regions are divided into three areas. The region with the higher proportion has a higher site selection priority, because such regions need small hydropower pumped storage to absorb new energy and stabilize the power grid.

[0015] Step S102: Construct a cost analysis system based on site selection-related costs.

[0016] In this step, the costs involved in site selection are calculated. Plant construction costs are calculated based on plant size and construction standards; pumped-storage unit equipment costs are related to unit power and quantity; waterway construction costs depend on waterway length, materials, etc.; transmission line costs are related to the distance from the selected site to the microgrid access point; and operation and maintenance costs are referenced to the annual operation and maintenance costs of similar projects. The weights of each cost factor are determined using the Analytic Hierarchy Process (AHP), constructing a cost assessment system. In the AHP, the consistency test formula for the judgment matrix is ​​as follows: , The consistency ratio; As a consistency indicator; The average random consistency index is denoted as , where , To determine the largest eigenvalue of a matrix; To determine the order of a matrix, when When the judgment matrix has satisfactory consistency.

[0017] Step S103: Based on the site selection priority and the cost analysis system, establish a comprehensive site selection evaluation model and select the optimal site.

[0018] In this step, a comprehensive site selection evaluation model is established. The priority score corresponding to the proportion of renewable energy generation (5 points for high proportion, 3 points for medium proportion, and 1 point for low proportion) is weighted with the cost assessment score (higher score for lower cost, maximum score 5 points). The comprehensive score formula is as follows: , For the overall score; Priority weight; Score based on priority; Cost weighting; Cost assessment score; The comprehensive score of each potential site is obtained, and the site with the highest comprehensive score is selected as the optimal site.

[0019] Step S104: Analyze the power load demand and the fluctuation characteristics of new energy power generation within the microgrid to determine the basic capacity requirement of small hydropower pumped storage.

[0020] In this step, the power load curve within the microgrid is analyzed to determine peak and off-peak periods and the corresponding power demand. Simultaneously, the fluctuations in renewable energy generation are analyzed to identify periods of excess and insufficient renewable energy output, as well as the corresponding amounts of renewable energy generated. Based on this data, the power gap that small hydropower pumped storage needs to fill and the renewable energy that can be stored are determined, resulting in the basic capacity requirement.

[0021] Step S105: Based on preset constraints, with the goals of minimizing costs and maximizing renewable energy consumption, a fixed-capacity optimization model is constructed. The fixed-capacity optimization model is then solved using a preset solution algorithm to obtain the optimal energy storage capacity and installed power.

[0022] In this step, a fixed-capacity optimization model is constructed with the objectives of minimizing the total life-cycle cost (including plant construction cost, pumped-storage unit equipment cost, waterway construction cost, transmission line cost, and operation and maintenance cost) and maximizing the absorption of new energy. The objective function formula is: , in, Total lifecycle cost; For factory construction costs; For pumped storage unit equipment costs; For the cost of waterway construction; For transmission line costs; For operation and maintenance costs (cost minimization); , in, This refers to the amount of new energy consumed; Annual wind power generation; Annual solar power generation; To address the issue of wasted electricity (maximizing the consumption of renewable energy); Model constraints include real-time power balance constraints for microgrids. , for Wind power output during a given time period; for Solar power output during different time periods; for Small hydropower pumped storage output during certain periods; for The switching power between the time period and the main network; for Load demand during different time periods; for Network losses during specific time periods and the maximum charging and discharging power limits of small hydropower pumped storage. , Maximum charging and discharging power of small hydropower pumped storage for The charging and discharging power of small hydropower pumped storage during a given time period is represented by positive values ​​for discharging and negative values ​​for the upper and lower limits of charging and energy storage capacity. , This represents the minimum energy storage capacity. for Energy storage capacity for a given time period; This represents the maximum energy storage capacity.

[0023] It should be noted that the jackal optimization algorithm and the projective iterative optimization algorithm are used to solve the model. The jackal optimization algorithm simulates the behavior of a jackal pack searching for and hunting prey to perform global optimization, and its individual update formula is: , For the first Individuals The position at that moment; For the first Individuals The position at that moment; For following factors; A random number between 0 and 1; for The optimal individual position at any given time; As a competing factor; For the first A random individual in The position at time; the projection iterative optimization algorithm achieves local optimization by projecting the solution space onto a subspace and iteratively updating it. The iterative formula is: , For the first The solution for the next iteration; For the first The solution for the next iteration; This is the iteration step size; for In the feasible region The projection points on the surface are combined to obtain the optimal energy storage capacity and installed power.

[0024] In summary, the method of this application takes the proportion of renewable energy generation and various costs (plant construction cost, waterway construction cost, transmission line cost, and operation and maintenance cost) as the main factors when selecting a site. It constructs a site selection model in combination with the characteristics of microgrids. The capacity determination process comprehensively considers the power supply and demand, renewable energy consumption, and cost-effectiveness within the microgrid. It uses the jackal optimization algorithm and the projection iterative optimization algorithm to solve the problem. This method can improve the economy and adaptability of small hydropower pumped storage projects in microgrids, promote the efficient utilization of renewable energy, and enhance the stability of microgrids.

[0025] Please see Figure 2 The diagram shows a structural block diagram of an optimized system for the location and capacity determination of pumped storage in a microgrid according to this application.

[0026] like Figure 2 As shown, the optimization system 200 for the location and capacity determination of pumped storage hydropower in microgrids includes an acquisition module 210, a construction module 220, a screening module 230, a determination module 240, and a solution module 250.

[0027] The module 210 is configured to acquire new energy power generation data within the microgrid, calculate the proportion of new energy power generation based on the new energy power generation data, and define the site selection priorities corresponding to different intervals of the proportion of new energy power generation. The module 220 is configured to construct a cost analysis system based on site selection-related costs. The module 230 is configured to establish a comprehensive site selection evaluation model based on the site selection priorities and the cost analysis system, and select the optimal site. The module 240 is configured to analyze the power load demand and new energy power generation fluctuation characteristics within the microgrid, and determine the basic capacity requirements of small hydropower pumped storage. The module 250 is configured to construct a fixed-capacity optimization model based on preset constraints, with the objectives of minimizing costs and maximizing new energy consumption, and solve the fixed-capacity optimization model using a preset solution algorithm to obtain the optimal energy storage capacity and installed power.

[0028] It should be understood that Figure 1 The modules and references described in the document Figure 1 The steps described in the text correspond to those in the method described above. Therefore, the operations, features, and corresponding technical effects described above also apply to the method described in the text. Figure 1 The various modules in the document will not be described in detail here.

[0029] In other embodiments, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the optimization method for the location and capacity determination of small hydropower pumped storage in any of the above method embodiments. In one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions, which are configured as follows: Acquire renewable energy power generation data within the microgrid, calculate the renewable energy power generation ratio based on the renewable energy power generation data, and define the site selection priority corresponding to different intervals of renewable energy power generation ratio; Construct a cost analysis system based on site selection-related costs; Based on the site selection priority and the cost analysis system, a comprehensive site selection evaluation model is established to select the optimal site. Analyze the power load demand and the fluctuation characteristics of new energy power generation in the microgrid to determine the basic capacity requirement of pumped storage for small hydropower. Based on preset constraints, and with the goals of minimizing costs and maximizing renewable energy consumption, a fixed-capacity optimization model is constructed. The model is then solved using a preset solution algorithm to obtain the optimal energy storage capacity and installed power.

[0030] Computer-readable storage media may include a stored program area and a stored data area, wherein the stored program area may store an operating system and an application program required for at least one function; the stored data area may store data created based on the use of the microgrid small hydropower pumped storage addressing and sizing optimization system. Furthermore, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include memory remotely disposed relative to a processor, which can be connected to the microgrid small hydropower pumped storage addressing and sizing optimization system via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0031] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention, such as... Figure 3As shown, the device includes a processor 310 and a memory 320. The electronic device may also include an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 can be connected via a bus or other means. Figure 3 Taking a bus connection as an example, the memory 320 is the computer-readable storage medium described above. The processor 310 executes various server functions and data processing by running non-volatile software programs, instructions, and modules stored in the memory 320, thereby implementing the microgrid small hydropower pumped storage addressing and capacity grading optimization method described in the above embodiment. The input device 330 can receive input digital or character information and generate key signal inputs related to user settings and function control of the microgrid small hydropower pumped storage addressing and capacity grading optimization system. The output device 340 may include a display screen or other display device.

[0032] The aforementioned electronic device can execute the method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in the embodiments of the present invention.

[0033] In one implementation, the above-described electronic device is applied to an optimization system for the site selection and capacity determination of small hydropower pumped storage in a microgrid, serving as a client, and includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to: Acquire renewable energy power generation data within the microgrid, calculate the renewable energy power generation ratio based on the renewable energy power generation data, and define the site selection priority corresponding to different intervals of renewable energy power generation ratio; Construct a cost analysis system based on site selection-related costs; Based on the site selection priority and the cost analysis system, a comprehensive site selection evaluation model is established to select the optimal site. Analyze the power load demand and the fluctuation characteristics of new energy power generation in the microgrid to determine the basic capacity requirement of pumped storage for small hydropower. Based on preset constraints, and with the goals of minimizing costs and maximizing renewable energy consumption, a fixed-capacity optimization model is constructed. The model is then solved using a preset solution algorithm to obtain the optimal energy storage capacity and installed power.

[0034] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0035] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An optimized method for site selection and capacity determination of pumped storage hydropower in microgrids, characterized in that, include: Acquire renewable energy power generation data within the microgrid, calculate the renewable energy power generation ratio based on the renewable energy power generation data, and define the site selection priority corresponding to different intervals of renewable energy power generation ratio; Construct a cost analysis system based on site selection-related costs; Based on the site selection priority and the cost analysis system, a comprehensive site selection evaluation model is established to select the optimal site. Analyze the power load demand and the fluctuation characteristics of new energy power generation in the microgrid to determine the basic capacity requirement of pumped storage for small hydropower. Based on preset constraints, and with the goals of minimizing costs and maximizing renewable energy consumption, a fixed-capacity optimization model is constructed. The model is then solved using a preset solution algorithm to obtain the optimal energy storage capacity and installed power.

2. The optimization method for site selection and capacity determination of pumped storage hydropower in a microgrid according to claim 1, characterized in that, The expression for calculating the proportion of renewable energy power generation is as follows: ， In the formula, The proportion of new energy power generation, For annual wind power generation, Annual solar power generation This represents the total power generation of the microgrid.

3. The optimization method for site selection and capacity determination of pumped storage hydropower in a microgrid according to claim 1, characterized in that, The site selection-related costs include plant construction costs, waterway construction costs, power transmission line costs, and operation and maintenance costs.

4. The optimization method for site selection and capacity determination of pumped storage hydropower in a microgrid according to claim 1, characterized in that, The constraints include microgrid power balance constraints, small hydropower pumped storage charging and discharging power constraints, and energy storage capacity constraints. The expression for the microgrid power balance constraint is: ， In the formula, for Wind power output during different time periods for Solar power output during different time periods for Small hydropower pumped storage output during certain periods for The switching power between the time period and the main network. for Load demand during different time periods for Network loss during a given time period; The expression for the charging and discharging power constraint of the small hydropower pumped storage is: ， In the formula, This represents the maximum charging and discharging power of small hydropower pumped storage. for The charging and discharging power of small hydropower pumped storage during a given time period; positive values ​​represent discharging, and negative values ​​represent charging. The expression for the energy storage capacity constraint is: ， In the formula, This represents the minimum energy storage capacity. for Energy storage capacity during a given time period This represents the maximum energy storage capacity.

5. The optimization method for site selection and capacity determination of pumped storage hydropower in a microgrid according to claim 1, characterized in that, in, The expression aimed at minimizing costs and maximizing the absorption of new energy sources is: ， In the formula, Total lifecycle cost For factory construction costs, For pumped storage unit equipment costs; For the cost of waterway construction, For the cost of transmission lines, For operation and maintenance costs; ， In the formula, For the amount of new energy consumed, For annual wind power generation, Annual solar power generation This refers to the amount of electricity wasted.

6. An optimized system for the site selection and capacity determination of pumped storage hydropower in microgrids, characterized in that, include: The acquisition module is configured to acquire new energy power generation data within the microgrid, calculate the proportion of new energy power generation based on the new energy power generation data, and define the site selection priority corresponding to different intervals of the proportion of new energy power generation. The module is configured to build a cost analysis system based on site selection-related costs. The screening module is configured to establish a comprehensive site selection evaluation model based on the site selection priority and the cost analysis system, and to screen out the optimal site. The module is configured to analyze the power load demand and the fluctuation characteristics of new energy power generation in the microgrid, and to determine the basic capacity requirements of small hydropower pumped storage. The solution module is configured to construct a fixed-capacity optimization model based on preset constraints, with the objectives of minimizing costs and maximizing renewable energy consumption. The fixed-capacity optimization model is then solved using a preset solution algorithm to obtain the optimal energy storage capacity and installed power.

7. An electronic device, characterized in that, include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the method described in any one of claims 1 to 5.

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