Distributed optimization planning method and device for multi-station fusion system

Abstract

The application discloses a kind of distributed optimization planning method and device of multi-station fusion system, the method includes: obtaining the historical data of substation, energy storage power station, charging station and data center station, constructs multiple optimization structure, the multiple optimization structure minimum data center station and charging station operating cost, energy storage power station investment and minimum loss cost as the first group of optimization target, minimum substation operating cost, energy storage power station investment and minimum loss cost as the second group of optimization target;According to the first group of optimization target, the first distributed optimization model of peak load shifting mode is constructed;According to the second group of optimization target, the second distributed optimization model of substation peak shaving mode is constructed;Using target cascade analysis method ATC, the above two distributed optimization models are solved, to obtain the first optimization planning result and the second optimization planning result, select the optimization planning result of larger value, configure energy storage power station, can improve the efficiency and rationality of configuration energy storage power station.

Description

Technical Field

[0001] This invention relates to the field of energy storage power station technology, and in particular to a distributed optimization planning method and apparatus for a multi-station fusion system. Background Technology

[0002] This section is intended to provide background or context for the embodiments of the invention set forth in the claims. The description herein is not an admission that it is prior art simply because it is included in this section.

[0003] Multi-station integrated systems transform traditional substations into integrated systems encompassing substations, energy storage stations, charging stations, and data center stations. This involves expanding the existing substation land resources to include these facilities. The data center station utilizes the power resources of the substation and energy storage station to improve power supply reliability. Simultaneously, it collects and analyzes load data and energy storage charging / discharging data within the station, providing data support for the substation, energy storage station, and charging station. The energy storage station, on the other hand, profits from peak-valley electricity price arbitrage, providing power support for the data center and charging station, and also offering peak-shaving capabilities when the substation is overloaded. Battery energy storage stations, due to their flexible and rapid adjustment capabilities, have been widely used in recent years on the generation, grid, and user sides.

[0004] With the decreasing price of batteries and continuous technological advancements, the demand for energy storage power stations is increasing from the grid side, the power supply side, and the user side, leading to a booming development of energy storage power stations. For energy storage power stations, when invested in, constructed, and operated by independent investors, multi-station integrated systems will involve multiple operating entities. Traditional optimization planning methods assume that all elements within the integrated power station belong to the same entity, centrally processing all data within the integrated power station before optimization planning. Under this assumption, the results suffer from several drawbacks: firstly, the massive amount of data exchanged affects the solution rate for the optimal configuration of the energy storage power station; secondly, all elements within the integrated station need to exchange all their own data, posing a threat to data security; and thirdly, traditional methods do not reasonably consider the losses of energy storage power stations, resulting in overly idealized results. Therefore, seeking an efficient, safe, and economical optimization planning method for multi-operator integrated power stations is particularly important. Summary of the Invention

[0005] This invention provides a distributed optimization planning method for a multi-station fusion system to ensure the security of data at each substation in the system and improve the efficiency and rationality of configuring energy storage power stations. The method includes:

[0006] Historical data from substations, energy storage power stations, charging stations, and data center stations are acquired separately.

[0007] Based on historical data from substations, energy storage stations, and data center stations, a multi-optimization structure is constructed. The multi-optimization structure sets the minimum operating cost of data center stations and charging stations, and the minimum investment and loss cost of energy storage stations as the first set of optimization objectives, and sets the minimum operating cost of substations, and the minimum investment and loss cost of energy storage stations as the second set of optimization objectives.

[0008] Based on the first set of optimization objectives, a first distributed optimization model for peak shaving and valley filling is constructed; the objective cascade analysis method (ATC) is used to solve the first distributed optimization model and obtain the first optimization planning result.

[0009] Based on the second set of optimization objectives, a second distributed optimization model for the substation peak-shaving mode is constructed; the objective cascade analysis method (ATC) is used to solve the second distributed optimization model and obtain the second optimization planning results.

[0010] The first and second optimization planning results are compared. Based on the comparison results, the optimization planning result with the larger value is selected to configure the energy storage power station. The optimization planning result includes the power and capacity of the energy storage power station.

[0011] This invention also provides a distributed optimization planning device for a multi-station fusion system, used to ensure the security of data at each substation in the multi-station fusion system and improve the efficiency and rationality of configuring energy storage power stations. The device includes:

[0012] The historical operation data acquisition module is used to acquire historical operation data of substations, energy storage power stations, charging stations and data center stations respectively;

[0013] The multi-optimization structure construction module is used to construct a multi-optimization structure based on the historical operating data of substations, energy storage power stations, and data center stations. The multi-optimization structure sets the minimum operating cost of data center stations and charging stations, and the minimum investment and loss cost of energy storage power stations as the first set of optimization objectives, and sets the minimum operating cost of substations, and the minimum investment and loss cost of energy storage power stations as the second set of optimization objectives.

[0014] The module for constructing and solving the first distributed optimization model is used to construct the first distributed optimization model of peak shaving and valley filling mode based on the first set of optimization objectives; the first distributed optimization model is solved using the objective cascade analysis method (ATC) to obtain the first optimization planning result.

[0015] The module for constructing and solving the second distributed optimization model is used to construct the second distributed optimization model of the substation peak-shaving mode based on the second set of optimization objectives; the second distributed optimization model is solved using the objective cascade analysis method (ATC) to obtain the second optimization planning results.

[0016] The comparison and configuration module is used to compare the first optimization planning result with the second optimization planning result. Based on the comparison result, the optimization planning result with the larger value is selected to configure the energy storage power station. The optimization planning result includes the power and capacity of the energy storage power station.

[0017] This invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the distributed optimization planning method for the multi-station fusion system described above.

[0018] This invention also provides a computer-readable storage medium, wherein the 3

[0019] A computer-readable storage medium stores a computer program that, when executed by a processor, implements the distributed optimization planning method for the aforementioned multi-station fusion system.

[0020] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the distributed optimization planning method for the multi-station fusion system described above.

[0021] In this embodiment of the invention, historical data from substations, energy storage power stations, charging stations, and data center stations are acquired respectively;

[0022] Based on historical data from substations, energy storage stations, and data center stations, a multi-optimization structure is constructed. This structure sets the following optimization objectives as follows: minimizing the operating costs of data center stations and charging stations, and minimizing the investment and loss costs of energy storage stations; minimizing the operating costs of substations, and minimizing the investment and loss costs of energy storage stations, respectively, sets the following optimization objectives as the second set. Based on the first set of optimization objectives, a first distributed optimization model for peak shaving and valley filling is constructed. The Objective Cascade Analysis (ATC) method is used to solve the first distributed optimization model, yielding the first optimization planning result. Based on the second set of optimization objectives, a second distributed optimization model for substation peak shaving is constructed. The ATC method is used to solve the second distributed optimization model, yielding the second optimization planning result. The first and second optimization planning results are compared, and based on the comparison results, the optimization planning result with the larger value is selected for configuring the energy storage station. The optimization planning result includes the power and capacity of the energy storage station.

[0023] This invention establishes distributed optimization models for peak shaving and valley filling modes and substation peak regulation modes when planning energy storage power stations in a multi-station integrated system. The ATC distributed optimization algorithm is used to solve the models. Each substation model only needs to exchange data information with coupling relationships during data processing, protecting the security of data between substations within the integrated power station. This also avoids memory waste and reduced solution speed caused by data redundancy. Furthermore, the invention considers the impact of energy storage losses and lifespan on the planning results during the solution process, avoiding unreasonable energy storage power station planning due to overly idealized system parameters, thus improving the rationality of energy storage power station configuration. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. In the drawings:

[0025] Figure 1 This is a flowchart illustrating the distributed optimization planning method for a multi-station fusion system in an embodiment of the present invention.

[0026] Figure 2 This is a flowchart of the method for constructing the first distributed optimization model in an embodiment of the present invention;

[0027] Figure 3 This is a flowchart of the method for constructing the second distributed optimization model in an embodiment of the present invention;

[0028] Figure 4 This is a flowchart illustrating a specific example of the distributed optimization planning method for a multi-station fusion system in this invention.

[0029] Figure 5 This is a distributed optimization planning device for a multi-station fusion system in this embodiment of the invention;

[0030] Figure 6 This is a schematic diagram of a computer device structure according to an embodiment of the present invention. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Here, the illustrative embodiments of the present invention and their descriptions are used to explain the present invention, but are not intended to limit the present invention.

[0032] First, the technical terms used in the embodiments of this invention will be introduced:

[0033] Multi-station integrated system: Transform the original substation into a multi-station integrated system consisting of substation, energy storage station, charging station and data center station, transforming the substation from a traditional power flow node into a hub that integrates energy flow, data flow and business flow.

[0034] Energy storage power station: A device system that stores, converts and releases cyclical electrical energy through electrochemical cells or electromagnetic energy storage media. This patent specifically refers to an electrochemical energy storage power station.

[0035] Target Cascading (ATC) is a novel method for solving decentralized, hierarchical coordination problems. It allows each element in the hierarchy to make autonomous decisions, with parent elements coordinating and optimizing the decisions of child elements to obtain the overall optimal solution to the problem. Compared with other optimization methods, ATC has advantages such as parallel optimization, unlimited number of stages, and rigorous convergence proof. Therefore, it is often used to solve large-scale system optimization problems.

[0036] The inventors discovered that existing technologies treat substations, energy storage stations, charging stations, and data center stations in a multi-station integrated system as a single entity. After uniformly processing all data from all elements within the station, an optimization planning model is established with the optimization objective of minimizing the overall cost of the energy storage station and considering the lifespan of the energy storage station as a constraint. The result is the energy storage configuration with the minimum overall investment. First, existing methods exchange and process data from all elements involved in the integrated station during planning. Substations, energy storage stations, charging stations, and data center stations each exchange all information with other stations. When an independent third-party investor invests, on the one hand, the amount of data received by each station is enormous, consuming significant memory; on the other hand, receiving excessive redundant information leads to storage waste; furthermore, the exchanged information contains a large amount of sensitive information, threatening the data privacy of the station itself and other stations. Second, existing methods require the integrated station to process and compute all received information, increasing workload and reducing solution speed. Third, existing methods only consider investment costs in the objective, while considering energy storage lifespan in the constraints. The results yield the energy storage configuration with the optimal investment cost within a reasonable lifespan, ignoring the impact of losses generated by the energy storage power station over many years of operation on the energy storage configuration capacity and operational lifespan. Therefore, the inventors propose a distributed optimization planning method for multi-station integrated systems to solve the above problems.

[0037] Figure 1 This is a flowchart illustrating the distributed optimization planning method for a multi-station fusion system in an embodiment of the present invention. Figure 1 As shown, the distributed optimization planning method for a multi-station fusion system in this embodiment of the invention may include:

[0038] Step 101: Obtain historical data from substations, energy storage power stations, charging stations, and data center stations respectively;

[0039] Step 102: Based on the historical data of substations, energy storage power stations and data center stations, construct a multi-optimization structure. The multi-optimization structure is as follows: the first set of optimization objectives is to minimize the operating cost of data center stations and charging stations and the investment and loss cost of energy storage power stations; the second set of optimization objectives is to minimize the operating cost of substations and the investment and loss cost of energy storage power stations.

[0040] Step 103: Based on the first set of optimization objectives, construct the first distributed optimization model of the peak shaving and valley filling mode; use the objective cascade analysis method (ATC) to solve the first distributed optimization model and obtain the first optimization planning result;

[0041] Step 104: Based on the second set of optimization objectives, construct the second distributed optimization model for the substation peak-shaving mode; use the objective cascade analysis method (ATC) to solve the second distributed optimization model and obtain the second optimization planning results.

[0042] Step 105: Compare the first optimization planning result with the second optimization planning result. Based on the comparison result, select the optimization planning result with the larger value and configure the energy storage power station. The optimization planning result includes the power and capacity of the energy storage power station.

[0043] The following describes the specific execution steps of the distributed optimization planning method for the multi-station fusion system in this embodiment of the invention:

[0044] First, in step 101, historical operating data of the substation, energy storage power station, charging station and data center station can be obtained respectively.

[0045] In one embodiment, the historical data may include: load data, time-of-use electricity price data, energy storage battery unit price data, PCS unit price data, energy storage power station efficiency, initial state of charge data, substation load rate data, charging pile model parameter data, and electric vehicle model data, etc.

[0046] Next, step 102 can be executed: based on the historical data of the substation, energy storage power station and data center station, construct a multi-optimization structure; in the multi-optimization structure, the minimum operating cost of the data center station and charging station and the minimum investment and loss cost of the energy storage power station can be taken as the first set of optimization objectives, and the minimum operating cost of the substation and the minimum investment and loss cost of the energy storage power station can be taken as the second set of optimization objectives.

[0047] In practice, during the construction of the multi-optimization structure, multiple sets of optimization targets can be established by referring to the actual operation of the multi-station fusion system.

[0048] After constructing the multi-optimization structure, steps 103-104 can be executed simultaneously: based on the first set of optimization objectives, construct the first distributed optimization model for peak shaving and valley filling mode; use the objective cascade analysis method (ATC) to solve the first distributed optimization model and obtain the first optimization planning result; based on the second set of optimization objectives, construct the second distributed optimization model for substation peak shaving mode; use the objective cascade analysis method (ATC) to solve the second distributed optimization model and obtain the second optimization planning result.

[0049] Figure 2 This is a flowchart illustrating the method for constructing the first distributed optimization model in an embodiment of the present invention. Figure 2 As shown, in one embodiment, constructing a first distributed optimization model for peak shaving and valley filling based on a first set of optimization objectives may include:

[0050] Step 201: Establish the objective function for minimizing the operating cost of data center stations and charging stations, and the objective function for minimizing the investment and loss costs of energy storage power stations; establish the lifespan constraints of energy storage power stations and the load constraints of data center stations and charging stations.

[0051] Step 202: Using the charging and discharging power of the energy storage power station as a coupling variable that is mutually transferred between the two optimization objectives, construct the first distributed optimization model of the peak shaving and valley filling mode.

[0052] Figure 3 This is a flowchart illustrating the method for constructing a second distributed optimization model in an embodiment of the present invention. Figure 3 As shown, in one embodiment, constructing a second distributed optimization model for the substation peak-shaving mode based on the second set of optimization objectives may include:

[0053] Step 301: Establish the objective function for minimizing the substation operating cost and the objective function for minimizing the investment and loss costs of the energy storage power station; establish the lifespan constraint of the energy storage power station and the peak-shaving constraint of the substation.

[0054] Step 302: Using the charging and discharging power of the energy storage power station as a coupling variable that is mutually transferred between the two optimization objectives, construct the second distributed optimization model of the substation peak-shaving mode.

[0055] In practical implementation, the multi-optimization structure mainly includes the cost objectives for data center and charging station operation, substation operation cost objective, and comprehensive objective for investment returns and loss costs of energy storage power stations; as well as safety operation constraints, substation peak-shaving operation constraints, and energy storage power station operation constraints related to the multi-station integrated system. The objective function expression for this multi-optimization is as follows:

[0056]

[0057]

[0058]

[0059]

[0060] Among them, equation (1) For the operating costs of charging stations and data center stations; (2) The penalty cost for the overload rate of the substation; (3) Equation F 13 (P PCS,t (4) Equation F 23 (P PCS This refers to the comprehensive investment cost of energy storage power stations participating in substation peak shaving. The output coupling variable of the energy storage power station is optimized among the data center station, charging station and energy storage power station under the peak shaving and valley filling mode; E represents the output coupling variable of the energy storage power station, optimized between the substation and the energy storage power station under peak-shaving mode. BESS P PCS The rated capacity and rated power of the energy storage power station; P PCS,t Powering energy storage power stations; L n,t The electrical load supplied by the grid to the charging station; Pr net,t Pr BESS,t These are the grid electricity price and the energy storage power station electricity price, respectively; ψ st L is the penalty factor for the substation overload rate. st,t For substation load; n tr C represents the number of transformers in the substation. tr,t The capacity of each transformer; C BES C represents the unit investment price of the battery system; PCS α is the unit investment price of the converter boost system; β is the operation and maintenance cost coefficient; r is the depreciation cost coefficient; D is the discount rate. p-v For the number of operating days that the energy storage power station participates in peak shaving and valley filling; loss DOD1 The loss of the energy storage power station under the DOD1 discharge depth in peak shaving and valley filling mode; τ loss D represents the loss cost coefficient of the energy storage power station. p The number of days the energy storage power station participates in substation peak shaving; loss DOD2 η is the loss of the energy storage station under the DOD2 discharge depth in the peak shaving mode of the substation; η is the peak shaving subsidy; L'1 and L”1 are the first and second Lagrange penalty functions in the peak shaving and valley filling mode, respectively, and their expressions are as shown in equation (5); L'2 and L”2 are the first and second Lagrange penalty functions in the peak shaving mode of the substation, respectively, and their expressions are as shown in equation (6).

[0061]

[0062]

[0063] Where, ξ 1,t ζ 1,t These are the first and second Lagrange penalty coefficients under the peak shaving and valley filling model, respectively; ξ 2,t ζ 2,t These are the primary and secondary Lagrange penalty coefficients under the peak-shaving mode of the substation, respectively.

[0064] In practical implementation, the method for establishing charging station load constraints is as follows: Based on the probability density function of electric vehicle charging behavior, Monte Carlo random sampling is used to generate electric vehicle charging behavior data, including daily mileage, time to reach the charging station, dwell time, and initial SOC state, to establish an electric vehicle charging behavior model. Data processing is then performed to remove unreasonable data. Lifetime constraints for the energy storage station are established and linearized.

[0065] The electric vehicle charging behavior model is as follows:

[0066] Data analysis revealed that the daily departure and return times of electric vehicles follow a standard normal distribution; it is expected that the SOC and daily mileage will follow a log-normal distribution.

[0067] 1) Standard normal distribution sampling function:

[0068]

[0069] Where: μ s and σ s These are the mean and standard deviation of the standard normal distribution, respectively.

[0070] 2) Log-normal distribution sampling function:

[0071]

[0072] In the formula: μ1 and σ1 are the mean and standard deviation of the log-normal distribution, respectively.

[0073] 3) The State of Charge (SOC) of an electric vehicle at the time of its return trip is determined by its daily mileage:

[0074]

[0075] In the formula: d n Let ω be the daily mileage of the nth electric vehicle; 100 Electricity consumption per 100 kilometers for electric vehicles.

[0076] Energy storage system operating constraints include:

[0077] -P PCS ≤P PCS,t ≤P PCS (10)

[0078]

[0079] E SOC,min ≤E SOC,t ≤E SOC,max (12)

[0080]

[0081] In the formula, (10) represents the charging and discharging power constraint, P PCS,t For energy storage charging and discharging power, P PCS,t A negative value represents charging, P PCS,t (11) represents a positive value indicating discharge; (12) represents the total energy constraint for charging and discharging within one cycle; (13) represents the remaining energy constraint of the energy storage system; and (14) represents the remaining energy of the energy storage power station at time t. SOC,t Let E be the energy stored in the energy storage station at time t. The stored energy in the energy storage station at time t must be sufficient to sustain the data center at maximum load for 30 minutes. SOC,max E SOC,min These are the upper and lower limits of the energy stored in the energy storage station; E SOC,0 is the initial power of the energy storage station; k is the time retrieval.

[0082] The method for establishing the lifespan constraint of an energy storage power station is as follows: The lifespan constraint of an energy storage power station is shown in the following formula:

[0083]

[0084]

[0085] Equation (14) represents the relationship between energy storage cycle life and depth of discharge; Equation (15) represents the relationship between energy storage loss and depth of discharge; N life For energy storage batteries in DOD i Cycle life at depth of charge / discharge; N0 is the cycle life at 100% depth of charge / discharge; k p The loss is a constant obtained from testing by the battery manufacturer. DOD denoted as , where i represents the energy storage power station loss; and i represents the number of charge / discharge cycles within a scheduling period.

[0086] After constructing the first distributed optimization model for peak shaving and valley filling and the second distributed optimization model for substation peak shaving, the objective cascade analysis method (ATC) can be used to solve these two distributed optimization models.

[0087] In one embodiment, the Objective Cascade Analysis (ATC) method is used to solve the first distributed optimization model and obtain the first optimization planning result, which may include:

[0088] The power and capacity of the energy storage power station are taken as variables to be solved, and the charging and discharging power of the energy storage power station is taken as coupling variables. The objective cascade analysis method (ATC) is used to solve the first distributed optimization model. When the error of the coupling variables is less than or equal to the preset value, the first optimization planning result is obtained.

[0089] In one embodiment, the Objective Cascade Analysis (ATC) method is used to solve the second distributed optimization model, and the second optimization planning result can include:

[0090] The power and capacity of the energy storage power station are taken as variables to be solved, and the charging and discharging power of the energy storage power station is taken as coupling variables. The objective cascade analysis method (ATC) is used to solve the second distributed optimization model. When the error of the coupling variables is less than or equal to the preset value, the second optimization planning result is obtained.

[0091] Then step 105 can be executed, comparing the first optimization planning result with the second optimization planning result, and selecting the optimization planning result with the larger value according to the comparison result to configure the energy storage power station. The optimization planning result includes the power and capacity of the energy storage power station.

[0092] In practice, the ATC optimization algorithm is used to solve the large-scale optimization in two modes. When the accuracy of the coupling variables reaches the preset value, the optimization planning result is output. The two optimization planning results are compared. Since the limit range corresponding to the larger value is larger, it can be applied to both modes at the same time. Therefore, based on the comparison result, the larger value of the energy storage power station optimization result is taken as the final energy storage power station configuration.

[0093] Among them, the coupling variables of peak shaving and valley filling mode and substation peak regulation mode need to satisfy equation (16) and equation (17) respectively. In the specific optimization process, the coupling variables and objective function need to satisfy the minimum error constraint, as shown in equation (18)-(19) and (20)-(21) respectively.

[0094]

[0095]

[0096]

[0097]

[0098]

[0099]

[0100] Where i is the iteration number; ε1, ε2, ε3, and ε4 are the errors that need to be satisfied.

[0101] The beneficial effects of the distributed optimization planning method for the multi-station fusion system in this embodiment of the invention are as follows:

[0102] (1) When planning energy storage power stations in a multi-station fusion system, the present invention uses a distributed optimization algorithm to solve the model. When each sub-station model processes data, it only needs to exchange data information with coupling relationship and process it, which protects the privacy of data between sub-stations in the fusion power station, and avoids memory waste and reduction in solution speed caused by data redundancy.

[0103] (2) The loss of the energy storage power station is taken into account in the objective of the optimization planning model, and the life constraint of the energy storage power station is taken into account in the constraints. The results ensure the safety of system operation, the economy of energy storage power station investment and the optimal operating state of energy storage power station.

[0104] Figure 4 This is a flowchart illustrating a specific example of a distributed optimization planning method for a multi-station fusion system according to an embodiment of the present invention. Figure 4 As shown, firstly, the system parameters of the multi-station fusion system can be initialized and the coupling variables determined. Then, a multi-optimization structure is established with the objectives of minimizing the operating costs F1 of the data center station and charging station, the minimum operating cost F2 of the substation, and the minimum investment and loss costs F13 and F23 of the energy storage station. Further, the minimum operating costs of the data center station and charging station, and the minimum investment and loss costs of the energy storage station are taken as the first set of optimization objectives. The constraints take into account the lifespan model of the energy storage station, the load model of the data center station, and the load model of the charging station. Simultaneously, the charging and discharging power of the energy storage station is taken as the coupling variable 1 that needs to be transferred between the two objectives, establishing a distributed optimization model based on peak shaving and valley filling. Secondly, the minimum operating costs of the substation and the minimum investment and loss costs of the energy storage station are taken as the second set of optimization objectives. The constraints take into account the peak-shaving demand of the substation and the lifespan model of the energy storage station. Simultaneously, the charging and discharging power of the energy storage station is taken as the coupling variable 2 that needs to be transferred between the two objectives, establishing a distributed optimization model based on substation peak shaving.

[0105] Finally, the ATC distributed optimization algorithm is used to solve the optimization planning model under the two modes. It is determined whether the solution results of the optimization planning model under the two modes meet the minimum error constraint of the coupling variables. If so, the larger value of the two optimization planning results is selected as the result of this energy storage power station configuration. If not, the ATC distributed optimization algorithm is used to solve the optimization planning model under the corresponding mode until the solution results meet the minimum error constraint of the coupling variables.

[0106] This invention also provides a distributed optimization planning device for a multi-station fusion system, as described in the following embodiments. Since the principle by which this device solves the problem is similar to the distributed optimization planning method for a multi-station fusion system, the implementation of this device can refer to the implementation of the distributed optimization planning method for a multi-station fusion system; repeated details will not be elaborated further.

[0107] Figure 5 This is a schematic diagram of the distributed optimization planning device of the multi-station fusion system in an embodiment of the present invention. Figure 5 As shown, the distributed optimization planning device of the multi-station fusion system in this embodiment of the invention may specifically include:

[0108] The historical operation data acquisition module 501 is used to acquire historical operation data of substations, energy storage power stations, charging stations and data center stations respectively;

[0109] The multi-optimization structure construction module 502 is used to construct a multi-optimization structure based on historical data of substations, energy storage power stations and data center stations. The multi-optimization structure sets the minimum operating cost of data center stations and charging stations and the minimum investment and loss cost of energy storage power stations as the first set of optimization objectives, and sets the minimum operating cost of substations and the minimum investment and loss cost of energy storage power stations as the second set of optimization objectives.

[0110] The first distributed optimization model construction and solution module 503 is used to construct the first distributed optimization model of peak shaving and valley filling mode based on the first set of optimization objectives; and to solve the first distributed optimization model using the objective cascade analysis method (ATC) to obtain the first optimization planning result.

[0111] The second distributed optimization model construction and solution module 504 is used to construct the second distributed optimization model of the substation peak-shaving mode based on the second set of optimization objectives; and to solve the second distributed optimization model using the objective cascade analysis method (ATC) to obtain the second optimization planning results.

[0112] The comparison and configuration module 505 is used to compare the first optimization planning result with the second optimization planning result. Based on the comparison result, the optimization planning result with the larger value is selected to configure the energy storage power station. The optimization planning result includes the power and capacity of the energy storage power station.

[0113] In one embodiment, the historical data includes the following data:

[0114] Load data, time-of-use electricity price data, energy storage battery unit price data, PCS unit price data, energy storage power station efficiency, initial state of charge data, substation load rate data, charging pile model parameter data, and electric vehicle model data, etc.

[0115] In one embodiment, the first distributed optimization model construction and solution module 503 is specifically used for:

[0116] Establish objective functions to minimize the operating costs of data center stations and charging stations, and to minimize the investment and loss costs of energy storage power stations. Also establish life constraints for energy storage power stations and load constraints for data center stations and charging stations.

[0117] By using the charging and discharging power of the energy storage power station as a coupling variable that is mutually transferred between two optimization objectives, the first distributed optimization model of peak shaving and valley filling mode is constructed.

[0118] In one embodiment, the second distributed optimization model construction and solution module 504 is specifically used for:

[0119] Establish objective functions to minimize substation operating costs and energy storage power station investment and loss costs, and establish life constraints for energy storage power stations and peak-shaving constraints for substations.

[0120] By using the charging and discharging power of the energy storage power station as a coupling variable that is mutually transferred between the two optimization objectives, a second distributed optimization model for the substation peak-shaving mode is constructed.

[0121] In one embodiment, the first distributed optimization model construction and solution module 503 is specifically used for:

[0122] The power and capacity of the energy storage power station are taken as variables to be solved, and the charging and discharging power of the energy storage power station is taken as coupling variables. The objective cascade analysis method (ATC) is used to solve the first distributed optimization model. When the error of the coupling variables is less than or equal to the preset value, the first optimization planning result is obtained.

[0123] In one embodiment, the second distributed optimization model construction and solution module 504 is specifically used for:

[0124] The power and capacity of the energy storage power station are taken as variables to be solved, and the charging and discharging power of the energy storage power station is taken as coupling variables. The objective cascade analysis method (ATC) is used to solve the second distributed optimization model. When the error of the coupling variables is less than or equal to the preset value, the second optimization planning result is obtained.

[0125] Based on the aforementioned inventive concept, such as Figure 6 As shown, the present invention also proposes a computer device 600, including a memory 610, a processor 620, and a computer program 630 stored in the memory 610 and executable on the processor 620. When the processor 620 executes the computer program 630, it implements the aforementioned distributed optimization planning method for the multi-station fusion system.

[0126] This invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the distributed optimization planning method for the multi-station fusion system described above.

[0127] This invention also provides a computer program product, which includes a computer program that, when executed by a processor, implements the distributed optimization planning method for the multi-station fusion system described above.

[0128] In summary, in this embodiment of the invention, historical data of substations, energy storage power stations, charging stations, and data center stations are acquired respectively. Based on the historical data of substations, energy storage power stations, and data center stations, a multi-optimization structure is constructed. The multi-optimization structure sets the minimum operating cost of data center stations and charging stations, and the minimum investment and loss cost of energy storage power stations as the first set of optimization objectives. The minimum operating cost of substations, and the minimum investment and loss cost of energy storage power stations are set as the second set of optimization objectives. Based on the first set of optimization objectives, a first distributed optimization model for peak shaving and valley filling is constructed. The first distributed optimization model is solved using the Objective Cascade Analysis (ATC) method to obtain the first optimization planning result. Based on the second set of optimization objectives, a second distributed optimization model for substation peak shaving is constructed. The second distributed optimization model is solved using the Objective Cascade Analysis (ATC) method to obtain the second optimization planning result. The first optimization planning result is compared with the second optimization planning result. Based on the comparison result, the optimization planning result with the larger value is selected to configure the energy storage power station. The optimization planning result includes the power and capacity of the energy storage power station.

[0129] This invention establishes distributed optimization models for peak shaving and valley filling modes and substation peak regulation modes when planning energy storage power stations in a multi-station integrated system. The ATC distributed optimization algorithm is used to solve the models. Each substation model only needs to exchange data information with coupling relationships during data processing, protecting the security of data between substations within the integrated power station. This also avoids memory waste and reduced solution speed caused by data redundancy. Furthermore, the invention considers the impact of energy storage losses and lifespan on the planning results during the solution process, avoiding unreasonable energy storage power station planning due to overly idealized system parameters, thus improving the rationality of energy storage power station configuration.

[0130] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present 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.

[0131] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0132] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0133] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0134] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A distributed optimization planning method for a multi-station fusion system, characterized in that: Historical data from substations, energy storage power stations, charging stations, and data center stations are acquired separately. Based on historical data from substations, energy storage stations, and data center stations, a multi-optimization structure is constructed. The multi-optimization structure sets the minimum operating cost of data center stations and charging stations, and the minimum investment and loss cost of energy storage stations as the first set of optimization objectives, and sets the minimum operating cost of substations, and the minimum investment and loss cost of energy storage stations as the second set of optimization objectives. Based on the first set of optimization objectives, a first distributed optimization model for peak shaving and valley filling is constructed; the objective cascade analysis method (ATC) is used to solve the first distributed optimization model and obtain the first optimization planning result. Based on the second set of optimization objectives, a second distributed optimization model for the substation peak-shaving mode is constructed. The second distributed optimization model is solved using the objective cascade analysis method (ATC) to obtain the second optimization planning result; The first and second optimization planning results are compared. Based on the comparison results, the optimization planning result with the larger value is selected to configure the energy storage power station. The optimization planning result includes the power and capacity of the energy storage power station.

2. The method as described in claim 1, characterized in that, The historical data includes at least the following data: Load data, time-of-use electricity price data, energy storage battery unit price data, PCS unit price data, energy storage power station efficiency, initial state of charge data, substation load rate data, charging pile model parameter data, and electric vehicle model data.

3. The method as described in claim 1, characterized in that, The first distributed optimization model for peak shaving and valley filling is constructed, including: Establish objective functions for minimizing the operating costs of data center stations and charging stations, and for minimizing the investment and loss costs of energy storage power stations. Also establish lifespan constraints for energy storage power stations and load constraints for data center stations and charging stations. By using the charging and discharging power of the energy storage power station as a coupling variable that is mutually transferred between two optimization objectives, the first distributed optimization model of peak shaving and valley filling mode is constructed.

4. The method as described in claim 1, characterized in that, Construct a second distributed optimization model for substation peak-shaving mode, including: Establish objective functions for minimizing substation operating costs and energy storage power station investment and loss costs, and establish life constraints for energy storage power stations and peak-shaving constraints for substations. By using the charging and discharging power of the energy storage power station as a coupling variable that is mutually transferred between the two optimization objectives, a second distributed optimization model for the substation peak-shaving mode is constructed.

5. The method as described in claim 3, characterized in that, The Objective Cascade Analysis (ATC) method is used to solve the first distributed optimization model, yielding the first optimization planning results, including: The power and capacity of the energy storage power station are taken as variables to be solved, and the charging and discharging power of the energy storage power station is taken as coupling variables. The objective cascade analysis method (ATC) is used to solve the first distributed optimization model. When the error of the coupling variables is less than or equal to the preset value, the first optimization planning result is obtained.

6. The method as described in claim 4, characterized in that, The Objective Cascade Analysis (ATC) method is used to solve the second distributed optimization model, yielding the second optimization programming results, including: The power and capacity of the energy storage power station are taken as variables to be solved, and the charging and discharging power of the energy storage power station is taken as coupling variables. The objective cascade analysis method (ATC) is used to solve the second distributed optimization model. When the error of the coupling variables is less than or equal to the preset value, the second optimization planning result is obtained.

7. A distributed optimization planning device for a multi-station fusion system, characterized in that, include: The historical data acquisition module is used to acquire historical data from substations, energy storage power stations, charging stations, and data center stations, respectively. The multi-optimization structure construction module is used to construct a multi-optimization structure based on historical data of substations, energy storage power stations, and data center stations. The multi-optimization structure sets the minimum operating cost of data center stations and charging stations, and the minimum investment and loss cost of energy storage power stations as the first set of optimization objectives, and sets the minimum operating cost of substations, and the minimum investment and loss cost of energy storage power stations as the second set of optimization objectives. The module for constructing and solving the first distributed optimization model is used to construct the first distributed optimization model of peak shaving and valley filling mode based on the first set of optimization objectives; the first distributed optimization model is solved using the objective cascade analysis method (ATC) to obtain the first optimization planning result. The module for constructing and solving the second distributed optimization model is used to construct the second distributed optimization model of the substation peak-shaving mode based on the second set of optimization objectives; the second distributed optimization model is solved using the objective cascade analysis method (ATC) to obtain the second optimization planning results. The comparison and configuration module is used to compare the first optimization planning result with the second optimization planning result. Based on the comparison result, the optimization planning result with the larger value is selected to configure the energy storage power station. The optimization planning result includes the power and capacity of the energy storage power station.

8. The apparatus as claimed in claim 7, characterized in that, The historical data includes at least the following data: Load data, time-of-use electricity price data, energy storage battery unit price data, PCS unit price data, energy storage power station efficiency, initial state of charge data, substation load rate data, charging pile model parameter data, and electric vehicle model data.

9. The apparatus as claimed in claim 7, characterized in that, The first distributed optimization model construction and solution module is specifically used for: Establish objective functions for minimizing the operating costs of data center stations and charging stations, and for minimizing the investment and loss costs of energy storage power stations. Also establish lifespan constraints for energy storage power stations and load constraints for data center stations and charging stations. By using the charging and discharging power of the energy storage power station as a coupling variable that is mutually transferred between two optimization objectives, the first distributed optimization model of peak shaving and valley filling mode is constructed.

10. The apparatus as claimed in claim 7, characterized in that, The second distributed optimization model construction and solution module is specifically used for: Establish objective functions for minimizing substation operating costs and energy storage power station investment and loss costs, and establish life constraints for energy storage power stations and peak-shaving constraints for substations. By using the charging and discharging power of the energy storage power station as a coupling variable that is mutually transferred between the two optimization objectives, a second distributed optimization model for the substation peak-shaving mode is constructed.

11. The apparatus as claimed in claim 9, characterized in that, The first distributed optimization model construction and solution module is specifically used for: The power and capacity of the energy storage power station are taken as variables to be solved, and the charging and discharging power of the energy storage power station is taken as coupling variables. The objective cascade analysis method (ATC) is used to solve the first distributed optimization model. When the error of the coupling variables is less than or equal to the preset value, the first optimization planning result is obtained.

12. The apparatus as claimed in claim 10, characterized in that, The second distributed optimization model construction and solution module is specifically used for: The power and capacity of the energy storage power station are taken as variables to be solved, and the charging and discharging power of the energy storage power station is taken as coupling variables. The objective cascade analysis method (ATC) is used to solve the second distributed optimization model. When the error of the coupling variables is less than or equal to the preset value, the second optimization planning result is obtained.

13. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method of any one of claims 1 to 6.

14. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method of any one of claims 1 to 6.

15. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the method of any one of claims 1 to 6.

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