Low-voltage distribution network hybrid energy storage capacity planning method and related device

Abstract

The invention provides a low-voltage distribution network hybrid energy storage capacity planning method and a related device, and belongs to the technical field of low-voltage distribution network energy storage in a power system. According to the method, the year-by-year influence of social and economic development on the energy storage income is fully considered in the energy storage configuration optimization model, so that the actual income level of the energy storage in the whole life cycle can be reflected more truly and accurately; the problem that the profit calculation deviation is large due to the fact that social and economic development factors are not considered in traditional energy storage capacity planning is effectively solved. Meanwhile, the initial energy storage configuration scheme is subjected to return on investment calculation and economic evaluation, and the configuration scheme is subjected to iterative adjustment when the economic requirement is not met, so that the finally obtained energy storage configuration scheme can be ensured to have reliable economic feasibility and engineering landing performance; and therefore, the rationality and practicability of low-voltage distribution network hybrid energy storage capacity planning are remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of energy storage technology for low-voltage distribution networks in power systems, specifically relating to a hybrid energy storage capacity planning method and related devices for low-voltage distribution networks. Background Technology

[0002] In modern power systems, with the widespread integration of distributed energy resources and increasing user demands for power quality, energy storage technology, as an effective regulation tool, is playing an increasingly important role in grid operation and planning. Energy storage systems can smooth power fluctuations, improve power quality, and optimize grid operation and dispatch, which is of great significance for enhancing the stability and reliability of the power grid.

[0003] In low-voltage distribution networks, the planning and configuration of energy storage is crucial. Accurate and reasonable energy storage capacity planning can effectively address changes in user load and improve grid operating efficiency and economic benefits. Currently, various methods are commonly used for grid risk identification and analysis, followed by energy storage capacity planning.

[0004] Traditional energy storage capacity planning methods often employ overly simplified life-cycle cost models that fail to adequately consider the actual impact of socio-economic development factors on energy storage capacity planning, and thus cannot accurately reflect changes in the profitability of energy storage during long-term operation. Consequently, energy storage configuration schemes derived from these models often deviate from the economic benefits of actual projects, making it difficult to guarantee the economic rationality and feasibility of the configuration results, and ultimately failing to meet the actual needs of hybrid energy storage planning for low-voltage distribution networks. Summary of the Invention

[0005] Therefore, it is necessary to provide a method and related devices for planning the hybrid energy storage capacity of low-voltage distribution networks to address the aforementioned technical problems.

[0006] In a first aspect, the present invention provides a method for planning hybrid energy storage capacity in low-voltage distribution networks, comprising the following steps:

[0007] For low-voltage distribution network areas that require energy storage, determine the type of energy storage to be installed and obtain the energy storage parameters;

[0008] Based on the grid parameters and energy storage parameters of the low-voltage distribution network area, the pre-constructed energy storage configuration optimization model is solved to obtain a preliminary energy storage configuration scheme. The energy storage configuration optimization model aims to maximize the difference between energy storage revenue and energy storage cost. The energy storage revenue changes year by year with the changes in social development costs after the investment in energy storage.

[0009] Calculate the return on investment of the preliminary energy storage configuration scheme and evaluate its economic efficiency. If the economic efficiency requirements are not met, adjust the energy storage parameters and resolve the energy storage configuration optimization model until the economic efficiency requirements are met, and then obtain the final energy storage configuration scheme.

[0010] Furthermore, the mathematical expression for the energy storage configuration optimization model is as follows:

[0011] ;

[0012] In the formula, F is the objective function; B j Let m be the demand reduction revenue for month j, m be the number of months in the planning period, and B be the revenue from demand reduction. i Let d be the peak shaving and valley filling revenue on day i, and d be the number of days in the planning period. Let Y be the social development coefficient in year y, where Y is the lifespan of the energy storage and C is the monthly equivalent energy storage cost.

[0013] The benefits of demand reduction are:

[0014] ;

[0015] In the formula, This is the unit price for basic electricity charges; and These represent the maximum monthly load value before and after the installation of energy storage in month j, respectively.

[0016] The benefits of peak shaving and valley filling are:

[0017] ;

[0018] In the formula, t is the time index; The real-time operating power of the hybrid energy storage configured at time t on day i; Let be the peak-valley time-of-use electricity price corresponding to the t-th time. The length of the time interval;

[0019] The social development coefficient is:

[0020] ;

[0021] In the formula, Inflation rate; The discount rate;

[0022] The monthly equivalent energy storage cost is:

[0023] ;

[0024] In the formula, , These are the unit investment prices per kilowatt-hour for energy storage units of batteries and supercapacitors, respectively. , These are the purchase prices per kilowatt for battery and supercapacitor energy storage, respectively. , These are the operation and maintenance costs per kilowatt for battery and supercapacitor energy storage, respectively. and The rated capacity is configured for the battery and the supercapacitor, respectively; and The rated power is configured for the battery and the supercapacitor, respectively.

[0025] Furthermore, a scenario linearization reshaping method is used to determine the real-time operating power of hybrid energy storage, including:

[0026] A linear transformation is performed on the power fluctuation curve of hybrid energy storage in a typical scenario to obtain:

[0027] ;

[0028] In the formula, Let be the real-time operating power vector of the hybrid energy storage on day i. This is the real-time energy storage power vector corresponding to the o-th typical scenario; It is a vector in which all elements are 1; , The coefficients are represented linearly. The remaining items;

[0029] By using the least squares method, the linear transformation result is optimally approximated to the power fluctuation curves of all natural scenarios within the same category, thus obtaining the power fluctuation curves of hybrid energy storage under natural scenarios.

[0030] The real-time operating power of hybrid energy storage is determined based on the power fluctuation curve under natural scenarios.

[0031] Furthermore, the least squares method is used to make the linear transformation result optimally approximate the power fluctuation curves of all natural scenes within the same class, including:

[0032] The optimization problem of the linear representation coefficients is constructed with the goal of minimizing the sum of squares of the elements of the linear transformation remainder.

[0033] The optimization problem is transformed into a problem of finding the best approximate solution to a system of linear equations.

[0034] The linear equation system is solved using a weighted recursive least squares identification algorithm with a forgetting factor to obtain the linear representation coefficients;

[0035] Based on the obtained linear representation coefficients, the linear mapping relationship between typical scenarios and natural scenarios is determined, so as to achieve the optimal approximation of the power fluctuation curves of all natural scenarios within the same category.

[0036] Furthermore, the mathematical expression for the weighted recursive least squares identification algorithm with a forgetting factor is:

[0037] ;

[0038] In the formula, and These are the linear representation coefficient estimates for time t and time t-1, respectively; The real-time energy storage power value for the t hour on the i-th day of the year; Represents the regression vector for the t-th hour on the i-th day of the year. transpose; This is the gain matrix; and Let be the covariance matrices at time t and time t-1, respectively. Forgetting factor; It is the identity matrix;

[0039] The forgetting factor is calculated as follows:

[0040] ;

[0041] In the formula, Let be the forgetting factor for the k-th iteration; Let be the state of charge of the battery at time t. and These are the lower and upper limits of the battery's state of charge, respectively.

[0042] Furthermore, in typical scenarios, user daily load data is clustered using an improved k-means clustering algorithm, including:

[0043] Based on electricity user characteristic indicators, feature dimensionality reduction processing is performed on the normalized user daily load curve to extract feature indicators that reflect the user's electricity consumption behavior throughout the day and at different times, forming a clustered feature dataset.

[0044] Based on the clustering feature dataset, different numbers of clusters are tried to generate candidate clustering results corresponding to different numbers of clusters;

[0045] The candidate clustering results are quantitatively evaluated using the effectiveness evaluation index, and the number of clusters corresponding to the maximum effectiveness evaluation index is selected as the optimal number of clusters.

[0046] The daily load curve corresponding to the cluster center under the optimal number of clusters is taken as the typical daily load curve, and each typical daily load curve corresponds to a typical scenario.

[0047] Furthermore, the mathematical expression for the effectiveness evaluation index is:

[0048] ;

[0049] In the formula, The effectiveness evaluation index is defined as follows: N is the total number of samples after principal component dimensionality reduction, and k is the number of clusters. The trace of the scatter matrix between different classes of the samples obtained after dimensionality reduction. The trace of the intra-class scatter matrix corresponding to the s-th power load feature. Let S be the weighting coefficient for the load characteristics of the s-th electricity user, and S be the number of electricity user characteristic indicators.

[0050] Secondly, the present invention provides a hybrid energy storage capacity planning device for low-voltage distribution networks, comprising:

[0051] The energy storage type determination module is used to determine the type of energy storage to be installed in low-voltage distribution network areas that require energy storage, and to obtain the energy storage parameters.

[0052] The energy storage configuration optimization solution module is used to solve a pre-built energy storage configuration optimization model based on the grid parameters and energy storage parameters of the low-voltage distribution network area, and obtain a preliminary energy storage configuration scheme. The energy storage configuration optimization model aims to maximize the difference between energy storage revenue and energy storage cost. The energy storage revenue changes year by year with the cost generated by social development after the investment in energy storage.

[0053] The energy storage scheme determination module is used to calculate the return on investment of the preliminary energy storage configuration scheme and evaluate its economics. If the economic requirements are not met, the energy storage parameters are adjusted and the energy storage configuration optimization model is solved again until the economic requirements are met, and the final energy storage configuration scheme is obtained.

[0054] Thirdly, the present invention provides a computer device, the device including a processor and a memory:

[0055] The memory is used to store computer programs and send the instructions of the computer programs to the processor;

[0056] The processor executes the following steps according to the instructions of the computer program:

[0057] For low-voltage distribution network areas that require energy storage, determine the type of energy storage to be installed and obtain the energy storage parameters;

[0058] Based on the grid parameters and energy storage parameters of the low-voltage distribution network area, the pre-constructed energy storage configuration optimization model is solved to obtain a preliminary energy storage configuration scheme. The energy storage configuration optimization model aims to maximize the difference between energy storage revenue and energy storage cost. The energy storage revenue changes year by year with the changes in social development costs after the investment in energy storage.

[0059] Calculate the return on investment of the preliminary energy storage configuration scheme and evaluate its economic efficiency. If the economic efficiency requirements are not met, adjust the energy storage parameters and resolve the energy storage configuration optimization model until the economic efficiency requirements are met, and then obtain the final energy storage configuration scheme.

[0060] Fourthly, the present invention provides a computer-readable storage medium on which a computer program is stored, and when executed by a processor, the computer program performs the following steps:

[0061] For low-voltage distribution network areas that require energy storage, determine the type of energy storage to be installed and obtain the energy storage parameters;

[0062] Based on the grid parameters and energy storage parameters of the low-voltage distribution network area, the pre-constructed energy storage configuration optimization model is solved to obtain a preliminary energy storage configuration scheme. The energy storage configuration optimization model aims to maximize the difference between energy storage revenue and energy storage cost. The energy storage revenue changes year by year with the changes in social development costs after the investment in energy storage.

[0063] Calculate the return on investment of the preliminary energy storage configuration scheme and evaluate its economic efficiency. If the economic efficiency requirements are not met, adjust the energy storage parameters and resolve the energy storage configuration optimization model until the economic efficiency requirements are met, and then obtain the final energy storage configuration scheme.

[0064] Fifthly, the present invention also provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0065] For low-voltage distribution network areas that require energy storage, determine the type of energy storage to be installed and obtain the energy storage parameters;

[0066] Based on the grid parameters and energy storage parameters of the low-voltage distribution network area, the pre-constructed energy storage configuration optimization model is solved to obtain a preliminary energy storage configuration scheme. The energy storage configuration optimization model aims to maximize the difference between energy storage revenue and energy storage cost. The energy storage revenue changes year by year with the changes in social development costs after the investment in energy storage.

[0067] Calculate the return on investment of the preliminary energy storage configuration plan and evaluate its economic viability. If the economic viability requirements are not met, adjust the preliminary energy storage configuration plan until the economic viability requirements are met, and then obtain the final energy storage configuration plan.

[0068] In summary, this invention provides a method and related apparatus for planning hybrid energy storage capacity in low-voltage distribution networks. By fully considering the annual impact of socio-economic development on energy storage revenue in the energy storage configuration optimization model, it can more realistically and accurately reflect the actual revenue level of energy storage throughout its entire life cycle, effectively solving the problem of large deviations in revenue calculation caused by the failure to consider socio-economic development factors in traditional energy storage capacity planning. Furthermore, by calculating the rate of return on investment and evaluating the economic viability of the preliminary energy storage configuration scheme, and iteratively adjusting the scheme when economic requirements are not met, it ensures that the final energy storage configuration scheme has reliable economic feasibility and engineering implementation, thereby significantly improving the rationality and practicality of hybrid energy storage capacity planning in low-voltage distribution networks. Attached Figure Description

[0069] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0070] Figure 1 This is a flowchart illustrating a hybrid energy storage capacity planning method for low-voltage distribution networks in one embodiment of the present invention.

[0071] Figure 2 This is a schematic diagram of adding energy storage to a power distribution network in one embodiment of the present invention;

[0072] Figure 3 This is a block diagram of a low-voltage distribution network hybrid energy storage capacity planning device according to one embodiment of the present invention;

[0073] Figure 4 This is an internal structural diagram of a computer device according to one embodiment of the present invention. Detailed Implementation

[0074] To make the objectives, features, and advantages of this invention more apparent and understandable, the technical solutions of the embodiments of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described below are only some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0075] The background technology of this invention will be further introduced below.

[0076] In modern power systems, with the widespread integration of distributed energy resources and increasing user demands for power quality, energy storage technology, as an effective regulation tool, plays an increasingly important role in grid operation and planning. Energy storage systems can smooth power fluctuations, improve power quality, and optimize grid operation and dispatch, which is crucial for enhancing grid stability and reliability. In low-voltage distribution networks, the configuration and planning of energy storage is paramount. Accurate and reasonable energy storage capacity planning can effectively address changes in user load, improve grid operating efficiency and economic benefits. Currently, the industry commonly uses various methods for grid risk identification and analysis, and then plans energy storage capacity accordingly. Among these, the existing technologies most similar to this invention mainly include energy storage life-cycle cost models, multi-objective combined operation optimization methods, empirical scenario reduction planning methods, and cost-effective optimization methods.

[0077] Specifically, the energy storage lifecycle cost model is a method for assessing the total cost of a product, system, or project throughout its entire lifecycle, covering all stages from design, manufacturing, operation to disposal. Its core principle is to help decision-makers optimize resource allocation and select the most economically efficient solution by quantifying all relevant costs such as initial investment, operation and maintenance, and end-of-life disposal, in order to achieve long-term cost minimization and value maximization. Multi-objective combined operation optimization establishes an optimization model based on multiple objectives, covering the needs of multiple project scenarios, and can achieve the optimal solution for combining multiple objectives. It is suitable for energy storage projects that can participate in multiple markets and obtain multiple benefits. The experience-based scenario reduction planning method selects several representative typical scenarios from the annual scenario set to simulate the energy storage operating environment. The scenario selection criteria often rely on the planning experience of historical years and the operating status of energy storage, and based on this, the energy storage is planned and solved to determine the optimal capacity.

[0078] However, the aforementioned existing technologies have many shortcomings in the practical application of hybrid energy storage capacity planning in low-voltage distribution networks, making it difficult to meet actual engineering needs: In traditional energy storage capacity planning, the life-cycle cost models of energy storage are mostly simplified, failing to fully consider the impact of energy storage operation and scheduling constraints on energy storage capacity planning, considering only a few factors, and failing to further consider the long-term impact of socio-economic development factors on energy storage revenue, thus failing to truly reflect the changes in energy storage revenue during long-term operation. The energy storage configuration schemes obtained based on these models often deviate from the economic benefits of actual projects, making it difficult to guarantee the economic rationality and feasibility of the configuration results; The multi-objective combined operation optimization method is applicable to complex scenarios, requiring consideration of a large number of influencing factors, which significantly impacts the efficiency of planning and configuration, making it difficult to promote and apply at the actual planning level; The experience-based scenario reduction method relies heavily on historical data and the experience of planners, has low adaptability to new regions and new technologies, has a narrow application scope, and its planning accuracy is difficult to guarantee as the planning years increase, making it unable to accurately adapt to the planning needs of hybrid energy storage in low-voltage distribution networks.

[0079] To address the numerous shortcomings of existing technologies, this invention aims to solve the following technical problems: It establishes an energy storage optimization configuration model with the goal of maximizing the monthly comprehensive benefits after adding energy storage to the distribution network. Considering the need for hybrid energy storage combining batteries and supercapacitors to cope with changes in user load, it proposes a hybrid energy storage capacity planning method and related devices applicable to multiple scenarios in low-voltage distribution networks to meet the personalized needs of low-voltage distribution network energy storage configuration. Based on typical scenarios from clustering results, it uses a weighted recursive least squares identification algorithm with a forgetting factor to approximately linearly represent extreme scenarios throughout the year. The linearized scenario results are embedded into the annual operation constraints of energy storage, and the scale of the optimization problem is reduced by rationally constructing decision variables, achieving annual operation simulation with a smaller computational scale. Ultimately, it obtains an economically feasible, highly adaptable, and accurately planned hybrid energy storage capacity configuration scheme for low-voltage distribution networks. The various embodiments of this invention are described in detail below.

[0080] In one embodiment, such as Figure 1 As shown, a method for planning hybrid energy storage capacity in low-voltage distribution networks is provided, including the following steps:

[0081] S101: For low-voltage distribution network areas that require energy storage, determine the type of energy storage to be installed and obtain the energy storage parameters.

[0082] Among them, the low-voltage distribution network area that needs to be equipped with energy storage refers to a specific distribution network area where the grid voltage level is low (usually 0.4kV and below) and there is a need for energy storage configuration (such as large load fluctuations, power quality improvement, etc.); the type of energy storage refers to the type of energy storage device that is suitable for the needs of the low-voltage distribution network area (such as battery energy storage, supercapacitor energy storage, etc.); the energy storage parameters refer to the key parameters that characterize the core performance of the energy storage device, including but not limited to the basic parameters such as the rated capacity, rated power, investment unit price, and operation and maintenance cost of the energy storage device.

[0083] Optionally, this step can be achieved by investigating the actual operational needs of the low-voltage distribution network area to be planned (such as load characteristics, power quality requirements, planning objectives, etc.), combining the analysis of the adaptability of energy storage technology, determining the type of energy storage that meets the needs of the area, and then collecting and organizing the various energy storage parameters corresponding to the type of energy storage by consulting energy storage product specifications and equipment suppliers.

[0084] For example, for areas with demand and initially determined to be suitable for energy storage installation, historical load data or predicted load values ​​can be used to select a wide range of battery types available on the market.

[0085] S102: Based on the grid parameters and energy storage parameters of the low-voltage distribution network area, solve the pre-constructed energy storage configuration optimization model to obtain a preliminary energy storage configuration scheme; the energy storage configuration optimization model aims to maximize the difference between energy storage revenue and energy storage cost; the energy storage revenue changes year by year with the cost generated by social development after the investment in energy storage.

[0086] Among them, grid parameters refer to the core operating parameters of the low-voltage distribution network area to be planned, including but not limited to the maximum and minimum load of users in the area, peak-valley time-of-use electricity prices, and grid transmission capacity; energy storage configuration optimization model refers to a mathematical model built based on preset optimization objectives and constraints, used to calculate the optimal energy storage configuration scheme; energy storage revenue refers to the economic benefits generated after the energy storage device is put into operation (such as peak shaving and valley filling revenue, demand reduction revenue, etc.); energy storage cost refers to the total investment cost of the energy storage device over its entire life cycle (such as initial investment cost, operation and maintenance cost, etc.); and cost changes caused by social development refer to cost fluctuations caused by changes in socio-economic factors such as inflation and discount rates over time.

[0087] Optionally, this step first collects the grid parameters of the low-voltage distribution network area to be planned, and then, in combination with the energy storage parameters, substitutes them into a pre-constructed energy storage configuration optimization model. This model aims to maximize the difference between energy storage revenue and energy storage cost, and fully considers the annual impact of social development factors on energy storage revenue. By selecting an appropriate mathematical solution method, the model is solved to obtain the combination of parameters such as rated capacity and rated power of energy storage that meet the optimization objective, i.e., the preliminary energy storage configuration scheme.

[0088] S103: Calculate the return on investment of the preliminary energy storage configuration scheme and evaluate its economic efficiency. If the economic efficiency requirements are not met, adjust the energy storage parameters and resolve the energy storage configuration optimization model until the economic efficiency requirements are met, and then obtain the final energy storage configuration scheme.

[0089] Among them, the return on investment (ROI) refers to the ratio of the investment income of an energy storage project to the total investment, which is the core indicator for measuring the economic efficiency of an energy storage configuration scheme; economic requirements refer to the preset feasibility standards for energy storage projects (such as an ROI not lower than the industry benchmark value, an investment payback period not exceeding the preset number of years, etc.); preliminary energy storage configuration scheme refers to the energy storage configuration scheme obtained from the solution that has not undergone economic verification; and final energy storage configuration scheme refers to the energy storage configuration scheme that has undergone economic verification and meets the preset economic requirements and is feasible for implementation.

[0090] Optionally, the first step is to calculate the project's return on investment (ROI) based on the total investment (initial investment + total life-cycle operation and maintenance costs) and expected total revenue (energy storage revenue over the entire life-cycle) of the preliminary energy storage configuration scheme. The calculated ROI is then compared with the preset economic requirements. If the ROI reaches or exceeds the benchmark value, the preliminary scheme meets the economic requirements and is thus the final scheme. If it does not meet the requirements, the energy storage parameters, such as the rated capacity and rated power, are adjusted, and the energy storage configuration optimization model is re-solved to obtain a new energy storage configuration scheme. The calculation and evaluation process of step S102 and this step is repeated until the scheme meets the economic requirements.

[0091] In addition, for areas with personalized needs or special regions, some parameters of the energy storage configuration scheme can be adjusted to ultimately determine the rationality of installing energy storage and provide configuration suggestions.

[0092] The method provided in this embodiment fully considers the annual impact of socio-economic development on energy storage revenue in the energy storage configuration optimization model, which can more realistically and accurately reflect the actual revenue level of energy storage throughout its entire life cycle. This effectively solves the problem of large deviations in revenue calculation caused by the failure to consider socio-economic development factors in traditional energy storage capacity planning. At the same time, by calculating the rate of return on investment and evaluating the economic efficiency of the preliminary energy storage configuration scheme, and iteratively adjusting the configuration scheme when it does not meet the economic requirements, it can ensure that the final energy storage configuration scheme has reliable economic feasibility and engineering implementation, thereby significantly improving the rationality and practicality of hybrid energy storage capacity planning for low-voltage distribution networks.

[0093] In one exemplary embodiment, an energy storage configuration optimization model design is provided. The specific construction, constraints, parameter settings, and optimization objectives of this energy storage configuration optimization model are described below.

[0094] 1. Modeling for the Benefit Analysis of Adding Energy Storage to Low-Voltage Distribution Networks:

[0095] To accurately calculate the economic benefits of energy storage deployment, and as a basis for optimizing energy storage configuration, a revenue analysis model for adding energy storage to low-voltage distribution networks is first constructed. It is known that grid electricity charges mainly consist of two parts: basic electricity charges and energy consumption charges. The basic electricity charges adopt the most common demand-based charging method, calculated based on the maximum monthly electricity demand; energy consumption charges are calculated and collected based on real-time electricity consumption.

[0096] The benefits of adding energy storage can be divided into two parts: demand reduction benefits and peak shaving and valley filling benefits, as detailed below:

[0097] (1) Demand Reduction Benefits: After the energy storage system is put into operation, it can effectively reduce the maximum monthly electricity demand of users by rationally scheduling and allocating the distribution network load and optimizing users' electricity consumption behavior, thereby reducing the expenditure of basic electricity fees and forming demand reduction benefits. The specific calculation formula is as follows:

[0098] ;

[0099] In the formula, B j To reduce revenue due to demand reduction in month j, This is the unit price for basic electricity charges; and These are the maximum monthly load values ​​before and after the installation of energy storage in month j, respectively. The difference between the two values ​​is the monthly demand that can be reduced.

[0100] (2) Peak shaving and valley filling revenue: Currently, low-voltage distribution networks generally implement a time-of-use pricing system, which means that different electricity prices are applied at different times. The peak electricity price is higher than the valley electricity price. Energy storage batteries can be charged during the valley when the electricity price is lower, storing the electrical energy, and then discharged during the peak when the electricity price is higher to meet the electricity demand of users. Thus, the difference between peak and valley electricity prices is used to earn economic benefits. This part of the revenue is called peak shaving and valley filling revenue, and its specific calculation formula is as follows:

[0101] ;

[0102] In the formula, B i Let t be the peak-shaving and valley-filling revenue on day i, and t be the time index. The real-time operating power of the hybrid energy storage configured at time t on day i; Let be the peak-valley time-of-use electricity price corresponding to the t-th time. The interval length between times.

[0103] In this embodiment, to balance the energy storage capacity, response speed, and operational stability of the energy storage system, a hybrid energy storage structure is adopted, specifically composed of a combination of batteries and supercapacitors. The real-time operating power of this hybrid energy storage system is the sum of the real-time operating power of the batteries and supercapacitors, and their power relationship satisfies the following expression:

[0104] ;

[0105] In the formula, This represents the real-time operating power of the battery at time t on day i. This represents the real-time operating power of the supercapacitor at time t on day i.

[0106] To achieve a reasonable configuration of batteries and supercapacitors in a hybrid energy storage system, ensure the capacity and power matching of the two energy storage devices, and meet the power demand of the low-voltage distribution network and the operational requirements of the energy storage system, a hybrid energy storage capacity model and a hybrid energy storage charge model are constructed to determine the rated capacity and real-time state of charge of the batteries and supercapacitors, as detailed below:

[0107] (3) Hybrid Energy Storage Capacity Model: This model is used to calculate the rated capacity of batteries and supercapacitors to ensure that their capacity can meet the needs of peak and valley regulation and load dispatching of the distribution network, while avoiding the cost waste caused by excessive capacity and the problem of insufficient capacity to meet the operation requirements. The specific formula is as follows:

[0108] ;

[0109] In the formula: , These are the rated capacities of the storage battery and the supercapacitor, respectively. and , and These are the upper and lower limits for batteries and supercapacitors, respectively.

[0110] (4) Hybrid Energy Storage Charge: This model is used to calculate the charge of the battery and supercapacitor at each operating moment, monitor the charge status of the energy storage device in real time, avoid overcharging and over-discharging, and ensure the service life and operational safety of the energy storage device. The specific formula is as follows:

[0111] ;

[0112] In the formula: , These represent the charge levels of the battery and the supercapacitor at time t, respectively. , These are the initial charge values ​​for the battery and the supercapacitor, respectively. The choice depends on the situation, but 0.5 is generally selected.

[0113] 2. Establishment of constraints for hybrid energy storage operation:

[0114] To ensure the safe, stable, and reliable operation of hybrid energy storage systems in low-voltage distribution networks, prevent disruptions to normal power supply due to system malfunctions, and guarantee the lifespan of the energy storage devices themselves, the following four types of operational constraints are established based on the operational requirements of low-voltage distribution networks and the performance characteristics of hybrid energy storage systems to comprehensively regulate the operating status of energy storage systems:

[0115] (1) Real-time power constraints for hybrid energy storage:

[0116] In a hybrid energy storage system, the real-time operating power of both the battery and the supercapacitor cannot exceed their respective rated power, nor can it fall below the negative value of the rated power (i.e., reverse rated power). This limits the charging and discharging power of the energy storage device to prevent damage caused by excessive power. The specific constraint formula is as follows:

[0117] ;

[0118] ;

[0119] In the formula, , These are the rated power settings for the storage battery and the supercapacitor, respectively.

[0120] (2) Capacity constraints of hybrid energy storage batteries:

[0121] The total charge and discharge capacity of energy storage batteries (including batteries and supercapacitors) throughout their entire operating cycle must be strictly controlled within their rated capacity range to ensure that the batteries do not overcharge or over-discharge, avoid damage due to exceeding the rated capacity, and guarantee battery lifespan. The specific constraint formula is as follows:

[0122] ;

[0123] (3) Charge constraints of hybrid energy storage batteries:

[0124] Hybrid energy storage battery charge constraint: In addition to constraining the total charge over the entire operating cycle, it is also necessary to constrain the charge of the battery and supercapacitor at each operating moment in real time to ensure that the charge remains within the preset upper and lower limits at each moment, further guaranteeing the operational stability and service life of the energy storage device. The specific constraint formula is as follows:

[0125] ;

[0126] (4) Real-time power constraints in low-voltage distribution networks:

[0127] After installing a hybrid energy storage system in a low-voltage distribution network, the actual operating load of the distribution network is the sum of the real-time user load before the energy storage system was installed and the real-time output of the energy storage battery, such as... Figure 2 As shown. To avoid abnormal load conditions that could lead to power backflow into the grid (affecting grid safety), and to ensure the energy storage system's demand reduction optimization goals are achieved, strict constraints must be imposed on this actual operating load. The specific constraint formula is as follows:

[0128] ;

[0129] In the formula, This represents the real-time load of the user at time t on day i of the month.

[0130] 3. Parameter settings considering the impact of social development on energy storage:

[0131] The calculation results of the energy storage configuration optimization model need to accurately reflect the actual benefits and costs of the hybrid energy storage system throughout its entire life cycle. Therefore, during the model construction process, it is necessary to reasonably set energy storage cost parameters and fully consider the impact of social development factors on energy storage benefits and costs. To this end, the relevant parameters are set as follows:

[0132] (1) Calculation of monthly discounted cost of energy storage:

[0133] The total lifecycle cost of energy storage batteries mainly consists of two parts: the initial investment cost, which is the expense incurred in purchasing energy storage devices such as batteries and supercapacitors; and the lifecycle operation and maintenance cost, which is the expense incurred in routine maintenance, repairs, and replacement of parts throughout the entire operating life of the energy storage system. Considering the fixed service life of energy storage batteries, to facilitate calculations that match monthly revenue, the total lifecycle cost needs to be amortized to each month, resulting in the monthly discounted energy storage cost C. The specific calculation formula is as follows:

[0134] ;

[0135] In the formula, , These are the unit investment prices per kilowatt-hour for energy storage units of batteries and supercapacitors, respectively. , These are the purchase prices per kilowatt for battery and supercapacitor energy storage, respectively. , These are the operation and maintenance costs per kilowatt for battery and supercapacitor energy storage, respectively. and The rated capacity is configured for the battery and the supercapacitor, respectively; and These are the rated power configurations for the battery and supercapacitor, respectively. The numerator of the formula is the sum of the total investment cost and total operation and maintenance cost of the energy storage system over its entire life cycle, while the denominator is the product of 12 (12 months in a year) and the lifespan Y, which is the total number of months in the entire life cycle. Dividing the two gives the monthly equivalent energy storage cost C.

[0136] (2) Setting the social development impact coefficient:

[0137] After an energy storage system is put into operation, its operating cycle is typically long (usually several years). During this period, socioeconomic factors such as inflation and discount rates will change, leading to annual variations in the system's revenue and operation and maintenance costs. To accurately quantify the impact of these socioeconomic factors on energy storage systems and ensure the authenticity and accuracy of the model calculation results, a coefficient for the impact of socioeconomic development on energy storage is specifically introduced. The specific calculation formula is as follows:

[0138] ;

[0139] In the formula, Inflation rate; This is the discount rate.

[0140] 4. Energy storage configuration optimization model design:

[0141] The energy storage configuration optimization model constructed in this embodiment aims to maximize the monthly benefits after the user side installs a hybrid energy storage system. The optimal rated power and rated capacity of the battery and supercapacitor in the hybrid energy storage system are obtained by solving the model, so as to ensure that the energy storage system can meet the operation requirements of the distribution network and achieve the best economic benefits.

[0142] The objective function F of the energy storage configuration optimization model is defined as the difference between the total monthly revenue of the energy storage system and the monthly equivalent energy storage cost. It also incorporates parameters related to the impact of social development on energy storage, adjusting for revenue in different years to ensure that the objective function accurately reflects the actual monthly benefits of the energy storage system. The specific calculation is as follows:

[0143] ;

[0144] In the formula, F is the objective function; m is the number of months in the planning period, and d is the number of days in the planning period; Let Y be the social development coefficient in year y, where Y is the lifespan of the energy storage and C is the monthly equivalent energy storage cost.

[0145] When applying this energy storage configuration optimization model in practice, it is necessary to first collect the grid parameters (including real-time user load, monthly maximum load, peak-valley time-of-use electricity price, etc.) of the low-voltage distribution network area to be planned and the parameters of the hybrid energy storage system (including the investment unit price of energy storage devices, operation and maintenance costs, rated power, upper and lower limits of load capacity, etc.). Substitute all the aforementioned revenue analysis model, operating constraints of each dimension and related parameter settings into the optimization model, and use appropriate mathematical solution methods (such as genetic algorithm, particle swarm optimization algorithm, etc.) to solve the model. The optimal rated power and rated capacity of the battery and supercapacitor in the hybrid energy storage system can be obtained, thus forming an energy storage configuration scheme.

[0146] This embodiment proposes a hybrid energy storage optimization configuration model that considers the benefits of both peak shaving and demand reduction, constraining various operating states of the energy storage. The energy storage model, which considers both batteries and supercapacitors, combines the advantages of high energy density from batteries and high power output from supercapacitors, improving its tolerance to load fluctuations and making the configuration optimization model more accurate and practically effective. Furthermore, this embodiment simplifies the decision-making process by adding additional configuration capacity constraints for extreme scenarios to prevent excessively large configurations from leading to excessively high investment costs, and employs simplified methods to reduce computational load and improve planning efficiency.

[0147] In one exemplary embodiment, a scenario linearization reshaping method is proposed. Through linear transformation and approximation, the power fluctuation curve of hybrid energy storage under typical scenarios is processed to obtain a more realistic real-time operating power for energy storage in natural scenarios. Specifically, this includes:

[0148] S201: A linear transformation is performed on the power fluctuation curve of hybrid energy storage in a typical scenario to obtain:

[0149] ;

[0150] In the formula, Let i be the real-time power vector of energy storage on day i of the year. This is the real-time energy storage power vector corresponding to the o-th typical scenario; It is a vector in which all elements are 1; , The coefficients are represented linearly. The remaining items.

[0151] This formula uses the real-time power vector of typical daily energy storage and the full... Using vectors as the basis, a linear combination representation of the real-time energy storage power vector of a natural day is performed. Since the vector dimension is 24 (corresponding to 24 hours in a day), it cannot be strictly linearly represented, so a remainder term is introduced to represent the error.

[0152] S202: By using the least squares method, the linear transformation result is optimally approximated to the power fluctuation curves of all natural scenarios within the same class, thus obtaining the power fluctuation curves of hybrid energy storage under natural scenarios.

[0153] Optionally, the optimization objective can be to minimize the sum of squares of the elements of the linear transformation remainder. The coefficients of the linear representation can be optimized by the least squares method, so that the linear transformation result can best approximate the power fluctuation curves of all natural scenarios within the same category. Finally, the power fluctuation curves of hybrid energy storage under various natural scenarios can be obtained, ensuring that the fitting accuracy meets the requirements of energy storage operation and control.

[0154] S203: Determine the real-time operating power of hybrid energy storage based on the power fluctuation curve under natural scenarios.

[0155] This embodiment aims to solve the problem of power fluctuation differences between typical scenarios and natural scenarios. Through linear transformation and optimal approximation, the characteristics of typical scenarios are extended to all natural scenarios, enabling the determination of real-time operating power of hybrid energy storage. This provides reliable support for the stable operation of energy storage systems and accurate estimation of state of charge, while simplifying the calculation process of power curves under natural scenarios and improving efficiency.

[0156] In one exemplary embodiment, to address the problem of insufficient approximation accuracy of linear transformation results, this embodiment introduces a weighted recursive least squares identification algorithm with a forgetting factor based on the traditional least squares method. This algorithmic improvement enhances the accuracy of solving the linear representation coefficients, thereby achieving optimal approximation of the power fluctuation curve of the natural scene by the linear transformation result. Specifically, as follows:

[0157] S301: With the goal of minimizing the sum of squares of all elements in the linear transformation remainder, construct the following optimization problem for the coefficients of the linear representation:

[0158] ;

[0159] S302: Transform the optimization problem into a problem of finding the best approximate solution to a system of linear equations.

[0160] The optimization problem described above can be transformed into finding the best approximate solution to a system of linear equations:

[0161] ;

[0162] In the formula:

[0163] ;

[0164] S303: The linear equation system is solved using a weighted recursive least squares identification algorithm with a forgetting factor to obtain the linear representation coefficients.

[0165] Compared to the traditional least squares method, this algorithm can effectively overcome the shortcomings of the traditional method in terms of limited accuracy of approximate solutions by introducing a forgetting factor. At the same time, it can dynamically adapt to changes in the characteristics of energy storage batteries such as state of charge, temperature, and aging degree, ensuring the real-time performance and accuracy of coefficient solving.

[0166] S304: Based on the solved linear representation coefficients, determine the linear mapping relationship between typical scenarios and natural scenarios to achieve optimal approximation of the power fluctuation curves of all natural scenarios within the same category.

[0167] This embodiment considers that existing planning methods based on typical scenario simulation cannot fully account for the impact of extreme scenarios on planning. It proposes an improved scenario linearization modeling method. By introducing a weighted recursive least squares method with a forgetting factor, the linearization fit is improved, a linear mapping relationship between natural scenarios and typical scenarios is established, and it is embedded into the planning problem. While reducing the computational cost required for the planning operation model, it achieves more accurate planning results than existing approximation methods.

[0168] In one exemplary embodiment, a weighted recursive least squares identification algorithm with a forgetting factor is proposed.

[0169] First, define the information vector and parameter vector of the identification model:

[0170] ;

[0171] In the formula: Represents the real-time energy storage power of the i-th day of the year, hour t. ; Represents the regression vector for the t-th hour on the i-th day of the year. ,in This represents the real-time energy storage power corresponding to the oth typical scenario t hours. A parameter vector representing the t-th hour of the i-th day of the year. ,in and Let each represent a linear coefficient for hour t on day i of the whole year. and ; Let be the residual value at time t.

[0172] Therefore, the parameter vector with the forgetting factor The least squares estimate is:

[0173] ;

[0174] In the formula: λ is the forgetting factor, which ranges from 0 to 1.

[0175] definition:

[0176] ;

[0177] Therefore, the weighted recursive least squares identification algorithm with a forgetting factor is expressed as:

[0178] ;

[0179] In the formula, and These are the linear representation coefficient estimates for time t and time t-1, respectively; The real-time energy storage power value for the t hour on the i-th day of the year; Represents the regression vector for the t-th hour on the i-th day of the year. transpose; This is the gain matrix; and Let be the covariance matrices at time t and time t-1, respectively. Forgetting factor; It is the identity matrix;

[0180] Since the characteristics of energy storage batteries change with variations in state of charge, temperature, and aging, it is necessary to quickly adjust parameters when the battery state changes and output smooth and reliable results when the state is stable, thereby laying the foundation for accurate state of charge estimation.

[0181] Therefore, the forgetting factor is defined as:

[0182] ;

[0183] In the formula, Let be the forgetting factor for the k-th iteration; Let be the state of charge of the battery at time t. and These are the lower and upper limits of the battery's state of charge, respectively.

[0184] This embodiment provides a recursive least squares identification algorithm that can adapt to the dynamic characteristics of energy storage batteries. By designing a dynamic forgetting factor, the algorithm can flexibly adjust parameters according to changes in the state of charge of the battery, solving the parameter drift problem caused by changes in the state of charge, temperature, and aging of energy storage batteries, ensuring the solution accuracy and real-time performance of linear representation coefficients, and thus improving the reliability of scenario linearization reshaping.

[0185] In one exemplary embodiment, typical scenarios form the basis for scenario linearization reshaping, and their accuracy directly affects the rationality of subsequent energy storage configuration. This embodiment improves the k-means clustering algorithm, combines load feature dimensionality reduction and effectiveness evaluation indicators, to address the shortcomings of traditional clustering algorithms and achieve accurate generation of typical scenarios, as detailed below:

[0186] S401: Based on electricity user characteristic indicators, feature dimensionality reduction processing is performed on the normalized user daily load curve to extract feature indicators that reflect the user's electricity consumption behavior throughout the day and at different times, forming a clustered feature dataset.

[0187] First, the daily load data of users is normalized to eliminate the influence of data of different magnitudes. Then, based on the characteristic indicators of power users, the normalized daily load curve is subjected to feature dimensionality reduction processing to extract core indicators that can comprehensively reflect the user's electricity consumption characteristics throughout the day and electricity consumption behavior in different time periods, forming a standardized clustering feature dataset.

[0188] For example, electricity user characteristic indicators include:

[0189] ;

[0190] ;

[0191] ;

[0192] ;

[0193] ;

[0194] In the formula, This is the normalized daily load data curve. This represents the average daily load. This indicates the maximum daily load. This indicates the total daily load. This represents the average daily load during peak periods. This represents the average daily load during the trough period. This represents the average daily load during off-peak periods; For load factor, For the highest utilization hours, For peak load rate, For the off-season load rate, The load factor during the trough period; among which, the indicator and The indicators reflect the user's electricity consumption characteristics throughout the day from different perspectives. , and These reflect the characteristics of users' electricity consumption behavior at different times.

[0195] S402: Based on the clustering feature dataset, try different numbers of clusters to generate candidate clustering results corresponding to different numbers of clusters.

[0196] Based on the clustering feature dataset, a traversal approach is used to try different numbers of clusters. The initial value of the number of clusters, k min The value is usually 2, with a maximum value of k. max Based on the number of samples and feature distribution of the dimensionality-reduced dataset, the optimal number of clusters is determined and flexibly adjusted to generate multiple candidate clustering results corresponding to different numbers of clusters, providing sufficient basis for selecting the optimal number of clusters.

[0197] S403: Use the effectiveness evaluation index to quantitatively evaluate the candidate clustering results, and select the number of clusters corresponding to the maximum effectiveness evaluation index as the optimal number of clusters.

[0198] A validity metric, Va, is introduced to obtain the optimal k-value, i.e., the cluster center data, within the computational search range, overcoming the need for manual setting of the k-value in traditional k-means clustering. During clustering calculations, a validity metric is typically used to evaluate the clustering results and determine their practicality. Furthermore, it addresses the limitation of traditional k-means clustering in determining the optimal number of clusters. The Va metric is introduced as a validity evaluation indicator for the clustering results obtained after iteration. For a dataset containing P-dimensional variables, the Va metric is defined as follows:

[0199] ;

[0200] In the formula, N is the total number of samples after principal component dimensionality reduction, and k is the number of clusters obtained after clustering. The trace of the scatter matrix between different classes of the samples obtained after dimensionality reduction. This is the trace of the matrix of the same set during the clustering process of the samples obtained after dimensionality reduction.

[0201] S404: The daily load curve corresponding to the cluster center under the optimal number of clusters is taken as the typical daily load curve, and each typical daily load curve corresponds to a typical scenario.

[0202] This embodiment aims to address the shortcomings of the traditional k-means algorithm, which requires manual setting of the number of clusters k, thus affecting the accuracy and efficiency of the clustering results. By focusing on the differences in load curve shape through load feature dimensionality reduction, an effectiveness evaluation index is introduced to determine the optimal number of clusters, generating typical scenarios that can accurately reflect different electricity consumption patterns.

[0203] In one exemplary embodiment, to further improve the practicality of the clustering results and adapt to the actual needs of energy storage capacity configuration, a load characteristic weight ratio is established based on the introduction of the Va evaluation index, and the evaluation index is optimized by setting reasonable characteristic weight coefficients. Specifically, characteristic weight coefficients are established based on energy storage capacity configuration requirements and historical engineering experience. These correspond to the user load characteristics mentioned above. The mathematical expression for the revised effectiveness evaluation index is:

[0204] ;

[0205] In the formula, The effectiveness evaluation index is defined as follows: N is the total number of samples after principal component dimensionality reduction, and k is the number of clusters. The trace of the scatter matrix between different classes of the samples obtained after dimensionality reduction. The trace of the intra-class scatter matrix corresponding to the s-th power load feature. is the weighting coefficient for the load characteristic of the s-th power user, and S is the number of power user characteristic indicators (such as the 5 load characteristic indicators extracted in the previous embodiment).

[0206] For example, the calculation steps of the improved k-means clustering algorithm are as follows:

[0207] (1) The initial data is subjected to load feature extraction and dimensionality reduction processing;

[0208] (2) Select the number of clusters k min ;

[0209] (3) Randomly select k samples from the n samples as the initial cluster centers;

[0210] (3) Calculate the distance between each sample and the center sample based on the mean obtained for each class;

[0211] (4) Re-divide the samples according to the principle of minimum distance (i.e., sum of squared errors);

[0212] (5) Increment the number of clusters and repeat steps 2-5 until the number of clusters reaches the maximum value k. max ;

[0213] (6) Calculate the evaluation index Va'. When Va' reaches its maximum value, the number of clusters reaches the optimal value.

[0214] This embodiment provides a quantitative index that can accurately evaluate the effectiveness of k-means clustering results. By introducing load feature weight coefficients, the importance of different load features can be flexibly adjusted according to the actual needs of energy storage capacity configuration. This solves the problem that traditional effectiveness indicators cannot take into account the needs of energy storage configuration and the clustering results are not practical enough. At the same time, it enables the scientific selection of the optimal number of clusters, ensuring that the typical scenarios generated by clustering can meet the actual needs of energy storage configuration.

[0215] As can be seen from the above embodiments, the present invention also uses typical scenarios in the clustering results as a basis, and uses a weighted recursive method with a forgetting factor to approximately linearly represent the extreme scenarios throughout the year; embeds the linearized scenario results into the annual operation constraints of energy storage, and reduces the scale of the optimization problem by reconstructing decision variables, thereby achieving annual operation simulation with a smaller computational scale.

[0216] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0217] Based on the same inventive concept, this application also provides a low-voltage distribution network hybrid energy storage capacity planning device for implementing the aforementioned low-voltage distribution network hybrid energy storage capacity planning method. The solution provided by this device is similar to the solution described in the above method; therefore, the specific limitations in the embodiments of the low-voltage distribution network hybrid energy storage capacity planning device provided below can be found in the limitations of the low-voltage distribution network hybrid energy storage capacity planning method described above, and will not be repeated here.

[0218] Please see Figure 3 In one exemplary embodiment, the present invention also provides a low-voltage distribution network hybrid energy storage capacity planning device, comprising:

[0219] The energy storage type determination module is used to determine the type of energy storage to be installed in low-voltage distribution network areas that require energy storage, and to obtain the energy storage parameters.

[0220] The energy storage configuration optimization solution module is used to solve a pre-built energy storage configuration optimization model based on the grid parameters and energy storage parameters of the low-voltage distribution network area, and obtain a preliminary energy storage configuration scheme. The energy storage configuration optimization model aims to maximize the difference between energy storage revenue and energy storage cost. The energy storage revenue changes year by year with the cost generated by social development after the investment in energy storage.

[0221] The energy storage scheme determination module is used to calculate the return on investment of the preliminary energy storage configuration scheme and evaluate its economics. If the economic requirements are not met, the energy storage parameters are adjusted and the energy storage configuration optimization model is solved again until the economic requirements are met, and the final energy storage configuration scheme is obtained.

[0222] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0223] Reference Figure 4 This invention also provides a computer device, including: a memory and a processor, and a computer program stored in the memory. When the computer program is executed on the processor, it performs the following steps:

[0224] For low-voltage distribution network areas that require energy storage, determine the type of energy storage to be installed and obtain the energy storage parameters;

[0225] Based on the grid parameters and energy storage parameters of the low-voltage distribution network area, the pre-constructed energy storage configuration optimization model is solved to obtain a preliminary energy storage configuration scheme. The energy storage configuration optimization model aims to maximize the difference between energy storage revenue and energy storage cost. The energy storage revenue changes year by year with the changes in social development costs after the investment in energy storage.

[0226] Calculate the return on investment of the preliminary energy storage configuration scheme and evaluate its economic efficiency. If the economic efficiency requirements are not met, adjust the energy storage parameters and resolve the energy storage configuration optimization model until the economic efficiency requirements are met, and then obtain the final energy storage configuration scheme.

[0227] The computer device may be a desktop computer, laptop, handheld computer, or cloud server, etc. This computer device may include, but is not limited to, a processor and memory. Those skilled in the art will understand that... Figure 4 The examples of computer devices are merely examples and do not constitute a limitation on computer devices. They may include more or fewer components than shown in the illustration, or combinations of certain components, or different components. For example, they may also include input / output devices, network access devices, etc.

[0228] The processor referred to can be a Central Processing Unit (CPU), but it can also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor.

[0229] In some embodiments, the memory may be an internal storage unit of the computer device, such as a hard drive or RAM. In other embodiments, the memory may be an external storage device of the computer device, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card. Furthermore, the memory may include both internal and external storage units of the computer device. The memory is used to store the operating system, applications, boot loader, data, and other programs, such as the program code of the computer program. The memory can also be used to temporarily store data that has been output or will be output.

[0230] This invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, performs the following steps:

[0231] For low-voltage distribution network areas that require energy storage, determine the type of energy storage to be installed and obtain the energy storage parameters;

[0232] Based on the grid parameters and energy storage parameters of the low-voltage distribution network area, the pre-constructed energy storage configuration optimization model is solved to obtain a preliminary energy storage configuration scheme. The energy storage configuration optimization model aims to maximize the difference between energy storage revenue and energy storage cost. The energy storage revenue changes year by year with the changes in social development costs after the investment in energy storage.

[0233] Calculate the return on investment of the preliminary energy storage configuration scheme and evaluate its economic efficiency. If the economic efficiency requirements are not met, adjust the energy storage parameters and resolve the energy storage configuration optimization model until the economic efficiency requirements are met, and then obtain the final energy storage configuration scheme.

[0234] In this embodiment, if the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include at least: any entity or device capable of carrying computer program code to a photographing device / terminal device, a recording medium, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium. Examples include USB flash drives, portable hard drives, magnetic disks, or optical disks. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electrical carrier signals or telecommunication signals.

[0235] This invention provides a computer program product, including a computer program that, when executed by a processor, performs the following steps:

[0236] For low-voltage distribution network areas that require energy storage, determine the type of energy storage to be installed and obtain the energy storage parameters;

[0237] Based on the grid parameters and energy storage parameters of the low-voltage distribution network area, the pre-constructed energy storage configuration optimization model is solved to obtain a preliminary energy storage configuration scheme. The energy storage configuration optimization model aims to maximize the difference between energy storage revenue and energy storage cost. The energy storage revenue changes year by year with the changes in social development costs after the investment in energy storage.

[0238] Calculate the return on investment of the preliminary energy storage configuration scheme and evaluate its economic efficiency. If the economic efficiency requirements are not met, adjust the energy storage parameters and resolve the energy storage configuration optimization model until the economic efficiency requirements are met, and then obtain the final energy storage configuration scheme.

[0239] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0240] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0241] In the embodiments disclosed in this application, it should be understood that the disclosed devices / terminal equipment and methods can be implemented in other ways. For example, the device / terminal equipment embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0242] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for planning hybrid energy storage capacity in low-voltage distribution networks, characterized in that, Includes the following steps: For low-voltage distribution network areas that require energy storage, determine the type of energy storage to be installed and obtain the energy storage parameters; Based on the grid parameters of the low-voltage distribution network area and the energy storage parameters, a pre-constructed energy storage configuration optimization model is solved to obtain a preliminary energy storage configuration scheme. The energy storage configuration optimization model aims to maximize the difference between energy storage revenue and energy storage cost. The energy storage revenue changes year by year with the costs generated by social development after the investment in energy storage. Calculate the return on investment of the preliminary energy storage configuration scheme and evaluate its economic viability. If the economic viability requirement is not met, adjust the energy storage parameters and re-solve the energy storage configuration optimization model until the economic viability requirement is met, and then obtain the final energy storage configuration scheme.

2. The method for planning hybrid energy storage capacity in low-voltage distribution networks according to claim 1, characterized in that, The mathematical expression for the energy storage configuration optimization model is: ； In the formula, F is the objective function; B j Let m be the demand reduction revenue for month j, m be the number of months in the planning period, and B be the revenue from demand reduction. i Let d be the peak shaving and valley filling revenue on day i, and d be the number of days in the planning period. Let Y be the social development coefficient in year y, where Y is the lifespan of the energy storage and C is the monthly equivalent energy storage cost. The benefits of demand reduction are: ； In the formula, This is the unit price for basic electricity charges; and These represent the maximum monthly load value before and after the installation of energy storage in month j, respectively. The benefits of peak shaving and valley filling are: ； In the formula, t is the time index; The real-time operating power of the hybrid energy storage configured at time t on day i; Let be the peak-valley time-of-use electricity price corresponding to the t-th time. The length of the time interval; The social development coefficient is: ； In the formula, Inflation rate; The discount rate; The monthly equivalent energy storage cost is: ； In the formula, , These are the unit investment prices per kilowatt-hour for energy storage units of batteries and supercapacitors, respectively. , These are the purchase prices per kilowatt for battery and supercapacitor energy storage, respectively. , These are the operation and maintenance costs per kilowatt for battery and supercapacitor energy storage, respectively. and The rated capacity is configured for the battery and the supercapacitor, respectively; and The rated power is configured for the battery and the supercapacitor, respectively.

3. The method for planning hybrid energy storage capacity in low-voltage distribution networks according to claim 2, characterized in that, The real-time operating power of the hybrid energy storage is determined using a scenario linearization reshaping method, including: A linear transformation is performed on the power fluctuation curve of hybrid energy storage in a typical scenario to obtain: ； In the formula, Let be the real-time operating power vector of the hybrid energy storage on day i. This is the real-time energy storage power vector corresponding to the o-th typical scenario; It is a vector in which all elements are 1; , The coefficients are represented linearly. The remaining items; The power fluctuation curve of the hybrid energy storage under natural scenarios is obtained by using the least squares method to make the linear transformation result optimally approximate the power fluctuation curve of all natural scenarios within the same category. The real-time operating power of the hybrid energy storage is determined based on the power fluctuation curve under the described natural scenario.

4. The method for planning hybrid energy storage capacity in low-voltage distribution networks according to claim 3, characterized in that, The least squares method is used to make the linear transformation result optimally approximate the power fluctuation curves of all natural scenes within the same class, including: The optimization problem of the linear representation coefficients is constructed with the goal of minimizing the sum of squares of the elements of the linear transformation remainder. The optimization problem is transformed into a problem of finding the best approximate solution to a system of linear equations. The linear equation system is solved using a weighted recursive least squares identification algorithm with a forgetting factor to obtain the linear representation coefficients; Based on the obtained linear representation coefficients, the linear mapping relationship between typical scenarios and natural scenarios is determined, so as to achieve the optimal approximation of the power fluctuation curves of all natural scenarios within the same category.

5. The method for planning hybrid energy storage capacity in low-voltage distribution networks according to claim 4, characterized in that, The mathematical expression for the weighted recursive least squares identification algorithm with a forgetting factor is: ； In the formula, and These are the linear representation coefficient estimates for time t and time t-1, respectively; The real-time energy storage power value for the t hour on the i-th day of the year; Represents the regression vector for the t-th hour on the i-th day of the year. transpose; This is the gain matrix; and Let be the covariance matrices at time t and time t-1, respectively. Forgetting factor; It is the identity matrix; The calculation expression for the forgetting factor is as follows: ； In the formula, Let be the forgetting factor for the k-th iteration; Let be the state of charge of the battery at time t. and These are the lower and upper limits of the battery's state of charge, respectively.

6. The method for planning hybrid energy storage capacity in low-voltage distribution networks according to claim 3, characterized in that, The typical scenario described above uses an improved k-means clustering algorithm to cluster user daily load data, including: Based on electricity user characteristic indicators, feature dimensionality reduction processing is performed on the normalized user daily load curve to extract feature indicators that reflect the user's electricity consumption behavior throughout the day and at different times, forming a clustered feature dataset. Based on the clustering feature dataset, different numbers of clusters are tried to generate candidate clustering results corresponding to different numbers of clusters; The candidate clustering results are quantitatively evaluated using an effectiveness evaluation index, and the number of clusters corresponding to the maximum effectiveness evaluation index is selected as the optimal number of clusters. The daily load curve corresponding to the cluster center under the optimal number of clusters is taken as the typical daily load curve, and each typical daily load curve corresponds to a typical scenario.

7. The method for planning hybrid energy storage capacity in low-voltage distribution networks according to claim 6, characterized in that, The mathematical expression for the effectiveness evaluation index is: ； In the formula, The effectiveness evaluation index is defined as follows: N is the total number of samples after principal component dimensionality reduction, and k is the number of clusters. The trace of the scatter matrix between different classes of the samples obtained after dimensionality reduction. The trace of the intra-class scatter matrix corresponding to the s-th power load feature. Let S be the weighting coefficient for the load characteristics of the s-th electricity user, and S be the number of electricity user characteristic indicators.

8. A hybrid energy storage capacity planning device for low-voltage distribution networks, characterized in that, include: The energy storage type determination module is used to determine the type of energy storage to be installed in low-voltage distribution network areas that require energy storage, and to obtain the energy storage parameters. The energy storage configuration optimization solution module is used to solve a pre-constructed energy storage configuration optimization model based on the grid parameters of the low-voltage distribution network area and the energy storage parameters to obtain a preliminary energy storage configuration scheme. The energy storage configuration optimization model aims to maximize the difference between energy storage revenue and energy storage cost. The energy storage revenue changes year by year with the changes in social development costs after energy storage investment. The energy storage scheme determination module is used to calculate the return on investment of the preliminary energy storage configuration scheme and evaluate its economic efficiency. If the economic efficiency requirements are not met, the energy storage parameters are adjusted and the energy storage configuration optimization model is re-solved until the economic efficiency requirements are met, and then the final energy storage configuration scheme is obtained.

9. A computer device, characterized in that, The device includes a processor and a memory: The memory is used to store computer programs and send the instructions of the computer programs to the processor; The processor executes a hybrid energy storage capacity planning method for low-voltage distribution networks according to the instructions of the computer program as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which, when executed by a processor, implements a hybrid energy storage capacity planning method for low-voltage distribution networks as described in any one of claims 1-7.

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