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

Abstract

The invention discloses a distributed optimization planning method and device for a multi-station fusion system, and the method comprises the steps: obtaining the historical data of a transformer substation, an energy storage power station, a charging station, and a data center station, and constructing a multi-optimization structure, according to the multi-optimization structure, the minimum operation cost of a data center station and a charging station and the minimum investment and loss cost of an energy storage power station serve as a first set of to-be-optimized targets, and the minimum operation cost of a transformer substation and the minimum investment and loss cost of the energy storage power station serve as a second set of to-be-optimized targets. According to the first group of to-be-optimized targets, constructing a first distributed optimization model of a peak clipping and valley filling mode; according to the second group of targets to be optimized, constructing a second distributed optimization model of the transformer substation peak regulation mode; and solving the two distributed optimization models by adopting a target cascade analysis method ATC to obtain a first optimization planning result and a second optimization planning result, selecting the optimization planning result with a relatively large numerical value, and configuring the energy storage power station, so that the efficiency and rationality of configuring the energy storage power station can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage power stations, and in particular to a distributed optimization planning method and device for a multi-station fusion system. Background Art

[0002] This section is intended to provide a background or context to the embodiments of the invention recited in the claims. No admission is made that the description herein is prior art by inclusion in this section.

[0003] The multi-station fusion system transforms the traditional substation into a multi-station fusion system including substations, energy storage power stations, charging stations and data center stations, and uses the original substation land resources to expand energy storage power stations, charging stations and data center stations. The data center station uses the power resources of the substation and energy storage power station to improve the reliability of power supply. At the same time, it collects the power load and energy storage charging and discharging data in the station for calculation and analysis, and provides data support for the substation, energy storage power station and charging station; the energy storage power station uses the arbitrage of the peak-flat-valley electricity price difference to obtain profits, and provides power support for the data center station and charging station. At the same time, it can provide a certain peak-shaving capacity when the substation is overloaded. Battery energy storage power stations have been widely used in the power generation side, the grid side, and the user side in recent years due to their flexible and fast adjustment capabilities.

[0004] With the price reduction of batteries and the continuous advancement of their technology, the demand for energy storage power stations is increasing, whether on the grid side, power supply side or user side, and the development of energy storage power stations is becoming increasingly hot. For energy storage power stations, when independent investors invest, build and operate them, the multi-station fusion system will include multiple operating entities. The traditional optimization planning method assumes that all elements in the fusion power station belong to the same entity, and all data in the fusion power station are processed centrally for optimization planning. The results are also based on this assumption. On the one hand, the amount of exchanged data is huge, which affects the solution rate of the optimal configuration of the energy storage power station; on the other hand, all elements in the fusion station need to exchange all their own data, which poses a threat to data security; furthermore, the traditional method does not reasonably consider the loss of the energy storage power station, making the results too ideal. It is particularly important to seek an efficient, safe and economical energy storage power station optimization planning method for multi-operator fusion power stations. Summary of the invention

[0005] An embodiment of the present invention provides a distributed optimization planning method for a multi-station fusion system, which is used to ensure the security of data of each substation in the multi-station fusion system and improve the efficiency and rationality of configuring an energy storage power station. The method includes:

[0006] Obtain historical data of substations, energy storage stations, charging stations, and data center stations respectively;

[0007] Based on the historical data of the substation, energy storage power station and data center station, a multiple optimization structure is constructed. The multiple optimization structure: the operation cost of the data center station and the charging station is minimized, and the investment and loss cost of the energy storage power station are minimized as the first group of optimization targets, and the operation cost of the substation is minimized, and the investment and loss cost of the energy storage power station are minimized as the second group of optimization targets;

[0008] According to the first group of targets to be optimized, a first distributed optimization model of the peak shaving and valley filling mode is constructed; the target cascade analysis method ATC is used to solve the first distributed optimization model and obtain the first optimization planning result;

[0009] According to the second group of targets to be optimized, a second distributed optimization model of the substation peak load regulation mode is constructed; the target cascade analysis method ATC is used to solve the second distributed optimization model and obtain the second optimization planning result;

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

[0011] The embodiment of the present invention further provides a distributed optimization planning device for a multi-station fusion system, which is used to ensure the security of data of each substation in the multi-station fusion system and improve the efficiency and rationality of configuring an energy storage power station. The device includes:

[0012] A historical operation data acquisition module is used to respectively acquire the historical operation data of the substation, energy storage power station, charging station and data center station;

[0013] A multiple optimization structure construction module is used to construct a multiple optimization structure based on the historical operation data of the substation, the energy storage power station and the data center station. The multiple optimization structure: minimizes the operation cost of the data center station and the charging station, and minimizes the investment and loss cost of the energy storage power station as the first group of optimization targets, and minimizes the operation cost of the substation, and minimizes the investment and loss cost of the energy storage power station as the second group of optimization targets;

[0014] The first distributed optimization model construction and solution module is used to construct a first distributed optimization model of the peak shaving and valley filling mode according to the first group of optimization targets; adopt the target cascade analysis method ATC to solve the first distributed optimization model and obtain the first optimization planning result;

[0015] The second distributed optimization model construction and solution module is used to construct a second distributed optimization model of the substation peak load regulation mode according to the second group of optimization targets; adopt the target cascade analysis method ATC to solve the second distributed optimization model and obtain the second optimization planning result;

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

[0017] An embodiment of the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned distributed optimization planning method for the multi-station fusion system when executing the computer program.

[0018] The embodiment of the present invention further provides a computer-readable storage medium, wherein the 3

[0019] The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the distributed optimization planning method of the multi-station fusion system is implemented.

[0020] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the distributed optimization planning method of the multi-station fusion system is implemented.

[0021] In the embodiment of the present invention, historical data of the substation, energy storage station, charging station and data center station are obtained respectively;

[0022] According to the historical data of the substation, energy storage power station and data center station, a multiple optimization structure is constructed, wherein the multiple optimization structure: minimizes the operating cost of the data center station and the charging station, and minimizes the investment and loss cost of the energy storage power station as the first group of optimization targets, and minimizes the operating cost of the substation, and minimizes the investment and loss cost of the energy storage power station as the second group of optimization targets; according to the first group of optimization targets, a first distributed optimization model of the peak shaving and valley filling mode is constructed; the target cascade analysis method ATC is used to solve the first distributed optimization model to obtain the first optimization planning result; according to the second group of optimization targets, a second distributed optimization model of the substation peak shaving mode is constructed; the target cascade analysis method ATC is used to solve the second distributed optimization model to obtain the second optimization planning result; the first optimization planning result is compared with the second optimization planning result, and according to the comparison result, the optimization planning result with a larger value is selected to configure the energy storage power station, wherein the optimization planning result includes the power and capacity of the energy storage power station.

[0023] When planning the energy storage power station in a multi-station fusion system, the present invention establishes a distributed optimization model under the peak shaving and valley filling mode and the substation peak regulation mode, and adopts the ATC distributed optimization algorithm to solve the model. When each substation model processes data, it only needs to exchange and process data information with a coupling relationship, thereby protecting the security of data between substations in the fusion power station, while avoiding memory waste and reduction in solution rate caused by data redundancy; and in the solution process, the influence of energy storage loss and life on the planning results is taken into account, thereby avoiding unreasonable energy storage power station planning due to over-idealized system parameters, and improving the rationality of configuring the energy storage power station. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the prior art descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work. In the drawings:

[0025] Figure 1 It is a processing flow chart of a distributed optimization planning method for a multi-station fusion system in an embodiment of the present invention;

[0026] Figure 2 A flow chart of a method for constructing a first distributed optimization model in an embodiment of the present invention;

[0027] Figure 3 A flow chart of a method for constructing a second distributed optimization model in an embodiment of the present invention;

[0028] Figure 4 A flowchart of a specific example of a distributed optimization planning method for a multi-station fusion system in an embodiment of the present invention;

[0029] Figure 5 A distributed optimization planning device for a multi-station fusion system in an embodiment of the present invention;

[0030] Figure 6 The figure is a schematic diagram of the structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0031] To make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the embodiments of the present invention are further described in detail below in conjunction with the accompanying drawings. Here, the exemplary 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 in the embodiments of the present invention are introduced:

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

[0034] Energy storage power station: an equipment system that stores, converts and releases cyclic electrical energy through electrochemical batteries or electromagnetic energy storage media. This patent specifically refers to electrochemical energy storage power stations.

[0035] Analysis target cascading (ATC): Target cascading, also known as target cascading analysis, is a new method for solving decentralized, hierarchical coordination problems. It allows each element in the hierarchy to make its own decisions, and the parent element coordinates and optimizes the decisions of the child elements to obtain the overall optimal solution to the problem. Compared with other optimization methods, the target cascading method has the advantages of parallel optimization, unlimited number of levels, and strict convergence proof. Therefore, it is often used to solve large-scale system optimization problems.

[0036] The inventors found that the prior art treats the substation, energy storage power station, charging station and data center station in the multi-station fusion system as the same entity, uniformly processes all the data of all elements in the station, and establishes an optimization planning model with the optimization goal of minimizing the comprehensive cost of the energy storage power station and considering the life of the energy storage power station in the constraints. The result is the energy storage configuration with the minimum comprehensive investment. First, the existing method exchanges and processes all the element data involved in the fusion power station during planning, and the substation, energy storage power station, charging station and data center station exchange all information with other stations. When the investment is made by an independent third party, on the one hand, the amount of data received by each station is huge and occupies data memory; on the other hand, too much redundant information is received, resulting in storage waste; furthermore, the exchanged information contains a large amount of sensitive information, which poses a threat to the data privacy of itself and other stations. Second, the existing method of fusion power station needs to process and calculate all the received information, which increases the workload and reduces the solution rate. Third, the existing method only considers the investment cost in the target and the energy storage life in the constraint. The result is the energy storage configuration with the best investment cost within the reasonable life range, ignoring the impact of the loss of the energy storage power station under many years of operation on the energy storage configuration capacity and operating life. Therefore, the inventor proposes a distributed optimization planning method for a multi-station fusion system to solve the above problems.

[0037] Figure 1 FIG. 1 is a processing flow chart of a distributed optimization planning method for a multi-station fusion system according to an embodiment of the present invention. Figure 1 As shown, the distributed optimization planning method of the multi-station fusion system in the embodiment of the present invention may include:

[0038] Step 101, respectively obtain historical data of the substation, energy storage power station, charging station and data center station;

[0039] Step 102: construct a multiple optimization structure based on historical data of the substation, energy storage power station and data center station. The multiple optimization structure: minimize the operating costs of the data center station and the charging station, and minimize the investment and loss costs of the energy storage power station as the first group of optimization targets, and minimize the operating costs of the substation, and minimize the investment and loss costs of the energy storage power station as the second group of optimization targets;

[0040] Step 103: construct a first distributed optimization model of the peak shaving and valley filling mode according to the first group of targets to be optimized; use the target cascade analysis method ATC to solve the first distributed optimization model and obtain a first optimization planning result;

[0041] Step 104: construct a second distributed optimization model of the substation peak load regulation mode according to the second group of targets to be optimized; use the target cascade analysis method ATC to solve the second distributed optimization model and obtain a second optimization planning result;

[0042] Step 105: compare the first optimization planning result with the second optimization planning result, and select the optimization planning result with a larger value according to the comparison result to configure the energy storage power station, wherein the optimization planning result includes the power and capacity of the energy storage power station.

[0043] The specific execution steps of the distributed optimization planning method for a multi-station fusion system in an embodiment of the present invention are described below:

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

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

[0046] Next, step 102 may be executed to construct a multiple optimization structure based on the historical data of the substation, energy storage power station and data center station; in the multiple optimization structure, the minimum operating cost of the data center station and the charging station, and the minimum investment and loss cost of the energy storage power station may be taken as the first group of objectives to be optimized, and at the same time, the minimum operating cost of the substation, and the minimum investment and loss cost of the energy storage power station may be taken as the second group of objectives to be optimized.

[0047] During specific implementation, in the process of constructing multiple optimization structures, it is also possible to refer to the actual operation of the multi-station fusion system and establish multiple groups of targets to be optimized.

[0048] After the multiple optimization structures are constructed, steps 103-104 can be executed simultaneously to construct a first distributed optimization model of the peak shaving and valley filling mode according to the first set of objectives to be optimized; use the target cascade analysis method ATC to solve the first distributed optimization model and obtain a first optimization planning result; construct a second distributed optimization model of the substation peak shaving mode according to the second set of objectives to be optimized; use the target cascade analysis method ATC to solve the second distributed optimization model and obtain a second optimization planning result.

[0049] Figure 2 FIG. 4 is a flow chart of a method for constructing a first distributed optimization model in an embodiment of the present invention. Figure 2 As shown, in one embodiment, according to the first group of objectives to be optimized, constructing a first distributed optimization model of the peak shaving and valley filling mode may include:

[0050] Step 201: Establish a minimum objective function for the operation cost of a data center station and a charging station, a minimum objective function for the investment and loss cost of an energy storage station, and establish a life constraint for the energy storage station and a load constraint for the data center station and the charging station;

[0051] Step 202: Taking the charging and discharging power of the energy storage power station as a coupling variable transmitted between two optimization targets, a first distributed optimization model of the peak shaving and valley filling mode is constructed.

[0052] Figure 3 FIG. 4 is a flow chart of a method for constructing a second distributed optimization model in an embodiment of the present invention. Figure 3 As shown, in one embodiment, according to the second group of objectives to be optimized, constructing a second distributed optimization model of the substation peak load regulation mode may include:

[0053] Step 301: Establish a substation operation cost minimization objective function, an energy storage power station investment and loss cost minimization objective function, and establish an energy storage power station life constraint and a substation peak load constraint;

[0054] Step 302: Taking the charging and discharging power of the energy storage power station as a coupling variable transmitted between two optimization objectives, a second distributed optimization model of the substation peak load regulation mode is constructed.

[0055] In specific implementation, the multiple optimization structure mainly includes the cost targets of data center stations and charging stations, the cost targets of substation operation, the comprehensive targets of investment income and loss cost of energy storage power stations; the safety operation constraints related to the multi-station integration system, the substation peak load operation constraints, and the energy storage power station operation constraints. The objective function expression of the multiple optimization is as follows:

[0056]

[0057]

[0058]

[0059]

[0060] Among them, formula (1) is the operating cost of the charging station and data center station; (2) is the penalty cost of the substation overload rate; (3) Formula F 13 (P PCS,t ) is the comprehensive investment cost of energy storage power station participating in peak load shifting and valley filling of data center station and charging station; (4) Formula F 23 (P PCS ) is the comprehensive investment cost of the energy storage power station participating in the substation peak load regulation. Optimize the output coupling variables of the energy storage power station between the data center station, charging station and energy storage power station in the peak shaving and valley filling mode; The output coupling variable of the energy storage power station is optimized between the substation and the energy storage power station under the peak load mode; E BESS , P PCS is the rated capacity and rated power of the energy storage power station; P PCS,t Provide power for energy storage power station; L n,t The power load supplied from the grid to the charging station; Pr net,t , Pr BESS,t are the electricity price of the power grid and the electricity price of the energy storage power station respectively; st is the penalty coefficient of substation overload rate; L st,t is the substation load; n tr is the number of transformers in the substation; C tr,t is the capacity of each transformer; C BES is the unit price of battery system investment; C PCS is the unit price of the power conversion and boosting system investment; α is the operation and maintenance cost coefficient; β is the depreciation cost coefficient; r is the discount rate; D p-v The number of days that the energy storage power station participates in peak load shifting and valley filling operation; loss DOD1 is the loss of the energy storage power station under the discharge depth of DOD1 in the peak shaving and valley filling mode; τ loss is the loss cost coefficient of the energy storage power station; D p The number of days that the energy storage power station participates in the substation peak load regulation; loss DOD2 is the loss of the energy storage power station under the discharge depth of substation peak-shaving mode DOD2; η is the peak-shaving subsidy; L'1 and L"1 are the first and second Lagrangian penalty functions of the peak-shaving and valley-filling mode, respectively, and their expressions are as shown in formula (5); L'2 and L"2 are the first and second Lagrangian penalty functions of the substation peak-shaving mode, respectively, and their expressions are as shown in formula (6).

[0061]

[0062]

[0063] Among them, ξ 1,t , 1,t They are the first and second Lagrangian penalty coefficients in the peak shaving and valley filling mode respectively; ξ 2,t , 2,t They are the Lagrange first and second penalty coefficients in substation peak load regulation mode.

[0064] In specific implementation, the method of establishing the load constraint of the charging station is as follows: based on the probability density function of the charging behavior of electric vehicles, the Monte Carlo random sampling method is used to generate the charging behavior of electric vehicles, including daily mileage, time to reach the charging station, length of stay, initial SOC state, etc., to establish the charging behavior model of electric vehicles. And perform data processing to eliminate unreasonable data. Establish the life constraint of the energy storage power station and perform linearization processing.

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

[0066] Through data statistics, it is found that the daily travel time and daily return time of electric vehicles satisfy the standard normal distribution; the expected SOC and daily mileage satisfy the logarithmic normal distribution.

[0067] 1) Standard normal distribution sampling function:

[0068]

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

[0070] 2) Lognormal distribution sampling function:

[0071]

[0072] Where: μ1, σ1 are the mean and standard deviation of the lognormal distribution respectively.

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

[0074]

[0075] Where: d n is the daily mileage of the nth electric car; ω 100 The power consumption of an electric vehicle per 100 kilometers.

[0076] Energy storage system operation 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] Where (10) is the charge and discharge power constraint, P PCS,t is the energy storage charging and discharging power, P PCS,t A negative value represents charging, P PCS,t A positive value represents discharge; (11) is the total energy constraint for charge and discharge in a cycle; (12) is the remaining energy constraint for the energy storage system; (13) is the remaining energy of the energy storage station at time t; E SOC,t E is the stored energy of the energy storage power station at time t. The stored energy of the energy storage power station at time t must be guaranteed to maintain the normal operation of the data center at maximum load for 30 minutes; SOC,max 、E SOC,min are the upper and lower limits of the energy storage station’s storage energy respectively; E SOC,0 is the initial power of the energy storage station; k is the time retrieval.

[0082] The method for establishing the life constraint of the energy storage power station is as follows: The life constraint of the energy storage power station is as follows:

[0083]

[0084]

[0085] Wherein, (14) is the relationship between energy storage cycle life and discharge depth; (15) is the relationship between energy storage loss and discharge depth; N life For energy storage batteries in DOD i Cycle life under charge and discharge depth; N0 is the cycle life under 100% charge and discharge depth; k p is a constant, obtained by the battery manufacturer; loss DOD is the loss of the energy storage power station; i is the number of charge and discharge cycles in a scheduling period.

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

[0087] In one embodiment, using the target cascade analysis method ATC to solve the first distributed optimization model and obtain the first optimization planning result may include:

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

[0089] In one embodiment, using the target cascade analysis method ATC to solve the second distributed optimization model and obtain the second optimization planning result may include:

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

[0091] Then, step 105 may be executed to compare the first optimization planning result with the second optimization planning result. According to the comparison result, the optimization planning result with a larger value is selected to configure the energy storage power station, wherein the optimization planning result includes the power and capacity of the energy storage power station.

[0092] In the specific implementation, the ATC optimization algorithm is used to solve the optimization scale of the two modes. When the accuracy of the coupling variables reaches the preset value, the optimization planning results are output; the two optimization planning results are compared. Since the larger value corresponds to a larger limit range, it can be applied to both modes at the same time. Therefore, according to the comparison results, the larger value of the energy storage power station optimization result is used as the final energy storage power station configuration.

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

[0094]

[0095]

[0096]

[0097]

[0098]

[0099]

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

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

[0102] (1) When planning energy storage power stations in a multi-station fusion system, the present invention adopts a distributed optimization algorithm to solve the model. When each substation model processes data, it only needs to exchange data information with a coupling relationship and process it, thereby protecting the privacy of data between substations in the fusion power station and avoiding memory waste and reduced solution rate caused by data redundancy.

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

[0104] Figure 4 FIG. 1 is a flow chart of a specific example of a distributed optimization planning method for a multi-station fusion system in an embodiment of the present invention. Figure 4 As shown, first, the system parameters of the multi-station fusion system can be initialized and the coupling variables can be determined. Then, a multiple optimization structure with the goal of minimizing the operating cost F1 of the data center station and the charging station, minimizing the operating cost F2 of the substation, and minimizing the investment and loss costs F13 and F23 of the energy storage station is established. Furthermore, the minimization of the operating cost of the data center station and the charging station, and the minimization of the investment and loss costs of the energy storage station in the multiple optimization structure are taken as the first group of optimization targets. The energy storage station life model, the data center station load model, and the charging station load model are taken into account in the constraints. At the same time, 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 targets, and a distributed optimization model with peak shaving and valley filling as the mode is established. Secondly, the minimization of the substation operating cost and the minimization of the investment and loss costs of the energy storage station are taken as the second group of optimization targets. The substation peak shaving demand and the energy storage station life model are taken into account in the constraints. At the same time, 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 targets, and a distributed optimization model with substation peak shaving as the mode is established.

[0105] Finally, the ATC distributed optimization algorithm is used to solve the optimization planning model under the two modes, and it is judged 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 sets of optimization planning results is selected as the result of this energy storage power station configuration; if not, the ATC distributed optimization algorithm is continued to be used to solve the optimization planning model under the corresponding mode until the solution result meets the minimum error constraint of the coupling variables.

[0106] The embodiment of the present invention also provides a distributed optimization planning device for a multi-station fusion system, as described in the following embodiment. Since the principle of solving the problem by the device is similar to the distributed optimization planning method for the multi-station fusion system, the implementation of the device can refer to the implementation of the distributed optimization planning method for the multi-station fusion system, and the repeated parts will not be repeated.

[0107] Figure 5 FIG. 1 is a schematic diagram of the structure of a distributed optimization planning device for a 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 the embodiment of the present invention may specifically include:

[0108] The historical operation data acquisition module 501 is used to respectively acquire the historical operation data of the substation, the energy storage station, the charging station and the data center station;

[0109] The multiple optimization structure building module 502 is used to build a multiple optimization structure based on the historical data of the substation, the energy storage power station and the data center station. The multiple optimization structure: minimizes the operating cost of the data center station and the charging station, and minimizes the investment and loss cost of the energy storage power station as the first group of optimization targets, and minimizes the operating cost of the substation, and minimizes the investment and loss cost of the energy storage power station as the second group of optimization targets;

[0110] The first distributed optimization model construction and solution module 503 is used to construct a first distributed optimization model of the peak shaving and valley filling mode according to the first group of optimization targets; adopt the target cascade analysis method ATC to solve the first distributed optimization model and obtain the first optimization planning result;

[0111] The second distributed optimization model construction and solution module 504 is used to construct a second distributed optimization model of the substation peak load regulation mode according to the second group of optimization targets; adopt the target cascade analysis method ATC to solve the second distributed optimization model and obtain the second optimization planning result;

[0112] The comparison and configuration module 505 is used to compare the first optimization planning result with the second optimization planning result, and select the optimization planning result with a larger value according to the comparison result to configure the energy storage power station, wherein 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 condition data, time-of-use electricity price data, energy storage battery unit price data, PCS unit price data, energy storage power station efficiency, initial charge state 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 to:

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

[0117] The charging and discharging power of the energy storage power station is used as the coupling variable transmitted between the two optimization objectives to construct the first distributed optimization model of peak shaving and valley filling mode.

[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 operation cost, energy storage power station investment and loss cost, and establish energy storage power station life constraints and substation peak load constraints;

[0120] The charging and discharging power of the energy storage power station is used as the coupling variable transmitted between the two optimization objectives to construct the second distributed optimization model of the substation peak-shaving mode.

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

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

[0125] Based on the above invention concept, 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, wherein the processor 620 implements the aforementioned distributed optimization planning method for the multi-station fusion system when executing the computer program 630.

[0126] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the distributed optimization planning method of the multi-station fusion system is implemented.

[0127] An embodiment of the present invention further provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the distributed optimization planning method of the multi-station fusion system is implemented.

[0128] To summarize, in an embodiment of the present invention, historical data of a substation, an energy storage power station, a charging station, and a data center station are obtained respectively; based on the historical data of the substation, the energy storage power station, and the data center station, a multiple optimization structure is constructed, wherein the multiple optimization structure: minimizes the operating costs of the data center station and the charging station, and minimizes the investment and loss costs of the energy storage power station as the first group of targets to be optimized, and minimizes the operating costs of the substation, and minimizes the investment and loss costs of the energy storage power station as the second group of targets to be optimized; based on the first group of targets to be optimized, a first distributed optimization model of a peak shaving and valley filling mode is constructed; the target cascade analysis method ATC is used to solve the first distributed optimization model to obtain a first optimization planning result; based on the second group of targets to be optimized, a second distributed optimization model of a substation peak shaving mode is constructed; the target cascade analysis method ATC is used to solve the second distributed optimization model to obtain a second optimization planning result; the first optimization planning result is compared with the second optimization planning result, and based on the comparison result, the optimization planning result with a larger value is selected to configure the energy storage power station, wherein the optimization planning result includes the power and capacity of the energy storage power station.

[0129] When planning the energy storage power station in a multi-station fusion system, the present invention establishes a distributed optimization model under the peak shaving and valley filling mode and the substation peak regulation mode, and adopts the ATC distributed optimization algorithm to solve the model. When each substation model processes data, it only needs to exchange and process data information with a coupling relationship, thereby protecting the security of data between substations in the fusion power station, while avoiding memory waste and reduction in solution rate caused by data redundancy; and in the solution process, the influence of energy storage loss and life on the planning results is taken into account, thereby avoiding unreasonable energy storage power station planning due to over-idealized system parameters, and improving the rationality of configuring the energy storage power station.

[0130] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented 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] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0132] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0133] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0134] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is 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 in the scope of protection of the present invention.

Claims

1. A distributed optimization planning method for a multi-station fusion system, characterized in that: Obtain historical data of substations, energy storage stations, charging stations, and data center stations respectively; Based on the historical data of the substation, energy storage power station and data center station, a multiple optimization structure is constructed. The multiple optimization structure: the operation cost of the data center station and the charging station is minimized, and the investment and loss cost of the energy storage power station are minimized as the first group of optimization targets, and the operation cost of the substation is minimized, and the investment and loss cost of the energy storage power station are minimized as the second group of optimization targets; According to the first group of targets to be optimized, a first distributed optimization model of the peak shaving and valley filling mode is constructed; the target cascade analysis method ATC is used to solve the first distributed optimization model and obtain the first optimization planning result; According to the second group of objectives to be optimized, a second distributed optimization model of the substation peak load regulation mode is constructed; The target cascade analysis method ATC is used to solve the second distributed optimization model and obtain the second optimization planning result; The first optimization planning result is compared with the second optimization planning result. According to the comparison result, the optimization planning result with a larger value is selected to configure the energy storage power station, wherein the optimization planning result includes the power and capacity of the energy storage power station.

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

3. The method according to claim 1, characterized in that The first distributed optimization model for peak load shaving and valley filling is constructed, including: Establish the objective function of minimizing the operating cost of data center stations and charging stations, the objective function of minimizing the investment and loss cost of energy storage power stations, and establish the life constraints of energy storage power stations and the load constraints of data center stations and charging stations; The charging and discharging power of the energy storage power station is used as the coupling variable transmitted between the two optimization objectives to construct the first distributed optimization model of peak shaving and valley filling mode.

4. The method according to claim 1, characterized in that Construct the second distributed optimization model of substation peak load regulation mode, including: Establish the objective function of minimizing the substation operation cost, the objective function of minimizing the investment and loss cost of the energy storage power station, and establish the energy storage power station life constraint and the substation peak load constraint; The charging and discharging power of the energy storage power station is used as the coupling variable transmitted between the two optimization objectives to construct the second distributed optimization model of the substation peak-shaving mode.

5. The method according to claim 3, characterized in that The target cascade analysis method ATC is used to solve the first distributed optimization model and obtain the first optimization planning result, including: The power and capacity of the energy storage power station are taken as the variables to be solved, and the charging and discharging power of the energy storage power station is taken as the coupling variable. The target cascade analysis method ATC is used to solve the first distributed optimization model. When the error of the coupling variable is less than or equal to the preset value, the first optimization planning result is obtained.

6. The method according to claim 4, characterized in that The target cascade analysis method ATC is used to solve the second distributed optimization model and obtain the second optimization planning results, including: The power and capacity of the energy storage power station are taken as the variables to be solved, and the charging and discharging power of the energy storage power station is taken as the coupling variable. The target cascade analysis method ATC is used to solve the second distributed optimization model. When the error of the coupling variable 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: A historical data acquisition module is used to respectively acquire historical data of the substation, energy storage station, charging station and data center station; A multiple optimization structure construction module is used to construct a multiple optimization structure based on historical data of the substation, energy storage power station and data center station. The multiple optimization structure: minimizes the operating cost of the data center station and the charging station, and minimizes the investment and loss cost of the energy storage power station as the first group of optimization targets, and minimizes the operating cost of the substation, and minimizes the investment and loss cost of the energy storage power station as the second group of optimization targets; The first distributed optimization model construction and solution module is used to construct the first distributed optimization model of the peak shaving and valley filling mode according to the first group of optimization targets; adopt the target cascade analysis method ATC to solve the first distributed optimization model and obtain the first optimization planning result; The second distributed optimization model construction and solution module is used to construct a second distributed optimization model of the substation peak load regulation mode according to the second group of optimization targets; adopt the target cascade analysis method ATC to solve the second distributed optimization model and obtain the second optimization planning result; The comparison and configuration module is used to compare the first optimization planning result with the second optimization planning result, and select the optimization planning result with a larger value according to the comparison result to configure the energy storage power station, wherein the optimization planning result includes the power and capacity of the energy storage power station.

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

9. The device according to claim 7, characterized in that The first distributed optimization model construction and solution module is specifically used for: Establish the objective function of minimizing the operating cost of data center stations and charging stations, the objective function of minimizing the investment and loss cost of energy storage power stations, and establish the life constraints of energy storage power stations and the load constraints of data center stations and charging stations; The charging and discharging power of the energy storage power station is used as the coupling variable transmitted between the two optimization objectives to construct the first distributed optimization model of peak shaving and valley filling mode.

10. The device according to claim 7, characterized in that The second distributed optimization model construction and solution module is specifically used for: Establish the objective function of minimizing the substation operation cost, the objective function of minimizing the investment and loss cost of the energy storage power station, and establish the energy storage power station life constraint and the substation peak load constraint; The charging and discharging power of the energy storage power station is used as the coupling variable transmitted between the two optimization objectives to construct the second distributed optimization model of the substation peak-shaving mode.

11. The device according to 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 the variables to be solved, and the charging and discharging power of the energy storage power station is taken as the coupling variable. The target cascade analysis method ATC is used to solve the first distributed optimization model. When the error of the coupling variable is less than or equal to the preset value, the first optimization planning result is obtained.

12. The device according to 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 the variables to be solved, and the charging and discharging power of the energy storage power station is taken as the coupling variable. The target cascade analysis method ATC is used to solve the second distributed optimization model. When the error of the coupling variable 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, the method according to any one of claims 1 to 6 is implemented.

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

15. A computer program product, characterized in that The computer program product comprises a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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