New energy consumption-oriented high-capacity energy storage partition optimization configuration method

Abstract

The invention belongs to the technical field of electric power system analysis, and mainly relates to a new energy consumption-oriented high-capacity energy storage partition optimization configuration method, which comprises the following steps of: firstly, dividing a power grid model into a plurality of regions according to geographic and electrical characteristic data of the selected power grid model, determining regional energy storage requirements after determining the partitions of the power grid model, and determining the energy storage requirements of the regions; further constructing a total cost optimization scheduling model of the maximum new energy consumption and the minimum energy storage system, determining constraint conditions of the target according to a load balance constraint, an energy storage system capacity constraint and a new energy processing limit, processing by using a mixed integer linear investment and operation cost planning method, and solving an optimal solution through iteration; more importantly, after the optimization scheduling model is solved preliminarily, the energy storage configuration function of each region is adjusted in real time according to time, system load and new energy consumption amount by updating the energy storage configuration step by step, and finally, the precision of energy storage configuration parameters is guaranteed.

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 energy, hydropower, etc.) in the power system continues to rise. However, the intermittent and volatile characteristics of these new energy sources have brought many challenges to the power system when accepting large-scale renewable energy. In order to ensure the stability and reliability of the power grid, energy storage systems are widely used to balance the supply and demand of electricity 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] In modern power systems, energy storage technology plays a vital role. It is mainly used to balance power supply and demand, stabilize grid frequency, smooth the volatility of renewable energy, and provide emergency backup power during peak power demand periods. Typical examples of energy storage technologies include battery energy storage, pumped storage, and compressed air energy storage. In response to the challenges of large-scale renewable energy grid connection, existing technologies have proposed a variety of energy storage capacity configuration and optimization strategies. However, these strategies usually rely on historical data and fixed power load patterns to perform one-time energy storage capacity configuration, which makes them unable to flexibly respond to dynamic changes in actual operation. In addition, current optimization methods mainly focus on using real-time data to dispatch energy storage systems to optimize the charging and discharging process of electricity, but they often do not fully consider the complementary effects between different renewable energy sources. Since these methods often independently configure energy storage for different regions, they lack global considerations, which may 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 prior art and to 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 the operation process of optimizing the energy storage configuration.

[0006] The technical solution to achieve the above technical objectives is as follows:

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

[0008] S101, representing the geographic data, electrical characteristic data and new energy characteristic data of each node in the selected power grid model as at least one feature vector, clustering the multidimensional data composed of the feature vectors by using a K-Means clustering algorithm, and dividing the power grid model into at least one region;

[0009] After determining the partitions of the power grid model, determine the initial energy storage configuration of each area, wherein the initial energy storage configuration of each area is the initial number of inverters and the initial battery capacity;

[0010] S201. Constructing a total cost optimization scheduling model with the goal of maximizing the consumption of new energy and minimizing the energy storage system based on the initial energy storage configuration data. The constraints of the total cost optimization scheduling model include: load balance constraints, energy storage system capacity constraints, energy storage capacity constraints, and new energy processing restrictions.

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

[0012] S401, after initially solving the total cost optimization scheduling model, dynamically adjust by gradually updating the energy storage configuration of each area so that the solution meets the constraints of different areas;

[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 in the dynamic adjustment process, For system load, is the amount of new energy consumed, 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 consumed.

[0016] The technical effects of the above technical solution are as follows:

[0017] This technical solution constructs an optimization scheduling model that aims to maximize energy consumption and minimize the total cost of the energy storage system through the grid model selected by effective partitioning. During the optimization process, by setting constraints and iterating, this solution can adjust the energy storage configuration in real time according to changes in time, system load and new energy consumption. This mechanism further ensures the adjustability of the final energy storage inverter power, battery capacity and configuration energy storage investment cost values.

[0018] As a further improvement to the present invention, the function of optimizing the scheduling model objective is as follows:

[0019]

[0020] Among them, C total represents the total cost of n regions, including the investment and operation cost 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 in the entire optimization period T, The amount of new energy consumed by the nth region at time t.

[0021] As a further improvement of the present invention, the dynamic adjustment process further includes the following steps:

[0022] S402: Determine the new energy consumption rate η in each area within the time t or the optimization period T RE whether it is lower than a first preset threshold;

[0023] 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 in 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 within the time t or the optimization period T;

[0024] S403: Determine and calculate the local electricity self-sufficiency rate η local whether it is lower than a second preset threshold;

[0025] The self-sufficiency rate is lower than the second preset threshold, indicating that the local electricity self-sufficiency rate is insufficient, and the self-sufficiency rate is improved by optimizing the distributed energy storage system.

[0026] As a further improvement to the present invention, the types of new energy involved in the multi-energy complementary consumption in step S402 are: wind energy, solar energy and hydro energy.

[0027] As a further improvement of the present invention, the optimization function in the multi-energy complementary consumption in step S402 is:

[0028]

[0029] in, and Modulate the wind energy, solar energy and water energy consumption of the nth region in time t respectively, is the total renewable energy consumption in the optimization period T;

[0030] The constraints of the above complementary consumption 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.

[0031] As a further improvement to the present invention, in step S402, the constraint conditions of the multi-energy complementary absorption optimization function also include the coupling relationship between different types of new energy sources.

[0032] As a further improvement of 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:

[0033]

[0034] 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 They are battery charging cost and battery discharging cost respectively;

[0035] The constraint condition of the above user-side energy storage optimization function is the user-side load balancing condition.

[0036] As a further improvement to the large-capacity energy storage partition optimization configuration method for new energy consumption of the present invention, the iterative solution process in step S301 includes the following steps:

[0037] S302, determining an initial value of an initial energy storage configuration, wherein the initial energy storage configuration includes an initial number of inverters and an initial battery capacity of initial n regions;

[0038] S303, using the dichotomy method to further optimize the energy storage configuration scale, the objective function is the number of inverters and the number of inverters in n areas, and the solution of the objective function is the optimal value;

[0039] S304, comparing the initial value with the currently known optimal value, and updating the initial value if the optimal value is better than the initial value;

[0040] Secondly, select the half area where the optimal value in the binary division method is located and repeat steps S302 to S304;

[0041] 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.

[0042] As a further improvement of 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 is limited to the maximum charge and discharge capacity of the device. The function of the energy storage charge and discharge constraint is:

[0043]

[0044] in, is the maximum charging power of the battery in the nth region, is the maximum discharge power of the battery in the nth region.

[0045] As a further improvement of 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 relational expression of the energy storage capacity constraint is:

[0046]

[0047] Among them, SoC (n) (t) The charging state and SoC of the energy storage system in the nth region at time t min With SoC max are the minimum and maximum charging states of the energy storage system respectively. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] 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:

[0049] Figure 1 This is a flow chart of Example 1 of the present invention;

[0050] Figure 2 This is a flow chart of Example 2 of the present invention;

[0051] Figure 3 This is a flow chart of Example 3 of the present invention. DETAILED DESCRIPTION

[0052] 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 the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians 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.

[0053] In order to make the purpose, technical solution and advantages of the embodiment of the present application clearer, the embodiment of the present application will be further described in detail with reference to the accompanying drawings. Below, some terms in the embodiment of the present application are explained to facilitate understanding by those skilled in the art.

[0054] Total system cost refers to all the costs involved in the planning and operation of an energy storage system. It includes the construction cost, operation and maintenance cost of energy storage equipment. Minimizing the total system cost is one of the goals of optimizing energy storage configuration.

[0055] Total renewable energy consumption refers to the total amount of renewable energy (such as wind energy and solar energy) that the power system can effectively absorb and utilize within a certain period of time. Maximizing renewable energy consumption is another goal of optimizing energy storage configuration.

[0056] The bisection method is an iterative algorithm for solving optimization problems. It gradually approaches the optimal solution by continuously splitting the search interval in half. In energy storage configuration optimization, the bisection method is used to find the optimal scale of the energy storage system so that the total system cost is minimized or the total new energy consumption is maximized.

[0057] Dynamic adjustment refers to adjusting the charging and discharging strategy of the energy storage system according to the real-time power demand and supply during the actual operation of the power system to optimize system performance. Dynamic adjustment is designed to cope with load fluctuations and the instability of renewable energy generation.

[0058] Multi-energy complementarity refers to optimizing power supply by utilizing the temporal and spatial complementarity of different types of renewable energy (such as wind energy, solar energy, and hydropower) in the power system. Through multi-energy complementarity, the overall energy consumption efficiency can be improved and the phenomenon of wind and solar power abandonment can be reduced.

[0059] SOC (State of Charge) indicates the current state of charge of the energy storage system, usually expressed as a percentage, indicating the current battery capacity as a percentage of its maximum capacity. The range of SOC is usually between 0% and 100%, and in actual operation, certain upper and lower limits are set to protect the battery life.

[0060] Mixed integer linear programming is a mathematical optimization method used to solve linear optimization problems involving integer variables and continuous variables. In energy storage configuration optimization, MILP is often used to determine the optimal energy storage capacity configuration and power allocation.

[0061] Load balancing constraint is a basic requirement for the operation of power systems, which means that at any time, the power supply must be equal to the power demand (load). In energy storage systems, this constraint ensures that the system can meet the power demand during the charging and discharging process.

[0062] Renewable energy refers to energy that can be reused in nature, such as wind energy, solar energy, hydropower, biomass energy, etc. They are usually unstable and intermittent, and require energy storage technology to balance supply and demand. They are specifically referred to as new energy in this application document.

[0063] Wind and solar power abandonment refers to the fact that during the process of renewable energy generation, part of the electricity is wasted because the power grid cannot fully absorb the power generated by wind and solar energy. By optimizing the energy storage configuration, the phenomenon of wind and solar power abandonment can be reduced and the utilization rate of new energy can be improved.

[0064] 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 amount of new energy consumed.

[0065] An iterative algorithm is a method that gradually approaches the optimal solution to a problem by repeating calculation steps. The binary search method is an iterative algorithm that finds the optimal energy storage configuration scale by continuously narrowing the search interval.

[0066] The main grid refers to the main backbone network of the power system, connecting power plants and major load centers. The main grid is responsible for dispatching electricity between different regions and maintaining a balance between power supply and demand.

[0067] Although the present application is disclosed as follows in the form of a preferred embodiment, 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.

[0068] The present invention is further described in detail below in conjunction with specific embodiments, but the embodiments of the present invention are not limited thereto.

[0069] Example 1

[0070] like Figure 1 As shown, in order to solve the problem that the prior art cannot dynamically adjust the energy storage configuration parameters in the process of optimizing the configuration of large-capacity energy storage partitions within the unit time t of the entire optimization cycle T, the present application improves the existing optimization configuration method, specifically, including the following steps:

[0071] S101, representing the geographic data and electrical characteristic data of each node in the selected power grid model as at least one feature vector, clustering the multidimensional data formed by the feature vectors by using a K-Means clustering algorithm, and dividing the power grid model into at least one region;

[0072] In this embodiment, the specific implementation of the K-Means clustering algorithm will be introduced in detail:

[0073] First, in order to use the multidimensional data of power grid nodes for clustering, we must first construct the feature vectors of the nodes. These feature vectors can include:

[0074] 1) Geographic information: For example, geographic coordinates (latitude and longitude): describe the spatial location of a node.

[0075] 2) Electrical characteristic data: such as the node's power generation capacity, load demand, existing regulation capability, etc., representing the power system characteristics of the node.

[0076] 3) New energy characteristic parameters: such as the available intensity of wind or photovoltaic power generation resources near the node.

[0077] Furthermore, suppose that each of the above nodes is represented by a feature vector—x i =[x i ,y i , p i , l i , r i ], where x i ,y i is the latitude and longitude coordinates of node i; p i is the generation capacity or load demand of node i; l i is the power transmission capacity between node i and other nodes; r i is the availability of new energy resources at node i.

[0078] After determining the eigenvectors, selecting the number of clusters K is a key step in K-Means clustering. You can determine a reasonable K by the following methods:

[0079] 1) Elbow method: Plot the sum of squared clustering errors (SSE) under different K values, find the "elbow point" where the curve begins to flatten, and determine the optimal number of clusters.

[0080] 2) Silhouette coefficient: By calculating the compactness and separation after clustering, the clustering effect under different K values ​​is evaluated and the optimal K is selected.

[0081] Furthermore, the specific steps of K-Means clustering are as follows:

[0082] 1. Randomly select K initial cluster centers. Each cluster center represents a potential power grid partition center.

[0083] 2. Calculate the distance from the node to the cluster center: Calculate the distance between each node and each cluster center. The distance can be defined as geographic distance (latitude and longitude), or weighted distance can be calculated in combination with electrical characteristics. For example:

[0084] d(i,c)=w1·GeoDistance(i,c)+w2·PowerCapacityDistance(i,c)+w3·RenewableDistance(i,c)

[0085] Among them, w1, w2, ω3 are the weight coefficients of the distance metric, which ensure a reasonable balance between the impact of geographical distance, electrical characteristics and new energy distribution.

[0086] 3. Update node affiliation: Assign each node to the area where the nearest cluster center is located.

[0087] 4. Update the cluster center: For each cluster, calculate the centroid of all nodes belonging to the cluster (that is, the average value of all node feature vectors) and update the position of the cluster center.

[0088] 5. Repeat iteration: Repeat steps 2 to 4 until the location of the cluster center no longer changes significantly or the maximum number of iterations is reached.

[0089] After the partitions of the power grid model are determined by the above algorithm, the initial energy storage configuration of each area is determined, wherein the initial energy storage configuration of each area is the initial number of inverters and the initial battery capacity.

[0090] S201. Construct a total cost optimization scheduling model based on the initial energy storage configuration data, with the goal of maximizing the absorption of new energy and minimizing the energy storage system. The constraints of the total cost optimization scheduling model include: load balance constraints, energy storage system capacity constraints, energy storage capacity constraints, and new energy processing restrictions.

[0091] Specifically, the load balance constraint mentioned in this application essentially requires that the load balance constraint requires the system to be able to meet the balance of power supply and demand at any time t. Specifically, the balance relationship is as follows:

[0092]

[0093] 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.

[0094] Furthermore, the energy storage charging and discharging constraint requires that the charging and discharging power of the energy storage system is limited to the maximum charging and discharging capacity of the equipment. The function of the energy storage charging and discharging constraint is:

[0095]

[0096] in, is the maximum charging power of the battery in the nth region, is the maximum discharge power of the battery in the nth region.

[0097] Furthermore, the energy storage capacity constraint 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:

[0098]

[0099] Among them, SoC (n) (t) The charging state and SoC of the energy storage system in the nth region at time t min With SoC max are the minimum and maximum charging states of the energy storage system respectively.

[0100] From the above, it can be seen that by applying various constraints to the process of solving the optimal solution for energy storage configuration, the energy storage configuration of the entire power system can be optimized, thereby achieving the goal of improving the efficiency of new energy consumption and reducing the total cost of the system.

[0101] S301, processing integer and continuous variables in the total cost optimization scheduling model by using a mixed integer linear investment and operating cost planning method, and determining the optimal solution that meets the constraints by iterative solution;

[0102] S401, after initially solving the total cost optimization scheduling model, dynamically adjust by gradually updating the energy storage configuration of each area so that the solution meets the constraints of different areas;

[0103] During the dynamic adjustment process, the dynamic adjustment relationship related to time is as follows:

[0104]

[0105] 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 in the dynamic adjustment process, For system load, is the amount of new energy consumed, 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 consumed.

[0106] S501, output the corresponding energy storage inverter power, battery capacity and energy storage investment cost. (Although this application focuses on the adjustment of energy storage configuration, the data of the corresponding energy storage inverter power, battery capacity and energy storage investment cost can provide more uses, and this application will not repeat them here).

[0107] According to the above steps, the optimization configuration method proposed in this application can effectively solve the energy storage configuration optimization problem, improve the utilization rate of new energy, reduce the total system cost, and ensure the stable operation of the power system through the set constraints and the optimization scheduling model. In addition, this application applies the mixed integer linear programming (MILP) method to the optimization scheduling model, specifically, the mixed integer linear investment and operating cost planning method is adopted. This method is particularly suitable for dealing with optimization problems involving integers and continuous variables, such as the configuration problem of energy storage systems. Specifically, the number of energy storage system configurations (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 equipment, such as the number of battery packs and inverters, is usually an integer. 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 meets the constraints, so that the energy storage configuration scheme can maximize the absorption of new energy and minimize investment and operating costs.

[0108] 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 period T, wherein 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.

[0109] 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, the amount of solar power generation is large, but the electricity load is relatively low. At night, when the load is peak, the amount of 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, real-time monitoring of the load demand and solar power generation in area 1 is achieved, 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.

[0110] By adjusting the number of inverters and batteries in real time, the system can effectively handle load changes between day and night, maximize the use of solar energy, and reduce idle or overload conditions of the energy storage system. At the same time, in actual operation, it avoids the excessive investment and operating costs that may be caused by fixed configurations.

[0111] 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 power is stronger in winter, and solar power 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 power is strong in the winter, more inverters need to be determined to handle the charging power, while in the summer, the light is stable and the inverter demand is reduced; if there is excess wind in the winter, it is determined that more battery capacity is needed to store energy, and the battery capacity demand in the summer is smaller and the setting can be reduced. By adjusting the configuration of the energy storage system in real time, the system can flexibly respond to fluctuations in new energy power generation between different seasons and reduce waste caused by over-configuration.

[0112] 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 unpredictable and requires a quick response from the system. The above adjustment mechanism of the present 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 changes, provide sufficient power support, and avoid grid overload or collapse.

[0113] Example 2

[0114] like Figure 1-2 As shown, different from Example 1, in order to further optimize the dynamic adjustment mechanism of the present application, the dynamic adjustment process further includes the following steps:

[0115] S402: Determine the new energy consumption rate η in each area within the time t or the optimization period T RE whether it is lower than a first preset threshold;

[0116] in, When the new energy consumption rate is lower than the first preset threshold, it indicates that a single type of renewable energy cannot be effectively consumed in a certain area at the current time t or the optimization period T. At the same time, as a supplement to the judgment basis, if the current single renewable energy (such as wind energy or solar energy) output fluctuates greatly, resulting in frequent adjustments to the power grid, and the frequency or amplitude of the fluctuation exceeds a certain set threshold, multi-energy complementarity is required to balance the system; if the imbalance between the load and the renewable energy generation of the power grid in a certain period of time (i.e., the difference in power supply and demand) exceeds the preset threshold, multi-energy complementarity optimization is also required, where the load imbalance can be defined as:

[0117] △P=|P load -P RE |

[0118] The maximum allowable range of ΔP is set to ΔP max , when ΔP>ΔPmax When considering multi-energy complementation.

[0119] Specifically, in the specific implementation process, multi-energy complementary absorption is performed according to the absorption amount of different types of new energy in the region within the time t or the optimization period T; considering that the commonly used renewable new 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 protection scope of this application is not limited to the above three renewable new energy sources. The optimization function in multi-energy complementary absorption is:

[0120]

[0121] in, and Modulate the wind energy, solar energy and water energy consumption of the nth region in time t respectively, is the total renewable energy consumption in the optimization period T;

[0122] The constraints of the above complementary consumption optimization function include the output constraints of the above-mentioned various types of renewable energy, the charging and discharging constraints of energy storage equipment, the load balance constraints of the system and the coupling relationship between different types of renewable energy.

[0123] In the specific implementation process, the wind energy absorption is limited by the output of the wind farm and the dispatching capacity of the power grid; the solar energy absorption is affected by the power generation capacity of the solar power station and its changes within a day; the water energy absorption is limited by the dispatching capacity of the hydropower station and the water resources allocation capacity; so the above constraints are defined as follows:

[0124] In the wind energy constraint, in is the maximum wind power output in region n at time t.

[0125] In the solar energy constraint, in is the maximum solar output power in area n at time t, which usually varies with the light intensity.

[0126] In the water energy constraint condition, in is the maximum hydropower output of region n at time t.

[0127] Furthermore, this application takes into account that different types of energy consumption may be coupled, such as wind power and solar power can complement each other in power supply, or in some cases use energy storage systems together:

[0128]

[0129] in, It is the total renewable energy absorption capacity that the power grid in region n can accommodate within time t.

[0130] S403: Determine and calculate the local electricity self-sufficiency rate η local Is it lower than a second preset threshold?

[0131] 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 self-sufficiency rate is improved by optimizing the distributed energy storage system. Specifically, the user-side energy storage optimization function is:

[0132]

[0133] 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; 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:

[0134]

[0135] To sum up, this application sets up a multi-energy complementary new energy consumption mode in the dynamic adjustment process of multiple regions. The multi-energy complementary mode 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.

[0136] The other details that are the same as those in Example 1 are not described in detail in this example.

[0137] Example 3

[0138] 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 as the basis for accurate solution in 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, so as to obtain the optimal solution for the energy storage configuration under the condition of minimizing the total cost of the system or maximizing the total consumption. Specifically, the iterative solution process in step S301 includes the following steps:

[0139] S302, determining an initial value of an initial energy storage configuration, wherein the initial energy storage configuration includes an initial number of inverters and an initial battery capacity of initial n regions;

[0140] S303, using the dichotomy method to further optimize the energy storage configuration scale, the objective function is the number of inverters and the number of inverters in n areas, and the solution of the objective function is the optimal value;

[0141] S304, comparing the initial value with the currently known optimal value, and updating the initial value if the optimal value is better than the initial value;

[0142] Secondly, select the half area where the optimal value in the binary division method is located and repeat steps S302 to S304;

[0143] 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.

[0144] In summary, by redefining the initial configuration scale as the total energy storage configuration scale of multiple regions, the dichotomy method can be effectively applied for optimization to find the solution for the optimal energy storage configuration under the conditions of minimizing the total system cost or maximizing the total new energy consumption. This method simplifies the comprehensive optimization process of the multi-regional system and provides a more intuitive and efficient optimization path.

[0145] The above are only preferred embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope 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, representing the geographic data, electrical characteristic data and new energy characteristic data of each node in the selected power grid model as at least one feature vector, clustering the multidimensional data formed by the feature vectors by using a K-Means clustering algorithm, and dividing the power grid model into at least one region; After determining the partitions of the power grid model, determine the initial energy storage configuration of each area, wherein the initial energy storage configuration of each area is the initial number of inverters and the initial battery capacity; S201, constructing a total cost optimization scheduling model with the goal of maximizing the consumption of new energy and minimizing the energy storage system according to the initial energy storage configuration data, wherein the constraints of the total cost optimization scheduling model include: load balance constraints, energy storage system capacity constraints, energy storage capacity constraints, and new energy processing restrictions; S301, processing integer and continuous variables in the total cost optimization scheduling model by a mixed integer linear investment and operating cost planning method, and determining the optimal solution that meets the constraint conditions by iterative solution; S401, after preliminarily solving the total cost optimization scheduling model, dynamically adjust by gradually updating the energy storage configuration of each area so that the solution meets the constraint conditions of different areas; 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 in the dynamic adjustment process, For system load, is the amount of new energy consumed, 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 consumed.

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

3. The large-capacity energy storage zoning optimization configuration method for new energy consumption according to claim 1 is characterized in that: The dynamic adjustment process also includes the following steps: S402: Determine the new energy consumption rate η in each area within the time t or the 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 in 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 within the time t or 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; The self-sufficiency rate is lower than the second preset threshold, indicating that the local electricity self-sufficiency rate is insufficient, and the self-sufficiency rate is improved by optimizing the distributed energy storage system.

4. The large-capacity energy storage zoning optimization configuration method for new energy consumption according to claim 3 is characterized in that: In step S402, the types of new energy involved in the multi-energy complementary consumption are: wind energy, solar energy and hydro energy.

5. The large-capacity energy storage zoning optimization configuration method for new energy consumption according to claim 4 is characterized in that: The optimization function in the multi-energy complementary consumption in step S402 is: in, and are the wind energy, solar energy and water energy consumption of the nth region in time t, is the total renewable energy consumption in the optimization period T; The constraints of the above complementary consumption 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.

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

7. The large-capacity energy storage zoning optimization configuration method for new energy consumption according to claim 3 is characterized in that: 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: 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 They are 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 balancing condition.

8. 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, determining an initial value of an initial energy storage configuration, wherein the initial energy storage configuration includes an initial number of inverters and an initial battery capacity of initial n regions; S303, using the dichotomy method to further optimize the energy storage configuration scale, the objective function is the number of inverters and the number of inverters in n areas, and the solution of the objective function is the optimal value; S304, comparing the initial value with the currently known optimal value, and updating the initial value if the optimal value is better than the initial value; Secondly, select the half area where the optimal value in the binary division 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.

9. The large-capacity energy storage zoning optimization configuration method for new energy consumption according to any one of claims 1-2 is 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 region, is the maximum discharge power of the battery in the nth region.

10. The large-capacity energy storage zoning optimization configuration method for new energy consumption according to any one of claims 1-2, 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 relational expression of the energy storage capacity constraint is: Among them, SoC (n) (t) The charging state and SoC of the energy storage system in the nth region at time t min With SoC max are the minimum and maximum charging states of the energy storage system respectively.

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