A large-capacity energy storage partition optimization configuration method for new energy consumption

Abstract

The present invention belongs to the technical field of power system analysis and mainly relates to a large-capacity energy storage zoning optimization configuration method for new energy consumption. First, the power grid model is divided into multiple regions according to the geographical and electrical characteristics of the selected power grid model. After determining the zoning of the power grid model, the regional energy storage demand is determined. Further, an optimization scheduling model with the goal of maximizing the consumption of new energy while minimizing the total cost of the energy storage system is constructed. The constraints of the above goals are determined according to load balancing constraints, energy storage system capacity constraints, and new energy processing limitations, and the optimal solution is solved through iteration using a mixed integer linear investment and operating cost planning method. More importantly, in this application, after the preliminary solution of the optimization scheduling model, the energy storage configuration of each region is gradually updated to adjust the energy storage configuration function in real time according to time, system load, and new energy consumption, ultimately ensuring the accuracy of the energy storage configuration parameters.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system analysis, and mainly relates to a large-capacity energy storage zoning optimization configuration method for new energy consumption. Background Art

[0002] With the transformation of the global energy structure, the proportion of renewable energy (such as wind power, solar power, and hydropower) in the power system continues to rise. However, the intermittent and fluctuating characteristics of these new energy sources pose many challenges to the power system when accommodating large-scale renewable energy. To ensure the stability and reliability of the power grid, energy storage systems are widely used to balance electricity supply and demand and improve the absorption capacity of renewable energy. However, in actual operation, many systems are unable to quickly adjust the energy storage configuration when responding to actual operating fluctuations, which leads to low utilization of renewable energy and even the phenomenon of wind and solar power abandonment. Therefore, the zoning optimization configuration method of large-capacity energy storage has become a hot topic in the current industry.

[0003] Energy storage technology plays a vital role in modern power systems, primarily used to balance electricity supply and demand, stabilize grid frequency, smooth the volatility of renewable energy, and provide emergency backup power during peak electricity demand periods. Typical examples of energy storage technologies include battery storage, pumped hydro, and compressed air storage. To address the challenges of large-scale renewable energy grid integration, existing technologies have proposed a variety of energy storage capacity configuration and optimization strategies. However, these strategies typically rely on historical data and fixed power load patterns to perform one-time energy storage capacity configuration, making them inflexible to dynamic changes in actual operation. Furthermore, current optimization methods primarily focus on utilizing real-time data to dispatch energy storage systems to optimize the charging and discharging process of electricity, but they often fail to fully consider the complementary effects between different renewable energy sources. Because these methods often independently configure energy storage for different regions, they lack a global perspective, which can lead to unbalanced or inefficient resource allocation.

[0004] Based on this, it is urgent to improve the existing large-capacity energy storage zoning optimization configuration method for new energy consumption to solve the defects of the aforementioned technology. Summary of the Invention

[0005] One of the purposes of the present invention is to address the deficiencies of the existing technology and propose a method for dynamically adjusting the configuration of an energy storage system by adjusting the energy storage configuration in real time according to time, system load, and new energy consumption during operation, which can optimize the energy storage configuration.

[0006] The technical solutions for achieving the above technical objectives are as follows:

[0007] A large-capacity energy storage zoning optimization configuration method for new energy consumption includes the following steps:

[0008] S101. Divide the selected power grid model into multiple regions based on its geographical and electrical characteristics, wherein the regional division is based on the distribution of new energy resources, the power demand pattern and load characteristics of each region, and the regulation capacity of the power grid in each region;

[0009] After determining the zoning of the grid model, determine the need for regional energy storage;

[0010] S201. Constructing an optimization scheduling model with the goal of maximizing the consumption of renewable energy while minimizing the total cost of the energy storage system, and determining the constraints for the above goals based on load balancing constraints, energy storage system capacity constraints, energy storage capacity constraints, and renewable energy processing limitations;

[0011] S301, processing integer and continuous variables in the above optimization scheduling model by using a mixed integer linear investment and operating cost planning method, and determining the optimal solution that meets the constraints through iterative solution;

[0012] S401. After initially solving the optimization scheduling model, dynamic adjustment is achieved by gradually updating the energy storage configuration of each region so that the solution satisfies the constraints of different regions.

[0013] During the dynamic adjustment process, the dynamic adjustment relationship related to time is as follows:

[0014]

[0015] in, Configure the number of inverters for the nth zone, is the number of batteries configured in the nth area; t is the time value during the dynamic adjustment process, For system load, is the amount of new energy consumption, and f(·) is a function that adjusts the energy storage configuration in real time according to time, system load, and the amount of new energy consumption.

[0016] S501. Output the corresponding energy storage inverter power, battery capacity, and energy storage investment cost.

[0017] The technical effects of the above technical solution are as follows:

[0018] This technical solution, through effectively partitioning and selecting a grid model, constructs an optimized scheduling model designed to maximize energy consumption and minimize the total cost of the energy storage system. During the optimization process, by iterating through constraint settings, this solution can adjust the energy storage configuration in real time based on time, system load, and changes in renewable energy consumption. This mechanism further ensures the accuracy of the final values ​​for energy storage inverter power, battery capacity, and configured energy storage investment costs.

[0019] As a further improvement to the present invention, the function of optimizing the scheduling model objective is as follows:

[0020]

[0021] Among them, C total represents the total cost of n regions, including the investment and operation costs of the energy storage system, C inverter is the investment cost per unit capacity of the inverter, C battery is the investment cost of the battery capacity; represents the total new energy consumption within the entire optimization period T, The amount of new energy consumed by the nth region at time t.

[0022] As a further improvement to the present invention, the dynamic adjustment process further includes the following steps:

[0023] S402: Determine the new energy consumption rate η in each region within time t or optimization period T. RE whether it is lower than a first preset threshold;

[0024] If the new energy consumption rate is lower than the first preset threshold, it indicates that a single type of renewable energy in a certain area cannot be effectively consumed at the current time t or during the optimization period T, and multi-energy complementary consumption is carried out according to the consumption of different types of new energy in the area during the time t or during the optimization period T;

[0025] S403: Determine and calculate the local electricity self-sufficiency rate η local whether it is lower than a second preset threshold;

[0026] If the self-sufficiency rate is lower than the second preset threshold, it indicates that the local electricity self-sufficiency rate is insufficient, and the self-sufficiency rate is improved through distributed energy storage system optimization.

[0027] As a further improvement to the present invention, the types of new energy involved in the multi-energy complementary absorption in step S402 are: wind energy, solar energy and hydropower.

[0028] As a further improvement to the present invention, the optimization function in the multi-energy complementary absorption in step S402 is:

[0029]

[0030] in, and Modulate the wind energy, solar energy and water energy consumption of the nth region within time t respectively, is the total renewable energy consumption during the optimization period T;

[0031] The constraints of the complementary absorption optimization function include the output constraints of the above-mentioned various types of new energy, the charging and discharging constraints of the energy storage equipment, and the load balance constraints of the system.

[0032] As a further improvement to the present invention, the constraint conditions of the multi-energy complementary absorption optimization function in step S402 also include the coupling relationship between different types of new energy sources.

[0033] As a further improvement to the present invention, in step S403, the distributed energy storage system optimization is achieved by optimizing the user-side energy storage, and the user-side energy storage optimization function is:

[0034]

[0035] Among them, C user is the total cost on the user side, C gen (t) is the power generation cost at time t, C battery_charge with C battery_dis are the battery charging cost and the battery discharging cost respectively;

[0036] The constraint condition of the above user-side energy storage optimization function is the user-side load balance condition.

[0037] As a further improvement to the present invention, the iterative solution process in step S301 includes the following steps:

[0038] S302: Determine initial values ​​for an initial energy storage configuration, where the initial energy storage configuration includes the total system cost of the initial n regions and the total new energy consumption within the initial optimization period T;

[0039] S303. Use a dichotomy method to further optimize the energy storage configuration scale. The objective function is the total system cost of the n regions and the total renewable energy consumption within the initial optimization period T. The solution of the objective function is the optimal value.

[0040] S304, compare the initial value with the currently known optimal value, and update the initial value if the optimal value is better than the initial value;

[0041] Next, select the half region where the optimal value in the binary search method is located and repeat steps S302 to S304;

[0042] S305: When the upper and lower limits of the energy storage configuration determined by the optimal value are less than a third preset threshold, the optimal solution is output as the optimal solution for the energy storage configuration scale.

[0043] As a further improvement to the present invention, the energy storage charge and discharge constraint in step S201 requires that the charge and discharge power of the energy storage system be limited to the maximum charge and discharge capacity of the device. The function of the energy storage charge and discharge constraint is:

[0044]

[0045] in, is the maximum charging power of the battery in the nth area, is the maximum discharge power of the battery in the nth region.

[0046] As a further improvement to the present invention, the energy storage capacity constraint in step S201 requires that the charging state of the energy storage system should be within a preset range. The relationship between the energy storage capacity constraint is:

[0047]

[0048] Among them, SoC (n) (t) The charging state of the energy storage system in the nth region at time t, SoC min and SoC max are the minimum and maximum charge states of the energy storage system respectively. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0050] Figure 1 This is a flow chart of Example 1 of the present invention;

[0051] Figure 2 This is a flow chart of Example 2 of the present invention;

[0052] Figure 3 This is a flow chart of Example 3 of the present invention. DETAILED DESCRIPTION

[0053] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art in the technical field of the present application. The terms used herein in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application.

[0054] In order to make the purpose, technical solutions and advantages of the embodiments of the present application more clear, the embodiments of the present application will be further described in detail with reference to the accompanying drawings. Below, some terms in the embodiments of the present application are explained to facilitate understanding by those skilled in the art.

[0055] Total system cost refers to the total expenses involved in planning and operating an energy storage system. It includes the construction, operation, and maintenance costs of the energy storage equipment. Minimizing total system cost is one of the goals of optimizing energy storage configuration.

[0056] Total renewable energy consumption refers to the total amount of renewable energy (such as wind and solar energy) that the power system can effectively absorb and utilize over a given period of time. Maximizing renewable energy consumption is another goal of optimizing energy storage configuration.

[0057] The bisection method is an iterative algorithm for solving optimization problems. It gradually approaches the optimal solution by repeatedly splitting the search interval in half. In energy storage configuration optimization, the bisection method is used to find the optimal size of the energy storage system, minimizing total system cost or maximizing total renewable energy consumption.

[0058] Dynamic adjustment refers to adjusting the energy storage system's charging and discharging strategies based on real-time power demand and supply during power system operation to optimize system performance. Dynamic adjustment is designed to address load fluctuations and the instability of renewable energy generation.

[0059] Multi-energy complementarity refers to optimizing power supply within a power system by leveraging the temporal and spatial complementarities of different types of renewable energy (such as wind, solar, and hydro). This can improve overall energy efficiency and reduce wind and solar curtailment.

[0060] SOC (State of Charge) represents the current state of charge of an energy storage system, typically expressed as a percentage, indicating the current battery capacity relative to its maximum capacity. The SOC range is typically between 0% and 100%, with upper and lower limits set in practice to protect battery life.

[0061] Mixed integer linear programming (MILP) is a mathematical optimization method used to solve linear optimization problems involving both integer and continuous variables. In energy storage configuration optimization, MILP is often used to determine the optimal energy storage capacity configuration and power allocation.

[0062] Load balancing is a fundamental requirement for power system operation, meaning that at any given moment, power supply must equal power demand (load). In energy storage systems, this constraint ensures that the system can meet power demand during charging and discharging.

[0063] Renewable energy refers to naturally reusable energy sources, such as wind, solar, hydro, and biomass. These are often unstable and intermittent, requiring energy storage technologies to balance supply and demand. In this application document, they are specifically referred to as new energy.

[0064] Wind and solar curtailment refers to the waste of renewable energy generation due to the grid's inability to fully absorb the power generated by wind and solar power. Optimizing energy storage configurations can reduce this curtailment and improve the utilization of renewable energy.

[0065] The objective function is the function to be minimized or maximized in an optimization problem. In energy storage configuration optimization, the objective function is usually the total system cost or the total renewable energy consumption.

[0066] An iterative algorithm is a method that gradually approaches the optimal solution to a problem by repeating calculation steps. The bisection method is an iterative algorithm that finds the optimal energy storage configuration size by continuously narrowing the search interval.

[0067] The primary grid is the main backbone of the power system, connecting power plants and major load centers. It is responsible for dispatching electricity between different regions and maintaining a balance between power supply and demand.

[0068] Although the present application discloses the preferred embodiments below, it is not intended to limit the claims. Any person skilled in the art may make several possible changes and modifications without departing from the concept of the present application. Therefore, the scope of protection of the present application shall be based on the scope defined by the claims of the present application.

[0069] The present invention will be further described in detail below with reference to specific embodiments, but the embodiments of the present invention are not limited thereto.

[0070] Example 1

[0071] like Figure 1 As shown, in order to solve the problem in the prior art that the energy storage configuration parameters cannot be dynamically adjusted during the unit time t of the entire optimization cycle T during the optimization configuration of the large-capacity energy storage partition, the present application improves the existing optimization configuration method, specifically, including the following steps:

[0072] S101. Divide the selected power grid model into multiple regions based on its geographical and electrical characteristics. The regional division is based on the distribution of new energy resources, the electricity demand pattern and load characteristics of each region, and the regulation capacity of the power grid in each region. Specifically, during the regional division process, the characteristics of each region are analyzed to identify the main factors limiting the absorption of new energy and set corresponding optimization goals.

[0073] After determining the zoning of the grid model, determine the need for regional energy storage;

[0074] S201. Constructing an optimization scheduling model with the goal of maximizing the consumption of renewable energy while minimizing the total cost of the energy storage system, and determining the constraints for the above goals based on load balancing constraints, energy storage system capacity constraints, energy storage capacity constraints, and renewable energy processing limitations;

[0075] Furthermore, the function of optimizing the scheduling model objective is as follows:

[0076]

[0077] Among them, C total represents the total cost of n regions, including the investment and operation costs of the energy storage system, C inverter is the investment cost per unit capacity of the inverter, C battery is the investment cost of the battery capacity; represents the total new energy consumption within the entire optimization period T, The amount of new energy consumed by the nth region at time t.

[0078] Specifically, the load balance constraint mentioned in this application essentially requires that the system can meet the balance of power supply and demand at any time t. Specifically, the balance relationship is as follows:

[0079]

[0080] In the above relationship, is the power generation of the nth region at time t (including power generation from renewable energy), is the battery discharge power of the nth region at time t, is the power load demand of the nth region at time t, is the battery charging power required by the power load of the nth region at time t.

[0081] Furthermore, the energy storage charge and discharge constraint requires that the charge and discharge power of the energy storage system be limited to the maximum charge and discharge capacity of the equipment. The function of the energy storage charge and discharge constraint is:

[0082]

[0083] in, is the maximum charging power of the battery in the nth area, is the maximum discharge power of the battery in the nth region.

[0084] Furthermore, the energy storage capacity constraint requires that the charging state of the energy storage system should be within a preset range. The energy storage capacity constraint relationship is:

[0085]

[0086] Among them, SoC (n) (t) The charging state of the energy storage system in the nth region at time t, SoC min and SoC max are the minimum and maximum charge states of the energy storage system respectively.

[0087] From the above, we can see that in the process of solving the optimal solution of the total cost of n areas and the total cost of n areas through various constraints,

[0088] It can optimize the energy storage configuration of the entire power system, thereby improving the efficiency of new energy consumption and reducing the total cost of the system.

[0089] S301, processing integer and continuous variables in the above optimization scheduling model by using a mixed integer linear investment and operating cost planning method, and determining the optimal solution that meets the constraints through iterative solution;

[0090] S401. After initially solving the optimization scheduling model, dynamic adjustment is achieved by gradually updating the energy storage configuration of each region so that the solution satisfies the constraints of different regions.

[0091] During the dynamic adjustment process, the dynamic adjustment relationship related to time is as follows:

[0092]

[0093] in, Configure the number of inverters for the nth zone, is the number of batteries configured in the nth area; t is the time value during the dynamic adjustment process, For system load, is the amount of new energy consumption, and f(·) is a function that adjusts the energy storage configuration in real time according to time, system load, and the amount of new energy consumption.

[0094] S501. Output the corresponding energy storage inverter power, battery capacity, and energy storage investment cost.

[0095] According to the above steps, the optimization configuration method proposed in this application, through the set constraints and optimization scheduling model, can effectively solve the energy storage configuration optimization problem, improve the utilization rate of renewable energy, reduce the total system cost, and ensure the stable operation of the power system. In addition, this application applies the mixed integer linear programming (MILP) method to the optimization scheduling model. Specifically, a mixed integer linear investment and operating cost planning method is adopted. This method is particularly suitable for solving optimization problems involving integer and continuous variables, such as the configuration of energy storage systems. Specifically, the configuration quantity of the energy storage system (including the capacity of inverters and batteries) and the charge and discharge power at each moment are decision variables, while the configuration of the number of system devices, such as the number of battery packs and inverters, are typically integers. At the same time, the charge and discharge power of the energy storage system at each moment is usually a continuous variable. These data are all within the adjustment range of the mixed integer linear programming (MILP) method. Therefore, through iterative solution, the MILP method can find the optimal solution that satisfies the constraints, so that the energy storage configuration plan can maximize the absorption of renewable energy while minimizing investment and operating costs.

[0096] Specifically, this application proposes a method that can adjust the energy storage configuration in real time according to the system load and the amount of new energy consumption at any time t within the entire optimization cycle T. The dynamic adjustment mechanism refers to dynamically adjusting the charging and discharging strategy of the energy storage system according to the real-time power demand, renewable energy generation and the state of the energy storage system during the operation of the power system to optimize the system performance. This mechanism can improve the stability of the power grid, increase the absorption rate of renewable energy, and effectively reduce operating costs. The following application will verify the function of the above-mentioned dynamic adjustment mechanism with multiple specific examples.

[0097] Assume that in a certain power grid area (such as area 1), there is a significant difference in electricity load during the day and at night. During the day, solar power generation is large, but the electricity load is relatively low. At night, when the load is peak, solar power generation decreases, and it is necessary to rely on the energy storage system. According to the dynamic adjustment method related to the practice of this application, the load demand and solar power generation of area 1 are monitored in real time, and the configuration of the energy storage system is dynamically adjusted. That is, if the load is low and the power generation is high during the day, it is determined that more inverters are needed to handle the charging power. If the load is high and the power generation is low at night, it is determined that discharge is prioritized. If there is a lot of excess solar power generation during the day, it is determined that the battery capacity needs to be increased to store energy. If the power demand is high at night, the battery capacity can be released to meet the demand.

[0098] By adjusting the number of inverters and batteries in real time, the system can effectively handle load fluctuations between day and night, maximizing solar energy utilization and reducing idle or overloaded energy storage systems. Furthermore, in actual operation, it avoids the high investment and operating costs that may be associated with a fixed configuration.

[0099] Assume that in a certain power grid area (such as area 2), seasonal factors cause large fluctuations in wind and solar power generation. For example, wind energy is stronger in winter and solar energy is stronger in summer. The dynamic adjustment method related to the practice of this application can be used to adjust the energy storage configuration according to seasonal changes to adapt to the power generation characteristics of different seasons. Specifically, if the wind energy is strong in winter, more inverters need to be determined to handle the charging power, while in summer, the sunlight is stable and the inverter demand is reduced; if there is excess wind in winter, more battery capacity is determined to be needed to store energy, and the battery capacity demand in summer is smaller and can be reduced. By adjusting the configuration of the energy storage system in real time, the system can flexibly respond to fluctuations in renewable energy power generation between different seasons and reduce waste caused by over-configuration.

[0100] Assume that a certain area (such as Area 3) may experience a surge in load due to a special event (such as a large-scale event or an emergency), and this situation is unforeseen and requires a rapid system response. The above-mentioned adjustment mechanism of this application can monitor load demand in real time and quickly adjust the energy storage system configuration. In the event of a sudden surge in load, the dynamic adjustment mechanism ensures that the energy storage system can quickly adapt to the changes, provide sufficient power support, and avoid grid overload or collapse.

[0101] Example 2

[0102] like Figure 1-2 As shown, different from Example 1, in order to further optimize the dynamic adjustment mechanism of this application, the dynamic adjustment process further includes the following steps:

[0103] S402: Determine the new energy consumption rate η in each region within time t or optimization period T. RE whether it is lower than a first preset threshold;

[0104] in, When the new energy absorption rate is lower than the first preset threshold, it indicates that a single type of renewable energy in a certain area cannot be effectively absorbed at the current time t or optimization period T. At the same time, as a supplement to the judgment basis, if the output of a single renewable energy source (such as wind energy or solar energy) fluctuates greatly, resulting in frequent adjustments to the power grid, and the frequency or amplitude of the fluctuation exceeds a certain set threshold, then multi-energy complementarity is also needed to balance the system; if the imbalance between the load and renewable energy generation of the power grid in a certain period of time (i.e., the difference between power supply and demand) exceeds the preset threshold, then multi-energy complementarity optimization is also required, where the load imbalance can be defined as:

[0105] ΔP=|P load -P RE |

[0106] Among them, the maximum allowable range of ΔP is set to ΔP max , when ΔP>ΔPmax When considering multi-energy complementation.

[0107] Specifically, during the specific implementation process, multi-energy complementary absorption is performed based on the absorption capacity of different types of new energy in the region within time t or optimization period T; considering that commonly used renewable energy sources are wind energy, solar energy and hydropower, this application specifically uses the above three as examples to illustrate a multi-energy complementary absorption method of this application, but the scope of protection of this application is not limited to the above three renewable energy sources. The optimization function in multi-energy complementary absorption is:

[0108]

[0109] in, and Modulate the wind energy, solar energy and water energy consumption of the nth region within time t respectively, is the total renewable energy consumption during the optimization period T;

[0110] The constraints of the complementary absorption optimization function include the output constraints of the above-mentioned various types of new energy, the charging and discharging constraints of the energy storage equipment, the load balance constraints of the system, and the coupling relationship between different types of new energy.

[0111] In the specific implementation process, the amount of wind energy absorbed is limited by the output of the wind farm and the dispatching capacity of the power grid; the amount of solar energy absorbed is affected by the power generation capacity of the solar power station and its changes within a day; the amount of hydropower absorbed is limited by the dispatching capacity of the hydropower station and the water resource allocation capacity. Therefore, the above constraints are defined as follows:

[0112] In the wind energy constraint, in is the maximum wind power output in region n at time t.

[0113] In the solar energy constraint, in is the maximum solar power output in region n at time t, which usually varies with the light intensity.

[0114] In the water energy constraint condition, in is the maximum hydropower output of region n at time t.

[0115] Furthermore, this application considers that different types of energy consumption may be coupled, such as wind and solar energy can complement each other in power supply, or in some cases use energy storage systems together:

[0116]

[0117] in, It is the total renewable energy absorption capacity that the power grid in region n can accept within time t.

[0118] S403: Determine and calculate the local electricity self-sufficiency rate η local Is it lower than a second preset threshold, where

[0119] If the self-sufficiency rate is lower than the second preset threshold, it indicates that the local power self-sufficiency rate is insufficient, and the distributed energy storage system is optimized to improve the self-sufficiency rate. Specifically, the user-side energy storage optimization function is:

[0120]

[0121] Among them, C user is the total cost on the user side, C gen (t) is the power generation cost at time t, C battery_charge with C battery_dis are the battery charging cost and battery discharging cost respectively; the constraint condition of the above user-side energy storage optimization function is the user-side load balance condition, and its balance relationship is as follows:

[0122]

[0123] To sum up, this application sets up a multi-energy complementary new energy consumption method in the dynamic adjustment process of multiple regions. The multi-energy complementary method includes the joint optimization configuration of wind energy, solar energy and hydropower to achieve stable operation of the power grid and efficient utilization of new energy. The optimized configuration of the distributed energy storage system takes into account the load demand and energy supply situation on the user side. By optimizing the configuration of the distributed energy storage system, the user's self-generation and self-use rate is improved and the dependence on the main power grid is reduced.

[0124] Other details that are the same as those in Example 1 are not described in detail in this example.

[0125] Example 3

[0126] like Figure 1-3 As shown, the difference from Example 1 is that: in order to further improve the accuracy of the iterative solution process of this application, this application uses the dichotomy method to perform accurate solutions during the dynamic adjustment process. The core of this method is to directly transform the energy storage configuration optimization problem into a comprehensive optimization problem, and find an optimal solution for the energy storage scale through the dichotomy method to minimize the total cost of the system or maximize the total absorption capacity. Specifically, the iterative solution process in step S301 includes the following steps:

[0127] S302: Determine initial values ​​for an initial energy storage configuration, where the initial energy storage configuration includes the total system cost of the initial n regions and the total new energy consumption within the initial optimization period T;

[0128] In this embodiment, the total energy storage configuration scale is defined as M i is the energy storage configuration scale of the i-th region, and n is the number of regions. From this, we can know the total system cost C total (M total ) and total new energy consumption They can be expressed as functions of the total scale of energy storage configuration:

[0129]

[0130] S303. Use a dichotomy method to further optimize the energy storage configuration scale. The objective function is the total system cost of the n regions and the total renewable energy consumption within the initial optimization period T. The solution of the objective function is the optimal value.

[0131] Specifically, the dichotomy application process is to first set the total energy storage configuration scale (initial value at the beginning and optimal value later) M total The initial upper and lower limits are [L0, U0]. For example, the minimum is set to L0MWh and the maximum is set to U0MWh. These upper and lower limits can also be estimated based on system requirements, experience or historical data.

[0132] Second, in each iteration, the midpoint of the current total energy storage configuration size is calculated:

[0133]

[0134] At this time, the total configuration scale M needs to be mid The allocation to each region can be assumed to be proportional, or it can be dynamically allocated according to the needs of each region.

[0135] Further, calculate the current key configuration M mid The total system cost C under total (M mid ) or total new energy consumption The goal of the bisection method can be to minimize the total cost minimizeC total (M mid ) or maximize the total new energy consumption

[0136] S304, compare the initial value with the currently known optimal value, and update the initial value if the optimal value is better than the initial value;

[0137] Secondly, select the half area where the optimal value in the binary search method is located and repeat steps S302 to S304; specifically, adjust the upper and lower limits of the calculation area. If the current objective function indicates that the energy storage scale needs to be increased, adjust the lower limit; if the energy storage scale needs to be reduced, adjust the upper limit until the difference between the upper and lower limits is less than the preset threshold ∈, then stop the calculation.

[0138] S305: When the upper and lower limits of the energy storage configuration determined by the optimal value are less than a third preset threshold, the optimal solution is output as the optimal solution for the energy storage configuration scale.

[0139] In summary, by redefining the initial configuration size as the total energy storage configuration size across multiple regions, we can effectively apply a bisection approach to optimization, finding the optimal energy storage size that minimizes total system costs or maximizes total renewable energy consumption. This approach simplifies the comprehensive optimization process for multi-region systems and provides a more intuitive and efficient optimization path.

[0140] The above are merely preferred embodiments of the present application and are not intended to limit the present application. Persons skilled in the art will readily appreciate that various modifications and variations are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. A large-capacity energy storage partition optimization configuration method for new energy consumption, characterized in that: The following steps are involved: S101. Divide the selected power grid model into multiple regions based on its geographical and electrical characteristics, wherein the regional division is based on the distribution of new energy resources, the power demand pattern and load characteristics of each region, and the regulation capacity of the power grid in each region; After determining the zoning of the grid model, determine the need for regional energy storage; S201. Constructing an optimization scheduling model with the goal of maximizing the consumption of renewable energy while minimizing the total cost of the energy storage system, and determining the constraints for the above goals based on load balancing constraints, energy storage system capacity constraints, energy storage capacity constraints, and renewable energy processing limitations; S301, processing integer and continuous variables in the above optimization scheduling model by using a mixed integer linear investment and operating cost planning method, and determining the optimal solution that meets the constraints through iterative solution; S401. After initially solving the optimization scheduling model, dynamic adjustment is achieved by gradually updating the energy storage configuration of each region so that the solution satisfies the constraints of different regions. During the dynamic adjustment process, the dynamic adjustment relationship related to time is as follows: in, Configure the number of inverters for the nth zone, is the number of batteries configured in the nth area; t is the time value during the dynamic adjustment process, For system load, is the amount of new energy consumption, f(·) is the function that adjusts the energy storage configuration in real time according to time, system load, and new energy consumption; The dynamic adjustment process further includes the following steps: S402: Determine the new energy consumption rate η in each region within time t or optimization period T. RE whether it is lower than a first preset threshold; If the new energy consumption rate is lower than the first preset threshold, it indicates that a single type of renewable energy in a certain area cannot be effectively consumed at the current time t or during the optimization period T, and multi-energy complementary consumption is carried out according to the consumption of different types of new energy in the area during the time t or during the optimization period T; S403: Determine and calculate the local electricity self-sufficiency rate η local whether it is lower than a second preset threshold; If the self-sufficiency rate is lower than the second preset threshold, indicating that the local power self-sufficiency rate is insufficient, the self-sufficiency rate is improved by optimizing the distributed energy storage system; S501. Output the corresponding energy storage inverter power, battery capacity, and energy storage investment cost.

2. The large-capacity energy storage zoning optimization configuration method for new energy consumption according to claim 1 is characterized in that: In step S402, the types of new energy involved in the multi-energy complementary absorption are: wind energy, solar energy and hydropower.

3. The large-capacity energy storage zoning optimization configuration method for new energy consumption according to claim 1 is characterized in that: The function to optimize the scheduling model objective is as follows: Among them, C total represents the total cost of n regions, including the investment and operation costs of the energy storage system, C inverter is the unit investment cost of the inverter, C battery is the investment cost of the battery capacity; represents the total new energy consumption within the entire optimization period T, The amount of new energy consumed by the nth region at time t.

4. The large-capacity energy storage zoning optimization configuration method for new energy consumption according to claim 1 is characterized in that: The optimization function in the multi-energy complementary absorption in step S402 is: in, and are the wind energy, solar energy and hydropower consumption of the nth region in time t, is the total renewable energy consumption during the optimization period T; The constraints of the complementary consumption optimization function include the output constraints of various types of new energy, the charging and discharging constraints of energy storage equipment, and the load balance constraints of the system.

5. The large-capacity energy storage zoning optimization configuration method for new energy consumption according to claim 4 is characterized in that: In step S402, the constraints of the multi-energy complementary absorption optimization function also include the coupling relationship between different types of new energy sources.

6. The large-capacity energy storage zoning optimization configuration method for new energy consumption according to claim 1 is characterized in that: In step S403, the distributed energy storage system is optimized by optimizing the user-side energy storage. The user-side energy storage optimization function is: Among them, C user is the total cost on the user side, C gen (t) is the power generation cost at time t, C battery_charge with C battery_dis are the battery charging cost coefficient and the battery discharging cost respectively; The constraint condition of the above user-side energy storage optimization function is the user-side load balance condition.

7. The large-capacity energy storage zoning optimization configuration method for new energy consumption according to claim 1 is characterized in that: The iterative solution process in step S301 includes the following steps: S302: Determine initial values ​​for an initial energy storage configuration, where the initial energy storage configuration includes the total system cost of the initial n regions and the total new energy consumption within the initial optimization period T; S303. Use a dichotomy method to further optimize the energy storage configuration scale. The objective function is the total system cost of the n regions and the total renewable energy consumption within the initial optimization period T. The solution of the objective function is the optimal value. S304, compare the initial value with the currently known optimal value, and update the initial value if the optimal value is better than the initial value; Next, select the half region where the optimal value in the binary search method is located and repeat steps S302 to S304; S305: When the upper and lower limits of the energy storage configuration determined by the optimal value are less than a third preset threshold, the optimal solution is output as the optimal solution for the energy storage configuration scale.

8. The large-capacity energy storage zoning optimization configuration method for new energy consumption according to any one of claims 1 to 3, characterized in that: The energy storage charge and discharge constraint in step S201 requires that the charge and discharge power of the energy storage system be limited to the maximum charge and discharge capacity of the device. The function of the energy storage charge and discharge constraint is: in, is the maximum charging power of the battery in the nth area, is the maximum discharge power of the battery in the nth region.

9. The large-capacity energy storage zoning optimization configuration method for new energy consumption according to any one of claims 1 to 3, characterized in that: The energy storage capacity constraint in step S201 requires that the charging state of the energy storage system should be within a preset range. The energy storage capacity constraint relationship is: Among them, SoC (n) (t) The charging state of the energy storage system in the nth region at time t, SoC min and SoC max are the minimum and maximum charge states of the energy storage system respectively.

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