Regional energy autonomous power network energy storage capacity configuration method and system

Abstract

The invention discloses a regional energy autonomous power network energy storage capacity configuration method and system, and the method is based on the income modes of a demand side response scene, a green power consumption scene and a demand management scene. The method comprises the following steps: respectively calculating an energy storage capacity demand of a demand side response scene, an energy storage capacity demand of a green power consumption scene and an energy storage capacity demand of a demand management scene; superposing the energy storage capacity requirements in each scene, and comparing the superposed result with the actual operation condition; and in combination with a comparison result, determining an optimal superposition strategy of each energy storage capacity demand by taking the maximum investment income ratio as a principle, and completing the configuration of the total energy storage capacity according to the optimal superposition strategy. According to the invention, the problems of large calculation amount and high cost of energy storage capacity planning in the regional energy autonomous power network are solved, and the overall economy of the project can be improved.

Description

Technical Field

[0001] This invention belongs to the field of regional energy autonomy technology, specifically relating to a method and system for configuring energy storage capacity in a regional energy autonomous power network. Background Technology

[0002] Currently, regional autonomous power grids, primarily powered by renewable energy, represent a significant development direction for absorbing renewable energy. Energy storage has become a crucial means of addressing the instability of renewable energy supply. The configuration of energy storage capacity within these grids is a critical factor influencing their efficient operation, requiring a comprehensive consideration of construction costs, operating costs, and investment returns. Current energy storage capacity configurations primarily focus on ensuring the stable operation of the regional autonomous power grid, with less consideration given to financial returns and cost recovery periods. While energy storage capacity planning can potentially calculate power and energy consumption differences based on historical and forecasted load and power generation data across 8760 hours of the year, combined with electricity prices at different times, yielding optimal power and capacity values, this method is costly and computationally intensive, generally suitable for large-scale projects. Simply configuring energy storage capacity according to a fixed proportion of renewable energy construction scale fails to differentiate between source-load time differences and peak-valley electricity price variations across various scenarios, lacking applicability to specific projects and thus reducing overall project economics. Summary of the Invention

[0003] (a) Purpose of the invention The purpose of this invention is to provide a method and system for configuring energy storage capacity in regional autonomous power networks. This method can effectively solve the problems of large calculation volume and high cost in energy storage capacity planning in regional autonomous power networks, and enhances the applicability to specific projects of small-scale regional autonomous power networks, thereby improving the overall economic efficiency of the project.

[0004] (II) Technical Solution To address the aforementioned problems, a first aspect of the present invention provides a method for configuring energy storage capacity in a regional energy autonomous power network. This method is based on revenue models for demand-side response scenarios, green energy consumption scenarios, and demand management scenarios. The method includes: Calculate the energy storage capacity requirements for demand-side response scenarios, green electricity consumption scenarios, and demand management scenarios respectively. The energy storage capacity requirements of various scenarios are superimposed, and the superimposed result is compared with the actual operating conditions. Based on the comparison results, and taking the principle of maximizing the return on investment, the optimal superposition strategy for each energy storage capacity requirement is determined, and the total energy storage capacity is configured according to the optimal superposition strategy.

[0005] Preferably, the energy storage capacity requirement for the computing demand-side response scenario includes: Collect raw electricity consumption data at peak daily electricity prices before energy storage configuration, and determine typical peak daily electricity consumption based on the raw electricity consumption data; Collect raw data of the average grid disconnection power during the 15-minute peak of the daily electricity price before energy storage configuration, and determine the maximum value of the average grid disconnection power during the 15-minute peak of the day based on the raw data of the average grid disconnection power. The battery capacity of the first energy storage system is determined based on the typical peak electricity consumption of the day, and the rated capacity of the first PCS is determined based on the maximum value of the average grid-connected power over 15 minutes during the typical peak period of the day. Based on the battery capacity of the first energy storage system and the rated capacity of the first PCS, configuration optimization is performed to obtain the energy storage capacity requirements for the demand-side response scenario.

[0006] Preferably, the energy storage capacity requirement for calculating green electricity consumption scenarios includes: Collect basic parameters related to the surplus or shortage of self-generated power and electricity load before energy storage configuration; Several typical values ​​are determined based on the aforementioned basic parameters; The battery capacity of the second energy storage system and the rated capacity of the second PCS are determined based on the typical values. Based on the battery capacity of the second energy storage system and the rated capacity of the second PCS, configuration optimization is performed to obtain the energy storage capacity requirements for green electricity consumption scenarios.

[0007] Preferably, the basic parameters related to the surplus and shortage of self-generated power and load power consumption before energy storage configuration include: the surplus and shortage power, power consumption, duration and cycle of self-generated power and load power consumption before energy storage configuration.

[0008] Preferably, the plurality of typical values ​​include a fixed surplus / deficit period, the maximum continuous surplus power within a typical fixed period, the maximum average on-grid power within a typical fixed period over 15 minutes, and the uncontrollable load power value.

[0009] Preferably, determining the battery capacity of the second energy storage system and the rated capacity of the second PCS based on the typical values ​​includes: The battery capacity of the second energy storage system is determined based on the maximum continuous surplus electricity within the typical fixed period. The rated capacity of the second PCS is determined based on the maximum average power of the network over a typical fixed period of 15 minutes and the power value of the uncontrollable load.

[0010] Preferably, the energy storage capacity requirements for the computational demand management scenario include: Collect raw average load data for 15 minutes or 30 minutes during the first year before configuring energy storage; Based on the aforementioned raw average load data, the typical load difference for the year is determined; Based on the typical load difference within the year, the rated capacity of the third PCS is determined, and the battery capacity of the third energy storage system is determined in combination with the rated power of the PCS. Based on the battery capacity of the third energy storage system and the rated capacity of the third PCS, configuration optimization is performed to obtain the energy storage capacity requirements for the demand management scenario.

[0011] Furthermore, a second aspect of the present invention provides a regional energy autonomous power network energy storage capacity configuration system, which is applied to demand-side response scenarios, green electricity consumption scenarios, and demand management scenarios for revenue generation. The system includes: The capacity demand calculation module is used to calculate the energy storage capacity demand for demand-side response scenarios, green electricity consumption scenarios, and demand management scenarios, respectively. The comparison module is used to superimpose the energy storage capacity requirements under various scenarios and compare the superimposed result with the actual operating conditions. The configuration module is used to combine the comparison results and determine the optimal superposition strategy for each energy storage capacity requirement based on the principle of maximizing the return on investment, and complete the configuration of the total energy storage capacity according to the optimal superposition strategy.

[0012] Preferably, the computing capacity requirement module is used for: Collect raw electricity consumption data at peak daily electricity prices before energy storage configuration, and determine typical peak daily electricity consumption based on the raw electricity consumption data; Collect raw data of the average grid disconnection power during the 15-minute peak of the daily electricity price before energy storage configuration, and determine the maximum value of the average grid disconnection power during the 15-minute peak of the day based on the raw data of the average grid disconnection power. The battery capacity of the first energy storage system is determined based on the typical peak electricity consumption of the day, and the rated capacity of the first PCS is determined based on the maximum value of the average grid-connected power over 15 minutes during the typical peak period of the day. Based on the battery capacity of the first energy storage system and the rated capacity of the first PCS, configuration optimization is performed to obtain the energy storage capacity requirements for the demand-side response scenario.

[0013] Preferably, the computing capacity requirement module is further used for: Collect basic parameters related to the surplus or shortage of self-generated power and electricity load before energy storage configuration; Several typical values ​​are determined based on the aforementioned basic parameters; The battery capacity of the second energy storage system and the rated capacity of the second PCS are determined based on the typical values. Based on the battery capacity of the second energy storage system and the rated capacity of the second PCS, configuration optimization is performed to obtain the energy storage capacity requirements for green electricity consumption scenarios.

[0014] (III) Beneficial Effects The above-mentioned technical solution of the present invention has the following beneficial technical effects: The present invention provides a method and system for configuring energy storage capacity in a regional energy autonomous power network. This application solves the problem of large calculation volume and high cost of energy storage capacity planning in a regional energy autonomous power network. The present invention includes three benefit modes: participating in demand-side response, participating in green electricity consumption, and participating in demand management. Statistical calculations are performed based on the internal load and internal power sources of the regional energy autonomous power network, specifically as follows: The energy storage capacity demand in the demand-side response scenario, the energy storage capacity demand in the green electricity consumption scenario, and the energy storage capacity demand in the demand management scenario are calculated respectively; the energy storage capacity demand under each scenario is superimposed, and the superimposed result is compared with the actual operating conditions; based on the comparison results, the optimal superposition strategy for each energy storage capacity demand is determined according to the principle of maximizing the investment return ratio, and the total energy storage capacity is configured according to the optimal superposition strategy. This application can significantly improve the adaptability of specific projects in small-scale regional energy autonomous power networks and effectively optimize the overall economic efficiency of the projects. The energy storage capacity planning takes low-cost electricity use as the core consideration, which can effectively reduce the construction and operation costs of regional energy autonomous power networks, while providing solid technical support for the promotion and application of renewable energy local grids and regional energy autonomous grids. Attached Figure Description

[0015] Figure 1 This is a flowchart of the regional energy autonomous power network energy storage capacity configuration method of the present invention; Figure 2 This is a schematic diagram of the regional energy autonomous power network energy storage capacity configuration system of the present invention; Figure 3 This is a schematic diagram of the regional energy autonomous power network according to a specific embodiment of the present invention; Figure 4 This is a flowchart illustrating the calculation of energy storage capacity configuration in a regional energy autonomous power network according to a specific embodiment of the present invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments and the accompanying drawings. It should be understood that these descriptions are merely exemplary and not intended to limit the scope of the invention. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concept of the invention.

[0017] like Figure 1 As shown, the first aspect of the present invention provides a method for configuring energy storage capacity in a regional energy autonomous power network. This method is based on the revenue models of demand-side response scenarios, green energy consumption scenarios, and demand management scenarios. The method includes: S1 calculates the energy storage capacity requirements for the demand-side response scenario, the green electricity consumption scenario, and the demand management scenario, respectively. The energy storage capacity requirements for the three scenarios are as follows: S11, calculating the energy storage capacity requirement for the demand-side response scenario includes: collecting raw data on daily peak electricity consumption before energy storage configuration, and determining the typical peak electricity consumption based on the raw data; collecting raw data on the average grid disconnection power over 15 minutes of daily peak electricity consumption before energy storage configuration, and determining the maximum value of the average grid disconnection power over 15 minutes of daily typical peak electricity consumption based on the raw data; determining the battery capacity of the first energy storage system based on the typical peak electricity consumption, and determining the rated capacity of the first PCS based on the maximum value of the average grid disconnection power over 15 minutes of daily typical peak electricity consumption; and optimizing the configuration based on the battery capacity of the first energy storage system and the rated capacity of the first PCS to obtain the energy storage capacity requirement for the demand-side response scenario.

[0018] S12, the energy storage capacity requirement for calculating the green electricity consumption scenario includes: S121, collect basic parameters related to the surplus and shortage of self-generated power and electricity load before energy storage configuration, specifically including: collecting the surplus and shortage power, electricity volume, duration and cycle of self-generated power and load electricity before energy storage configuration.

[0019] S122, Based on the basic parameters, determine a number of typical values, including a fixed surplus / deficit period, the maximum continuous surplus power within a typical fixed period, the maximum average on-grid power within a typical fixed period over 15 minutes, and the uncontrollable load power value.

[0020] S123, determine the battery capacity of the second energy storage system and the rated capacity of the second PCS according to the typical values; S124, Based on the battery capacity of the second energy storage system and the rated capacity of the second PCS, configuration optimization is performed to obtain the energy storage capacity requirement for the green electricity consumption scenario, specifically including: determining the battery capacity of the second energy storage system based on the maximum continuous surplus electricity within the typical fixed period; and determining the rated capacity of the second PCS based on the maximum average grid-connected power value over 15 minutes within the typical fixed period and the uncontrollable load power value.

[0021] S13, the calculation of energy storage capacity requirements for the demand management scenario includes: collecting raw average load data for 15 minutes or 30 minutes in the first year before configuring energy storage; determining the typical load difference within the year based on the raw average load data; determining the rated capacity of the third PCS based on the typical load difference within the year, and determining the battery capacity of the third energy storage system in combination with the rated power of the PCS; and optimizing the configuration based on the battery capacity of the third energy storage system and the rated capacity of the third PCS to obtain the energy storage capacity requirements for the demand management scenario.

[0022] S2 superimposes the energy storage capacity requirements under various scenarios and compares the superimposed result with the actual operating conditions. S3. Based on the comparison results, and taking the principle of maximizing the return on investment, determine the optimal superposition strategy for each energy storage capacity requirement, and complete the configuration of the total energy storage capacity according to the optimal superposition strategy.

[0023] like Figure 2 As shown, a second aspect of the present invention provides a regional energy autonomous power network energy storage capacity configuration system. This system is applied to demand-side response scenarios, green electricity consumption scenarios, and demand management scenarios, and the system includes: The capacity demand calculation module 21 is used to calculate the energy storage capacity demand for demand-side response scenarios, green electricity consumption scenarios, and demand management scenarios, respectively. The capacity demand calculation module 21 is used to: collect raw electricity consumption data during peak daily electricity prices before energy storage configuration; determine the typical peak daily electricity consumption based on the raw electricity consumption data; collect raw data of the average grid disconnection power over 15 minutes during peak daily electricity prices before energy storage configuration; determine the maximum value of the average grid disconnection power over 15 minutes during typical daily peak times based on the raw data of the average grid disconnection power; determine the battery capacity of the first energy storage system based on the typical peak daily electricity consumption; determine the rated capacity of the first PCS based on the maximum value of the average grid disconnection power over 15 minutes during typical daily peak times; and perform configuration optimization based on the battery capacity of the first energy storage system and the rated capacity of the first PCS to obtain the energy storage capacity demand for demand-side response scenarios. In addition, the capacity demand calculation module 21 is also used to: collect basic parameters related to the surplus and shortage of self-generated power and electricity load before energy storage configuration; determine multiple typical values ​​based on the basic parameters; determine the battery capacity of the second energy storage system and the rated capacity of the second PCS according to the typical values; and optimize the configuration based on the battery capacity of the second energy storage system and the rated capacity of the second PCS to obtain the energy storage capacity demand for the green electricity consumption scenario.

[0024] The comparison module 22 is used to superimpose the energy storage capacity requirements under various scenarios and compare the superimposed result with the actual operating conditions. Configuration module 23 is used to combine the comparison results and determine the optimal superposition strategy for each energy storage capacity requirement based on the principle of maximizing the return on investment, and complete the configuration of the total energy storage capacity according to the optimal superposition strategy.

[0025] The following is in conjunction with the appendix Figure 4 This application will be described in detail.

[0026] The purpose of this invention is to provide a method and system for configuring energy storage capacity in a regional energy autonomous power network. This method calculates the total energy storage capacity configuration based on the revenue-generating mechanisms available for energy storage, overcoming the poor adaptability of configuring energy storage capacity according to a fixed ratio and the high design costs of configuring energy storage capacity based on 8760 hours per year. This improves the economic efficiency of small-scale regional energy autonomous power networks. The specific configuration is as follows: (1) Calculation of capacity configuration for different revenue models of energy storage in regional energy autonomous power networks.

[0027] Capacity configuration calculation under the demand-side response (DSR) revenue model: Demand-side response revenue mainly refers to the revenue generated by users actively changing their electricity consumption behavior through changes in retail electricity prices, utilizing energy storage systems. Assuming one peak and one valley period per day, and the energy storage system completes one charge and one discharge cycle, if the levelized cost of electricity (LCOE) over the energy storage's lifespan is 0.4 yuan (LCOE is estimated based on the energy storage system's cycle life and service life), then investing in energy storage is feasible when the peak-valley price difference is greater than 0.4 yuan. The revenue comes from the peak-valley price difference generated by shifting peak load consumption to off-peak periods using energy storage. The energy storage PCS capacity can be selected as the maximum value of the average 15-minute grid-connected power (excluding energy storage) during peak hours in a typical month of the year (guaranteeing peak load power). The energy storage battery capacity can be selected as the grid-connected power during peak hours on a typical day of the year (guaranteeing peak load power), or it can be optimized by referencing the mode of the daily peak-hour grid-connected power statistics for the year and considering the depth of discharge. Because peak and off-peak periods are similar in length throughout the day, and the maximum average grid-connected power during the 15-minute peak period is usually greater than the average load power during peak periods, the electricity stored by the PCS (Power Storage System) at its rated power during off-peak periods can generally meet the electricity demand during peak periods. If the off-peak period is significantly shorter than the peak period, the PCS capacity should be increased to ensure that the energy storage batteries can be fully charged during off-peak periods. Existing methods for configuring energy storage based on the proportion of new energy installed capacity are not sufficiently comparable to peak load electricity measurements, while the method described in this application, which is configured based on the grid-connected electricity during peak periods, better ensures the transfer of peak electricity demand to off-peak periods and maximizes the utilization of energy storage.

[0028] Capacity configuration calculation under the green electricity consumption revenue model: Green electricity consumption revenue refers to the economic benefits derived from allocating corresponding energy storage capacity based on the price difference between the grid-connected electricity price and the off-grid electricity price. This capacity stores electricity that cannot be consumed in real time within the regional energy autonomous grid, adjusting the time difference between internal renewable energy generation and load consumption. Unconsumable distributed power (renewable energy) generation is stored in energy storage batteries. When the internal power output cannot meet the load demand, the storage capacity is called upon. The revenue comes from the difference between the grid-connected electricity price of renewable energy generation (or zero or negative revenue due to non-grid access) and the off-grid electricity price of the load. Investment and construction are feasible when the difference between the grid-connected and off-grid electricity prices is greater than the cost per kilowatt-hour of the energy storage's lifespan (e.g., 0.4 yuan). When the internal power generation far exceeds the regional load demand, energy storage cannot generate revenue from the charge-discharge plan based on the grid-connected electricity price difference because self-generation is consistently sufficient, with little or no time for calling upon stored electricity. It only becomes effective when there is a surplus or deficit between self-generation and load demand within a fixed period (e.g., one day). When planning the construction of an energy-autonomous power grid, the principle is usually that the annual power generation of the self-owned power source (clean energy), after deducting reserves and losses, should be equivalent to the power consumption of the load. However, in actual operation, there is a time difference between the output of the self-owned power source and the power consumption of the load. Besides controllable loads whose power demand time can be adjusted, it is necessary to meet the power supply time requirements of uncontrollable loads in production and daily life. The capacity of the PCS can be selected as the maximum average on-grid power output over a typical fixed period of the year (defined as PCS). 绿电消纳容量1 The battery capacity should be selected based on the greater of the power of the load and the uncontrollable load (ensuring charging and discharging power). The maximum continuous surplus capacity within a typical fixed period (ensuring green electricity storage capacity) should also be chosen. To ensure the investment payback period, the typical fixed period used for energy storage capacity configuration calculations should not be too long. It should include the ratio of primary self-generated electricity and the surplus / deficit power of the load to the PCS rated power, both greater than 1-2. The number of charge / discharge cycles within the fixed period can be estimated based on the duration of both surplus and deficit scenarios. Configuring energy storage capacity based on relevant data within a typical fixed period is more adaptable to the specific project's source-load operation patterns. Compared to configuring energy storage capacity based on the daily load deficit or the surplus of new energy generation, this approach is more precise, allowing for maximum utilization of the configured energy storage.

[0029] Capacity configuration calculation under the demand management revenue model: Demand management revenue refers to the revenue gained by reducing peak load. When power companies charge industrial electricity fees, a portion is charged based on demand, which is the product of the maximum value M of the average load over 15 minutes or 30 minutes in a month and the demand unit price a. Configuring energy storage to reduce M can lead to a reduction in demand-based electricity fees. The change in demand resulting from configuring energy storage is ΔM. ΔM*a*12*n represents the demand management revenue over the energy storage's lifespan (n years) due to the reduction in M. For industrial users with insignificant peak loads, the demand management revenue from energy storage configuration is relatively small, and configuration may not be necessary. When the reduction in electricity fees due to demand reduction over the energy storage's lifespan (e.g., 5 years) exceeds the energy storage investment and operating costs, investment in energy storage construction is recommended (e.g., energy storage construction and operating costs are 2 yuan / Wh (1C), and the demand price is greater than 33 yuan / kW·month). The energy storage PCS capacity is selected based on the difference between the maximum value and the fifth (or third) largest value of the 15-minute (or 30-minute) average load in a typical month. The battery capacity is configured based on the energy storage PCS rated power being the battery's 1C charge / discharge rate. This configuration principle reduces energy storage construction costs while ensuring the reduction of the maximum and second-largest values ​​even under extreme conditions where the maximum and second-largest values ​​occur consecutively. Configuring energy storage capacity based on the difference between the maximum value M of the 15-minute or 30-minute average load and other largest values ​​allows for refined calculations targeting specific values, maximizing investment feasibility and maximizing payback period.

[0030] The benefits of energy storage participating in the electricity market are largely uncontrollable and are not considered in the construction of regional autonomous energy grids.

[0031] (2) Calculation of total energy storage capacity configuration for regional energy autonomous power networks.

[0032] The capacity configuration of an energy storage system is divided into two parts: the capacity configuration of the PCS (Power Conversion System), representing the rated charging and discharging power of the energy storage system, and the capacity configuration of the energy storage battery, representing the total stored energy of the energy storage system. The main revenue of an energy storage system comes from the peak-valley electricity price difference and the difference between grid and upstream electricity prices. The revenue from demand management is strongly correlated with the time-limited nature of the control system, requiring forecasting and control on a shorter timescale. Therefore, it increases the cost of the control system in addition to the energy storage cost, and thus is not configured separately. Therefore, the calculation of the total energy storage capacity configuration mainly consists of two parts: demand-side response and green energy consumption. Due to the influence of the simultaneity rate, the capacity selection range of the PCS is Max(PCS). 需求响应容量 PCS 绿电消纳容量 )≤PCS 总容量 ≤PCS 需求响应容量 +PCS 绿电消纳容量 The energy storage battery capacity selection range is Max(C 需求响应容量 C 绿电消纳容量 )≤C 总容量 ≤C 需求响应容量+C 绿电消纳容量 Energy storage charging and discharging for demand-side response operates under a highly deterministic mode, while energy storage charging and discharging for green electricity consumption is less deterministic. This is because, in most cases, the output of renewable energy in regional energy autonomous power networks fluctuates between surplus and deficit with load power. While the annual power generation of renewable energy is similar to the annual electricity consumption of the load, the timing and duration of these surpluses and deficits are uncertain. The maximum sustained surplus electricity (W) of renewable energy generation during typical daytime periods of low electricity prices is statistically analyzed. 低谷盈余 To ensure electricity storage during off-peak hours and the absorption of renewable energy, PCS 总容量 =PCS 需求响应容量 +PCS 绿电消纳容量1 (The typical fixed cycle here is selected as the typical off-peak period of the daily electricity price); ensuring the grid connection of electricity when the load does not use the peak electricity price, and also ensuring the local consumption of renewable energy, C 总容量 =C 需求响应容量 +W 低谷盈余 Excessive battery capacity in energy storage leads to low utilization, long investment payback periods, and reduced economic efficiency. As the price difference between upstream and downstream electricity increases, the battery capacity can be increased accordingly. Battery capacity configurations that ensure energy storage during off-peak hours are close to the economic optimal level. Demand management does not require additional energy storage capacity; by combining real-time monitoring data with load and generation forecast data for control, demand management benefits can be achieved.

[0033] The following is in conjunction with the appendix Figure 3 The invention will be further illustrated by the examples and implementation cases.

[0034] An example of the structure of a regional energy autonomous power network with renewable energy is attached. Figure 3 As shown, it includes components such as photovoltaics, wind power, energy storage, and controllable loads.

[0035] The energy storage capacity configuration is calculated using the following method: Demand-side response energy storage configuration calculation: Statistical calculations are performed based on the internal load of the regional energy autonomous power network to be constructed. First, the annual operating curve data of the internal load and the peak and off-peak electricity price periods of the construction area are obtained. For example, in a certain area, the peak period is 16:00 to 24:00; the off-peak period is 0:00 to 7:00. The peak period is 8 hours, and the off-peak period is 7 hours. The capacity of the energy storage PCS is selected based on the maximum 15-minute average grid-connected power during peak hours (16:00–24:00) in a typical month of the year. For example, if April is selected as a typical month, and the maximum 15-minute average grid-connected power is 500kW according to the load operation curve, then the energy storage PCS capacity is selected as 500kW. The energy storage battery capacity can be selected based on the grid-connected electricity during peak hours (16:00–24:00) on a typical day of the year. For example, if April 15th is selected as a typical day, and the grid-connected electricity is 2MWh according to the load operation curve, then the energy storage battery capacity is selected as 2MWh. If the optimal depth of discharge (DoD) of the energy storage battery is 80%, then the optimized energy storage battery capacity is selected as 2.5MWh. The selection of typical days and months is generally based on days and months close to the average value throughout the year, rather than special extremes.

[0036] Green electricity consumption and energy storage configuration calculation: Statistical calculations are performed based on the internal load and internal power sources of the regional energy autonomous power network to be constructed. First, the annual operating curve data of the internal load and internal power sources are obtained, and the fixed surplus / deficit period is determined. For example, the fixed surplus / deficit period for a certain park is 24 hours (with at least one significant sustained surplus / deficit occurrence within 24 hours). Based on the annual operating curve, the maximum sustained surplus electricity in a typical fixed surplus / deficit period is determined to be 1 MWh. The maximum 15-minute average grid-connected power in a typical fixed surplus / deficit period is 200 kW, and the uncontrollable load power is 300 kW. Therefore, the energy storage PCS capacity is selected as 300 kW, and the energy storage battery capacity is selected as 1 MWh. If the optimal depth of discharge (DoD) of the energy storage battery during operation is 80%, then the optimized energy storage battery capacity is selected as 1.25 MWh. The selection of the typical fixed surplus / deficit period is generally close to the average value throughout the year, rather than a special extreme.

[0037] Demand Management Energy Storage Configuration Calculation: Statistical calculations are performed based on the internal load (or the difference between load and power output if there is an internal power source) of the regional energy autonomous power network to be constructed. First, the annual operating curve data of the internal load is obtained, and the average power output over 15 minutes or 30 minutes in a typical month is calculated and sorted according to the numerical values. For example, if the difference between the maximum and fifth largest 15-minute average load values ​​in a typical month is 100kW, then the energy storage PCS capacity is selected as 100kW, and the energy storage battery capacity is selected as 100kWh. If the optimal depth of discharge (DoD) for the energy storage battery is 80%, then the optimized energy storage battery capacity is selected as 125kWh.

[0038] Calculation of Energy Storage Configuration for Regional Energy Autonomous Power Networks: Statistical calculations are performed based on the internal loads and power sources of the proposed regional energy autonomous power network. First, the energy storage capacity is calculated using a demand-side response energy storage configuration method, for example, selecting a PCS capacity of 500kW and a battery capacity of 2MWh. Then, using a green electricity consumption energy storage configuration method, the maximum continuous surplus electricity and the maximum average grid-connected power are calculated based on the typical fixed surplus / deficit period during the low-price period of the daily electricity bill, thus deriving the energy storage capacity configuration, for example, selecting a PCS capacity of 200kW and a battery capacity of 1MWh. The configuration capacities obtained from the two methods are added together, resulting in a PCS capacity of 700kW and a battery capacity of 3MWh (optimized to 3.75MWh).

[0039] It should be understood that the specific embodiments described above are merely illustrative or explanatory of the principles of the invention and do not constitute a limitation thereof. Therefore, any modifications, equivalent substitutions, improvements, etc., made without departing from the spirit and scope of the invention should be included within the protection scope of the invention. Furthermore, the appended claims are intended to cover all variations and modifications falling within the scope and boundaries of the appended claims, or equivalent forms of such scope and boundaries. Those skilled in the art will understand that embodiments of the invention can be provided as methods, systems, or computer program products. Therefore, the invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. These computer program instructions can also be stored in a computer-readable storage medium capable of directing a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. Those skilled in the art will understand that all or part of the steps in the methods of 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, it includes the flows of the embodiments of the methods described above. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc. The steps in the methods of the embodiments of the present invention can be adjusted, merged, and deleted according to actual needs. The modules in the system of the embodiments of the present invention can be merged, divided, and deleted according to actual needs.

Claims

1. A method for configuring energy storage capacity in a regional energy autonomous power network, characterized in that, The energy storage capacity configuration method is based on the revenue models of demand-side response scenarios, green electricity consumption scenarios, and demand management scenarios. The method includes: Calculate the energy storage capacity requirements for demand-side response scenarios, green electricity consumption scenarios, and demand management scenarios respectively. The energy storage capacity requirements of various scenarios are superimposed, and the superimposed result is compared with the actual operating conditions. Based on the comparison results, and taking the principle of maximizing the return on investment, the optimal superposition strategy for each energy storage capacity requirement is determined, and the total energy storage capacity is configured according to the optimal superposition strategy.

2. The method for configuring energy storage capacity in a regional energy autonomous power network according to claim 1, characterized in that, The energy storage capacity requirements for the computing demand-side response scenario include: Collect raw electricity consumption data at peak daily electricity prices before energy storage configuration, and determine typical peak daily electricity consumption based on the raw electricity consumption data; Collect raw data of the average grid disconnection power during the 15-minute peak of the daily electricity price before energy storage configuration, and determine the maximum value of the average grid disconnection power during the 15-minute peak of the day based on the raw data of the average grid disconnection power. The battery capacity of the first energy storage system is determined based on the typical peak electricity consumption of the day, and the rated capacity of the first PCS is determined based on the maximum value of the average grid-connected power over 15 minutes during the typical peak period of the day. Based on the battery capacity of the first energy storage system and the rated capacity of the first PCS, configuration optimization is performed to obtain the energy storage capacity requirements for the demand-side response scenario.

3. The method for configuring energy storage capacity in a regional energy autonomous power network according to claim 1, characterized in that, The energy storage capacity requirements for the green electricity consumption scenario include: Collect basic parameters related to the surplus or shortage of self-generated power and electricity load before energy storage configuration; Several typical values ​​are determined based on the aforementioned basic parameters; The battery capacity of the second energy storage system and the rated capacity of the second PCS are determined based on the typical values. Based on the battery capacity of the second energy storage system and the rated capacity of the second PCS, configuration optimization is performed to obtain the energy storage capacity requirements for green electricity consumption scenarios.

4. The method for configuring energy storage capacity in a regional energy autonomous power network according to claim 3, characterized in that, The basic parameters related to the surplus and shortage of self-generated power and load power consumption before energy storage configuration are collected include: the surplus and shortage power, power, duration and cycle of self-generated power and load power consumption before energy storage configuration.

5. The method for configuring energy storage capacity in a regional energy autonomous power network according to claim 3, characterized in that, The typical values ​​include a fixed surplus / deficit period, the maximum continuous surplus power within a typical fixed period, the maximum average on-grid power within a 15-minute period within a typical fixed period, and the uncontrollable load power value.

6. The method for configuring energy storage capacity in a regional energy autonomous power network according to claim 5, characterized in that, The step of determining the battery capacity of the second energy storage system and the rated capacity of the second PCS based on the typical values ​​includes: The battery capacity of the second energy storage system is determined based on the maximum continuous surplus electricity within the typical fixed period. The rated capacity of the second PCS is determined based on the maximum average power of the network over a typical fixed period of 15 minutes and the power value of the uncontrollable load.

7. The method for configuring energy storage capacity in a regional energy autonomous power network according to claim 1, characterized in that, The energy storage capacity requirements for the computational demand management scenario include: Collect raw average load data for 15 minutes or 30 minutes during the first year before configuring energy storage; Based on the aforementioned raw average load data, the typical load difference for the year is determined; Based on the typical load difference within the year, the rated capacity of the third PCS is determined, and the battery capacity of the third energy storage system is determined in combination with the rated power of the PCS. Based on the battery capacity of the third energy storage system and the rated capacity of the third PCS, configuration optimization is performed to obtain the energy storage capacity requirements for the demand management scenario.

8. A regional energy autonomous power network energy storage capacity configuration system, characterized in that, The energy storage capacity configuration system is applied to demand-side response scenarios, green electricity consumption scenarios, and demand management scenarios, and the system includes: The capacity demand calculation module is used to calculate the energy storage capacity demand for demand-side response scenarios, green electricity consumption scenarios, and demand management scenarios, respectively. The comparison module is used to superimpose the energy storage capacity requirements under various scenarios and compare the superimposed result with the actual operating conditions. The configuration module is used to combine the comparison results and determine the optimal superposition strategy for each energy storage capacity requirement based on the principle of maximizing the return on investment, and complete the configuration of the total energy storage capacity according to the optimal superposition strategy.

9. The regional energy autonomous power network energy storage capacity configuration system according to claim 8, characterized in that, The computing capacity requirement module is used for: Collect raw electricity consumption data at peak daily electricity prices before energy storage configuration, and determine typical peak daily electricity consumption based on the raw electricity consumption data; Collect raw data of the average grid disconnection power during the 15-minute peak of the daily electricity price before energy storage configuration, and determine the maximum value of the average grid disconnection power during the 15-minute peak of the day based on the raw data of the average grid disconnection power. The battery capacity of the first energy storage system is determined based on the typical peak electricity consumption of the day, and the rated capacity of the first PCS is determined based on the maximum value of the average grid-connected power over 15 minutes during the typical peak period of the day. Based on the battery capacity of the first energy storage system and the rated capacity of the first PCS, configuration optimization is performed to obtain the energy storage capacity requirements for the demand-side response scenario.

10. The method for configuring energy storage capacity in a regional energy autonomous power network according to claim 8, characterized in that, The computing capacity requirement module is also used for: Collect basic parameters related to the surplus or shortage of self-generated power and electricity load before energy storage configuration; Several typical values ​​are determined based on the aforementioned basic parameters; The battery capacity of the second energy storage system and the rated capacity of the second PCS are determined based on the typical values. Based on the battery capacity of the second energy storage system and the rated capacity of the second PCS, configuration optimization is performed to obtain the energy storage capacity requirements for green electricity consumption scenarios.

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