Source-grid-storage multi-time-scale optimization method and device based on interval time period division

Abstract

The application provides a source network storage multi-time scale optimization method and device based on interval period division. The method comprises the following steps: dividing the fluctuation interval of the day-ahead stage load power prediction data and the photovoltaic power prediction data into periods to obtain discrete device regulation periods; based on the regulation periods, the day-ahead optimization target is the minimum distribution network operation cost according to the load power prediction data and the photovoltaic power prediction data, and various controllable devices are coordinated and optimized under the safety index constraint to determine the optimal economic operation scheme of the day-ahead stage; based on the action plan period of the discrete device of the day-ahead stage, the day-in stage distribution network operation cost loss is minimized as the day-in optimization target by using the full level scheduling method, the day-in rolling prediction of the photovoltaic power prediction data and the load power prediction data is carried out, and the day-in stage photovoltaic active and reactive power and the storage charging and discharging power are rolling optimized. The application can fully utilize the controllable resources of the distribution network and ensure the safe and economic operation of the distribution network.

Description

Technical Field

[0001] This application relates to the field of power distribution network operation optimization technology, and in particular to a multi-timescale optimization method and device based on interval time division of source, grid and storage. Background Technology

[0002] With the integration of distributed photovoltaic, energy storage, and reactive power compensation devices into the distribution network, the traditional distribution network is gradually evolving into an active distribution network with numerous controllable resources. However, the output of distributed power generation is random, intermittent, and fluctuating, and its penetration rate in the distribution network has been increasing in recent years, posing a significant challenge to the stable operation of the distribution network.

[0003] Currently, most distribution networks are equipped with integrated photovoltaic (PV) and energy storage units, which can meet part of the daily load demand during sunshine hours using PV power generation. Based on time-of-use pricing, the energy storage units' "low storage, high release" principle maximizes economic benefits, smoothing load fluctuations through peak shaving and valley filling. However, when there is a large deviation between the day-ahead load forecast and the intraday load forecast, intraday operation cannot achieve economic optimization, and the day-ahead plan cannot adapt to intraday dispatch. Therefore, research on intraday rolling optimization of integrated PV and energy storage units is particularly important, yet research in this area is limited. On the distribution network side, network reconfiguration, on-load tap-changing transformers, and parallel capacitor banks are also important means of operation control, which can change feeder power flow, improve voltage distribution, balance loads, and reduce network losses. However, these optimization models all optimize and control discrete equipment, belonging to mixed-integer nonlinear programming problems.

[0004] While there has been some research on optimal power flow methods for distribution networks based on integrated photovoltaic and energy storage systems and discrete devices, certain shortcomings and deficiencies still exist.

[0005] (1) Existing distribution network optimization methods cannot fully utilize all controllable resources. There is still a lack of research on the comprehensive and multi-stage coordinated optimization of "source-grid-storage". In the intraday stage of "source-storage", the optimization results of model predictive control still depend to some extent on the day-ahead reference trajectory. When the actual load change during the day deviates significantly from the day-ahead prediction, the final result still has a large error and cannot achieve economic optimization, resulting in a waste of "source-storage" resources.

[0006] (2) Most current time period division methods do not take into account the temporal nature of the load, and the number of divisions is also somewhat arbitrary. Moreover, the existing time period divisions are all based on the accurate value of the day-ahead forecast, without taking into account the uncertainty of intraday load and photovoltaics. This makes it impossible to guarantee that intraday load fluctuations will lead to errors in time period division. Summary of the Invention

[0007] This application provides a multi-timescale optimization method and apparatus for source-grid-storage based on interval time period division, in order to solve the problem that the distribution network cannot achieve full-time coordinated optimization during operation in the prior art.

[0008] Firstly, this application provides a multi-timescale optimization method for source-network-storage based on interval time period division, including:

[0009] By dividing the fluctuation range of the day-ahead load power forecast data and photovoltaic power forecast data into time periods, the control cycle of discrete equipment in the distribution network is obtained.

[0010] Based on the control cycle of the discrete equipment in the distribution network, and according to the load power forecast data and photovoltaic power forecast data, the goal of minimizing the operating cost of the distribution network is to optimize the day-ahead operation. Under the constraints of safety indicators, the various controllable equipment in the distribution network are coordinated and optimized to determine the optimal economic operation scheme for the day-ahead stage. The optimal economic operation scheme includes the operation plan period of the discrete equipment in the day-ahead stage.

[0011] Based on the operation plan time period of discrete equipment in the day-ahead phase, the full-level scheduling method is adopted. The goal of minimizing the operating cost loss of the distribution network in the day-ahead phase is to perform intraday rolling forecasts on photovoltaic power forecast data and load power forecast data, and to continuously optimize the photovoltaic active and reactive power output and energy storage charging and discharging power in the day-ahead phase.

[0012] Secondly, this application provides a source-network-storage multi-timescale optimization device based on interval time period division, including:

[0013] The time period segmentation module is used to segment the fluctuation range of the day-ahead load power forecast data and photovoltaic power forecast data into time periods, so as to obtain the control cycle of discrete equipment in the distribution network.

[0014] The day-ahead optimization module is used to optimize various controllable devices in the distribution network based on the control cycle of discrete devices in the distribution network, according to load power forecast data and photovoltaic power forecast data, with the goal of minimizing the operating cost of the distribution network, and under the constraint of safety indicators, to determine the best economic operation scheme for the day-ahead stage. The best economic operation scheme includes the operation plan period of the discrete devices in the day-ahead stage.

[0015] The intraday rolling forecasting and optimization module is used to perform intraday rolling forecasts on photovoltaic power forecast data and load power forecast data based on the operation plan time period of discrete equipment in the day-ahead period. It adopts the full-level scheduling method and takes the minimum operation cost loss of the distribution network in the day-ahead period as the intraday optimization objective. It also performs rolling optimization of photovoltaic active and reactive power output and energy storage charging and discharging power in the day-ahead period.

[0016] Thirdly, this application provides a terminal including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the method as described in the first aspect or any possible implementation of the first aspect above.

[0017] Fourthly, this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the method as described in the first aspect or any possible implementation of the first aspect.

[0018] This application provides a multi-timescale optimization method and apparatus for power generation, grid, and energy storage based on interval time period division. By dividing the fluctuating interval time periods of the day-ahead load power forecast data and photovoltaic power forecast data, the control cycle of discrete equipment is determined. Then, with the minimum operating cost of the distribution network as the day-ahead optimization objective, and under the constraint of safety indicators, the optimal economic operation scheme for the day-ahead stage is determined, making full use of all controllable equipment in the distribution network. Intraday rolling forecasts are performed on the photovoltaic power forecast data and load power forecast data, and the active and reactive power output of photovoltaics and the charging and discharging power of energy storage are optimized in the intraday stage, ensuring the safe and economical operation of the distribution network. Moreover, when performing intraday stage forecast optimization, discrete equipment executes according to the action plan time periods of discrete equipment in the day-ahead stage. The control cycle of discrete equipment is obtained by dividing the time periods based on the optimal Fisher segmentation method of interval data. This time period division, to a certain extent, ensures that the load in the intraday stage is still within the correct time period when it fluctuates within the fluctuation interval, thereby improving the economy of global optimization in the intraday stage. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating the implementation of the source-network-storage multi-timescale optimization method based on interval time period division provided in this application embodiment;

[0021] Figure 2 This is a timescale model diagram provided in the embodiments of this application;

[0022] Figure 3 This is a power distribution network topology diagram provided in the embodiments of this application;

[0023] Figure 4 This is a time period division result diagram provided in the embodiments of this application;

[0024] Figure 5 This is a diagram showing the optimization results at the day-ahead stage provided in the embodiments of this application;

[0025] Figure 6 This is a framework diagram of "source-network-storage" collaborative optimization provided in the embodiments of this application;

[0026] Figure 7 This is a schematic diagram of the structure of the source-network-storage multi-timescale optimization device based on interval time period division provided in the embodiments of this application;

[0027] Figure 8 This is a schematic diagram of the terminal provided in the embodiments of this application. Detailed Implementation

[0028] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0029] To make the objectives, technical solutions, and advantages of this application clearer, the following description will be provided in conjunction with the accompanying drawings and specific embodiments.

[0030] Figure 1 The implementation flowchart of the source-network-storage multi-timescale optimization method based on interval time period division provided in the embodiments of this application is described in detail below:

[0031] In step 101, the fluctuation range of the day-ahead load power forecast data and photovoltaic power forecast data is divided into time periods to obtain the control cycle of discrete equipment in the distribution network.

[0032] Among them, see Figure 2 In this embodiment, the day-ahead period is a 24-hour day, with 1-hour intervals used to optimize the operation of the distribution network for the next 24 hours. Both the load power forecast data and the photovoltaic power forecast data are forecast data for the 24 hours to be predicted.

[0033] Discrete equipment typically refers to equipment with only two states: motor operation and shutdown, and two-position valve switching. Controlling conventional discrete equipment is relatively simple: click the icon on the screen to open the equipment operation panel, and then click to turn the equipment on or off. The discrete equipment in this embodiment includes an on-load tap-changing transformer, a parallel capacitor bank, and a tie-line switch.

[0034] In this embodiment, during the day-ahead stage of optimized scheduling, the time period is divided according to the fluctuation range of the day-ahead stage load power prediction data and photovoltaic power prediction data. This is used as the control cycle of discrete equipment in the distribution network, which reduces the number of operations of discrete equipment such as switches, on-load tap-changing transformers, and parallel capacitor banks, thereby reducing the solution scale and accelerating the calculation speed.

[0035] In one possible implementation, step 101 may specifically include:

[0036] By forecasting the load power and photovoltaic power in the day-ahead period, the predicted load power and photovoltaic power data for the day-ahead period are obtained. The fluctuation ranges corresponding to the predicted load power and photovoltaic power data in the day-ahead period are then combined to obtain the net load fluctuation range for the intraday period.

[0037] The optimal Fisher segmentation method based on interval data divides the net load fluctuation interval into time periods to obtain the control cycle of discrete equipment in the distribution network.

[0038] Among them, the optimal Fisher segmentation method uses the sum of squared deviations to represent the degree of difference between samples of the same class. Through simple calculation steps and graphing, the optimal number of categories is determined to minimize the difference between samples of the same class and maximize the difference between samples of each category. The F test is used to verify the rationality of the optimal number of categories.

[0039] In the power flow optimization of the "source-grid-storage" system, the optimization of the distribution network side involves the optimization and control of discrete devices, which is a mixed integer nonlinear programming problem. In addition, the need to consider the coordinated optimization of multiple controllable resources in the distribution network makes it difficult to solve. By using the optimal Fisher partitioning method based on interval data, the time period is divided as the control cycle of discrete devices, thereby reducing the solution scale.

[0040] In this embodiment, the load power and photovoltaic power of the day-ahead period are first predicted to determine the predicted load power and photovoltaic power data of the day-ahead period. Then, the fluctuation ranges corresponding to the obtained predicted data are combined to obtain the net load prediction value and its corresponding fluctuation range of the day-ahead period. Finally, the optimal Fisher segmentation method based on interval data is used to divide the net load fluctuation range of the day-ahead period into time periods to obtain the control cycle of the discrete equipment of the distribution network.

[0041] In this embodiment, an improved IEEE 33-node system is used to simulate and verify the optimal economic operating scheme for the day-ahead phase. See [link to relevant documentation]. Figure 3 The diagram shows the distribution network topology of this application embodiment. The voltage regulation range of the secondary side of the on-load tap-changing transformer is 1.03*(0.95~1.05)μu, with a step size of 0.0125μu; the reference value is selected as S.B =10MVA, U B =12.66kV; Nodes 4 and 19 are simply connected to photovoltaic power generation, Nodes 13, 23, and 31 are connected to integrated photovoltaic and energy storage units, and Nodes 17, 24, and 32 are connected to parallel capacitor banks.

[0042] See Figure 4 The embodiments of this application are in Figure 3 Based on the simulation calculations, the net load fluctuation range was divided into five segments: 1:00~8:00, 9:00~15:00, 16:00~17:00, 18:00~21:00, and 22:00~24:00. This can, to a certain extent, ensure that the daily net load is still within the correct time period when it fluctuates within the predicted range.

[0043] In one possible implementation, the optimal Fisher segmentation method based on interval-type data divides the net load fluctuation interval into time periods to obtain the control cycle of discrete equipment in the distribution network, which may include:

[0044] The distance between the intervals of net load fluctuation range is calculated using the first formula;

[0045] Based on the distance between intervals, the net load fluctuation interval is divided into time periods to obtain the control cycle of discrete equipment in the distribution network;

[0046] The first formula is:

[0047]

[0048] Among them, E(u d ,u d+1 -1) represents the interval distance of the D-th segment, u d For the d-th time period, u d+1 For the (d+1)th time period, n is the number of nodes, and j is the j-th node. This represents the load forecast value for the d-th time period. Let d be the size of the fluctuation range in the d-th time period. Let be the load forecast mean for node j. Let be the size of the predicted mean fluctuation range for node j.

[0049] For the optimal Fisher segmentation method based on interval-type data: assuming the net power interval for the entire time period is represented by matrix A, then A = [X1, X2, ..., X...]. T ] T , where X m =[x m1 ,x m2 ,…,x mn Let ] be the load interval set for time period m, and n be the number of nodes. and These represent the load forecast value and the fluctuation range, respectively. When dividing the time period, the time periods included in the Dth segment are defined as {u d ,u d +1,…,u d+1 -1},u k For the first time period of the Dth segment, the corresponding predicted load mean set for the Dth segment is V. d =[v d1 ,v d2 ,…,v dn ], and Let be the load forecast mean and the range of fluctuation of the forecast mean for node j in the Dth segment. The specific calculation formula is shown in formula (1):

[0050]

[0051] in, This is the load forecast value. This represents the size of the fluctuation range.

[0052] In this matrix, matrix A represents the net power range throughout the entire time period, and segment D represents the division of the time period.

[0053] This application embodiment uses the optimal Fisher segmentation method for interval-type data to divide the net load fluctuation interval into time periods. The interval distance describes the similarity of the interval data within a segment. The interval distance within the Dth segment is calculated by the first formula. Based on the calculated interval distance, the net load fluctuation interval is divided into time periods to obtain the control cycle of the discrete equipment in the distribution network. The weighting factor λ is used to control the impact of interval size on clustering.

[0054] In one possible implementation, the net load fluctuation interval is divided into time periods based on the interval distance to obtain the control cycle of discrete equipment in the distribution network, which may include:

[0055] The optimal time period division scheme for the net load fluctuation range is determined by the objective function, thus obtaining the control cycle of discrete equipment in the distribution network. The objective function is:

[0056]

[0057] Where L[] is the objective function, b(T,D) is the optimal time period division scheme after D divisions, D is the number of time period divisions, and T is 24 hours.

[0058] In this application embodiment, the similarity of interval data within a segment is described by the interval number distance, and an objective function is defined accordingly. The segmentation scheme with the minimum objective function is the optimal segmentation scheme, and the optimal segmentation scheme is used as the control cycle of discrete equipment in the distribution network.

[0059] The recursive formula for the objective function is as follows:

[0060] L[b(T,2)]=min{E(1,a-1)+E(a,n)},a=2,3,…,T (2)

[0061] L[b(T,D)]=min{L[b(a-1,D-1)]+E(a,T)},a=D,D+1,…,T (3)

[0062] The specific steps for recursive ordered clustering time segmentation are as follows:

[0063] Step 1: Calculate the optimal 2-segment b(a,2) for the net load samples of the first a time periods according to formula (2), where a = 2, 3, ..., T.

[0064] Step 2: Calculate the optimal 3-segment b(a,3)(a=3,…,T), optimal 4-segment b(a,4)(a=4,…,T), …, optimal D-1 segment b(a,D-1)(a=D-1,…,T) for the net load sample in the first a time period according to formula (3).

[0065] Step 3: Determine the optimal split points for the T samples.

[0066] First, determine the D-th split point u. d To satisfy:

[0067] L[b(T,D)]=min{L[b(u d -1,D-1)]+E(u d ,T)} (4)

[0068] Then, find the (D-1)th split point u. d-1 To satisfy:

[0069] L[b(u d -1,D-1)]=min{L[b(u d-1 -1,D-2)]+E(u d-1 ,u d -1)} (5)

[0070] By following this method, the optimal time period division scheme can be obtained.

[0071] Step 4: Determine the optimal number of segments. Generally, as the number of segments D increases, the objective function L and its rate of change will decrease. In order to balance the similarity of data within segments and the number of actions of slow-motion discrete devices, the optimal number of segments is defined as the number of segments corresponding to the inflection point of the objective function trend graph, referring to the "inflection point method". The inflection point method is prior art in this application embodiment and will not be described in detail here.

[0072] In step 102, based on the control cycle of discrete equipment in the distribution network, and according to the load power forecast data and photovoltaic power forecast data, the goal of minimizing the operating cost of the distribution network is to optimize the day-ahead operation. Under the constraints of safety indicators, the various controllable equipment in the distribution network are coordinated and optimized to determine the optimal economic operation scheme for the day-ahead stage. The optimal economic operation scheme includes the operation plan period of the discrete equipment in the day-ahead stage.

[0073] In this embodiment of the application, based on the control cycle of the discrete equipment of the distribution network in step 101, and according to the complete forecast information one day in advance, namely the load power forecast data and photovoltaic power forecast data, the goal of minimizing the operating cost of the distribution network is to optimize the day-ahead. Under the constraint of various safety indicators, the coordinated optimization of all controllable equipment in the "source-grid-storage" system is carried out in all aspects and multiple links to obtain the best economic operation plan for the next day, namely the best economic operation plan for the day-ahead stage. The best economic operation plan includes the action plan period of the discrete equipment in the day-ahead stage.

[0074] The control variables include: photovoltaic active / reactive power, tap position of on-load tap changer, number of parallel capacitor banks switched on and off, switching status of tie switch, and charging / discharging power of photovoltaic-storage integrated unit.

[0075] The main operating costs of power distribution networks include electricity purchase costs, network loss costs, photovoltaic active power reduction costs, discrete equipment operation costs, and integrated photovoltaic and energy storage unit operating costs.

[0076] The current optimization goal is:

[0077]

[0078] Where f is the distribution network operating cost, ΔT is the day-ahead time period duration, i is node i, and j is node j. The unit electricity purchase cost for time period t. The power exchange between the main grid and the distribution network during time period t. Let c be the power loss of the distribution network during time period t. PV For the revenue from photovoltaic power generation, Where D is the reduction in active power of photovoltaics, and c is the number of time periods. OLTC The operating cost of single-gear adjustment of the shaft head, Let d be the tap position of the on-load tap-changing transformer on branch ij in the d-th time period. For the tap position of the on-load tap-changing transformer in branch ij during the (d-1)th time period, c CB The switching cost of a single capacitor bank. Let j be the number of capacitor banks connected in the d-th time period segment. Let c be the number of capacitor banks connected at node j in the (d-1)th time period. S The cost of a single opening and closing action of the switch. This refers to the switching state of feeder ij in the d-th time period. For the (d-1)th time period, N represents the switching state of feeder ij. ESS c represents the number of energy storage units. ESS To call the unit price of energy storage for integrated photovoltaic and energy storage machines, This refers to the charging power per unit time period (t) in a K-type photovoltaic-storage integrated unit. The discharge power per unit time period (t) in the k-type photovoltaic-storage integrated unit is denoted as k.

[0079] The embodiments of this application also include day-ahead optimization constraints, mainly including distribution network power flow equation constraints, safe operation constraints, photovoltaic active and reactive power constraints, photovoltaic-storage integrated machine charging and discharging power constraints, discrete equipment operation constraints, and system active power fluctuation constraints.

[0080] To analyze the rationality of the optimal economic operation scheme in the day-ahead phase of the embodiments of this application, four scenarios were simulated, as detailed in Table 1. Scenario 1 does not consider the charging and discharging of discrete equipment and integrated photovoltaic-storage units; Scenario 2 only considers discrete equipment; Scenario 3 only considers the charging and discharging of integrated photovoltaic-storage units; and Scenario 4 represents the day-ahead optimization constraints considered in the embodiments of this application. In Scenario 1, during the 18-20 hour period, photovoltaic output is low while load demand is high, leading to low-voltage over-limit situations at some nodes. This situation did not occur in other scenarios. Meanwhile, Scenario 4, through coordinated optimization of the "source-grid-storage" system, outperforms individual optimizations such as network reconfiguration for discrete equipment and integrated photovoltaic-storage units, reducing the global target cost by 5.34% and significantly reducing network loss costs.

[0081] Table 1 Comparison of simulation results for economic operation schemes at different current stages

[0082]

[0083] As shown in Table 1, the embodiments of this application have significant advantages over individual optimizations of "source," "network," and "storage." The time period division proposed in this application can effectively reduce the number of discrete device operations and accelerate model calculation.

[0084] In step 103, based on the operation plan time period of discrete equipment in the intraday stage, the full-level scheduling method is adopted. The intraday optimization objective is to minimize the operating cost loss of the distribution network in the intraday stage. Intraday rolling forecasts are performed on photovoltaic power forecast data and load power forecast data, and the photovoltaic active and reactive power output and energy storage charging and discharging power in the intraday stage are optimized in a rolling manner.

[0085] In this embodiment of the application, during the intraday phase, the operation plans of discrete equipment such as on-load tap-changing transformers, parallel capacitor banks and tie line switches are executed according to the intraday phase operation plan time period of discrete equipment in step 102. The full-level scheduling method is adopted, with the goal of minimizing the operating cost loss of the distribution network during the intraday phase. Intraday rolling forecasts are performed on photovoltaic power forecast data and load power forecast data, and the photovoltaic active and reactive power output and energy storage charging and discharging power during the intraday phase are continuously optimized.

[0086] In one possible implementation, step 103 may specifically include:

[0087] Based on the operation plan time period of discrete equipment in the daytime phase, and with the goal of minimizing the operating cost loss of the distribution network in the daytime phase, the system uses a preset optimization time granularity as a rolling window to slide in the daytime phase. It performs prediction optimization on the photovoltaic power prediction data and load power prediction data in each rolling window, and outputs the photovoltaic active and reactive power output and energy storage charging and discharging power in the daytime phase.

[0088] In this embodiment, the operation plan of discrete equipment is executed according to the optimal economic operation scheme of the day-ahead stage. To ensure overall economic efficiency within the day, rolling optimization employs a full-level scheduling method. The goal of rolling optimization is to minimize the operating cost loss of the distribution network for the remaining time of the day. A preset optimization time granularity is set as the rolling window. The active and reactive power output of photovoltaics and the charging and discharging power of energy storage are predicted from the current time to 24:00 within the day. The reduction in active power output of photovoltaics, reactive power output, and the charging and discharging power of the photovoltaic-energy storage integrated machine are used as control variables to obtain control commands within the control time domain and issue them for execution starting from the first time period. Every preset optimization time granularity, the prediction window is shifted backward, and rolling optimization is repeated. The preset optimization time granularity can be set to 1 hour or 0.5 hours; this embodiment does not limit the setting of the preset optimization time granularity.

[0089] The intraday operating costs of the distribution network include the main grid power purchase cost from the current time to 24:00, network loss costs, photovoltaic active power reduction costs, and the operating costs of the integrated photovoltaic and energy storage system. Therefore, the intraday optimization objective is to minimize the sum of these costs. The intraday optimization objective is:

[0090]

[0091] Where t0 is the current time, Δt is the duration of the intraday period, and N ESS The number of energy storage units. Let g be the time-of-use electricity price. The power exchanged between the main grid and the distribution network at time g. Let c be the power loss of the distribution network at time g. PV For the revenue from photovoltaic power generation, c represents the reduction in active power of photovoltaic power. ESS To call the unit price of energy storage for integrated photovoltaic and energy storage machines, The charging power per unit g in the k-type photovoltaic-storage integrated unit is given by the following value. Let g be the discharge power of the energy storage unit in the k-photovoltaic-energy storage integrated unit at time g.

[0092] The operation of discrete equipment such as on-load tap-changing transformers, parallel capacitor banks, and tie line switches during the intraday phase, as well as the demand response load scheme, are determined by the optimal economic operation scheme during the day-ahead phase. The main constraints considered during the intraday phase include: distribution network power flow equation constraints, safe operation constraints, photovoltaic active and reactive power constraints, and energy storage active power processing constraints.

[0093] See Figure 5 Based on time-of-use pricing, the photovoltaic-storage integrated machine obtains the maximum economic benefit through "low storage and high discharge". Photovoltaic output exceeds user load from 10:00 to 15:00. Since this period is when the time-of-use price is high, the excess photovoltaic output can be sold to the main grid to obtain higher revenue. Energy storage is stored when the time-of-use price is low. From 16:00 to 22:00, the photovoltaic output is lower than the load, and this period is when the time-of-use price is high. The discharge of photovoltaic and energy storage can reduce the power distribution network's purchase of electricity from the main grid, which is more economical.

[0094] Table 2 compares the intraday rolling optimization simulation results of this application embodiment with the intraday execution results of the best economic operation scheme in the daytime stage. The load and photovoltaic fluctuations in the intraday stage show that the network loss increases with the intraday execution results of the best economic operation scheme in the daytime stage, while the intraday rolling optimization in this application embodiment results in less network loss and a 2.51% reduction in global target optimization.

[0095] Table 2 compares the intraday rolling optimization simulation results with the intraday execution results of the optimal economic operation plan for the day-ahead phase.

[0096]

[0097] The framework diagram of the "source-network-storage" collaborative optimization in this application embodiment can be found in [reference needed]. Figure 6 The specific implementation process is as follows:

[0098] Step 1: Global optimization in the current phase.

[0099] (1) Based on the optimal Fisher segmentation method of interval data, the fluctuation range of the load power prediction data and photovoltaic power prediction data in the day-ahead stage is divided into time periods, which is used as the control cycle of discrete equipment to reduce the number of operations of discrete equipment such as switches, on-load tap-changing transformers, and parallel capacitor banks.

[0100] (2) Based on (1), and according to photovoltaic power forecast data, load power forecast data, and time-of-use electricity price, the goal of minimizing the operating cost of the distribution network is to optimize the day-ahead operation. Under the constraints of various safety indicators, the optimal economic operation plan for the day-ahead stage is obtained by coordinating and optimizing all controllable equipment in the "source-grid-storage" system in all aspects and multiple links.

[0101] Step 2: Intraday rolling optimization.

[0102] (1) During the daytime phase, the operation plans of discrete equipment such as on-load tap-changing transformers, parallel capacitor banks and tie switches shall be executed in accordance with the best economic operation plan of the daytime phase.

[0103] (2) The full-level scheduling method is adopted. The goal of intraday optimization is to minimize the operating cost loss of the distribution network during the remaining time of the intraday stage. Intraday rolling forecasts are made on photovoltaic power forecast data and load power forecast data. The active and reactive power output of photovoltaic power and the charging and discharging power of energy storage are optimized in the intraday stage to obtain the rolling optimization scheme for the intraday stage.

[0104] This application provides a multi-timescale optimization method for power generation, grid, and energy storage based on interval time period division. By dividing the fluctuating interval time periods of the day-ahead load power forecast data and photovoltaic power forecast data, the control cycle of discrete devices is determined. Then, with the minimum operating cost of the distribution network as the day-ahead optimization objective, and under the constraint of safety indicators, the optimal economic operation scheme for the day-ahead stage is determined, making full use of all controllable devices in the distribution network. Intraday rolling forecasts are performed on the photovoltaic power forecast data and load power forecast data, and the active and reactive power output of photovoltaics and the charging and discharging power of energy storage are optimized in the intraday stage, ensuring the safe and economical operation of the distribution network. Moreover, when performing intraday stage forecast optimization, discrete devices execute according to the action plan time periods of discrete devices in the day-ahead stage. The control cycle of discrete devices is obtained by dividing the time periods based on the optimal Fisher segmentation method of interval data. This time period division, to a certain extent, ensures that when the load fluctuates within the fluctuation interval during the intraday stage, the control cycle of discrete devices is still within the correctly divided time periods, thereby improving the economy of global optimization in the intraday stage.

[0105] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0106] The following are device embodiments of this application. For details not described in detail, please refer to the corresponding method embodiments described above.

[0107] Figure 7 The diagram shows a schematic of the source-network-storage multi-timescale optimization device based on interval time period division provided in this application embodiment. For ease of explanation, only the parts related to this application embodiment are shown, and are described in detail below:

[0108] like Figure 7 As shown, the source-network-storage multi-timescale optimization device 7 based on interval time period division includes:

[0109] The time period segmentation module 71 is used to segment the fluctuation range of the load power forecast data and photovoltaic power forecast data in the day-ahead stage into time periods to obtain the control cycle of discrete equipment in the distribution network.

[0110] The day-ahead optimization module 72 is used for the control cycle of discrete equipment based on the distribution network. Based on the load power forecast data and photovoltaic power forecast data, the day-ahead optimization objective is to minimize the operating cost of the distribution network. Under the constraint of safety indicators, the module coordinates and optimizes various controllable equipment in the distribution network to determine the best economic operation scheme in the day-ahead stage. The best economic operation scheme includes the action plan period of the discrete equipment in the day-ahead stage.

[0111] The intraday rolling forecast optimization module 73 is used to perform intraday rolling forecasts on the action plan time period of discrete equipment based on the day-ahead stage. It adopts the full-level scheduling method and takes the minimum operating cost loss of the distribution network in the intraday stage as the intraday optimization objective. It performs intraday rolling forecasts on photovoltaic power forecast data and load power forecast data, and continuously optimizes the photovoltaic active and reactive power output and energy storage charging and discharging power in the intraday stage.

[0112] This application provides a multi-timescale optimization device for power generation, grid, and energy storage based on interval time-segmentation. By dividing the fluctuating interval time-segment of load power forecast data and photovoltaic power forecast data in the day-ahead period, the control cycle of discrete equipment is determined. Then, with the minimum operating cost of the distribution network as the day-ahead optimization objective, and under the constraint of safety indicators, the optimal economic operation scheme for the day-ahead period is determined, making full use of all controllable equipment in the distribution network. Intraday rolling forecasts are performed on photovoltaic power forecast data and load power forecast data, and the active and reactive power output of photovoltaic power and the charging and discharging power of energy storage in the intraday period are continuously optimized, ensuring the safe and economical operation of the distribution network. Moreover, when performing intraday forecast optimization, discrete equipment executes according to the action plan time-segment of discrete equipment in the day-ahead period. The control cycle of discrete equipment is obtained by dividing the time-segmentation based on the optimal Fisher segmentation method of interval data. This time-segmentation ensures to a certain extent that when the load fluctuates in the fluctuating interval during the intraday period, the control cycle of discrete equipment is still within the correctly divided time-segmented period, thereby improving the economy of global optimization in the intraday period.

[0113] In one possible implementation, the time-segmentation module can specifically be used for:

[0114] By forecasting the load power and photovoltaic power in the day-ahead period, the predicted load power and photovoltaic power data for the day-ahead period are obtained. The fluctuation ranges corresponding to the predicted load power and photovoltaic power data in the day-ahead period are then combined to obtain the net load fluctuation range for the intraday period.

[0115] The optimal Fisher segmentation method based on interval data divides the net load fluctuation interval into time periods to obtain the control cycle of discrete equipment in the distribution network.

[0116] In one possible implementation, the time-segmentation module can also be used for:

[0117] The distance between the intervals of net load fluctuation range is calculated using the first formula;

[0118] Based on the distance between intervals, the net load fluctuation interval is divided into time periods to obtain the control cycle of discrete equipment in the distribution network;

[0119] The first formula is:

[0120]

[0121] Among them, E(u d ,u d+1 -1) represents the interval distance of the D-th segment, u d For the d-th time period, u d+1 For the (d+1)th time period, n is the number of nodes, and j is the j-th node. This represents the load forecast value for the d-th time period. Let d be the size of the fluctuation range in the d-th time period. Let be the load forecast mean for node j. Let be the size of the predicted mean fluctuation range for node j.

[0122] In one possible implementation, the time-segmentation module can also be used for:

[0123] The optimal time period division scheme for the net load fluctuation range is determined by the objective function, thus obtaining the control cycle of discrete equipment in the distribution network. The objective function is:

[0124]

[0125] Where L[] is the objective function, b(T,D) is the optimal time period division scheme after D divisions, D is the number of time period divisions, and T is 24 hours.

[0126] In one possible implementation, the operating cost of the distribution network includes electricity purchase cost, network loss cost, photovoltaic active power reduction cost, discrete equipment operation cost, and integrated photovoltaic-storage unit operating cost; the current optimization objective is:

[0127]

[0128] Where f is the distribution network operating cost, ΔT is the day-ahead time period duration, i is node i, and j is node j. The unit electricity purchase cost for time period t. The power exchange between the main grid and the distribution network during time period t. Let c be the power loss of the distribution network during time period t. PV For the revenue from photovoltaic power generation, Where D is the reduction in active power of photovoltaics, and c is the number of time periods. OLTC The operating cost of single-gear adjustment of the shaft head, Let d be the tap position of the on-load tap-changing transformer on branch ij in the d-th time period. For the tap position of the on-load tap-changing transformer in branch ij during the (d-1)th time period, c CB The switching cost of a single capacitor bank. Let j be the number of capacitor banks connected in the d-th time period segment. Let c be the number of capacitor banks connected at node j in the (d-1)th time period. S The cost of a single opening and closing action of the switch. This refers to the switching state of feeder ij in the d-th time period. For the (d-1)th time period, N represents the switching state of feeder ij. ESS c represents the number of energy storage units. ESS To call the unit price of energy storage for integrated photovoltaic and energy storage machines, This refers to the charging power per unit time period (t) in a K-type photovoltaic-storage integrated unit. The discharge power per unit time period (t) in the k-type photovoltaic-storage integrated unit is denoted as k.

[0129] In one possible implementation, the day-ahead optimization module can specifically be used for:

[0130] Based on the operation plan time period of discrete equipment in the daytime phase, and with the goal of minimizing the operating cost loss of the distribution network in the daytime phase, the system uses a preset optimization time granularity as a rolling window to slide in the daytime phase. It performs prediction optimization on the photovoltaic power prediction data and load power prediction data in each rolling window, and outputs the photovoltaic active and reactive power output and energy storage charging and discharging power in the daytime phase.

[0131] In one possible implementation, the intraday optimization objective is:

[0132]

[0133] Where t0 is the current time, Δt is the duration of the intraday period, and N ESS The number of energy storage units. Let g be the time-of-use electricity price. The power exchanged between the main grid and the distribution network at time g. Let c be the power loss of the distribution network at time g. PV For the revenue from photovoltaic power generation, c represents the reduction in active power of photovoltaic power. ESs To call the unit price of energy storage for integrated photovoltaic and energy storage machines, The charging power per unit g in the k-type photovoltaic-storage integrated unit is given by the following value. Let g be the discharge power of the energy storage unit in the k-photovoltaic-energy storage integrated unit at time g.

[0134] Figure 8 This is a schematic diagram of the terminal provided in an embodiment of this application. For example... Figure 8 As shown, the terminal 8 in this embodiment includes: a processor 80, a memory 81, and a computer program 82 stored in the memory 81 and executable on the processor 80. When the processor 80 executes the computer program 82, it implements the steps in the various embodiments of the source-network-storage multi-timescale optimization method based on interval time period division, for example... Figure 1 Steps 101 to 103 are shown. Alternatively, when the processor 80 executes the computer program 82, it implements the functions of each module in the above-described device embodiments, for example... Figure 7 The functions of modules 71 to 73 are shown.

[0135] For example, the computer program 82 can be divided into one or more modules, which are stored in the memory 81 and executed by the processor 80 to complete this application. The one or more modules can be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program 82 in the terminal 8. For example, the computer program 82 can be divided into... Figure 7 Modules 71 to 73 are shown.

[0136] The terminal 8 can be a computing device such as a desktop computer, laptop, handheld computer, or cloud server. The terminal 8 may include, but is not limited to, a processor 80 and a memory 81. Those skilled in the art will understand that... Figure 8 This is merely an example of terminal 8 and does not constitute a limitation on terminal 8. It may include more or fewer components than shown, or combine certain components, or different components. For example, the terminal may also include input / output devices, network access devices, buses, etc.

[0137] The processor 80 may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0138] The memory 81 can be an internal storage unit of the terminal 8, such as a hard disk or memory of the terminal 8. The memory 81 can also be an external storage device of the terminal 8, such as a plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the terminal 8. Furthermore, the memory 81 can include both internal storage units and external storage devices of the terminal 8. The memory 81 is used to store the computer program and other programs and data required by the terminal. The memory 81 can also be used to temporarily store data that has been output or will be output.

[0139] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0140] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0141] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0142] In the embodiments provided in this application, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling or direct coupling or communication connection may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0143] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0144] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0145] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above-described embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of the above-described embodiments of the source-network-storage multi-timescale optimization method based on interval time-segmentation. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc. It should be noted that the content contained in the computer-readable medium may be appropriately added to or subtracted from the content as required by the legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, the computer-readable medium may not include electrical carrier signals and telecommunication signals.

[0146] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A source-network-storage multi-timescale optimization method based on interval time period division, characterized in that, include: The fluctuation ranges of the day-ahead load power forecast data and photovoltaic power forecast data are divided into time periods to obtain the control cycle of discrete equipment in the distribution network. Based on the control cycle of the discrete equipment in the distribution network, and according to the load power forecast data and photovoltaic power forecast data, the minimum operating cost of the distribution network is taken as the day-ahead optimization objective. Under the constraint of safety indicators, the various controllable equipment of the distribution network are coordinated and optimized to determine the optimal economic operation scheme for the day-ahead stage. The optimal economic operation scheme includes the action plan time period of the discrete equipment in the day-ahead stage. Based on the action plan time period of the discrete equipment in the day-ahead stage, the full-level scheduling method is adopted, and the minimum operating cost loss of the distribution network in the intraday stage is taken as the intraday optimization objective. Intraday rolling forecasts are performed on the photovoltaic power forecast data and load power forecast data, and the photovoltaic active and reactive power output and energy storage charging and discharging power in the intraday stage are continuously optimized. The step of dividing the fluctuation range corresponding to the day-ahead load and photovoltaic power data into time periods to obtain the control cycle of discrete equipment in the distribution network includes: By forecasting the load power and photovoltaic power in the day-ahead period, the predicted load power and photovoltaic power data for the day-ahead period are obtained. The fluctuation ranges corresponding to the predicted load power and photovoltaic power data in the day-ahead period are then combined to obtain the net load fluctuation range for the intraday period. Based on the optimal Fisher segmentation method for interval data, the net load fluctuation interval is divided into time periods to obtain the control cycle of discrete equipment in the distribution network.

2. The source-network-storage multi-timescale optimization method based on interval time period division according to claim 1, characterized in that, The optimal Fisher segmentation method based on interval-type data divides the net load fluctuation interval into time periods to obtain the control cycle of discrete equipment in the distribution network, including: The interval distance of the net load fluctuation range is calculated using the first formula; Based on the distance between the intervals, the net load fluctuation interval is divided into time periods to obtain the control cycle of discrete equipment in the distribution network; The first formula is: in, The interval distance of the D-th segment. For the first Each period, For the first Each period, For the number of nodes, For the first 1 node For the first Load forecast values ​​for each time period For the first The size of the fluctuation range over a given period. For nodes The average load forecast, For nodes The size of the predicted mean fluctuation range, This is the weighting factor.

3. The source-network-storage multi-timescale optimization method based on interval time period division according to claim 2, characterized in that, The step of dividing the net load fluctuation interval into time periods based on the interval distance to obtain the control cycle of discrete equipment in the distribution network includes: The optimal time period division scheme for the net load fluctuation range is determined by the objective function, thereby obtaining the control cycle of discrete equipment in the distribution network. The objective function is: in, Let the objective function be... This represents the optimal time period division scheme after D divisions. Divide the time period into multiple times. It is 24 hours.

4. The source-network-storage multi-timescale optimization method based on interval time period division according to claim 1, characterized in that, The power distribution network operating cost includes electricity purchase cost, network loss cost, photovoltaic active power reduction cost, discrete equipment operation cost, and integrated photovoltaic-storage unit operating cost; the day-ahead optimization objective is: in, The operating cost of the aforementioned distribution network, The duration of the day before the event. For nodes , For nodes , For time period The unit's electricity purchase cost, For time period The power exchange between the main grid and the distribution network For time period Distribution network power loss For the revenue from photovoltaic power generation, This represents the reduction in active power output of photovoltaic systems. To divide the time period into numbers, The operating cost of single-gear adjustment of the shaft head, For the first Branch roads divided into time periods There are tap positions for the on-load tap-changing transformer. For the first Branch roads divided into time periods There are tap positions for the on-load tap-changing transformer. The switching cost of a single capacitor bank. For the first Each time period node The number of capacitor banks connected, For the first Each time period node The number of capacitor banks connected, The cost of a single opening and closing action of the switch. For the first Feeder divided into time periods The on / off state, For the first Feeder divided into time periods The on / off state, The number of energy storage units. To call the unit price of energy storage for integrated photovoltaic and energy storage machines, for Energy storage unit in photovoltaic-storage integrated machine Charging power during different time periods for Energy storage unit in photovoltaic-storage integrated machine Discharge power over time period The duration of a single time segment.

5. The source-network-storage multi-timescale optimization method based on interval time period division according to claim 1, characterized in that, The operation plan period based on the discrete equipment in the day-ahead phase adopts a full-level scheduling method, with the goal of minimizing the operating cost loss of the distribution network in the day-ahead phase. It performs rolling forecasts on photovoltaic power forecast data and load power forecast data in the day-ahead phase, and continuously optimizes the active and reactive power output of photovoltaic power and the charging and discharging power of energy storage in the day-ahead phase, including: Based on the operation plan time period of discrete equipment in the daytime phase, and with the goal of minimizing the operating cost loss of the distribution network in the daytime phase, the system uses a preset optimization time granularity as a rolling window to slide in the daytime phase. It performs prediction optimization on the photovoltaic power prediction data and load power prediction data in each rolling window, and outputs the photovoltaic active and reactive power output and energy storage charging and discharging power in the daytime phase.

6. The source-network-storage multi-timescale optimization method based on interval time period division according to claim 5, characterized in that, The intraday optimization target is: in, For the current moment, The duration of the daytime session. The number of energy storage units. for Time-of-use electricity pricing at any given moment for Power exchange between the main grid and the distribution network at any given time. for Power loss of the distribution network at any given time. For the revenue from photovoltaic power generation, This represents the reduction in active power output of photovoltaic systems. To call the unit price of energy storage for integrated photovoltaic and energy storage machines, for Energy storage unit in photovoltaic-storage integrated machine Constant charging power, for Energy storage unit in photovoltaic-storage integrated machine Discharge power at any given time.

7. A source-network-storage multi-timescale optimization device based on interval time period division, characterized in that, include: The time period segmentation module is used to segment the fluctuation range of the day-ahead load power forecast data and photovoltaic power forecast data into time periods, so as to obtain the control cycle of discrete equipment in the distribution network. The day-ahead optimization module is used to optimize various controllable devices in the distribution network based on the control cycle of discrete devices in the distribution network, according to load power forecast data and photovoltaic power forecast data, with the goal of minimizing the operating cost of the distribution network, and under the constraint of safety indicators, to determine the best economic operation scheme for the day-ahead stage. The best economic operation scheme includes the operation plan period of the discrete devices in the day-ahead stage. The intraday rolling forecasting and optimization module is used to perform intraday rolling forecasts on photovoltaic power forecast data and load power forecast data based on the operation plan time period of discrete equipment in the day-ahead phase. It adopts the full-level scheduling method and takes the minimum operating cost loss of the distribution network in the day-ahead phase as the intraday optimization objective. It also performs rolling optimization of photovoltaic active and reactive power output and energy storage charging and discharging power in the day-ahead phase. The time period division module is used for: By forecasting the load power and photovoltaic power in the day-ahead period, the predicted load power and photovoltaic power data for the day-ahead period are obtained. The fluctuation ranges corresponding to the predicted load power and photovoltaic power data in the day-ahead period are then combined to obtain the net load fluctuation range for the intraday period. Based on the optimal Fisher segmentation method for interval data, the net load fluctuation interval is divided into time periods to obtain the control cycle of discrete equipment in the distribution network.

8. A terminal, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the source-network-storage multi-timescale optimization method based on interval time period division as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the source-network-storage multi-timescale optimization method based on interval time period division as described in any one of claims 1 to 6.

Images