Micro-grid energy storage capacity optimization model generation and configuration optimization method and device

Abstract

This disclosure proposes a method and apparatus for generating and optimizing the energy storage capacity optimization model for microgrids, including: acquiring historical operating data and operating constraints; obtaining typical output scenarios based on historical operating data; establishing formulas for the daily operating cost change and the overall energy storage configuration cost of the target microgrid after energy storage configuration based on the typical output scenarios; and establishing an optimization model for the distributed energy storage capacity configuration of the target microgrid after configuration based on the daily operating cost change formula, the overall energy storage configuration cost formula, and the operating constraints. By establishing this optimization model, the optimal allocation of resources within the microgrid can be achieved, improving the economic efficiency of the target microgrid operation. By generating the daily operating cost change formula and the overall energy storage configuration cost formula, an optimal energy storage charging and discharging plan can be established while minimizing the total energy storage configuration cost, thus enhancing the practicality of this solution.

Description

Technical Field

[0001] This disclosure relates to the field of distributed energy storage technology, and in particular to a method and apparatus for generating and configuring a microgrid energy storage capacity optimization model. Background Technology

[0002] With rapid economic development, energy demand is increasing daily, and problems such as the shortage of traditional energy sources and environmental pollution are becoming increasingly serious, leading to vigorous development of renewable energy utilization technologies. Microgrids are an important technology for absorbing renewable energy. However, renewable energy sources such as photovoltaic and wind power are volatile and intermittent. To improve the absorption rate of renewable energy and smooth power fluctuations on interconnect lines, energy storage devices need to be installed in microgrids. How to rationally configure energy storage capacity to achieve safe, stable, and economical operation of microgrids has become an important issue in microgrid planning.

[0003] As a new type of regulation resource, distributed energy storage has advantages such as flexible installation, diverse forms, and significant economic benefits. It has broad application prospects in scenarios such as new energy consumption and microgrids. It can be optimized and configured during the microgrid planning stage, and during the operation stage, distributed energy storage operators can aggregate and centrally control distributed energy storage resources to provide services such as peak shaving, frequency regulation, and backup, and realize its own profitability by taking advantage of the peak-valley electricity price difference.

[0004] Extensive research has been conducted both domestically and internationally on the issue of energy storage capacity configuration in microgrids. Current research typically uses the lowest energy storage configuration cost as the objective function, considering constraints such as power balance, microgrid operation, and energy storage charging and discharging behavior to establish an energy storage capacity configuration optimization model. However, it rarely considers the impact of uncertainties in photovoltaic power output on the optimal configuration of energy storage, and the operation mode of microgrids is relatively simple. Summary of the Invention

[0005] This disclosure aims to at least partially address one of the technical problems in the related art.

[0006] Therefore, one objective of this disclosure is to propose a method for generating an optimization model for distributed energy storage capacity configuration in microgrids.

[0007] The second objective of this disclosure is to propose a microgrid optimization method.

[0008] The third objective of this disclosure is to propose a device for generating a microgrid distributed energy storage capacity configuration optimization model.

[0009] The fourth objective of this disclosure is to propose a microgrid optimization device.

[0010] The fifth objective of this disclosure is to provide an electronic device.

[0011] The sixth objective of this disclosure is to provide a non-transitory computer-readable storage medium.

[0012] The seventh objective of this disclosure is to provide a computer program product.

[0013] To achieve the above objectives, the first aspect of this disclosure proposes a method for generating a microgrid distributed energy storage capacity configuration optimization model, comprising: acquiring historical operating data of a target microgrid and operating constraints of the target microgrid; acquiring typical output scenarios of the target microgrid based on the historical operating data; establishing a formula for the daily operating cost change and an overall energy storage configuration cost formula for the configured target microgrid based on the typical output scenarios; and establishing a microgrid distributed energy storage capacity configuration optimization model for the configured target microgrid based on the daily operating cost change formula, the overall energy storage configuration cost formula, and the operating constraints.

[0014] According to one embodiment of this disclosure, obtaining the typical power output scenario of the target microgrid based on the historical operating data includes: clustering the application scenarios in the historical operating data using a clustering algorithm to generate multiple target clusters; and treating each target cluster as a typical power output scenario.

[0015] According to one embodiment of this disclosure, the step of clustering application scenarios in the historical operating data using a clustering algorithm to generate multiple target clusters includes: clustering application scenarios in the historical operating data using an improved K-means algorithm; determining the optimal number of clusters based on the clustering results of the improved K-means algorithm by combining the elbow rule and silhouette coefficient; and performing clustering again based on the improved K-means algorithm to generate the target clusters with the optimal number of clusters.

[0016] According to one embodiment of this disclosure, establishing the daily operating cost change formula and the overall energy storage configuration cost formula for the target microgrid based on the typical output scenario includes: for any typical output scenario, obtaining the scenario probability of the typical output scenario appearing in all operating scenarios of the target microgrid, and obtaining the configuration data of the target microgrid; establishing the overall energy storage configuration cost formula based on the scenario probability and the configuration data, wherein the overall energy storage configuration cost formula includes at least the expected daily operating cost change of the microgrid and the equipment purchase cost; and establishing the daily operating cost change formula based on the expected daily operating cost change of the microgrid, the scenario probability, and the configuration data in the overall energy storage configuration cost formula.

[0017] According to one embodiment of this disclosure, the formula for the daily operating cost change is: Wherein, the CO′ The term "pro" represents the expected increase in daily operating costs of the target microgrid after energy storage configuration. k For the probability of the scenario, the The electricity purchase price of the target microgrid from the upper-level grid during the t-th time period is... The power that the target microgrid purchases from the upper-level grid via the tie line during the t-th time period of scenario k after energy storage configuration, wherein c sub The feed-in tariff for photovoltaic power, the To configure the actual grid-connected power of the photovoltaic system after energy storage, the The power that the target microgrid purchases from the upper-level grid via the tie line in the t-th time period of scenario k, prior to the energy storage configuration. The actual grid-connected power of photovoltaic power before energy storage is configured.

[0018] According to one embodiment of this disclosure, the overall energy storage configuration cost formula is: min C A =C I +C M +C L +C O′ Wherein, the C A The average daily investment cost for energy storage configuration of the target microgrid, C I The average daily purchase cost of the energy storage system for the target microgrid, C L The average daily maintenance cost of the target microgrid, C O′ This represents the expected increase in daily operating costs of the target microgrid after energy storage configuration.

[0019] To achieve the above objectives, a second aspect of this disclosure proposes a microgrid optimization method, comprising: acquiring a microgrid distributed energy storage capacity configuration optimization model and energy storage configuration data of a target microgrid, wherein the microgrid distributed energy storage capacity configuration optimization model is generated by the microgrid distributed energy storage capacity configuration optimization model generation method as described in the first aspect embodiment; and processing the energy storage configuration data using the energy storage configuration data from the microgrid distributed energy storage capacity configuration optimization model to generate the overall energy storage configuration power and overall energy storage configuration capacity of the target microgrid.

[0020] To achieve the above objectives, a third aspect of this disclosure proposes a microgrid distributed energy storage capacity configuration optimization model generation device, comprising: an acquisition module for acquiring historical operating data of a target microgrid and operating constraints of the target microgrid; an analysis module for acquiring typical output scenarios of the target microgrid based on the historical operating data; an establishment module for establishing a daily operating cost change formula and an overall energy storage configuration cost formula for the configured target microgrid based on the typical output scenarios; and a generation module for establishing a microgrid distributed energy storage capacity configuration optimization model for the configured target microgrid based on the daily operating cost change formula, the overall energy storage configuration cost formula, and the operating constraints.

[0021] To achieve the above objectives, a fourth aspect of this disclosure provides a microgrid optimization device, comprising: a receiving module for acquiring a microgrid distributed energy storage capacity configuration optimization model and energy storage configuration data of a target microgrid, wherein the microgrid distributed energy storage capacity configuration optimization model is generated by the microgrid distributed energy storage capacity configuration optimization model generation method as described in the first aspect embodiment; and a processing module for processing the energy storage configuration data using the energy storage configuration data from the microgrid distributed energy storage capacity configuration optimization model to generate the overall energy storage configuration power and overall energy storage configuration capacity of the target microgrid.

[0022] To achieve the above objectives, a fifth aspect of this disclosure provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to implement the microgrid distributed energy storage capacity configuration optimization model generation method as described in the first aspect of this disclosure, or to implement the microgrid optimization method as described in the second aspect of this disclosure.

[0023] To achieve the above objectives, a sixth aspect of this disclosure provides a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to implement the microgrid distributed energy storage capacity configuration optimization model generation method as described in the first aspect of this disclosure, or to implement the microgrid optimization method as described in the second aspect of this disclosure.

[0024] To achieve the above objectives, a seventh aspect of this disclosure provides a computer program product, including a computer program that, when executed by a processor, is used to implement the microgrid distributed energy storage capacity configuration optimization model generation method as described in the first aspect of this disclosure, or to implement the microgrid optimization method as described in the second aspect of this disclosure.

[0025] Therefore, by establishing a microgrid distributed energy storage capacity configuration optimization model, it is helpful to achieve the optimal allocation of resources within the microgrid. It can also generate positive social, economic and environmental impacts on a larger scale, increase the efficiency of the target microgrid configuration, and improve the economic benefits of the target microgrid operation. By generating the daily operating cost change formula and the overall energy storage configuration cost formula, the optimal charging and discharging plan can be established under the premise of minimizing the total energy storage configuration cost, thereby improving the practicality of this scheme. Attached Figure Description

[0026] Figure 1 This is a schematic diagram of a method for generating a microgrid distributed energy storage capacity configuration optimization model according to one embodiment of this disclosure;

[0027] Figure 2 This is a schematic diagram of the structure of a target microgrid according to one embodiment of the present disclosure;

[0028] Figure 3 This is a schematic diagram of another method for generating a microgrid distributed energy storage capacity configuration optimization model according to one embodiment of this disclosure;

[0029] Figure 4 This is a schematic diagram of another method for generating a microgrid distributed energy storage capacity configuration optimization model according to one embodiment of this disclosure;

[0030] Figure 5 This is a schematic diagram of a microgrid optimization method according to one embodiment of the present disclosure;

[0031] Figure 6 This is a schematic diagram of a microgrid distributed energy storage capacity configuration optimization model generation device according to one embodiment of the present disclosure;

[0032] Figure 7 This is a schematic diagram of a microgrid optimization device according to one embodiment of the present disclosure;

[0033] Figure 8 This is a schematic diagram of an electronic device according to one embodiment of the present disclosure. Detailed Implementation

[0034] Embodiments of this disclosure are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this disclosure, and should not be construed as limiting this disclosure.

[0035] The acquisition, storage, use, and processing of data in this disclosed technical solution all comply with the relevant provisions of relevant laws and regulations.

[0036] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0037] Figure 1 This is a schematic diagram of a method for generating a microgrid distributed energy storage capacity configuration optimization model according to one embodiment of this disclosure, as shown below. Figure 1 As shown, the method for generating the microgrid distributed energy storage capacity configuration optimization model includes the following steps:

[0038] S101, Obtain historical operating data and operating constraints of the target microgrid.

[0039] The microgrid distributed energy storage capacity configuration optimization model generation method of this application embodiment can be applied to the scenario of microgrid optimization and control. The execution subject of the microgrid distributed energy storage capacity configuration optimization model generation method of this application embodiment can be the microgrid distributed energy storage capacity configuration optimization model generation device of this application embodiment. The microgrid distributed energy storage capacity configuration optimization model generation device can be installed on an electronic device.

[0040] It should be noted that a microgrid is a miniaturized power grid system that can operate independently of the main power grid or work in conjunction with it. Microgrids typically integrate various distributed energy resources (DERs), energy storage systems, loads, and necessary control and protection devices.

[0041] For example, such as Figure 2 As shown, the target microgrid can be a photovoltaic microgrid, which may include photovoltaic arrays, photovoltaic inverters, energy storage batteries, energy storage converters, a battery management system (BMS), various loads, and a microgrid central controller. The microgrid central controller acquires photovoltaic power generation, energy storage system information, and load power consumption information. The energy storage system information includes battery state of charge, voltage, current, and temperature. The central controller monitors the operating status of each component of the microgrid in real time, coordinates and manages the internal operating strategies of the microgrid, and ensures the safe and stable operation of the microgrid.

[0042] Photovoltaic microgrids are connected to the upper-level power grid via tie lines and have two operation modes: grid-connected and off-grid. In grid-connected mode, power is transmitted to the upper-level grid via the tie line. In off-grid mode, photovoltaic (PV) power and energy storage supply power to local loads. When PV power generation exceeds load demand, excess power is either transmitted to the upper-level grid or stored through charging batteries. When PV power generation is insufficient to meet load demand, priority is given to discharging energy storage batteries to supply power to the load. During the operation of the photovoltaic microgrid, energy storage batteries suppress PV output fluctuations through charging and discharging levels, ensuring stable tie line power and reducing the impact on the upper-level grid.

[0043] In the embodiments of this disclosure, there are various methods for obtaining historical operating data of the target microgrid, and no limitation is made here.

[0044] In one possible implementation, it can be extracted directly from the microgrid control system.

[0045] Alternatively, it can be obtained by analyzing the historical database or log files of the target microgrid.

[0046] It should be noted that the operational constraints of the target microgrid are a series of rules and technical requirements set to ensure its safe, reliable, and efficient operation. These constraints cover a wide range of aspects, from power balance to equipment operation limitations.

[0047] Due to differences in production capacity requirements, market demand, and other conditions, the constraints corresponding to different target microgrids may be different.

[0048] S102, obtaining typical power output scenarios of the target microgrid based on historical operating data.

[0049] It should be noted that target microgrids, especially photovoltaic microgrids, are affected by a variety of factors, including geographical location, weather conditions, seasonal changes, installation angle and orientation, etc. For example, typical power output scenarios may include typical sunny days, cloudy or overcast days, seasonal variations, and early morning / late evening times versus nighttime.

[0050] There are various methods for obtaining typical power output scenarios of a target microgrid based on historical operating data, and no limitation is made here.

[0051] In one possible implementation, historical operating data can be analyzed using a classic scenario determination model to generate typical output scenarios for the target microgrid. This classic scenario determination model is pre-trained and can be stored in the storage space of electronic devices for easy retrieval when needed.

[0052] S103, based on typical power output scenarios, establishes the formulas for the daily operating cost change of the target microgrid after configuration and the overall configuration cost of energy storage.

[0053] S104. Based on the formula for daily operating cost change, the formula for overall energy storage configuration cost, and operating constraints, an optimization model for the configuration of distributed energy storage capacity of the target microgrid is established.

[0054] In this embodiment, historical operating data and operating constraints of the target microgrid are first obtained. Then, typical output scenarios of the target microgrid are obtained based on the historical operating data. Next, formulas for the daily operating cost change and the overall energy storage configuration cost of the configured target microgrid are established based on these typical output scenarios. Finally, an optimization model for the distributed energy storage capacity configuration of the configured target microgrid is established based on the daily operating cost change formula, the overall energy storage configuration cost formula, and the operating constraints. Therefore, by establishing an optimization model for the distributed energy storage capacity configuration of the microgrid, optimal resource allocation within the microgrid can be achieved. This also generates positive social, economic, and environmental impacts on a larger scale, increases the efficiency of the target microgrid configuration, and improves the economic benefits of the target microgrid operation. By generating the daily operating cost change formula and the overall energy storage configuration cost formula, an optimal charging and discharging plan can be established while minimizing the total energy storage configuration cost, thus improving the practicality of this solution.

[0055] In the above embodiments, the typical output scenarios of the target microgrid can be obtained based on historical operating data, and can also be achieved through... Figure 3 To further explain, the method includes:

[0056] S301 uses a clustering algorithm to cluster application scenarios in historical operational data to generate multiple target clusters.

[0057] In this embodiment of the disclosure, the application scenarios in historical operating data can be clustered by improving the K-means algorithm. The optimal number of clusters is determined based on the clustering results of the improved K-means algorithm by combining the elbow rule and the silhouette coefficient. Clustering is performed again based on the improved K-means algorithm to generate the target cluster with the optimal number of clusters.

[0058] It's important to note that the K-means algorithm uses Euclidean distance to characterize the similarity between samples. By continuously updating the cluster center positions and re-dividing the clusters, it ultimately obtains the clustering results. However, the K-means algorithm requires pre-determining the number of clusters. Considering the significant impact of the number of clusters on the partitioning results, an improved K-means algorithm combining the elbow method and silhouette coefficient is used to reduce historical photovoltaic power output scenarios to obtain typical scenarios. The elbow method is a heuristic method for determining the optimal number of clusters (K value) in the K-means clustering algorithm. This method is based on the assumption that as the number of clusters K increases, the total variation within the cluster (usually expressed as the Within-Cluster Sum of Squares, WCSS) gradually decreases. However, after a certain point, the reduction in WCSS brought about by each additional cluster will significantly decrease, forming an "elbow"-shaped curve. This inflection point is the so-called "elbow," indicating the optimal number of clusters.

[0059] In the K-means algorithm, the more clusters there are, the more refined the sample partitioning becomes. Therefore, the sum of squared errors gradually decreases as the number of clusters increases. The elbow rule states that there exists a critical point where the decreasing trend of the sum of squared errors changes significantly, from a rapid decrease to a gradual slowdown. That is, after this critical point, increasing the number of clusters to reduce error becomes less efficient. The best-performing clusters are located near this critical point. The change in the sum of squared errors is used to measure the clustering effect of the number of clusters K.

[0060]

[0061] Where S1 represents the clustering performance index defined according to the elbow rule; K is the number of clusters, i.e., the number of clusters, with a minimum of two clusters; C i Let c represent the i-th cluster. i This represents the cluster center of the cluster; d(x,c i ) indicates that it belongs to C i Sample x and cluster center c i The Euclidean distance.

[0062] The silhouette coefficient, which combines cohesion and separation, is used to evaluate the clustering effect. Its expression is as follows:

[0063]

[0064] Among them, a n b represents the mean distance between sample n and other samples in the same cluster, i.e., the cluster cohesion; nThe silhouette coefficient represents the mean distance between sample n and samples from other clusters, i.e., the separation degree between different clusters; N represents the number of samples. The silhouette coefficient takes the value [-1, 1], and the closer it is to 1, the more reasonable the choice of the number of clusters.

[0065] Combining the elbow rule and the contour coefficient, the clustering performance index is defined as follows:

[0066] S=ζ1S1′+ζ1S2

[0067] Where S1′ represents the normalized index S1; ζ1 and ζ2 represent the weight coefficients of the two items, respectively. The larger the index S, the better the aggregation effect. The optimal number of clusters is determined based on the index S.

[0068] S302 treats each target cluster as a typical output scenario.

[0069] In this embodiment, application scenarios in historical operational data are first clustered using a clustering algorithm to generate multiple target clusters. Each target cluster is then used as a typical output scenario. Therefore, by using a clustering algorithm to process historical operational data and define typical output scenarios, not only is the internal management and operation efficiency of the microgrid improved, but the adaptability of the subsequently generated microgrid distributed energy storage capacity configuration optimization model to changes in the external environment is also enhanced, increasing the practicality of the microgrid distributed energy storage capacity configuration optimization model.

[0070] In the above embodiments, based on typical output scenarios, formulas are established for the daily operating cost change of the target microgrid after configuration and the overall energy storage configuration cost. Furthermore, [the following can be achieved] through [further methods]. Figure 4 To further explain, the method includes:

[0071] S401: For any typical output scenario, obtain the scenario probability of the typical output scenario occurring in all operating scenarios of the target microgrid, and obtain the configuration data of the target microgrid.

[0072] In this embodiment of the disclosure, there are various methods for obtaining the probability of a typical power output scenario occurring in all operating scenarios of the target microgrid, and no limitation is made here.

[0073] In one possible implementation, a certain number of samples can be obtained through random sampling, and then the probability of a typical output scenario occurring in the samples can be used as the scenario probability.

[0074] In another possible approach, operational data from one operating cycle of the target microgrid can be selected and analyzed to determine the probability of typical output scenarios occurring in all operating scenarios of the target microgrid.

[0075] S402, establish the overall configuration cost formula for energy storage based on scenario probability and configuration data, wherein the overall configuration cost formula for energy storage includes at least the expected daily operating cost change of the microgrid and the equipment purchase cost.

[0076] S403 establishes a formula for the daily operating cost change based on the expected daily operating cost change of microgrids, scenario probabilities, and configuration data in the overall energy storage configuration cost formula.

[0077] In this embodiment, considering the uncertainty of photovoltaic power generation and load in actual operation, the present disclosure establishes a two-stage optimization model for the energy storage capacity configuration of the target microgrid, taking into account both planning and operation. This two-stage optimization model makes decisions on energy storage capacity configuration before the energy storage charging and discharging plan is determined during the operation phase. The optimization objective of the first stage is to minimize the total cost of energy storage configuration, with the capacity and power of the energy storage configuration as the variables to be optimized. The optimization objective of the second stage is the expected increase in daily operating cost of the target microgrid after energy storage configuration, with the charging and discharging plan of energy storage under different scenarios as the variables to be optimized. In the model, the optimization objective of the first stage includes the increased daily operating cost of the target microgrid in the second stage, while the energy storage charging and discharging plan in the second stage is constrained by the energy storage capacity configuration in the first stage.

[0078] Therefore, by coupling the daily operating cost change formula with the overall energy storage configuration cost formula, an optimal charging and discharging plan can be established while minimizing the total energy storage configuration cost, thus improving the practicality of this solution.

[0079] In this embodiment of the disclosure, the formula for the overall energy storage configuration cost is:

[0080] min C A =C I +C M +C L +C O ′

[0081] Among them, C I =γ[(c bess +c bms +c re E bess +c pcs P bess ]

[0082] C M =γc m E bess

[0083]

[0084] Among them, C A C I CM C L and C O′ These represent the average daily investment cost of configuring energy storage in a photovoltaic microgrid, the average daily purchase cost of the energy storage system, the average daily maintenance cost, and the expected increase in the daily operating cost of the target microgrid after the energy storage configuration; c bess c bms and c re All represent the unit capacity cost coefficient of energy storage, corresponding to the purchase cost of energy storage batteries, battery energy management system equipment, and other energy storage-related equipment, respectively; c pcs This indicates the unit power purchase cost of the energy storage converter; c m This represents the average annual maintenance cost coefficient for energy storage. γ represents the energy storage battery capacity cost factor at the i-th battery replacement; γ represents the capital recovery factor. This is used to discount equipment investment to average daily investment; N represents the number of energy storage battery replacement cycles; β represents the discount rate; and Y represents the total project design life.

[0085] Considering that energy storage capacity configuration is limited by factors such as funding and land availability, the following constraints need to be met in the first stage of the model:

[0086] C I +C M ≤C max

[0087] E bess ≤E bess,max

[0088] P bess ≤P bess.max

[0089] In the formula, C max Indicates the upper limit of initial investment for energy storage systems; E bess,max Indicates the upper limit of the energy storage configuration capacity; P bess.max This indicates the upper limit of the energy storage configuration power.

[0090] Based on the parameters of existing energy storage products and the requirements for energy storage construction in conjunction with distributed photovoltaic systems, the rated capacity and rated power of the configured energy storage should meet the constraints of energy storage duration:

[0091] E bess =P bess T bess

[0092] T bess,min ≤T bess ≤T bess,max

[0093] In the formula, T bess Indicates the duration of continuous energy storage; T bess,min Tbess,max These represent the lower and upper limits of continuous energy storage duration, respectively. The lower and upper limits of continuous energy storage duration are pre-designed and can be determined based on the design parameters of the actual target microgrid. For example, the lower and upper limits of continuous energy storage duration can be 2 hours and 4 hours, respectively.

[0094] The second phase targets the increase in daily operating costs of microgrids.

[0095] It should be noted that in current technologies, industrial and commercial distributed photovoltaic systems can choose between full self-consumption or self-consumption with surplus power fed into the grid. Taking the energy storage optimization configuration under the self-consumption with surplus power fed into the grid as an example, the second stage of the model minimizes the expected change in the daily operating cost of the photovoltaic microgrid after configuring energy storage. The daily operating cost of the microgrid is:

[0096]

[0097] In the formula, pro k Δt represents the probability of scenario k occurring; Δt represents the runtime segment. express; express; and Indicates and configures the actual grid-connected power of photovoltaic power after energy storage; c represents the data from the upper-level grid and t respectively during the t-th time period, using historical spot market electricity price data; pv c sub These refer to the photovoltaic (PV) subsidy price and the PV feed-in tariff, respectively. It should be noted that the PV subsidy price and the PV feed-in tariff are determined based on actual circumstances. For example, the PV subsidy price could be 0.05 yuan / kWh, and the PV feed-in tariff could be 0.2 yuan / kWh. λ represents the tie-line power fluctuation penalty factor. This indicates the switching power of the tie line.

[0098] Before and after configuring energy storage, the photovoltaic output and load of the microgrid remain unchanged. Therefore, the change in the daily operating cost of the photovoltaic microgrid after configuring energy storage is:

[0099]

[0100] In the formula, This indicates the power that the microgrid purchases from the upper-level grid via the interconnect line before configuring energy storage; Before energy storage is configured, the actual grid-connected power of photovoltaic (PV) power is as follows: Before energy storage is configured, the power purchased by the microgrid from the upper-level grid through the interconnection line is the portion of photovoltaic power generation that does not meet the load. The actual grid-connected power of PV is the remaining portion after PV meets the local load.

[0101] The constraints for the second phase are:

[0102]

[0103] In the formula, These represent the charging power and discharging power of the energy storage battery in the t-th time period of scenario k, respectively.

[0104] Energy storage charge and discharge models typically include charge and discharge constraints and state of charge constraints:

[0105]

[0106] In the formula, This represents the charge / discharge state of the energy storage battery, a variable of 0 or 1. At any given time, the energy storage battery can only choose to charge or discharge, i.e., charge / discharge are mutually exclusive; E k,t E represents the energy storage battery's charge in the t-th time period of scenario k; k,t＝1 E k,t＝T These represent the electricity levels at the beginning and end of the scheduling cycle, respectively; η c η d These represent the energy storage charging efficiency and the discharging efficiency, respectively.

[0107] The charging and discharging time is constrained based on the energy storage duration:

[0108]

[0109] In the formula, n represents the number of time periods within an hour. In the operation phase of this paper, each time period is 15 minutes, so n is taken as 4.

[0110] Considering that spot market electricity prices may reach extreme levels during midday when photovoltaic (PV) power generation is high, with the feed-in price of PV power exceeding the spot market price, microgrid operators will tend to feed all PV power to the grid, while purchasing electricity from the grid to meet local load demand. To avoid this profit model, grid-connected PV power will be limited during periods of extreme electricity prices.

[0111]

[0112] In the formula, x i,j The variables are 0 and 1, with 0 representing the value when the photovoltaic feed-in tariff is higher than the spot market tariff; M is a large positive number. These constraints ensure that the maximum photovoltaic feed-in power is the remaining power generation after meeting the load, and that photovoltaic feed-in and purchasing electricity from the upstream grid cannot coexist.

[0113] Figure 5 This is a schematic diagram of a microgrid optimization method according to one embodiment of the present disclosure, as shown below. Figure 5 As shown, the microgrid optimization method includes the following steps:

[0114] S501, obtain the microgrid distributed energy storage capacity configuration optimization model and energy storage configuration data of the target microgrid.

[0115] It should be noted that the microgrid distributed energy storage capacity configuration optimization model in this embodiment is as follows: Figures 1-4 The example demonstrates the generation method for optimizing the capacity configuration of distributed energy storage in microgrids.

[0116] It should be noted that energy storage configuration data can include various types, and no limitations are set here. For example, it may include date data, operating parameters of the target microgrid, etc.

[0117] S502 processes the energy storage configuration data using the energy storage configuration data from the microgrid distributed energy storage capacity configuration optimization model to generate the overall energy storage configuration power and overall energy storage configuration capacity of the target microgrid.

[0118] In this embodiment, firstly, a microgrid distributed energy storage capacity configuration optimization model and energy storage configuration data of the target microgrid are obtained. Then, the energy storage configuration data is processed using the energy storage configuration data from the microgrid distributed energy storage capacity configuration optimization model to generate the overall energy storage configuration power and overall energy storage configuration capacity of the target microgrid. Thus, through... Figures 1-4 The microgrid distributed energy storage capacity configuration optimization model generated by the embodiment predicts and optimizes the microgrid distributed energy storage capacity configuration optimization model, which helps to achieve the optimal allocation of resources within the microgrid. It can also generate positive social, economic and environmental impacts on a larger scale, increase the efficiency of the target microgrid configuration, and improve the economic benefits of the target microgrid operation. By generating the daily operating cost change formula and the overall energy storage configuration cost formula, the optimal charging and discharging plan can be established while ensuring the minimization of the total energy storage configuration cost, thus improving the practicality of this scheme.

[0119] Corresponding to the microgrid distributed energy storage capacity configuration optimization model generation method provided in the above embodiments, one embodiment of this disclosure also provides a microgrid distributed energy storage capacity configuration optimization model generation device. Since the microgrid distributed energy storage capacity configuration optimization model generation device provided in this disclosure corresponds to the microgrid distributed energy storage capacity configuration optimization model generation method provided in the above embodiments, the implementation method of the above microgrid distributed energy storage capacity configuration optimization model generation method is also applicable to the microgrid distributed energy storage capacity configuration optimization model generation device provided in this disclosure, and will not be described in detail in the following embodiments.

[0120] Figure 6 This is a schematic diagram of a microgrid distributed energy storage capacity configuration optimization model generation device according to one embodiment of the present disclosure, such as... Figure 6 As shown, the microgrid distributed energy storage capacity configuration optimization model generation device 600 includes: an acquisition module 610, an analysis module 620, a building module 630, and a generation module 640.

[0121] The acquisition module is used to acquire historical operating data and operating constraints of the target microgrid.

[0122] The analysis module is used to obtain typical output scenarios of the target microgrid based on historical operating data.

[0123] A module is established to generate formulas for the daily operating cost change of the target microgrid and the overall energy storage configuration cost based on typical output scenarios.

[0124] The generation module is used to establish a microgrid distributed energy storage capacity configuration optimization model for the target microgrid after configuration, based on the daily operating cost change formula, the overall energy storage configuration cost formula, and operating constraints.

[0125] According to one embodiment of this disclosure, obtaining typical power output scenarios of a target microgrid based on historical operating data includes: clustering application scenarios in historical operating data using a clustering algorithm to generate multiple target clusters; and treating each target cluster as a typical power output scenario.

[0126] According to one embodiment of this disclosure, application scenarios in historical operating data are clustered using a clustering algorithm to generate multiple target clusters, including: clustering application scenarios in historical operating data using an improved K-means algorithm; determining the optimal number of clusters based on the clustering results of the improved K-means algorithm by combining the elbow rule and silhouette coefficient; and performing clustering again based on the improved K-means algorithm to generate target clusters with the optimal number of clusters.

[0127] According to one embodiment of this disclosure, a target operation formula for a configured target microgrid is established based on a typical output scenario. The target operation formula is one of a daily operating cost change formula and an overall energy storage configuration cost formula. The formula includes: for any typical output scenario, obtaining the scenario probability of the typical output scenario in all operating scenarios of the target microgrid, and obtaining the configuration data of the target microgrid; and establishing the target operation formula based on the scenario probability and the configuration data.

[0128] According to one embodiment of this disclosure, the formula for the daily operating cost change is: Among them, C O′ Pro represents the expected increase in daily operating costs of a photovoltaic microgrid after the configuration of energy storage. k For scenario probabilities, Let t be the electricity purchase price of the target microgrid from the upper-level grid during the t-th time period. c represents the power that the target microgrid purchases from the upper-level grid via the tie line during the t-th time period of scenario k after energy storage configuration. subFor photovoltaic feed-in tariffs, To configure the actual grid-connected power of the photovoltaic system after energy storage, Before configuring energy storage, the target microgrid's power purchase from the upper-level grid via the tie line in the t-th time period of scenario k. The actual grid-connected power of photovoltaic power before energy storage is configured.

[0129] According to one embodiment of this disclosure, the formula for the overall energy storage configuration cost is: min C A =C I +C M +C L +C O ′wherein, C A C represents the average daily investment cost for configuring photovoltaic microgrid energy storage in the target microgrid. I C represents the average daily purchase cost of the energy storage system for the target microgrid. L C represents the average daily maintenance cost of the target microgrid. O ′ represents the expected increase in daily operating cost of the target microgrid after energy storage configuration.

[0130] Therefore, by establishing a microgrid distributed energy storage capacity configuration optimization model, it is helpful to achieve the optimal allocation of resources within the microgrid. It can also generate positive social, economic and environmental impacts on a larger scale, increase the efficiency of the target microgrid configuration, and improve the economic benefits of the target microgrid operation. By generating the daily operating cost change formula and the overall energy storage configuration cost formula, the optimal charging and discharging plan can be established under the premise of minimizing the total energy storage configuration cost, thereby improving the practicality of this scheme.

[0131] Corresponding to the microgrid optimization methods provided in the above embodiments, one embodiment of this disclosure also provides a microgrid optimization device. Since the microgrid optimization device provided in this disclosure corresponds to the microgrid optimization methods provided in the above embodiments, the implementation methods of the above microgrid optimization methods are also applicable to the microgrid optimization device provided in this disclosure, and will not be described in detail in the following embodiments.

[0132] Figure 7 This is a schematic diagram of a microgrid optimization device according to one embodiment of the present disclosure, such as... Figure 7 As shown, the microgrid optimization device 700 includes: a receiving module 710 and a processing module 720.

[0133] The receiving module is used to acquire the microgrid distributed energy storage capacity configuration optimization model and energy storage configuration data of the target microgrid. The microgrid distributed energy storage capacity configuration optimization model is as follows: Figures 1-4 The example demonstrates the generation method for optimizing the capacity configuration of distributed energy storage in microgrids.

[0134] The processing module is used to process the energy storage configuration data through the energy storage configuration data of the microgrid distributed energy storage capacity configuration optimization model to generate the total energy storage configuration power and total energy storage configuration capacity of the target microgrid.

[0135] Therefore, through such Figures 1-4 The microgrid distributed energy storage capacity configuration optimization model generated by the embodiment predicts and optimizes the microgrid distributed energy storage capacity configuration optimization model, which helps to achieve the optimal allocation of resources within the microgrid. It can also generate positive social, economic and environmental impacts on a larger scale, increase the efficiency of the target microgrid configuration, and improve the economic benefits of the target microgrid operation. By generating the daily operating cost change formula and the overall energy storage configuration cost formula, the optimal charging and discharging plan can be established while ensuring the minimization of the total energy storage configuration cost, thus improving the practicality of this scheme.

[0136] To implement the above embodiments, this disclosure also proposes an electronic device 800. Figure 8 This is a schematic diagram of an electronic device according to one embodiment of the present disclosure, such as... Figure 8 As shown, the electronic device 800 includes: a processor 801 and a memory 802 communicatively connected to the processor. The memory 802 stores instructions executable by at least one processor. The instructions are executed by at least one processor 801 to achieve the functions described in this disclosure. Figures 1-4 The microgrid distributed energy storage capacity configuration optimization model generation method in the embodiment, or as follows: Figure 5 Microgrid optimization method of the embodiment.

[0137] To implement the above embodiments, this disclosure also proposes a non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to implement the present disclosure. Figures 1-4 The microgrid distributed energy storage capacity configuration optimization model generation method in the embodiment, or as follows: Figure 5 Microgrid optimization method of the embodiment.

[0138] To implement the above embodiments, this disclosure also proposes a computer program product, including a computer program, which, when executed by a processor, implements the features of this disclosure. Figures 1-4 The microgrid distributed energy storage capacity configuration optimization model generation method in the embodiment, or as follows: Figure 5 Microgrid optimization method of the embodiment.

[0139] It should be noted that personal information collected from users should be used for legitimate and reasonable purposes and should not be shared or sold outside of these legitimate uses. Furthermore, such collection / sharing should only be conducted after receiving the user's informed consent, including but not limited to notifying the user to read the user agreement / user notice and sign an agreement / authorization that includes authorization of relevant user information before the user uses the function. In addition, any necessary steps must be taken to protect and safeguard access to such personal information data and ensure that others with access to personal information data comply with their privacy policies and procedures.

[0140] This application is intended to provide an implementation scheme for users to selectively prevent the use or access to their personal information data. Specifically, this disclosure is intended to provide hardware and / or software to prevent or block access to such personal information data. Once personal information data is no longer needed, risks can be minimized by restricting data collection and deleting data. Furthermore, where applicable, such personal information is de-identified to protect user privacy.

[0141] In the foregoing descriptions of the embodiments, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0142] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0143] Any process or method description in the flowchart or otherwise herein can be understood as representing a module, segment, or portion of code comprising one or more executable instructions for implementing custom logic functions or processes, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed not in the order shown or discussed, including substantially simultaneously or in reverse order depending on the functions involved, as should be understood by those skilled in the art to which embodiments of this application pertain.

[0144] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that contains, stores, communicates, propagates, or transmits programs for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include: an electrical connection having one or more wires (electronic device), a portable computer disk drive (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Alternatively, the computer-readable medium may be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in a computer memory.

[0145] It should be understood that various parts of this application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0146] Those skilled in the art will understand that all or part of the steps of the methods in the above embodiments can be implemented by a program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, the program includes one or a combination of the steps of the method embodiments.

[0147] Furthermore, the functional units in the various embodiments of this application can be integrated into a processing module, or each unit can exist physically separately, or two or more units can be integrated into a module. The integrated module can be implemented in hardware or as a software functional module. If the integrated module is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0148] The storage medium mentioned above can be a read-only memory, a disk, or an optical disk, etc. Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of this application.

Claims

1. A method for generating an optimization model for distributed energy storage capacity configuration in microgrids, characterized in that, include: Obtain historical operating data and operating constraints of the target microgrid; Based on the historical operating data, typical power output scenarios of the target microgrid are obtained; Based on the typical output scenario, establish the formula for the daily operating cost change of the target microgrid after configuration and the formula for the overall energy storage configuration cost; Based on the formula for daily operating cost change, the formula for overall energy storage configuration cost, and the operating constraints, an optimization model for the configuration of distributed energy storage capacity of the target microgrid is established.

2. The method according to claim 1, characterized in that, The process of obtaining typical power output scenarios for the target microgrid based on the historical operating data includes: The application scenarios in the historical operation data are clustered using a clustering algorithm to generate multiple target clusters; Each target cluster is considered as a typical output scenario.

3. The method according to claim 2, characterized in that, The process of clustering application scenarios in the historical operational data using a clustering algorithm to generate multiple target clusters includes: The application scenarios in the historical operating data are clustered by improving the K-means algorithm, and the optimal number of clusters is determined based on the clustering results of the improved K-means algorithm by combining the elbow rule and the silhouette coefficient. Clustering is performed again based on the improved K-means algorithm to generate the target cluster with the optimal number of clusters.

4. The method according to any one of claims 1-3, characterized in that, The formulas for the daily operating cost change of the target microgrid and the overall energy storage configuration cost, established based on the typical output scenario, include: For any typical output scenario, obtain the scenario probability of the typical output scenario appearing in all operating scenarios of the target microgrid, and obtain the configuration data of the target microgrid; The overall energy storage configuration cost formula is established based on the scenario probability and the configuration data, wherein the overall energy storage configuration cost formula includes at least the expected daily operating cost change of the microgrid and the equipment purchase cost; The formula for the daily operating cost change is established based on the expected daily operating cost change of the microgrid in the overall energy storage configuration cost formula, the scenario probability, and the configuration data.

5. The method according to claim 4, characterized in that, The formula for the daily operating cost change is: Wherein, the C O′ The term "pro" represents the expected increase in daily operating costs of the target microgrid after energy storage configuration. k For the probability of the scenario, the The electricity purchase price of the target microgrid from the upper-level grid during the t-th time period is... The power that the target microgrid purchases from the upper-level grid via the tie line during the t-th time period of scenario k after energy storage configuration, wherein c sub The feed-in tariff for photovoltaic power, the To configure the actual grid-connected power of the photovoltaic system after energy storage, the The power that the target microgrid purchases from the upper-level grid via the tie line in the t-th time period of scenario k, prior to the energy storage configuration. The actual grid-connected power of photovoltaic power before energy storage is configured.

6. The method according to claim 4, characterized in that, The formula for the overall energy storage configuration cost is as follows: my C A =C I +C M +C L +C O ′ Wherein, the C A The average daily investment cost for energy storage configuration of the target microgrid, C I The average daily purchase cost of the energy storage system for the target microgrid, C L The average daily maintenance cost of the target microgrid, C O′ This represents the expected increase in daily operating costs of the target microgrid after energy storage configuration.

7. A microgrid optimization method, characterized in that, include: Obtain the microgrid distributed energy storage capacity configuration optimization model and energy storage configuration data of the target microgrid, wherein the microgrid distributed energy storage capacity configuration optimization model is generated by the microgrid distributed energy storage capacity configuration optimization model generation method as described in any one of claims 1-6; The energy storage configuration data is processed using the energy storage configuration optimization model of the microgrid distributed energy storage capacity configuration to generate the overall energy storage configuration power and overall energy storage configuration capacity of the target microgrid.

8. A device for generating a microgrid distributed energy storage capacity configuration optimization model, characterized in that, include: The acquisition module is used to acquire historical operating data of the target microgrid and the operating constraints of the target microgrid; The analysis module is used to obtain typical power output scenarios of the target microgrid based on the historical operating data; A module is established to create a formula for the daily operating cost change of the target microgrid and a formula for the overall energy storage configuration cost based on the typical output scenario. The generation module is used to establish a microgrid distributed energy storage capacity configuration optimization model for the target microgrid based on the daily operating cost change formula, the overall energy storage configuration cost formula, and the operating constraints.

9. A microgrid optimization device, characterized in that, include: The receiving module is used to acquire the microgrid distributed energy storage capacity configuration optimization model and energy storage configuration data of the target microgrid, wherein the microgrid distributed energy storage capacity configuration optimization model is generated by the microgrid distributed energy storage capacity configuration optimization model generation method as described in any one of claims 1-6; The processing module is used to process the energy storage configuration data through the energy storage configuration data of the microgrid distributed energy storage capacity configuration optimization model to generate the overall energy storage configuration power and overall energy storage configuration capacity of the target microgrid.

10. An electronic device, characterized in that, Including memory and processor; The processor reads executable program code stored in the memory to run a program corresponding to the executable program code, so as to implement the microgrid distributed energy storage capacity configuration optimization model generation method as described in any one of claims 1-6, or the microgrid optimization as described in claim 7.

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