Distributed energy site configuration method based on iterative optimization

Abstract

The application provides a distributed energy station site configuration method based on iterative optimization, comprising the following steps: a power grid control module acquires power generation fitting curves Qi(t) of each element in a candidate site list; the power output average Ei of each candidate site in a preset time period is calculated according to Qi(t); the power storage capacity Si and the maximum energy storage time Ti required for the site i to meet the power output average Ei are calculated according to Qi(t) and Ei; a queue management module calculates the power output value coefficient Pi of the candidate site i by Ei / Si, and then selects the first F sites meeting the total power demand Sum based on Pi, and puts the sites into a queue Q1, and puts the remaining sites into a queue Q2; a site configuration optimization module optimizes the power output average of Q2 to update the Q1 and Q2 queues; and a site selection configuration module determines the Q1 queue sites as selected sites, and outputs the power output average AEj and the site energy storage ASj of the Q1 queue sites, so as to improve the scientificity of site selection and optimize the quantitative configuration of site energy storage.

Description

[0001] This application is a divisional application, with parent application number 202210079289.5, application date January 24, 2022, and invention titled Distributed Energy System Site Selection and Energy Storage Configuration System. Technical Field

[0002] This application relates to the field of distributed site selection, and in particular to a distributed energy site configuration method based on iterative optimization. Background Technology

[0003] The statements in this section are merely background information related to this application and do not necessarily constitute prior art.

[0004] To alleviate the pressure on power supply, the government has introduced relevant policies to encourage distributed generation and grid connection. This has mobilized private resources in various regions to make extensive use of energy sources such as hydropower, wind power, solar energy, bioenergy (biogas), and tidal energy to effectively supplement electricity supply, thereby achieving the goal of alleviating power supply pressure through the extensive development of green energy.

[0005] However, the selection, evaluation, and management of distributed generation sites are currently lagging significantly, resulting in widespread problems such as severe oversupply and excessive energy storage capacity at distributed power stations after grid connection, leading to high costs and low overall efficiency. Summary of the Invention

[0006] To address the aforementioned issues, this application proposes an iterative optimization-based distributed energy site configuration method, which enhances the scientific rigor of site selection and optimizes the quantitative allocation of site energy storage.

[0007] This application provides a distributed energy site configuration method based on iterative optimization, including the following steps:

[0008] Step 1: The power grid control module obtains the power generation fitting curve Q for each element in the candidate site list ListOption. i (t), where i is the station number, which takes the value 1, 2, 3, ..., I, and I is the total number of stations in ListOption;

[0009] Step 2: The mean estimation module estimates the mean based on Q. i (t) Calculate the average power output E of each candidate site within the preset time period [TS, TE]. i ;

[0010] Step 3: The site energy cache estimation module estimates the energy based on Q. i (t) and E i The calculation site i satisfies the average power output E i Required energy storage capacity S iand maximum energy storage duration T i ;

[0011] Step 4: The queue management module calculates E. i / S i Obtain the energy output value coefficient P of candidate site i. i Then based on P i Sort the stations from highest to lowest, select the top F stations that meet the total power demand Sum, put them into queue Q1, and put the remaining stations in ListOption other than the F stations into queue Q2.

[0012] Step 5: The site configuration optimization module optimizes the average power output of Q2 based on the optimization iteration strategy and updates the queue elements of Q1 and Q2.

[0013] Step 6: The site selection and configuration module determines each site in the Q1 queue as the site selected by the distributed energy system, and outputs the average power output AE of each optimized site in the Q1 queue. j Site-based energy storage AS j j takes values ​​of 1, 2, 3, ..., K, where K is the total number of stations in queue Q1;

[0014] In step 5, the optimization of the average power output of Q2 based on the optimization iteration strategy and the updating of queue elements of Q1 and Q2 are specifically performed as follows:

[0015] Step 5.1: Set Index to 0, Q1_num to the total number of elements in queue Q1, Q2_num to the total number of elements in queue Q2, and UnAdjustCnt to 0. Then calculate the comprehensive value coefficient TK of queue Q1. The specific calculation method is as follows: obtain the number of stations in queue Q1, Q1_num; calculate the power output value SumPart and power storage capacity SPart of the first (Q1_num-1) stations in queue Q1; subtract SumPart from the total power demand Sum to obtain SumDelta; then calculate the power storage capacity S_F of the Q1_num station in queue Q1 based on the average power output value SumDelta; calculate Sum / (SPart+S_F) to obtain TK.

[0016] Step 5.2: Increment Index by 1 and assign the result to Index. If Index is greater than Q2_num, set Index to 1. Then determine whether UnAdjustCnt is greater than or equal to Q2_num. If yes, jump to step 5.5; otherwise, jump to step 5.3.

[0017] Step 5.3: Obtain the index-th element from queue Q2, assign the maximum energy storage duration of this element to t, the average energy output to E, and the energy storage capacity to S; then according to... Calculate δE, and recalculate the energy storage capacity value S_adjust and the maximum energy storage duration T_adjust of the element based on the average energy output value of (E-δE); then calculate (E-δE) / S_adjust to obtain the value coefficient TK_index of the index-th element in queue Q2; and assign (E-δE) to the average energy output value of the element, assign S_adjust to the energy storage capacity value of the element, and assign T_adjust to the maximum energy storage duration value of the element.

[0018] Step 5.4: Determine whether TK_index is greater than the value coefficient of the last element of queue Q1.

[0019] If not, increment UnAdjustCnt by 1 and assign it to UnAdjustCnt, then jump to step 5.2;

[0020] If so, move the index-th element in queue Q2 into queue Q1, sorted by value coefficient, and calculate the number of elements (NeedQ1) in queue Q1 required to satisfy the total energy demand (Sum).

[0021] If NeedQ1 is greater than Q1_num, then remove the index-th element from queue Q1 and return it to queue Q2, increment UnAdjustCnt by 1 and assign it to UnAdjustCnt, and jump to step 5.2;

[0022] If NeedQ1 is less than or equal to Q1_num, then UnAdjustCnt is cleared to zero, and the last (Q1_num - NeedQ1 + 1) elements in queue Q1 are moved into queue Q2. Q2_num + (Q1_num - NeedQ1) is assigned to Q2_num, NeedQ1 is assigned to Q1_num, and the comprehensive value coefficient TK in queue Q1 is updated according to the calculation method of "comprehensive value coefficient TK of queue Q1" in step 5.1. Then, the process jumps to step 5.2.

[0023] Step 5.5: Calculate the energy output value SumPart and energy storage capacity SPart of the first (Q1_num-1) stations in queue Q1; subtract SumPart from the total energy demand Sum to obtain SumDelta; then calculate the energy storage capacity S_F of the Q1_num station in queue Q1 based on the average energy output value SumDelta, and assign SumDelta to the average energy output value of the Q1_num station in queue Q1, and assign S_F to the energy storage capacity value of the Q1_num station in queue Q1, thus completing the iterative optimization operation.

[0024] Preferably, in step 1, the fitted curve Q i (t) is obtained by fitting the sensing parameters of station i, and the specific fitting method can be either interpolation or step method.

[0025] Preferably, in step 2, according to Q i (t) Integrate over the time interval [TS, TE] and then divide by (TE-TS) to obtain E. i .

[0026] Compared with the prior art, the beneficial effects of this application are as follows:

[0027] The method of this application first establishes an initial comprehensive value coefficient and the number of sites. Then, using the comprehensive value coefficient as a reference, it optimizes the average power output of the elements in the Q2 queue. Based on the improvement of the value coefficient and the number of sites, it updates the elements in the Q1 queue according to the optimization results of the elements in the Q2 queue. This process is iterated until the optimization operation of each element in the Q2 queue can no longer update the elements in the Q1 queue. Then, each site in the elements in the Q1 queue is determined as the site selected for the distributed energy system, and each site is configured to work according to the optimized average power output. By adopting an optimization iteration strategy, the value coefficient and the number of sites are continuously self-optimized, thereby achieving the optimal selection of sites in the distributed system, reducing energy storage costs and the number of sites, and improving the efficiency of site construction. Attached Figure Description

[0028] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments of this application and their descriptions are used to explain this application and do not constitute an undue limitation of this application.

[0029] Figure 1 This is a schematic diagram of a method flow according to one embodiment of this application;

[0030] Figure 2 This is a schematic diagram of the system principle of one embodiment of this application;

[0031] Figure 3 This is a schematic diagram of an implementation of one embodiment of this application. Detailed implementation method:

[0032] The present application will be further described below with reference to the accompanying drawings and embodiments.

[0033] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this disclosure. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0034] In this disclosure, terms such as "upper," "lower," "left," "right," "front," "back," "vertical," "horizontal," "side," and "bottom" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are merely relational terms determined for the convenience of describing the structural relationship of the various components or elements in this disclosure, and do not specifically refer to any component or element in this disclosure, nor should they be construed as limiting this disclosure.

[0035] like Figures 1 to 3 As shown, this application provides a distributed energy system site selection and energy storage configuration system, including: a grid control module, an average value estimation module, a site energy buffer estimation module, a queue management module, a site configuration optimization module, and a site selection configuration module. The functions of each module are as follows:

[0036] The power grid control module is responsible for obtaining the generation fitting curve Q for each element in the candidate site list ListOption. i (t), where i is the site number, taking values ​​from 1 to I, and I is the total number of sites in ListOption;

[0037] The mean estimation module is responsible for estimating the mean based on Q. i (t) Calculate the average power output E of each candidate site within the preset time period [TS, TE]. i ;

[0038] The site energy buffer estimation module is responsible for estimating the energy based on Q. i (t) and E i The calculation site i satisfies the average power output E i Required energy storage capacity S i and maximum energy storage duration T i ;

[0039] The queue management module is responsible for calculating E. i / S i Obtain the energy output value coefficient P of candidate site i. iThen based on P i Sort the stations from highest to lowest, select the top F stations that meet the total power demand Sum, put them into queue Q1, and put the remaining stations in ListOption other than the F stations into queue Q2.

[0040] The site configuration optimization module is responsible for optimizing the average power output of Q2 based on the optimization iteration strategy, and updating the queue elements of Q1 and Q2.

[0041] The site selection and configuration module is responsible for identifying each site in the Q1 queue as a site selected by the distributed energy system, and outputting the average power output AE of each site in the Q1 list after optimization. j Site-based energy storage AS j j takes values ​​of 1, ..., K, where K is the total number of stations in queue Q1.

[0042] This application also provides a distributed energy site configuration method based on iterative optimization, the specific steps of which are as follows:

[0043] Step 1: The power grid control module obtains the power generation fitting curve Q for each element in the candidate site list ListOption. i (t), where i is the station number, which takes the value 1, 2, 3, ..., I, and I is the total number of stations in ListOption;

[0044] Step 2: The mean estimation module estimates the mean based on Q. i (t) Calculate the average power output E of each candidate site within the preset time period [TS, TE]. i ;

[0045] Step 3: The site energy cache estimation module estimates the energy based on Q. i (t) and E i The calculation site i satisfies the average power output E i Required energy storage capacity S i and maximum energy storage duration T i ;

[0046] Step 4: The queue management module calculates E. i / S i Obtain the energy output value coefficient P of candidate site i. i Then based on P i Sort the stations from highest to lowest, select the top F stations that meet the total power demand Sum, put them into queue Q1, and put the remaining stations in ListOption other than the F stations into queue Q2.

[0047] Step 5: The site configuration optimization module optimizes the average power output of Q2 based on the optimization iteration strategy and updates the queue elements of Q1 and Q2.

[0048] Step 6: The site selection and configuration module determines each site in the Q1 queue as the site selected by the distributed energy system, and outputs the average power output AE of each site in the Q1 list after optimization. j Site-based energy storage AS j j takes values ​​of 1, 2, 3, ..., K, where K is the total number of stations in queue Q1.

[0049] In step 1, the fitted curve Q i (t) is obtained by fitting the sensing parameters of station i, and the specific fitting method can be either interpolation or step method.

[0050] In step 2, according to Q i (t) Integrate over the time interval [TS, TE] and then divide by (TE-TS) to obtain E. i .

[0051] In step 3, the electrical energy storage capacity S i and maximum energy storage duration T i The estimation can be performed using existing technologies, without any specific restrictions. For example, the energy storage node capacity requirement estimation method provided by the patent with application number 202111283987.9 and patent name "Energy Storage Node Capacity Requirement Estimation System for Distributed Power Systems".

[0052] In step 5, the optimization of the average power output of Q2 based on the optimization iteration strategy and the updating of queue elements of Q1 and Q2 are specifically performed as follows:

[0053] Step 5.1: Set Index to 0, Q1_num to the total number of elements in queue Q1, Q2_num to the total number of elements in queue Q2, and UnAdjustCnt to 0. Then calculate the comprehensive value coefficient TK of queue Q1. The specific calculation method is as follows: obtain the number of stations in queue Q1, Q1_num; calculate the power output value SumPart and power storage capacity SPart of the first (Q1_num-1) stations in queue Q1; subtract SumPart from the total power demand Sum to obtain SumDelta; then calculate the power storage capacity S_F of the Q1_num station in queue Q1 based on the average power output value SumDelta; calculate Sum / (SPart+S_F) to obtain TK.

[0054] Step 5.2: Increment Index by 1 and assign the result to Index. If Index is greater than Q2_num, set Index to 1. Then determine whether UnAdjustCnt is greater than or equal to Q2_num. If yes, jump to step 5.5; otherwise, jump to step 5.3.

[0055] Step 5.3: Obtain the index-th element from queue Q2, assign the maximum energy storage duration of this element to t, the average energy output to E, and the energy storage capacity to S; then according to... Calculate δE, and recalculate the energy storage capacity value S_adjust and the maximum energy storage duration T_adjust of the element based on the average energy output value of (E-δE); then calculate (E-δE) / S_adjust to obtain the value coefficient TK_index of the index-th element in queue Q2; and assign (E-δE) to the average energy output value of the element, assign S_adjust to the energy storage capacity value of the element, and assign T_adjust to the maximum energy storage duration value of the element.

[0056] Step 5.4: Determine whether TK_index is greater than the value coefficient of the last element of queue Q1.

[0057] If not, increment UnAdjustCnt by 1 and assign it to UnAdjustCnt, then jump to step 5.2;

[0058] If so, move the index-th element in queue Q2 into queue Q1, sorted by value coefficient, and calculate the number of elements (NeedQ1) in queue Q1 required to satisfy the total energy demand (Sum).

[0059] If NeedQ1 is greater than Q1_num, then remove the index-th element from queue Q1 and return it to queue Q2, increment UnAdjustCnt by 1 and assign it to UnAdjustCnt, and jump to step 5.2;

[0060] If NeedQ1 is less than or equal to Q1_num, then UnAdjustCnt is cleared to zero, and the last (Q1_num - NeedQ1 + 1) elements in queue Q1 are moved into queue Q2. Q2_num + (Q1_num - NeedQ1) is assigned to Q2_num, NeedQ1 is assigned to Q1_num, and the comprehensive value coefficient TK in queue Q1 is updated according to the calculation method of "comprehensive value coefficient TK of queue Q1" in step 5.1. Then, the process jumps to step 5.2.

[0061] Step 5.5: Calculate the energy output value SumPart and energy storage capacity SPart of the first (Q1_num-1) stations in queue Q1; subtract SumPart from the total energy demand Sum to obtain SumDelta; then calculate the energy storage capacity S_F of the Q1_num station in queue Q1 based on the average energy output value SumDelta, and assign SumDelta to the average energy output value of the Q1_num station in queue Q1, and assign S_F to the energy storage capacity value of the Q1_num station in queue Q1, thus completing the iterative optimization operation.

[0062] The following specific examples illustrate the detailed implementation of the distributed energy site configuration method based on iterative optimization:

[0063] like Figure 3 In this embodiment, the preset time period is as shown in [TS, TE], i.e. Figure 3 The entire horizontal axis span; in this embodiment, the candidate site list (ListOption) includes a total of six sites, as detailed in Table 1, with the average power output E of each site. i Energy storage capacity S i and maximum energy storage duration T i The detailed accounting results are shown in Table 1;

[0064] Next, the queue management module calculates E. i / S i Obtain the energy output value coefficient P of candidate site i. i Then based on P i Sort the stations from high to low and select the top F stations (4 in this embodiment) that meet the total power demand Sum. Put them into queue Q1 and put the remaining stations (i.e. the remaining two stations) in ListOption into queue Q2. After performing the above operations, the elements of queue Q1 and queue Q2 are detailed in the "Initialization" section of Table 4.

[0065] Next, the site configuration optimization module, based on the optimization iteration strategy and according to steps 5.1 to 5.5, optimizes the average power output of Q2 and updates the queue elements of Q1 and Q2. A total of 5 iterations of optimization were completed during this period. The first two iterations of optimization are as follows:

[0066] First iteration optimization: Based on TK equals 3.8, S equals 130, E equals 380, and t equals 6, according to... The calculated δE is 5.2. Then, based on the power generation fitting curve Q5(t) of station 5 and the average power output (E-δE), the power storage capacity value S_adjust is recalculated to be 125 and the maximum storage duration T_adjust is 5. Next, (E-δE) / S_adjust is calculated to obtain the value coefficient TK_1 of the first element in queue Q2 (i.e., station 5) as 3.0. (E-δE) is assigned to the average power output value of the element, S_adjust is assigned to the power storage capacity value of the element, and T_adjust is assigned to the maximum storage duration value of the element. See the "First Iteration Optimization" section in Tables 2 and 4 for details. At this time, since TK_1 is less than the value coefficient of the last element in queue Q1 (i.e., station 4), UnAdjustCnt is incremented by 1 and assigned to UnAdjustCnt (which is now equal to 1), and the process jumps to step 5.2 to start the optimization of the next element.

[0067] Second iteration optimization: Optimize the second element in queue Q2 (i.e., station 6), based on TK equals 3.8, S equals 180, E equals 500, and t equals 39. The calculated δE equals 1.25. Then, based on the power generation fitting curve Q6(t) of station 6 and the average power output (E-δE), the energy storage capacity value S_adjust is recalculated to 108, and the maximum storage duration T_adjust is recalculated to 10. Next, (E-δE) / S_adjust is calculated to obtain the value coefficient TK_2 of the second element in queue Q2 (i.e., station 6), which equals 4.6. (E-δE) is assigned to the average power output value of this element, S_adjust is assigned to the energy storage capacity value of this element, and T_adjust is assigned to the maximum storage duration value of this element. See Tables 3 and 4 for details in the "Second Iteration Optimization" section. At this point, since TK_2 is greater than the value coefficient of the last element in Q1 (i.e., station 4), the processing logic in step 5.4 is then followed. The process begins by moving the second element (site 6) from queue Q2 into queue Q1 according to its value coefficient. The number of elements needed to satisfy the total energy demand Sum in queue Q1, NeedQ1 (which is 3), is then calculated. Since NeedQ1 (3) is less than or equal to Q1_num (4), UnAdjustCnt is cleared. The last two elements (Q1_num - NeedQ1 + 1) from queue Q1 are moved into queue Q2. Q2_num + (Q1_num - NeedQ1) is assigned to Q2_num (3), and NeedQ1 is assigned to Q1_num (3). The comprehensive value coefficient TK in queue Q1 is updated according to the calculation method in step 5.1. The process then jumps to step 5.2.

[0068] In this embodiment, it is assumed that Q1 is not updated in the subsequent 3rd to 5th iterations of optimization. Therefore, after the 3rd to 5th iterations, UnAdjustCnt takes the value of 3, and it is determined that UnAdjustCnt is greater than or equal to Q2_num, so the iteration optimization stops.

[0069] Finally, the site selection and configuration module identifies each site in the Q1 queue as the site selected by the distributed energy system, and outputs the average power output AE of each site in the optimized Q1 list. j Site-based energy storage AS j j takes values ​​of 1, ..., K, where K is the total number of stations in queue Q1. See Table 3 for the configuration information of queue Q1 in "Second Iteration Optimization".

[0070] As can be seen from this embodiment, by using the method of the present invention and iteratively optimizing, the comprehensive value coefficient of the selected site increased from 3.8 to 4.7, the number of sites decreased from 4 to 3, and the overall efficiency was significantly improved.

[0071] Table 1. List of Alternate Sites ListOption Information - Initial State

[0072]

[0073] Table 2. List of candidate sites (ListOption information - First iteration optimization results)

[0074]

[0075] Table 3. List of candidate sites (ListOption information) - Results of the second iteration optimization

[0076]

[0077]

[0078] Table 4. Schematic diagram of iterative optimization process

[0079]

[0080]

[0081] As can be seen from the above embodiments, the present invention first establishes an initial comprehensive value coefficient and the number of sites, and then optimizes the average power output of the elements in the Q2 queue with the comprehensive value coefficient as a reference. Based on the improvement of the value coefficient and the number of sites, the elements in the Q1 queue are updated according to the optimization results of the elements in the Q2 queue. This process is iterated until the optimization operation of each element in the Q2 queue can no longer update the elements in the Q1 queue. Then, each site in the elements in the Q1 queue is determined as the site selected by the distributed energy system, and each site is configured to work according to the optimized average power output. The present invention adopts an optimization iteration strategy to continuously self-optimize the value coefficient and the number of sites, thereby realizing the optimal selection of sites in the distributed system, achieving the purpose of reducing energy storage expenses and the number of sites, and improving the efficiency of site construction.

[0082] The above are merely preferred embodiments of this application and are not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0083] While the specific embodiments of this application have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of this application. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of this application are still within the scope of protection of this application.

Claims

1. A distributed energy site configuration method based on iterative optimization, characterized in that, Includes the following steps: Step 1: The power grid control module obtains the power generation fitting curve Q for each element in the candidate site list ListOption. i (t), where i is the site number, which can be 1, 2, 3, ..., I, and I is the total number of sites in ListOption; Step 2: The mean estimation module estimates the mean based on Q. i (t) Calculate the average power output E of each candidate site within the preset time period [TS, TE]. i ; Step 3: The site energy cache estimation module estimates the energy based on Q. i (t) and E i The calculation site i satisfies the average power output E i Required energy storage capacity S i and maximum energy storage duration T i ; Step 4: The queue management module calculates E. i / S i Obtain the energy output value coefficient P of candidate site i. i Then based on P i Sort the stations from highest to lowest, select the top F stations that meet the total power demand Sum, put them into queue Q1, and put the remaining stations in ListOption other than the F stations into queue Q2. Step 5: The site configuration optimization module optimizes the average power output of Q2 based on the optimization iteration strategy and updates the elements of queues Q1 and Q2. The optimization goal of the optimization iteration strategy is to increase the comprehensive value coefficient TK of queue Q1 while reducing the total number of selected sites in queue Q1. Step 6: The site selection and configuration module determines each site in the Q1 queue as the site selected by the distributed energy system, and outputs the average power output AE of each optimized site in the Q1 queue. j Site-based energy storage AS j j takes values ​​of 1, 2, 3, ..., K, where K is the total number of stations in queue Q1; In step 5, the optimization of the average power output of Q2 based on the optimization iteration strategy and the updating of queue elements of Q1 and Q2 are specifically performed as follows: Step 5.1: Set Index to 0, Q1_num to the total number of elements in queue Q1, Q2_num to the total number of elements in queue Q2, and UnAdjustCnt to 0. Then calculate the comprehensive value coefficient TK of queue Q1. The specific calculation method is as follows: obtain the number of stations in queue Q1, Q1_num; calculate the power output value SumPart and power storage capacity SPart of the first (Q1_num-1) stations in queue Q1; subtract SumPart from the total power demand Sum to obtain SumDelta; then calculate the power storage capacity S_F of the Q1_num station in queue Q1 based on the average power output value SumDelta; calculate Sum / (SPart+S_F) to obtain TK. Step 5.2: Increment Index by 1 and assign the result to Index. If Index is greater than Q2_num, set Index to 1. Then determine whether UnAdjustCnt is greater than or equal to Q2_num. If yes, jump to step 5.5; otherwise, jump to step 5.

3. Step 5.3: Obtain the index-th element from queue Q2, assign the maximum energy storage duration of this element to t, the average energy output to E, and the energy storage capacity to S; then according to... Calculate δE, and recalculate the energy storage capacity value S_adjust and the maximum energy storage duration T_adjust of the element based on the average energy output value of (E-δE); then calculate (E-δE) / S_adjust to obtain the value coefficient TK_index of the index-th element in queue Q2; and assign (E-δE) to the average energy output value of the element, assign S_adjust to the energy storage capacity value of the element, and assign T_adjust to the maximum energy storage duration value of the element. Step 5.4: Determine whether TK_index is greater than the value coefficient of the last element of queue Q1. If not, increment UnAdjustCnt by 1 and assign it to UnAdjustCnt, then jump to step 5.2; If so, move the index-th element in queue Q2 into queue Q1, sorted by value coefficient, and calculate the number of elements (NeedQ1) in queue Q1 required to satisfy the total energy demand (Sum). If NeedQ1 is greater than Q1_num, then remove the index-th element from queue Q1 and return it to queue Q2, increment UnAdjustCnt by 1 and assign it to UnAdjustCnt, and jump to step 5.2; If NeedQ1 is less than or equal to Q1_num, then UnAdjustCnt is cleared to zero, and the last (Q1_num - NeedQ1 + 1) elements in queue Q1 are moved into queue Q2. Q2_num + (Q1_num - NeedQ1) is assigned to Q2_num, NeedQ1 is assigned to Q1_num, and the comprehensive value coefficient TK in queue Q1 is updated according to the calculation method of "comprehensive value coefficient TK of queue Q1" in step 5.

1. Then, the process jumps to step 5.

2. Step 5.5: Calculate the energy output value SumPart and energy storage capacity SPart of the first (Q1_num-1) stations in queue Q1; subtract SumPart from the total energy demand Sum to obtain SumDelta; then calculate the energy storage capacity S_F of the Q1_num station in queue Q1 based on the average energy output value SumDelta, and assign SumDelta to the average energy output value of the Q1_num station in queue Q1, and assign S_F to the energy storage capacity value of the Q1_num station in queue Q1, thus completing the iterative optimization operation; During the iteration steps from step 5.2 to step 5.4, if it is found that there is still no situation where the Q1 element of the queue is updated after UnAdjustCnt iterations for a consecutive number of iterations and UnAdjustCnt is greater than or equal to Q2_num, then the iteration process is stopped. In step 1, the fitted curve Q i (t) is obtained by fitting the sensing parameters of station i, and the specific fitting method can be any one of interpolation or step method; In step 2, according to Q i (t) Integrate over the time interval [TS, TE] and then divide by (TE - TS) to obtain E. i .

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