Multi-scene collaborative transformer area shared energy storage capacity configuration method

Abstract

The invention provides a multi-scene collaborative transformer area shared energy storage capacity configuration method, and relates to the technical field of electric power. The method comprises the following steps: carrying out scene classification on each transformer area power grid, and obtaining annual photovoltaic power generation prediction data and load prediction data of each scene; according to the type of each scene, the photovoltaic power generation prediction data and the load prediction data, establishing a total photovoltaic waste penalty term and a total power shortage penalty term considering multi-scene time division multiplexing; the method comprises the following steps of: establishing a target function by taking the sum of a total life cycle annual average cost term, a total photovoltaic waste penalty term and a total power shortage penalty term of shared energy storage as a target and taking the capacity and power of the shared energy storage as decision variables, and establishing a multi-dimensional constraint condition containing a multi-scene collaborative constraint; and solving the objective function to obtain the optimal capacity. According to the invention, the problem of unreasonable configuration of the shared energy storage capacity of the transformer area can be solved.

Description

Technical Field

[0001] This invention relates to the field of power technology, and in particular to a method for configuring shared energy storage capacity in multiple scenarios for coordinated distribution transformer areas. Background Technology

[0002] With the high penetration of distributed energy, the upgrading and transformation of rural power grids, and the surge in demand for electric vehicle charging, the power grid in the distribution area faces problems such as voltage fluctuations, insufficient power supply reliability, and imbalance between power source and load. Shared energy storage, as a flexible adjustment resource, can effectively solve these problems.

[0003] Shared energy storage can address issues such as insufficient grid regulation capacity and increased difficulty in frequency stabilization. Rational allocation of energy storage capacity can effectively absorb renewable energy from solar and wind power, smooth user load curves, thereby reducing energy waste and lowering electricity costs. Existing energy storage capacity configuration methods are generally based on initial dynamic investment and mainly include energy storage system integration methods that improve renewable energy absorption, energy storage utilization, and grid support. However, existing methods are primarily designed for a single application scenario and cannot consider the synergistic effects of multiple scenarios, impacting system operating efficiency and thus exhibiting problems of poor rationality and economic viability. Summary of the Invention

[0004] This invention provides a method for configuring shared energy storage capacity in multiple scenarios to address the problem of unreasonable configuration of shared energy storage capacity in different power distribution areas.

[0005] In a first aspect, embodiments of the present invention provide a method for configuring shared energy storage capacity in multiple scenarios, including: The power grid of each distribution area is classified into scenarios, and the annual photovoltaic power generation forecast data and load forecast data of each scenario are obtained. Based on the type of each scenario, photovoltaic power generation forecast data, and load forecast data, establish a total photovoltaic waste penalty item and a total power shortage penalty item that take into account time-division multiplexing of multiple scenarios. With the goal of minimizing the sum of the average annual cost of shared energy storage throughout its entire life cycle, the total photovoltaic waste penalty, and the total power shortage penalty, and with the capacity and power of shared energy storage as decision variables, an objective function is established, and multi-dimensional constraints including multi-scenario collaborative constraints are established. The objective function is solved based on the multidimensional constraints to obtain the optimal capacity.

[0006] In one possible implementation, a total photovoltaic waste penalty term is established that considers time-division multiplexing across multiple scenarios, including: Based on the type of each scenario, obtain the photovoltaic allocation coefficient and photovoltaic penalty weight factor for each scenario at different time periods; Based on the photovoltaic power generation forecast data, determine the photovoltaic output of each scenario at different times; Based on the photovoltaic allocation coefficient, the rechargeable energy storage capacity for each scenario at different times is determined; Based on the photovoltaic output and energy storage rechargeable capacity of each scenario at different times, determine the photovoltaic power wastage of each scenario at different times. Based on the photovoltaic penalty weighting factors of each scenario at different time periods, the weighted sum of the photovoltaic wasted electricity of each scenario at different time periods is calculated to obtain the total photovoltaic waste penalty item.

[0007] In one possible implementation, a total power outage penalty term considering time-division multiplexing across multiple scenarios is established, including: Based on the type of each scenario, obtain the discharge allocation coefficient and power shortage penalty weight factor for each scenario at different time periods; Based on the discharge allocation coefficient, the amount of energy that can be discharged in each scenario at different times is determined. Based on the load forecast data and energy storage discharge capacity of each scenario, the power shortage of each scenario at different times is determined, and the power shortage is corrected by the pre-calculated energy storage status correction coefficient and peak power shortage impact coefficient. Based on the power shortage penalty weighting factors of each scenario at different time periods, the weighted sum of the power shortage after correction for each scenario at different time periods is calculated to obtain the total power shortage penalty term.

[0008] In one possible implementation, determining the wasted photovoltaic power in each scenario at different times based on the photovoltaic output and energy storage rechargeability of each scenario at different times includes: Calculate the local load absorption, reuse conflict loss, and cross-time period retention of each scenario at different time periods; Based on the formula: Wasted photovoltaic power = Photovoltaic output - (Local load consumption + Energy storage rechargeable capacity - Conflict loss) - Retained power across time periods, the wasted photovoltaic power for each scenario at different time periods is determined.

[0009] In one possible implementation, determining the power shortage for each scenario at different times based on load forecast data and the discharge capacity of energy storage for each scenario, and correcting the power shortage using pre-calculated energy storage state correction coefficients and peak power shortage impact coefficients, includes: Calculate the photovoltaic output and grid power supply for each scenario at different times; Based on the formula: power shortage = load - grid power supply - photovoltaic output - energy storage discharge capacity, the power shortage of each scenario at different times can be determined. The product of the power shortage, energy storage status correction coefficient, and peak impact coefficient for each scenario at different times is calculated to obtain the corrected power shortage for each scenario at different times.

[0010] In one possible implementation, the average annual cost of the shared energy storage over its entire lifecycle includes: amortization of initial investment, operation and maintenance costs, and residual value recovery.

[0011] In one possible implementation, the multi-scenario collaborative constraints include: time-division multiplexing power allocation constraints and energy balance constraints; the multi-dimensional constraints also include: charging and discharging power constraints, energy storage safety constraints, distribution transformer load rate constraints, and investment benefit constraints.

[0012] In one possible implementation, solving the objective function based on the multidimensional constraints to obtain the optimal capacity includes: Based on the aforementioned multidimensional constraints, the initial optimal solution is determined using the particle swarm optimization algorithm. The initial optimal solution was filtered using Monte Carlo simulation to obtain a robust optimal solution; Based on the robust optimal solution, the optimal capacity is determined.

[0013] Secondly, embodiments of the present invention provide a multi-scenario collaborative transformer substation shared energy storage capacity configuration device, comprising: The acquisition module is used to classify the power grid of each distribution area into scenarios and acquire the annual photovoltaic power generation forecast data and load forecast data for each scenario. The calculation module is used to establish a total photovoltaic waste penalty and a total power shortage penalty, taking into account the time-division multiplexing of multiple scenarios, based on the type of each scenario, photovoltaic power generation forecast data, and load forecast data. A module is established to minimize the sum of the average annual cost of shared energy storage over its entire life cycle, the total photovoltaic waste penalty, and the total power shortage penalty, with the capacity and power of shared energy storage as decision variables. The objective function is established, and multi-dimensional constraints including multi-scenario collaborative constraints are also established. The solution module is used to solve the objective function based on the multidimensional constraints to obtain the optimal capacity.

[0014] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the method described in the first aspect or any possible implementation thereof.

[0015] Fourthly, embodiments of the present invention provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described in the first aspect or any possible implementation thereof.

[0016] Fifthly, embodiments of the present invention provide a computer program product, including a computer program that, when executed by a processor, implements the method described in the first aspect or any possible implementation thereof.

[0017] The beneficial effects of the embodiments of the present invention compared with the prior art are as follows: In this embodiment of the invention, the power grid of the distribution area is first classified into scenarios. By establishing a total photovoltaic waste penalty term and a total power shortage penalty term that consider time-division multiplexing in multiple scenarios, the dynamic allocation of shared energy storage among different scenarios is promoted. The objective function focuses on minimizing the average annual cost of shared energy storage over its entire life cycle, plus the total photovoltaic waste penalty term and the total power shortage penalty term. This avoids both insufficient energy storage capacity caused by solely pursuing the lowest cost and capacity redundancy caused by overemphasizing reliability, achieving a win-win situation of low cost and high reliability. By establishing multi-dimensional constraints, it is ensured that the optimal capacity obtained conforms to the actual power grid operation rules. At the same time, the optimal capacity adapts to the collaborative needs of multiple scenarios, reduces energy storage idle time, improves the utilization rate of energy storage equipment, further reduces the storage cost per unit of electricity, and enhances the overall operating efficiency of the distribution grid. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the implementation of the multi-scenario collaborative method for configuring shared energy storage capacity in transformer substations, as provided in this embodiment of the invention. Figure 2 This is a schematic diagram of the structure of the multi-scenario collaborative transformer area shared energy storage capacity configuration device provided in the embodiment of the present invention; Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0019] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of the invention. However, those skilled in the art will understand that the invention can be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods are omitted so as not to obscure the description of the invention with unnecessary detail.

[0020] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0021] See Figure 1 The document illustrates a flowchart of the implementation of a multi-scenario collaborative method for configuring shared energy storage capacity in transformer substations, as provided in an embodiment of the present invention. Details are as follows: Step S101: Classify the power grid of each distribution area into scenarios and obtain the annual photovoltaic power generation forecast data and load forecast data for each scenario.

[0022] Different scenarios have different electricity consumption characteristics and patterns. By classifying the power grid of each distribution area into different scenarios, a basis can be provided for time-division multiplexing in multiple scenarios.

[0023] For example, based on historical operating data of the transformer substation (including at least one year of 15-minute load, photovoltaic output, voltage, and current data), time-series analysis, probabilistic statistics, and other methods can be used to mine the source-load characteristics and operational requirements of differentiated scenarios, resulting in the following scenario types: (1) High proportion of distributed photovoltaic grid connection scenarios Source-side characteristics: Photovoltaic power output exhibits a bell-shaped curve, peaking between 12:00 and 14:00, with a standard deviation of power output fluctuation exceeding 20%; Load characteristics: Residential load exhibits a bi-peak pattern, with the morning peak from 7:00-9:00 and the evening peak from 18:00-21:00, resulting in a time-space mismatch rate of over 60% with photovoltaic power output; Operational requirements: Energy storage needs to have rapid power regulation capabilities (response time ≤ 100ms) to smooth photovoltaic fluctuations and suppress voltage over-limits.

[0024] (2) Rural power distribution network terminal scenario Source-side characteristics: If a small-capacity distributed photovoltaic system is connected, the output is significantly affected by the weather; if there is no photovoltaic system, the power supply depends on the upstream power grid. Load characteristics: Large seasonal differences in load (such as summer irrigation load and winter heating load). Operational requirements: Energy storage with a certain capacity reserve is required to reduce the duration of power outages.

[0025] (3) Integrated photovoltaic, energy storage and charging scenario.

[0026] Source-side characteristics: There is a time difference between peak photovoltaic output and peak charging load (peak photovoltaic output 12:00-14:00, peak charging output 8:00-10:00, 18:00-20:00). Load-side characteristics: The charging load has strong short-term impact, with the charging power fluctuation range of a single pile being 0-60kW, and the load change rate exceeding 40% when multiple piles are charging simultaneously; Operational requirements: Energy storage needs to have high-power charging and discharging capabilities (charging and discharging power ≥ 30% of the peak charging load) to achieve photovoltaic absorption and avoid transformer overload.

[0027] In this embodiment, based on the multi-scenario operation characteristics of the distribution area, historical operation data of each distribution area over the past three years at the 15-minute level can be collected, covering load data, photovoltaic output data, and voltage data, providing a comprehensive data foundation for subsequent analysis. Then, abnormal interference values ​​are removed through data cleaning to ensure the cleanliness and validity of the original data. Next, a seasonal trend decomposition method is used to accurately separate long-term trend terms, seasonal fluctuation terms, and random disturbance terms in load and photovoltaic data, providing structured input for the prediction model. Finally, prediction calculations are carried out based on a pre-trained high-precision time series prediction model to generate photovoltaic power generation prediction data and load prediction data that meet the needs of each scenario, ensuring that the prediction results match the actual operation patterns of the distribution area and providing reliable data support for subsequent shared energy storage capacity configuration.

[0028] Step S102: Based on the type of each scenario, photovoltaic power generation forecast data, and load forecast data, establish a total photovoltaic waste penalty item and a total power shortage penalty item that consider time-division multiplexing in multiple scenarios.

[0029] In one possible implementation, establishing a total photovoltaic waste penalty term that considers time-division multiplexing across multiple scenarios can be achieved through the following steps: (1) Based on the type of each scenario, obtain the photovoltaic allocation coefficient of each scenario at different time periods. And photovoltaic penalty weighting factor W i,t .

[0030] here, This represents the photovoltaic charging allocation coefficient for scenario i during time period t (reflecting time-division multiplexing priority). W i,t(光伏) This indicates the importance of photovoltaics in scenario i during time period t, allowing for quantitative adjustments to photovoltaic waste. Both can be determined through analysis of scenario importance.

[0031] (2) Based on the photovoltaic power generation forecast data, determine the photovoltaic output of each scenario at different times.

[0032] (3) Determine the energy storage chargeable amount for each scenario at different times based on the photovoltaic allocation coefficient.

[0033] (4) Based on the photovoltaic output and energy storage chargeable capacity of each scenario at different times, determine the photovoltaic power wastage of each scenario at different times.

[0034] In this embodiment, the local load absorption, reuse conflict losses, and cross-time period retained power for each scenario at different time periods can be considered. Based on the formula: Photovoltaic wasted power = Photovoltaic output - (Local load absorption + Energy storage rechargeable capacity - Conflict losses) - Cross-time period retained power, the photovoltaic wasted power for each scenario at different time periods is determined as follows:

[0035] in, Let be the amount of photovoltaic waste in scenario i during time period t; Provide photovoltaic power output for scenario i during time period t; For scenario i, the load is locally absorbed during time period t; The available energy storage capacity for scenario i during time period t. For photovoltaic allocation coefficient, Rated power; This represents the resource crowding out caused by the reuse conflict between scenarios i and j during time period t. To reuse the conflict coefficient, ; For charging efficiency; The amount of energy stored in scenario i during the time period t-1. =SOC(t-1) E ; This refers to the efficiency of energy transfer across time periods.

[0036] (5) Based on the photovoltaic penalty weighting factors of each scenario at different time periods, calculate the weighted sum of the photovoltaic wasted electricity of each scenario at different time periods to obtain the total photovoltaic waste penalty term:

[0037] in, The total amount of photovoltaic waste is penalized throughout the year, with scenarios A, B, and C representing the scenarios.

[0038] In one possible implementation, establishing a total power outage penalty term that considers time-division multiplexing across multiple scenarios can be achieved through the following steps: (1) Based on the type of each scenario, obtain the discharge distribution coefficient of each scenario at different time periods. and power shortage penalty weighting factor W i,t(缺电) .

[0039] here, This represents the discharge allocation coefficient for scenario i during time period t (reflecting time-division multiplexing priority). W i,t(缺电) This indicates the importance of power outage for scenario i during time period t, allowing for quantifiable adjustments to power outage losses. Both can be determined through analysis of the scenario's importance.

[0040] (2) Determine the amount of energy storage that can be discharged in different time periods for each scenario based on the discharge distribution coefficient.

[0041] (3) Based on the load forecast data and the discharge capacity of energy storage in each scenario, determine the power shortage in each scenario at different times, and correct the power shortage by using the pre-calculated energy storage status correction coefficient and peak power shortage impact coefficient.

[0042] In this embodiment, the power shortage = load - grid power supply - photovoltaic output - energy storage discharge capacity, and the specific calculation formula is as follows:

[0043] in, For scenario i, the load is locally absorbed during time period t; This represents the maximum power supply capacity of the power grid in the distribution area. Provide photovoltaic power output for scenario i during time period t; For energy storage, the amount of discharge is... This refers to the discharge efficiency.

[0044] (4) Based on the power shortage penalty weighting factor of each scenario at different time periods, calculate the weighted sum of the power shortage after correction for each scenario at different time periods to obtain the total power shortage penalty term.

[0045] Energy storage state correction factor This indicates that the lower the SOC, the more severe the power shortage penalty; peak impact factor This indicates that sudden load changes amplify losses. The adjusted total power outage penalty is calculated as follows:

[0046] Step S103: With the goal of minimizing the sum of the average annual cost of shared energy storage throughout its entire life cycle, the total photovoltaic waste penalty, and the total power shortage penalty, and with the capacity and power of shared energy storage as decision variables, establish an objective function and establish multi-dimensional constraints that include multi-scenario collaborative constraints.

[0047] In this embodiment, a two-dimensional decision variable is established: X=[E,P], where E is the rated capacity (kWh) and P is the rated power (kW).

[0048] The objective function is to minimize the average annual cost over the entire lifecycle: minC total =C inv-op +C pv,total +C peak,total The annual cost over the entire lifecycle, including initial investment amortization, operating costs, and residual value recovery, is calculated using the following formula:

[0049] in, The average annual cost over the entire lifecycle; Cost per unit capacity of battery; To share energy storage capacity; Cost per unit power of the inverter; For shared energy storage capacity; r is the benchmark rate of return; n is the energy storage lifetime; The annual maintenance cost per unit capacity plus power; This is the residual value.

[0050] In this embodiment, constraints may include, but are not limited to: Charge and discharge power constraints:

[0051]

[0052] SOC security constraints:

[0053] Voltage constraint:

[0054] Distribution transformer load rate constraint:

[0055] Time-division multiplexing power allocation:

[0056] Energy balance:

[0057] Investment recovery period:

[0058] in, This represents the average annual return.

[0059] Step S104: Solve the objective function based on the multidimensional constraints to obtain the optimal capacity.

[0060] Here, the initial optimal solution can be determined using the particle swarm optimization algorithm based on multidimensional constraints; the initial optimal solution can be screened using Monte Carlo simulation to obtain the robust optimal solution; and the optimal capacity can be determined based on the robust optimal solution.

[0061] In this embodiment of the invention, the power grid of the distribution area is first classified into scenarios. By establishing a total photovoltaic waste penalty term and a total power shortage penalty term that consider time-division multiplexing in multiple scenarios, the dynamic allocation of shared energy storage among different scenarios is promoted. The objective function focuses on minimizing the average annual cost of shared energy storage over its entire life cycle, plus the total photovoltaic waste penalty term and the total power shortage penalty term. This avoids both insufficient energy storage capacity caused by solely pursuing the lowest cost and capacity redundancy caused by overemphasizing reliability, achieving a win-win situation of low cost and high reliability. By establishing multi-dimensional constraints, it is ensured that the optimal capacity obtained conforms to the actual power grid operation rules. At the same time, the optimal capacity adapts to the collaborative needs of multiple scenarios, reduces energy storage idle time, improves the utilization rate of energy storage equipment, further reduces the storage cost per unit of electricity, and enhances the overall operating efficiency of the distribution grid.

[0062] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0063] The following are device embodiments of the present invention. For details not described in detail, please refer to the corresponding method embodiments described above.

[0064] Figure 2 The diagram shows a structural schematic of a multi-scenario collaborative shared energy storage capacity configuration for transformer substations provided by an embodiment of the present invention. For ease of explanation, only the parts related to the embodiment of the present invention are shown.

[0065] like Figure 2 As shown, the multi-scenario collaborative shared energy storage capacity configuration 2 for transformer substations includes: The acquisition module 21 is used to classify the power grid of each distribution area into scenarios and acquire the annual photovoltaic power generation forecast data and load forecast data of each scenario. Calculation module 22 is used to establish a total photovoltaic waste penalty item and a total power shortage penalty item that take into account time-division multiplexing of multiple scenarios, based on the type of each scenario, photovoltaic power generation forecast data and load forecast data. Module 23 is established to minimize the sum of the average annual cost of shared energy storage throughout its entire life cycle, the total photovoltaic waste penalty, and the total power shortage penalty, with the capacity and power of shared energy storage as decision variables. It establishes an objective function and multi-dimensional constraints that include multi-scenario collaborative constraints. Solution module 24 is used to solve the objective function based on multidimensional constraints to obtain the optimal capacity.

[0066] In one possible implementation, the computing module 22 is used for: Based on the type of each scenario, obtain the photovoltaic allocation coefficient and photovoltaic penalty weight factor for each scenario at different time periods; Based on photovoltaic power generation forecast data, determine the photovoltaic output of each scenario at different times; Based on the photovoltaic allocation coefficient, determine the rechargeable energy storage capacity for each scenario at different times; Based on the photovoltaic output and energy storage rechargeable capacity of each scenario at different times, determine the photovoltaic power wastage of each scenario at different times. Based on the photovoltaic penalty weighting factors of each scenario at different times, the weighted sum of the photovoltaic wasted electricity of each scenario at different times is calculated to obtain the total photovoltaic waste penalty item.

[0067] In one possible implementation, the computing module 22 is used for: Based on the type of each scenario, obtain the discharge allocation coefficient and power shortage penalty weight factor for each scenario at different time periods; Based on the discharge allocation coefficient, determine the amount of energy that can be discharged from the storage in different time periods for each scenario; Based on the load forecast data and energy storage discharge capacity of each scenario, the power shortage of each scenario at different times is determined, and the power shortage is corrected by the pre-calculated energy storage status correction coefficient and peak power shortage impact coefficient. Based on the power shortage penalty weighting factors of each scenario at different time periods, the weighted sum of the power shortage after correction for each scenario at different time periods is calculated to obtain the total power shortage penalty term.

[0068] In one possible implementation, the computing module 22 is used for: Calculate the local load absorption, reuse conflict loss, and cross-time period retention of each scenario at different time periods; Based on the formula: Wasted photovoltaic power = Photovoltaic output - (Local load consumption + Energy storage rechargeable capacity - Conflict loss) - Retained power across time periods, the wasted photovoltaic power for each scenario at different time periods is determined.

[0069] In one possible implementation, the computing module 22 is used for: Calculate the photovoltaic output and grid power supply for each scenario at different times; Based on the formula: power shortage = load - grid power supply - photovoltaic output - energy storage discharge capacity, the power shortage of each scenario at different times can be determined. The product of the power shortage, energy storage status correction coefficient, and peak impact coefficient for each scenario at different times is calculated to obtain the corrected power shortage for each scenario at different times.

[0070] In one possible implementation, the average annual cost of shared energy storage over its entire lifecycle includes: amortization of initial investment, operation and maintenance costs, and residual value recovery.

[0071] In one possible implementation, multi-scenario collaborative constraints include: time-division multiplexing power allocation constraints and energy balance constraints; multi-dimensional constraints also include: charging and discharging power constraints, energy storage safety constraints, distribution transformer load rate constraints, and investment benefit constraints.

[0072] In one possible implementation, the objective function is solved based on multidimensional constraints to obtain the optimal capacity, including: Based on multidimensional constraints, the initial optimal solution is determined using the particle swarm optimization algorithm. The initial optimal solution was filtered through Monte Carlo simulation to obtain the robust optimal solution; The optimal capacity is determined based on the robust optimal solution.

[0073] In this embodiment of the invention, the power grid of the distribution area is first classified into scenarios. By establishing a total photovoltaic waste penalty term and a total power shortage penalty term that consider time-division multiplexing in multiple scenarios, the dynamic allocation of shared energy storage among different scenarios is promoted. The objective function focuses on minimizing the average annual cost of shared energy storage over its entire life cycle, plus the total photovoltaic waste penalty term and the total power shortage penalty term. This avoids both insufficient energy storage capacity caused by solely pursuing the lowest cost and capacity redundancy caused by overemphasizing reliability, achieving a win-win situation of low cost and high reliability. By establishing multi-dimensional constraints, it is ensured that the optimal capacity obtained conforms to the actual power grid operation rules. At the same time, the optimal capacity adapts to the collaborative needs of multiple scenarios, reduces energy storage idle time, improves the utilization rate of energy storage equipment, further reduces the storage cost per unit of electricity, and enhances the overall operating efficiency of the distribution grid.

[0074] Figure 3 This is a schematic diagram of an electronic device provided in an embodiment of the present invention. For example... Figure 3 As shown, the electronic device 3 in this embodiment includes a processor 30 and a memory 31. The memory 31 stores a computer program 32. When the processor 30 executes the computer program 32, it implements the steps in the various method embodiments described above. Alternatively, when the processor 30 executes the computer program 32, it implements the functions of each module in the various device embodiments described above.

[0075] For example, computer program 32 may be divided into one or more modules / units, which are stored in memory 31 and executed by processor 30 to complete the present invention. The one or more modules / units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of computer program 32 in electronic device 3.

[0076] Electronic device 3 may include, but is not limited to, processor 30 and memory 31. Those skilled in the art will understand that... Figure 3This is merely an example of electronic device 3 and does not constitute a limitation on electronic device 3. It may include more or fewer components than shown, or combine certain components, or different components. For example, electronic device 3 may also include input / output devices, network access devices, buses, etc.

[0077] The processor 30 can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0078] The memory 31 can be an internal storage unit of the electronic device 3, such as a hard disk or memory of the electronic device 3. The memory 31 can also be an external storage device of the electronic device 3, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the electronic device 3. Furthermore, the memory 31 can include both internal and external storage units of the electronic device 3. The memory 31 is used to store the computer program 32 and other programs and data required by the electronic device 3. The memory 31 can also be used to temporarily store data that has been output or will be output.

[0079] For the sake of simplicity and clarity, only the above-described functional modules / units are used as examples. In practical applications, the functions described above can be assigned to different functional modules / units as needed. These modules / units can be implemented in hardware, software, or a combination of both.

[0080] This invention also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, it implements the methods described in the above-described method embodiments.

[0081] This invention also provides a computer program product, including a computer program. When the computer program is executed by a processor, it implements the methods described in the above-described method embodiments.

[0082] Computer programs include computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. Computer-readable media can include: any entity or device capable of carrying computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0083] In the above embodiments, the descriptions of each embodiment have their own emphasis. Parts not detailed or described in a particular embodiment can be referred to in the relevant descriptions of other embodiments. Unless otherwise specified or in conflict with logic, the terminology and / or descriptions between different embodiments are consistent and can be referenced interchangeably. Technical features in different embodiments can be combined to form new embodiments based on their inherent logical relationships.

[0084] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for configuring shared energy storage capacity in multiple scenarios, characterized in that, include: The power grid of each distribution area is classified into scenarios, and the annual photovoltaic power generation forecast data and load forecast data of each scenario are obtained. Based on the type of each scenario, photovoltaic power generation forecast data, and load forecast data, establish a total photovoltaic waste penalty item and a total power shortage penalty item that take into account time-division multiplexing of multiple scenarios. With the goal of minimizing the sum of the average annual cost of shared energy storage throughout its entire life cycle, the total photovoltaic waste penalty, and the total power shortage penalty, and with the capacity and power of shared energy storage as decision variables, an objective function is established, and multi-dimensional constraints including multi-scenario collaborative constraints are established. The objective function is solved based on the aforementioned multidimensional constraints to obtain the optimal capacity.

2. The method for configuring shared energy storage capacity in multiple scenarios in a transformer substation according to claim 1, characterized in that, Establish a total photovoltaic waste penalty item that considers time-division multiplexing in multiple scenarios, including: Based on the type of each scenario, obtain the photovoltaic allocation coefficient and photovoltaic penalty weight factor for each scenario at different time periods; Based on the photovoltaic power generation forecast data, determine the photovoltaic output of each scenario at different times; Based on the photovoltaic allocation coefficient, the rechargeable energy storage capacity for each scenario at different times is determined; Based on the photovoltaic output and energy storage rechargeable capacity of each scenario at different times, determine the photovoltaic power wastage of each scenario at different times. Based on the photovoltaic penalty weighting factors of each scenario at different time periods, the weighted sum of the photovoltaic wasted electricity of each scenario at different time periods is calculated to obtain the total photovoltaic waste penalty item.

3. The method for configuring shared energy storage capacity in multiple scenarios in a transformer substation according to claim 1, characterized in that, Establish a total power outage penalty term that considers time-division multiplexing across multiple scenarios, including: Based on the type of each scenario, obtain the discharge allocation coefficient and power shortage penalty weight factor for each scenario at different time periods; Based on the discharge allocation coefficient, the amount of energy that can be discharged in each scenario at different times is determined. Based on the load forecast data and energy storage discharge capacity of each scenario, the power shortage of each scenario at different times is determined, and the power shortage is corrected by the pre-calculated energy storage status correction coefficient and peak power shortage impact coefficient. Based on the power shortage penalty weighting factors of each scenario at different time periods, the weighted sum of the power shortage after correction for each scenario at different time periods is calculated to obtain the total power shortage penalty term.

4. The method for configuring shared energy storage capacity in multiple scenarios in a transformer substation according to claim 2, characterized in that, The determination of wasted photovoltaic power in each scenario at different times, based on the photovoltaic output and energy storage rechargeable capacity of each scenario at different times, includes: Calculate the local load absorption, reuse conflict loss, and cross-time period retention of each scenario at different time periods; Based on the formula: Wasted photovoltaic power = Photovoltaic output - (Local load consumption + Energy storage rechargeable capacity - Conflict loss) - Retained power across time periods, the wasted photovoltaic power for each scenario at different time periods is determined.

5. The method for configuring shared energy storage capacity in multiple scenarios in a transformer substation according to claim 3, characterized in that, The process involves determining the power shortage for each scenario at different times based on load forecast data and the available discharge capacity of energy storage, and then correcting the power shortage using pre-calculated energy storage status correction coefficients and peak power shortage impact coefficients. This includes: Calculate the photovoltaic output and grid power supply for each scenario at different times; Based on the formula: power shortage = load - grid power supply - photovoltaic output - energy storage discharge capacity, the power shortage of each scenario at different times can be determined. The product of the power shortage, energy storage status correction coefficient, and peak impact coefficient for each scenario at different times is calculated to obtain the corrected power shortage for each scenario at different times.

6. The method for configuring shared energy storage capacity in multiple scenarios in a transformer substation according to any one of claims 1-5, characterized in that, The average annual cost of the shared energy storage over its entire lifecycle includes: initial investment amortization, operation and maintenance costs, and residual value recovery.

7. The method for configuring shared energy storage capacity in multiple scenarios in a transformer substation according to any one of claims 1-5, characterized in that, The multi-scenario collaborative constraints include: time-division multiplexing power allocation constraints and energy balance constraints; the multi-dimensional constraints also include: charging and discharging power constraints, energy storage safety constraints, distribution transformer load rate constraints, and investment benefit constraints.

8. The method for configuring shared energy storage capacity in multiple scenarios according to any one of claims 1-5, characterized in that, The process of solving the objective function based on the multidimensional constraints to obtain the optimal capacity includes: Based on the aforementioned multidimensional constraints, the initial optimal solution is determined using the particle swarm optimization algorithm. The initial optimal solution was filtered using Monte Carlo simulation to obtain a robust optimal solution; Based on the robust optimal solution, the optimal capacity is determined.

9. A multi-scenario collaborative transformer substation shared energy storage capacity configuration device, characterized in that, include: The acquisition module is used to classify the power grid of each distribution area into scenarios and acquire the annual photovoltaic power generation forecast data and load forecast data for each scenario. The calculation module is used to establish a total photovoltaic waste penalty and a total power shortage penalty, taking into account the time-division multiplexing of multiple scenarios, based on the type of each scenario, photovoltaic power generation forecast data, and load forecast data. A module is established to minimize the sum of the average annual cost of shared energy storage over its entire life cycle, the total photovoltaic waste penalty, and the total power shortage penalty, with the capacity and power of shared energy storage as decision variables. The objective function is established, and multi-dimensional constraints including multi-scenario collaborative constraints are also established. The solution module is used to solve the objective function based on the multidimensional constraints to obtain the optimal capacity.

10. An electronic device, characterized in that, It includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the method as described in any one of claims 1 to 8.

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