Scheduling method and device for new energy power system containing small hydropower station, equipment and medium

Abstract

The invention discloses a scheduling method and device for a new energy power system containing small hydropower stations, equipment and a medium, and the method comprises the steps: constructing a hydropower space-time matrix model of the system based on a topological structure and water flow motion parameters between nodes of a watershed; substituting the load prediction value and the operation parameter prediction value of each unit into a deterministic model to obtain a reference value of a system optimization target; and according to the uncertainty model, the reference value and the certainty model of the system, constructing an IGDT scheduling model of the system and solving the IGDT scheduling model to obtain an output plan and an optimization target of each unit in the system. By constructing a hydropower space-time matrix model of the system, a topological structure and a water flow motion state between each small hydropower station and a conventional hydropower station are considered when a decision is made, and the accuracy and the reliability of a scheduling result are improved; the influence of water load uncertainty on a scheduling result is quantified through an information gap decision theory, a decision maker provides scheduling strategies under different risks and different effects, and the reliability and economical efficiency of a final scheduling scheme are guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of new energy technology, in particular to a new energy power system dispatching method and device containing small hydropower, equipment and medium. BACKGROUND

[0002] Due to the influence of meteorological environment and other factors, the output of new energy has randomness. Large-scale new energy grid connection increases the volatility of power grid power, which threatens the stable operation of the power grid. The difficulty in accommodating wind power and photovoltaic power makes the problem of light and wind curtailment increasingly serious. Hydropower has the advantages of large capacity and strong adjustability, and the joint operation of small hydropower and new energy is an effective way to solve the new energy accommodation.

[0003] However, the current joint operation of hydropower and new energy mainly focuses on large hydropower stations. Small hydropower with regulating reservoirs also has short-term regulation capacity of hours and long-term regulation capacity of days and weeks, but its power generation plan is usually independently formulated by the owner under the condition of meeting the safety constraints of the power grid. The power grid generally formulates a dispatching model containing small hydropower in the way of determining electricity according to water, that is, small hydropower is regarded as a negative load. However, due to the small capacity of small hydropower reservoirs and the uncertainty of natural water flow, the output of small hydropower has stronger uncertainty, which seriously restricts the regulation potential of small hydropower.

[0004] In order to cope with the uncertainty of renewable energy output, an optimization dispatching model is usually used to formulate the power generation plan in the prior art. When modeling the hydraulic coupling relationship of hydropower stations, the existing dispatching method often ignores or simplifies the key physical process, which leads to an inaccurate dispatching model and affects the reliability and economy of the final dispatching scheme. SUMMARY

[0005] Therefore, it is necessary to propose a new energy power system dispatching method and device containing small hydropower, equipment and medium to provide dispatching strategies under different risks and effects for decision makers and ensure the reliability and economy of the final dispatching scheme.

[0006] To achieve the above purpose, the first aspect of the present application provides a new energy power system dispatching method containing small hydropower, which comprises: obtaining the load prediction value of the system and the operation parameter prediction value of each unit, wherein the unit includes new energy units and hydropower units; based on the topological structure and water flow motion parameters between nodes of the basin involved in the system, constructing a hydropower space-time matrix model of the system, wherein a node represents a hydropower station in the basin, and the basin contains several small hydropower stations and conventional hydropower stations; The load prediction value of the system and the operation parameter prediction value of each unit are substituted into the deterministic model to obtain a benchmark value of the system optimization target, wherein the deterministic model is composed of a hydropower space-time matrix model, a preset objective function and constraint condition of the system scheduling model, and the objective function is designed to maximize the power generation and minimize the total fluctuation; Based on the information gap decision theory, the IGDT scheduling model of the system is constructed according to the uncertainty model, the benchmark value and the deterministic model of the system, and the IGDT scheduling model is solved to obtain the output plan of each unit in the system and the optimization target.

[0007] Further, based on the topological structure and the water flow movement parameters between the nodes of the basin involved in the system, the hydropower space-time matrix model of the system is constructed, specifically including: According to the topological structure and the water flow movement parameters between the nodes in the basin, a water flow time delay model between upstream and downstream hydropower stations is constructed; According to the water flow time delay model of the upstream and downstream hydropower stations and the topological structure between the hydropower stations, the hydropower space-time matrix model of the system is constructed.

[0008] Further, the water flow movement parameters at least include water flow velocity and runoff; According to the topological structure and the water flow movement parameters between the nodes in the basin, a water flow time delay model between upstream and downstream hydropower stations is constructed, specifically including: The historical water flow velocity and runoff between the target upstream node and the target downstream node are obtained, wherein the target upstream node and the target downstream node are any pair of upstream and downstream nodes in the basin; Based on the big M method and piecewise linearization, a water flow time delay model between the target upstream node and the target downstream node is constructed according to the historical water flow velocity and runoff between the target upstream node and the target downstream node.

[0009] Further, the hydropower space-time matrix model is composed of a basin node matrix, a basin correlation matrix, a water flow time delay matrix and a basin distribution matrix; The basin node matrix contains the types of the nodes in the basin; The basin correlation matrix contains the water flow direction of the nodes in the basin; The water flow time delay matrix contains the water flow time delay size of the nodes in the basin; The basin distribution matrix contains the water flow distribution proportion size of the nodes in the basin.

[0010] Further, the uncertainty model includes the following expressions:

[0011]

[0012] In the formula, Let be the total uncertainty radius of the system. , These are the uncertain radii of the hydropower station's output and the system load, respectively. , These are the uncertainty coefficients for the hydropower station's output and the system load, respectively. This represents the uncertain set of power output of the i-th hydropower station at time t. Let be the actual output of the i-th hydropower station at time t. Let be the predicted output of the i-th hydropower station at time t, and be the actual output of each hydropower station at time t. The absolute deviation from the corresponding predicted output value shall not exceed the uncertain radius of the hydropower station's output multiplied by the magnitude of the predicted output value. This represents the uncertain set of system load at time t. for The predicted value of the system load at any given time. for The actual value of the system load at any given time.

[0013] Furthermore, the system's IGDT scheduling model includes a robust scheduling model and an opportunistic scheduling model; Based on the information gap decision theory, and according to the system's uncertainty model, benchmark value, and deterministic model, an IGDT scheduling model for the system is constructed, specifically including: With the goal of minimizing the total uncertainty radius of the system, a robust scheduling model is constructed based on the system's uncertainty model, benchmark value, deterministic model, and a preset robustness level factor. With the goal of maximizing the risk avoidance capability of the scheduling model within the preset optimization target range, an opportunity scheduling model is constructed based on the system's uncertainty model, benchmark value, deterministic model, and preset opportunity seeking factors.

[0014] Furthermore, the system load forecast and the predicted operating parameters of each unit are substituted into the deterministic model for solution to obtain the baseline value of the system optimization objective, specifically including: Based on the genetic algorithm and NSGA-II method, the system load forecast and the operating parameter forecast of each unit are substituted into the deterministic model to generate the Pareto optimal solution set; Based on the ideal point method, the optimal solution is found in the Pareto optimal solution set to obtain the baseline value of the system optimization objective.

[0015] To achieve the above objectives, a second aspect of this application provides a dispatching device for a new energy power system including small hydropower, characterized in that the device comprises: A data collection module is configured to acquire a load prediction value of the system and an operation parameter prediction value of each unit, wherein the units include new energy units and hydropower units. A model construction module is configured to construct a hydropower space-time matrix model of the system based on a topological structure and a water flow movement parameter between each node of a basin involved in the system, wherein one node represents one hydropower station in the basin, and the basin includes several small hydropower stations and conventional hydropower stations; the load prediction value of the system and the operation parameter prediction value of each unit are substituted into a deterministic model to obtain a benchmark value of the optimization target of the system, wherein the deterministic model is composed of the hydropower space-time matrix model, a preset objective function and constraint condition of the system scheduling model, and the objective function is designed to maximize power generation and minimize total fluctuation. A strategy generation module is configured to construct an IGDT scheduling model of the system based on an information gap decision theory, according to an uncertainty model of the system, the benchmark value and the deterministic model, and to solve the IGDT scheduling model to obtain an output plan of each unit in the system and an optimization target, wherein the uncertainty model represents uncertainty and fluctuation of hydropower output and system load in the system.

[0016] To achieve the above object, the third aspect of the present application provides a computer readable storage medium, which stores a computer program, and the computer program, when executed by a processor, causes the processor to perform the steps of the method according to the first aspect.

[0017] To achieve the above object, the fourth aspect of the present application provides a computer device, which includes a memory and a processor, and the memory stores a computer program, and the computer program, when executed by the processor, causes the processor to perform the steps of the method according to the first aspect.

[0018] By adopting the embodiment of the present application, the following beneficial effects are achieved: The embodiment of the present application provides a new energy power system dispatching method containing small hydropower, and the method comprises the following steps: acquiring a load prediction value of a system and an operation parameter prediction value of each unit, wherein the unit comprises a new energy unit and a hydropower unit; constructing a hydropower space-time matrix model of the system based on a topological structure and a water flow movement parameter between nodes of a basin involved in the system, wherein one node represents a hydropower station in the basin, and the basin comprises a plurality of small hydropower stations and conventional hydropower stations; substituting the load prediction value of the system and the operation parameter prediction value of each unit into a deterministic model for solving to obtain a benchmark value of an optimization target of the system, wherein the deterministic model is composed of the hydropower space-time matrix model, a preset objective function and constraint conditions of the system dispatching model, and the objective function is designed with the maximum power generation and the minimum total fluctuation as the target; constructing an IGDT dispatching model of the system based on an information gap decision theory, according to an uncertainty model of the system, the benchmark value and the deterministic model, and solving the IGDT dispatching model to obtain an output plan of each unit in the system and an optimization target, wherein the uncertainty model represents the uncertainty and fluctuation of the hydropower output and the system load in the system. First, the hydropower space-time matrix model of the system is constructed, so that the topological structure and the water flow movement state between each small hydropower station and conventional hydropower station in the basin are considered when making a decision, the accuracy and reliability of the dispatching result are improved, and the decision-making error caused by model simplification is avoided; second, the information gap decision theory is applied, the influence of the uncertainty on the dispatching result can be quantified, and the dispatching strategies under different risks and different effects are provided for decision makers, so that the reliability and economy of the final dispatching scheme are ensured. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort on the basis of these drawings.

[0020] Among them: Figure 1 It is a flowchart of the new energy power system dispatching method containing small hydropower in the embodiment of the present application; Figure 2 It is a small hydropower cluster structure in the embodiment of the present application; Figure 3 It is a wind power typical output schematic diagram in the embodiment of the present application; Figure 4 It is a photovoltaic typical output schematic diagram in the embodiment of the present application; Figure 5 It is a deterministic dispatching processing plan diagram in the embodiment of the present application; Figure 6 A structure block diagram of a new energy power system dispatching device in an embodiment of the present application is shown in the figure. Figure 7 An internal structure diagram of a computer device in an embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0021] The technical solutions in the embodiments of the present application will be clearly and completely described in combination with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the protection scope of the present application.

[0022] The new energy dispatching research of the enrichment type small hydropower still has the following problems: 1. The spatial mapping of the upstream and downstream hydropower stations is not considered, and the model needs to be rebuilt when the cluster relationship between the hydropower stations changes, and the promotion and practicability of the model are limited; 2. The existing research is mostly based on the prediction of upper and lower bounds to construct an uncertainty set, and lacks fine modeling of the water flow time delay and the water flow-output coupling relationship, and it is difficult to quantify the influence of uncertainty on the optimization target.

[0023] In view of the above problems, the present application proposes an IGDT risk avoidance and opportunity seeking dispatching strategy for small hydropower spatial mapping relationship and water load uncertainty quantification. First, based on the space-time characteristics of small hydropower, a deterministic dispatching model of a new energy power system containing enrichment type small hydropower is constructed; the information gap decision theory (Information Gap Decision Theory, IGDT) is applied to model the uncertainty of small hydropower and load demand, and robust dispatching model and opportunity dispatching model are established from the perspectives of risk avoidance and risk speculation respectively, to provide a basis for dispatching decisions under different risk preferences.

[0024] In an embodiment of the present application, a new energy power system dispatching method containing small hydropower is proposed, which can be referred to as Figure 1 , Figure 1 A flowchart of the new energy power system dispatching method containing small hydropower in an embodiment of the present application is shown in the figure, and the method comprises the following steps: S100, obtaining a load prediction value of the system and a running parameter prediction value of each unit, wherein the unit includes a new energy unit and a hydropower unit.

[0025] In this embodiment, the load prediction value of the new energy power system and the running parameter prediction value of each unit need to be obtained. The new energy power system at least includes a new energy unit and a hydropower unit, and the new energy unit can include wind turbine units and photovoltaic units and other types of units. The running parameter of the unit can be the output prediction value of each unit.

[0026] In an implementable embodiment, the load prediction value and the operation parameter prediction value of each unit can be predicted by a preset prediction model.

[0027] S200, based on the topological structure and water flow motion parameters between nodes of a river basin involved in the system, a water-electricity space-time matrix model of the system is constructed, wherein one node represents one hydropower station in the river basin, and the river basin contains a plurality of small hydropower stations and conventional hydropower stations.

[0028] In the embodiment, the river basin involved in the new energy power system contains a large number of hydropower stations, and the hydropower stations include small hydropower stations and conventional hydropower stations. The small hydropower station refers to a small hydropower station with installed capacity < 50 MW, and the conventional hydropower station refers to a water flow receiving and dispatching influencing water power facility.

[0029] In the same river basin, the inflow of the downstream hydropower station is partially derived from the upstream hydropower station, so the output of the downstream hydropower station is affected by the upstream hydropower station. Therefore, the water-electricity space-time matrix model can be constructed according to the topological structure of different small hydropower basins and the water flow motion parameters between the hydropower stations, which is helpful to develop an accurate and efficient dispatching strategy. In the water-electricity space-time matrix model, one node represents one hydropower station in the river basin; and the water flow motion parameters at least include runoff and water flow velocity data.

[0030] The application is suitable for a new energy power system containing small hydropower stations, and the dispatching capacity of the small hydropower stations and the influence of the small hydropower stations on the dispatching results are fully tapped through the water-electricity space-time matrix model of the river basin, so as to improve the accuracy and effectiveness of the dispatching strategy.

[0031] S300, the load prediction value of the system and the operation parameter prediction value of each unit are substituted into the deterministic model for solving to obtain a reference value of the system optimization target, wherein the deterministic model is composed of the water-electricity space-time matrix model, a target function and constraint conditions of a preset system dispatching model, and the target function is designed with the maximum power generation and the minimum total fluctuation as the target.

[0032] In the embodiment, a dispatching model of a new energy power system containing rich small hydropower stations is constructed. The small hydropower stations have the characteristics of rapid start and stop and flexible regulation, and provide a key flexible resource for stabilizing the randomness and fluctuation of wind and light power output. The dispatching model aims to coordinate the operation of small hydropower stations and wind power, photovoltaic and other new energy, and improve the system new energy consumption capacity and operation stability. The optimization operation of the new energy power system containing rich small hydropower stations needs to consider clean energy consumption and output stability, and the maximum power generation can effectively reduce the wind and light abandonment rate, and the minimum total output fluctuation of the system can help to reduce the grid regulation pressure and improve the power quality, so the target function of the system dispatching model is designed with the maximum power generation and the minimum total fluctuation as the target.

[0033] The dispatching model of the new energy power system containing the rich small hydropower needs to follow the physical law of the power system operation and the dynamic characteristics of the hydraulic system, and the constraint conditions jointly constitute the feasible region of the decision variables. The main considerations of the dispatching model of the new energy power system are the following key constraints: the power flow constraint, based on the Kirchhoff's law, to ensure that the power generation output can be safely and reliably transmitted to the load center under a certain network topology; the power balance constraint, to make the total power generation output and the total load demand match in real time; the water power station conversion and output correlation constraint, including the instantaneous conversion relationship of water energy to electric energy and the hydraulic coupling relationship between cascade hydropower stations, the former is described by the power characteristic equation of the water turbine, and the latter is represented by considering the flow time delay and the flow balance of the flow coefficient, which jointly determine the space-time distribution of the hydropower output; the basin hydropower station reservoir capacity dynamic balance constraint, i.e. the continuity equation of the reservoir, which establishes the dynamic connection of the reservoir dispatching in the time dimension, and is the mathematical model basis for the hydropower to play the cross-period regulation capacity and smooth the new energy fluctuation. These constraints are coupled with each other and jointly constitute a high-dimensional, nonlinear and complex optimization problem.

[0034] In this embodiment, the deterministic model of the new energy power system is constructed according to the hydropower space-time matrix model of the new energy power system, the preset objective function and constraint condition of the system dispatching model, so as to determine the benchmark value of the optimization target of the new energy power system according to the deterministic function. The deterministic model is the most economic (or most consumption) operation point that the system can reach under the assumption that the prediction is completely accurate. Specifically, the load prediction value and the operation parameter prediction value of each unit of the new energy power system obtained by S100 are substituted into the deterministic model to obtain the benchmark value of the optimization target of the new energy power system.

[0035] S400, based on the information gap decision theory, the IGDT dispatching model of the system is constructed according to the uncertainty model, the benchmark value and the deterministic model of the system, and the IGDT dispatching model is solved to obtain the output plan of each unit in the system and the optimization target, wherein the uncertainty model represents the uncertainty and volatility of the hydropower output and the system load in the system.

[0036] In the deterministic dispatching, the small hydropower and the system load are often regarded as deterministic quantities, but in the actual system, the above parameters have uncertainty and volatility, therefore, the uncertainty model is constructed for the small hydropower and the system load, so as to improve the accuracy and effectiveness of the dispatching strategy based on the uncertainty model.

[0037] In actual scheduling work, it is often necessary to evaluate different risks, and quantify the influence of uncertainty on the scheduling result. Therefore, the embodiment applies IGDT to construct an IGDT scheduling model of the system from the perspectives of risk aversion and opportunity seeking according to the uncertainty model, the benchmark value and the deterministic model of the new energy power system, and then solves the IGDT scheduling model to find the load expected output plan of each unit in the system and the optimization target.

[0038] The new energy power system scheduling method with small hydropower stations provided by the embodiment of the application improves the accuracy and reliability of the scheduling result by constructing a hydropower space-time matrix model of the system, so as to consider the topological structure and water flow state between each small hydropower station and conventional hydropower station in the basin when making a decision, and avoid decision-making errors caused by model simplification. In addition, by applying the information gap decision theory, the influence of uncertainty on the scheduling result can be quantified, and the scheduling strategies under different risks and different effects can be provided for decision makers, so as to ensure the reliability and economy of the final scheduling scheme.

[0039] In an embodiment of the application, S100, the load prediction value of the system and the operation parameter prediction value of each unit are acquired, and the specific steps include: S110, data preparation and multi-dimensional feature construction. Historical output data of at least 2-3 years are prepared, and the longer the time span is, the more climate interannual variations can be captured. Months are converted into sine and cosine values to eliminate the boundary fracture between December and January, and a ring-shaped time representation is formed. The average output, fluctuation standard deviation and change trend of each record in the previous 7 days and 30 days are calculated to reflect the real operation mode in this period by using a sliding window statistic. The pth day of the year and the qth day of the week are extracted to capture the operation rules within a week and within a year. Finally, each data forms a feature vector, including time coding, recent statistical features and trend information.

[0040] S120, clustering with time continuity constraint. All data are sorted according to the pth day of the year to ensure the continuity of the time axis. K-means is run to obtain the initial 4 cluster centers. It is checked whether each cluster is continuous on the time axis. If there are time fragments (for example, March is classified into class A, April is classified into class B, and May is classified into class A), these fragments are forced to be merged to ensure that each class corresponds to a continuous time period. According to the order of appearance of each cluster in a year, they are sequentially marked as spring, summer, autumn and winter.

[0041] S130, Season mapping and rationality verification. The month with the highest data frequency in each cluster is taken as the representative month, and the natural seasons are matched in calendar order (March-May is spring, June-August is summer, September-November is autumn, and December-February is winter in the northern hemisphere; the order is reversed in the southern hemisphere). Draw a heat map of each season output (horizontal axis is month, vertical axis is hour of the day), intuitively check if the spring-summer-autumn-winter pattern difference is significant, and judge the rationality of the cluster.

[0042] S140, Establishing a prediction model for each season. The historical data is divided into four parts according to the season label. For each season, the historical output sequence is constructed as input. For example, the output values of the past 7 days, the detailed output of the last 24 hours, the average value, the standard deviation and the latest trend are used. Short-term prediction (1-24 hours) is suitable for tree-based models; ultra-short-term (less than 1 hour) can use ARIMA; high-precision requirements can integrate multiple algorithms. The four seasonal models share the feature engineering logic but are independently trained to capture their unique output patterns. Record the historical output mean, standard deviation and data volume of each season for subsequent prediction correction.

[0043] S150, Prediction execution and dynamic correction. Calculate the distance between the feature vector at the current time point and the four cluster centers, and select the nearest cluster as the belonging season. If the predicted value deviates from the historical mean of the season by more than 3 times the standard deviation (such as extreme weather), introduce the data of the same period last year or the recent trend for weighted smoothing to avoid unreasonable results.

[0044] S160, Continuous optimization and evaluation. Calculate the prediction error of spring, summer, autumn and winter respectively, and focus on the stability of the season boundary period. Online update: adjust the model with new data every month to adapt to the gradually changing climate. Drift monitoring: re-run the cluster analysis every quarter, if the cluster center deviates significantly, it means that the climate pattern has changed, and the model needs to be updated as a whole. Feature engineering is the core, periodic encoding and statistical features are more important than algorithms; time continuity constraints must be implemented; a dynamic correction mechanism is established to deal with extreme weather.

[0045] In this embodiment, the system load prediction value is obtained in a similar manner, which is not described here.

[0046] In an embodiment of the present application, S200, based on the topological structure and water flow motion parameters between nodes in the basin involved by the system, a water-electricity space-time matrix model of the system is constructed, specifically including: S210, according to the topological structure and water flow motion parameters between nodes in the basin, a water flow time delay model between upstream and downstream hydropower stations is constructed.

[0047] In the embodiment, the time required for the water flow of the upstream small hydropower station to reach the downstream hydropower station is referred to as water flow time lag. The size of the water flow time lag is affected by the change of river runoff. Small hydropower stations are mostly developed in a runoff mode, and lack the regulation capacity of large reservoirs, so the outflow process is directly affected by the change of natural runoff, resulting in stronger dynamic fluctuation characteristics of the water flow time lag. This is in sharp contrast to large hydropower stations with annual regulation capacity. Small hydropower stations are often distributed in a string of beads mode in small and medium-sized river basins, the distance between upstream and downstream power stations is short (usually < 10 km), the river channel slope is large, and the water flow propagation time can be as short as minutes. The time lag change in such a time and space scale requires high-frequency monitoring and fine modeling. The control parameters of small hydropower stations are sensitive, the Saint Venant equation can express the dynamic water flow time lag of small hydropower stations, and the degree of simplification of the Saint Venant equation set is closely related to the characteristics of the river channel. Based on this, the water flow time lag model between upstream and downstream hydropower stations is established for a new energy power system containing rich small hydropower stations.

[0048] In the embodiment, in a pair of upstream and downstream hydropower stations (i.e. a pair of upstream and downstream nodes), the upstream node is a small hydropower station. The downstream hydropower station must be a conventional hydropower station, and the downstream node must be a water flow receiving and dispatching influencing hydropower facility, otherwise the time lag has no practical modeling significance.

[0049] In a feasible embodiment, the upstream node can also be a conventional hydropower station (such as a seasonal regulation reservoir), and the discharge process of the conventional hydropower station already contains an artificial regulation component. At this time, the water flow time lag should be divided into a “natural propagation time lag + artificial dispatching time lag” composite model. As long as it is a water flow output node (such as a reservoir, a medium-sized hydropower station, and a stream source), it can be used, and the key is to obtain the water flow velocity and runoff data of the node. For a pure natural river section, a statistical model based on the measured flow velocity can be directly used.

[0050] Based on this, the water flow movement parameters at least include the water flow velocity and the runoff, and S210, according to the topological structure and the water flow movement parameters between the nodes in the river basin, a water flow time lag model between upstream and downstream hydropower stations is constructed, which specifically includes: S211, historical water flow velocity and runoff between a target upstream node and a target downstream node are obtained, wherein the target upstream node and the target downstream node are any pair of upstream and downstream nodes in the river basin.

[0051] In the embodiment, in order to obtain a fine and complete dynamic water flow time lag of a small hydropower station, a Saint Venant equation needs to be constructed. The accurate expression of the Saint Venant equation is relatively complex, and many factors are considered. In actual engineering, the water flow time lag model can be constructed by segmenting and linearizing the historical water flow velocity and runoff data.

[0052] S212. Based on the Big M method and piecewise linearization, a flow time delay model between the upstream and downstream nodes of the target is constructed according to the historical flow velocity and runoff between the upstream and downstream nodes of the target.

[0053] In this embodiment, the historical water flow time delay curve is determined by the historical water flow velocity between the upstream node and the downstream node of the target. The historical water flow time delay curve can be expressed as: (1) In the formula, Let represent the water flow time delay from node j to node i, and let represent the water flow time delay curve between node i and node j, where node i is a small hydropower node. Let be the water flow velocity between node i and node j at time t.

[0054] Furthermore, the water flow velocity can be calculated using the following formula: (2) In the formula, In order to be in Outbound flow from node i to node j at any given time.

[0055] Using the Big M method and piecewise linearization, the water flow velocity (or flow rate) is divided into... The time-delay model expression for the water flow is obtained by dividing the water flow velocity (or flow rate) into several intervals. Specifically, the water flow velocity (or flow rate) is divided to establish a time-delay model, which is a model of the time required for the water to flow from the upstream power station to the downstream power station. However, since the relationship between the water flow velocity (or flow rate) and the time delay may be nonlinear, direct modeling is difficult. Therefore, piecewise linearization technique is used to divide the variable of water flow velocity (or flow rate) into several discrete intervals, resulting in... C There are several intervals, and within each interval, a linear function is used to approximate the nonlinear relationship.

[0056] The expression for the water flow time delay model is: (3) In the formula, Indicates in Time, from the node To the node Time parameters, such as transportation time, processing time, etc.; Indicates in Time, node To the node Let c be the water flow time delay at the c-th discontinuity, where the c-th discontinuity is the starting point of the c-th interval, and the c-th interval is any one of the C intervals. This can be understood as scene index, disturbance factor, etc. , The first The runoff at the first breakpoint and the (c+1)th breakpoint; For the watershed between node i and node j, the first... A linear expression for each interval; In order to be in Outbound flow from node i to node j at any given time; , They represent the first Time-related parameters for the interval and the (c+1)th interval, such as stage duration and stage time nodes; Indicates the first interval from node To the node Time parameters; It is a sufficiently large positive number for the big M method.

[0057] S220. Based on the water flow time delay model of upstream and downstream hydropower stations and the topology between hydropower stations, construct the hydropower spatiotemporal matrix model of the system.

[0058] In this embodiment, a hydropower spatiotemporal matrix is ​​constructed based on the node topology of the watershed network. For different topologies, only the size and values ​​of each matrix need to be adjusted, eliminating the need for extensive repetitive modeling work. This approach can simply and effectively characterize the spatiotemporal coupling characteristics of new energy power systems containing abundant small hydropower.

[0059] Specifically, a hydropower spatiotemporal matrix model is constructed based on the hydropower station type, the watershed association between upstream and downstream small hydropower stations, the magnitude of water flow time delay between small hydropower stations, and the water flow allocation ratio of watershed network nodes in the small hydropower topology of the new energy power system. It mainly consists of a watershed node matrix, a watershed association matrix, a water flow time delay matrix, and a watershed allocation matrix. Among them, the watershed node matrix includes the watershed node type; the watershed association matrix includes the water flow direction of the watershed nodes; the water flow time delay matrix includes the magnitude of the water flow time delay of the watershed nodes; and the watershed allocation matrix includes the magnitude of the water flow allocation ratio of the watershed nodes.

[0060] (4) In the formula, This is a watershed node matrix, reflecting the types of watershed nodes. The value is 0-1, where 0 indicates that node i is a hydropower station without reservoir capacity, and 1 indicates that node i is a hydropower station with reservoir capacity. This is a watershed correlation matrix, reflecting the direction of water flow at the nodes of the watershed. The value represents the direction of water flow. A value of 0 indicates that there is no hydraulic connection between nodes i and j, a value of 1 indicates that the water flow direction is from node j to node i, and a value of -1 indicates that the water flow direction is from node i to node j. This is the water flow time delay matrix, reflecting the magnitude of the water flow time delay at the nodes of the watershed; The watershed allocation matrix reflects the proportion of water flow distribution among the nodes in the watershed. This represents the ratio of the traffic transmitted from node j to node i to the total traffic transmitted from node j.

[0061] In this embodiment, by using historical water flow time delay curves, the time it takes for water to travel from an upstream small hydropower station to a downstream hydropower station can be accurately calculated based on real-time or predicted water flow velocity. This allows upstream and downstream hydropower stations to plan their power generation schedules in advance (such as adjusting the timing of upstream flow release and simultaneously adjusting unit output downstream), avoiding water wastage or power supply gaps, and improving the accuracy and flexibility of scheduling. Based on the water flow time delay curve-based water flow time delay model, the impact of runoff changes on time delay can be quantified, thereby optimizing the water resource utilization efficiency of small hydropower stations (such as adjusting water storage and power generation rhythms according to time delays) and improving overall power generation efficiency.

[0062] In one embodiment of the present invention, for a new energy power system containing abundant small hydropower, the new energy power generation should be absorbed as much as possible, and hydropower should compensate for the fluctuations in new energy power generation as much as possible. The complementary operation effect can be quantified by utilizing the complementary power generation and output fluctuations. Therefore, this embodiment designs the objective function of the new energy power system dispatch model with the goal of maximizing power generation and minimizing total fluctuations. The objective function is expressed as follows: (5) In the formula, To complement power generation, The complementary output fluctuations within the cycle, , , The first Taiwan Water & Electricity, the Typhoon power, the Taiwan Solar's output at time t , , These represent the number of units used for hydropower, wind power, and solar power, respectively. For period The average internal output.

[0063] In one embodiment of the present invention, the new energy power system dispatch model mainly considers the following key constraints: power flow constraints, power balance constraints, hydropower station conversion and output correlation constraints, and basin hydropower station reservoir capacity dynamic balance constraints. The specific expressions are as follows: 1) Power flow constraints are represented as: (6) In the formula, , These are the active and reactive power flowing into node i at time t, respectively; , , where are the voltages of node i and node j at time t, respectively; N is the number of nodes; , Gij and B ij are the conductance and susceptance between node i and node j, respectively; is the phase angle difference between node i and node j at time t.

[0064] 2) The power balance constraint is expressed as: (7) where, is the system load at time t.

[0065] 3) The hydropower station conversion and output correlation constraint is expressed as (8) where, is the gravitational acceleration; is the power generation efficiency of node i; , are the net water head and flow rate of node i, respectively; is the sum of the outputs of all upstream hydropower stations considering the water flow time lag and water flow distribution coefficient at the scheduling time of node i is the mathematical expectation of the cost of indicates the water flow time lag (TL) and the set of hydropower stations that affect the power generation water head and flow rate of node i through the water flow distribution coefficient, and the sum of the outputs of these hydropower stations will indirectly change the power generation cost of the first hydropower station, so the influence needs to be quantified through the mathematical expectation

[0066] is the sum of the outputs of all upstream hydropower stations considering the water flow time lag and water flow distribution coefficient at the scheduling time of node i is obtained by the following formula: (9) where, indicates the set of hydropower stations that have direct hydraulic correlation with the node at the scheduling time t, and is used to define the grouping of upstream or same-level hydropower stations that have direct influence on the water flow state and output cost of the node at the scheduling time; T indicates the total duration of the scheduling period (upper limit of time), which is used to quantify the time node (such as ) considering the water flow time lag in formula (9), and reflects the association between the time span of water flow transmission between hydropower stations and the scheduling period; is the water flow time difference between node i and node j considering the hydraulic connection topology of the basin network.

[0067] In a feasible embodiment, hydrological monitoring equipment can be arranged in the river channel of node i and node j in the basin to actually measure the time of water flow transmission from node j to node i, and the average value is taken after multiple measurements, which is suitable for basins with long-term hydrological observation basis.

[0068] ​​​In another possible embodiment, a professional hydrological and hydraulic model such as MIKE can be used to input data such as basin terrain, river channel parameters, water flow velocity, and simulate the transmission process of water flow from node j to node i, and then obtain the time difference

[0069] In another possible embodiment, the river length between node i and node j (the river length is measured by GIS) and the average water flow velocity (the average water flow velocity is obtained by statistical analysis or model inversion of historical hydrological data) can be used to calculate the time difference by the formula .

[0070] 4) The dynamic balance constraint of the reservoir capacity of the basin hydropower station is represented as: (10) In the formula, is the associated flow of node i of the basin network at scheduling time t considering the hydraulic association and the water flow time lag between nodes; , are the reservoir capacity and the natural inflow of node i of the basin network at scheduling time t, respectively, is the scheduling time interval.

[0071] In an embodiment of the present application, the IGDT method is used to model water flow and load uncertainty from the perspectives of risk aversion and opportunity seeking, to form a robust optimization model and an opportunity optimization model, and to provide a basis for scheduling decisions under different risk preferences. Based on this, the IGDT scheduling model of the new energy power system includes a robust scheduling model and an opportunity scheduling model. Then, S400, based on the information gap decision theory, constructs an IGDT scheduling model of the system according to the uncertainty model, the benchmark value and the deterministic model of the system, and specifically includes: S410, taking the minimum total uncertainty radius of the system as the target, constructing a robust scheduling model according to the uncertainty model, the benchmark value and the deterministic model of the system, and a preset robust level factor.

[0072] In this embodiment, the robust scheduling model has the advantages of low parameter sensitivity and strong risk aversion ability, and can guarantee that the optimization target is within an acceptable range to seek the maximum uncertainty radius of the water power output and load uncertainty.

[0073] In a possible embodiment, the uncertainty model is represented by the following formula:

[0074] (11) In the formula, is the total uncertainty radius of the system, , uncertain radius of hydropower output and system load, respectively, , uncertain coefficient of hydropower output and system load, respectively, denotes the uncertain set of the output of the i-th hydropower station at time t, denotes the actual output of the i-th hydropower station at time t, denotes the predicted output of the i-th hydropower station at time t, and the actual output of each hydropower station at time t the absolute deviation between the actual output and the corresponding predicted output does not exceed the uncertain radius of the hydropower output multiplied by the size of the predicted output; denotes the uncertain set of the system load at time t, denotes the predicted value of the system load at time t, denotes the actual value of the system load at time t.

[0075] The uncertain coefficients of hydropower output and system load are quantitatively calculated according to "standard deviation of prediction error × confidence factor" or "maximum error quantile". The setting of uncertain coefficients usually combines data statistics and decision preferences, and the specific methods are as follows: 1) Historical data statistical method: analyze the historical fluctuation data of wind power, photovoltaic output, and small hydropower inflow (such as calculating the coefficient of variation and standard deviation), and determine the reasonable interval of uncertain coefficients based on the statistical results of fluctuation range. For example, if the historical maximum fluctuation of wind power output in a certain region is 15%, the upper limit of the uncertain coefficient can be set to 0.15 accordingly.

[0076] 2) Risk preference method: set by the decision maker according to the tolerance of risk. For example, conservative decision makers (pursuing absolute stability) choose smaller uncertain coefficients (such as 0.05), and aggressive decision makers (pursuing opportunity gains) choose larger uncertain coefficients (such as 0.2).

[0077] In the IGDT scheduling framework of new energy power systems containing small hydropower, the core role of the uncertain coefficient is to quantify the "tolerance boundary" of uncertainty. For the robust scheduling model: the uncertain coefficient defines the range of "worst uncertain scenarios", ensuring that the scheduling scheme can still meet power balance, safety constraints, etc. within this range, and realizes "risk aversion". For the opportunity scheduling model: the uncertain coefficient defines the "acceptable risk interval", within which the scheduling target is pursued (such as reducing cost, improving new energy consumption rate), and realizes "opportunity seeking".

[0078] ​​The uncertainty model is used to represent the error distribution of the system, which refers to the statistical distribution of the deviation between the actual value and the predicted value of the small hydropower flow or system load, and is used to quantify the uncertainty. In the actual system, the runoff of small hydropower and the load (i.e. the demand for electricity) are not completely determined, and they have volatility and uncertainty. In the deterministic scheduling model, these quantities are usually treated as known constants, and the possible errors in them are ignored, thereby affecting the final scheduling strategy. In fact, these quantities are random variables and will deviate from the predicted value. Therefore, the uncertainty model is constructed to consider this uncertainty in scheduling, thereby improving the accuracy of the scheduling result.

[0079] When the decision maker wants to ensure that the scheduling model has good risk aversion ability within an acceptable optimization target range, and the system is not sensitive to the fluctuation of water load uncertainty, the robust scheduling model can be used: (12) In the formula, is the expected value of risk aversion, is the benchmark value of the optimization target, is the robustness level factor, The greater the model is, the stronger the risk resistance ability is.

[0080] In this embodiment, the deterministic model is composed of a hydropower space-time matrix model, an objective function and constraint conditions (i.e. formulas (1)-(7)). In the deterministic model, the constraint conditions of the system scheduling model include power balance constraints and flow constraints. Only these two constraint conditions can ensure that the instantaneous power generation of the power grid is equal to the load, ensure that the water quantity-power-head physical relationship of hydropower is correct, and enable the optimizer to quickly give an interpretable and reproducible optimization target benchmark value. The remaining constraints belong to operation details, which can be temporarily relaxed in the benchmark stage to prevent the model from having no feasible solution due to small probability boundaries and affecting the acquisition of the benchmark value.

[0081] S420, the risk aversion ability of the scheduling model in the preset optimization target range is maximized, and an opportunity scheduling model is constructed according to the uncertainty model of the system, the benchmark value and the deterministic model, and a preset opportunity seeking factor.

[0082] In this embodiment, the opportunity scheduling model has a better optimization target, and can seek a smaller minimum uncertainty radius of the load uncertainty of the hydropower output under the premise that the system uncertainty quantity is expected to fluctuate less.

[0083] When the uncertainty quantity fluctuates less and the decision maker wants to seek a better optimization target, the opportunity seeking model can be selected. The opportunity seeking model takes the sum of the uncertainty level The minimum as the target, has a greater opportunity to achieve the expected optimization target, but the model is greatly affected by the water load uncertainty: (13) wherein, is the expected value of the cost of opportunity seeking, is the opportunity seeking factor, The smaller the value is, the smaller the degree of risk preference is.

[0084] In an embodiment of the present application, a genetic algorithm is combined with a Cplex linear solver to solve the IGDT scheduling model, and the uncertain coefficients and IGDT strategies are set in combination with the actual situation to solve the robust scheduling or opportunity scheduling model, so that the output plan and optimization target of the small hydropower under the robust scheduling model or the opportunity scheduling model under the uncertain radius are finally obtained, and the small hydropower optimization target is quantified from the two angles of risk avoidance and opportunity seeking.

[0085] In the present embodiment, the output plan and optimization target finally solved by the robust scheduling or opportunity scheduling model specifically include: 1) Output plan content. Power output sequence: time-varying output curves of small hydropower, wind power and photovoltaic power in a scheduling period (such as hourly or daily), which clearly indicate the active power values in each period. Auxiliary decision parameters: if conventional power sources (such as thermal power) are involved, the output arrangement of peak regulation and backup of the conventional power sources is also included; at the same time, the operating parameters (safety constraints need to be met) of the voltage of the key nodes of the power network and the line flow are covered.

[0086] 2) Optimization target content. Economic target: minimizing the total generation cost (including the generation cost of small hydropower, thermal power and the like), the cost of abandoned wind power, abandoned light and abandoned water, or maximizing the economic benefits brought by new energy consumption. Technical target: maximizing the new energy consumption rate (the effective utilization proportion of wind power, photovoltaic power and small hydropower), and meeting the power system power balance constraint (total generation output matches total load). Safety target: ensuring that the power network flow does not exceed the limit and the node voltage is in the qualified interval, and in the robust scheduling, the system still needs to be ensured to be able to stably operate under the "worst uncertain scenario". Risk-reward trade-off target: the robust scheduling focuses on "risk avoidance", and the cost / constraint satisfaction under the "worst scenario" is the core; the opportunity scheduling focuses on "opportunity seeking", and pursues a better economic or technical target within an acceptable risk interval.

[0087] In an embodiment of the present application, the load prediction value of the system and the operation parameter prediction value of each unit are substituted into the deterministic model to solve the baseline value of the system optimization target, specifically including: based on the genetic algorithm and the NSGA-II method, the load prediction value of the system and the operation parameter prediction value of each unit are substituted into the deterministic model to generate a Pareto optimal solution set; based on the ideal point method, the optimal solution is found in the Pareto optimal solution set to obtain the baseline value of the system optimization target.

[0088] In the embodiment of the present application, the Pareto optimal solution set is generated by the genetic algorithm and the NSGA-II method, and the ideal point method is applied to determine the benchmark target value of the deterministic scheduling model by taking the small hydropower output as a variable.

[0089] An embodiment of the present application proposes a scheduling method taking a small hydropower-photovoltaic-wind power complementary power generation system in a certain city as an example.

[0090] In the embodiment, the scheduling period of the system is 24 hours, and the scheduling time interval is 1 hour. The small hydropower is composed of a reservoir hydropower station 、 and a non-reservoir hydropower station 、 . Figure 2 For the small hydropower cluster structure in the embodiment of the present application, the small hydropowers A-D are H 1 -H 4, and the values between the hydropower stations are the water flow time lag. The parameters of the small hydropower are shown in Table 1. The local built wind power and photovoltaic data are selected and clustered into typical output curves of different seasons by using the K-means algorithm, as shown in Figure 3 、 4 , Figure 3 is a typical output diagram of wind power in the embodiment of the present application, Figure 4 is a typical output diagram of photovoltaic in the embodiment of the present application.

[0091] Table 1 Small hydropower parameters

[0092] The small hydropower parameters and the summer photovoltaic and wind power prediction data are substituted into the deterministic scheduling model to obtain the optimal total scheduling cost of the system F b = 5.3306 million yuan, and the output plan of each unit is shown in Figure 5 , Figure 5 is a deterministic scheduling processing plan diagram in the embodiment of the present application. From Figure 5It can be seen that the peak regulation task is mainly undertaken by small hydropower A, and at 4:00 and 11:00, due to the large change of load, it is affected by the climbing constraint; small hydropower C participates in the peak regulation, from 7:00 to 17:00, photovoltaic starts to generate electricity, and the load demand starts to decrease from 8:00, the output of small hydropower A gradually decreases with the decrease of peak regulation demand, and stops at 11:00; from 11:00 to 13:00, the load demand changes little, and the peak regulation task is mainly undertaken by small hydropower C; after 13:00, the output of photovoltaic gradually decreases with the decrease of light intensity, and the load demand gradually increases, small hydropower A starts at 14:00, and the output gradually increases to meet the load demand; the load demand increases to the peak at 19:00, small hydropower A is in full power state at 17:00 due to the maximum output constraint, and small hydropower B starts to participate in the peak regulation task; until 21:00, the load demand decreases significantly, small hydropower B stops, and the peak regulation task is undertaken by small hydropower A.

[0093] To further analyze the influence of different uncertainty factors on the scheduling strategy, the robustness level factor σ and the opportunity level factor κ are set to 0.03, a plurality of groups of weight coefficients are selected, the IGDT robust scheduling model and the opportunity scheduling model are optimized and solved respectively, and the corresponding results are shown in Table 2 and Table 3.

[0094] Table 2 Robust scheduling results under different weights

[0095] Table 3 Opportunity scheduling results under different weights

[0096] The response characteristics of the system to different uncertainty factors are significantly different. The value of the uncertainty coefficient can change the uncertainty radius corresponding to a single uncertainty factor, but the influence on the overall uncertainty radius of the system is relatively limited. In the weight configuration process, the scheduling personnel can follow the principle of "the higher the sensitivity, the greater the weight", and determine the weight value according to the actual system operation experience and historical statistical data.

[0097] Taking the uncertainty coefficient ( , ) = (1,5) as an example, the critical scheduling cost F obtained by solving the IGDT robust scheduling model is 54.9050. =0.6990, =0.0278, Under the condition of 0.8381, that is, when the actual fluctuation of hydropower does not exceed 69.9% of the predicted value and the load fluctuation does not exceed 2.78%, the total system dispatch cost can be controlled within 54.9050. It can be seen that the robust dispatch strategy is more conservative than the initial scheme, and tends to deal with potential uncertainties by increasing the output of conventional units, thus causing a corresponding increase in dispatch costs.

[0098] In the IGDT opportunistic scheduling model, the critical scheduling cost decreases to F=51.7070. =0, =0.0192, =0.1925, meaning that if the actual load decreases by 1.92% compared to the predicted value, the total system scheduling cost can be further reduced to 51.7070. Opportunistic scheduling strategies exhibit a more proactive scheduling style, optimizing total cost by reducing the output of conventional units in the hope that uncertainty will evolve in a favorable direction.

[0099] In one embodiment of the present invention, a new energy power system dispatching device incorporating small hydropower is also proposed, which can be referred to. Figure 6 , Figure 6 This is a structural block diagram of a new energy power system dispatching device according to an embodiment of the present invention. The device includes: The data acquisition module 601 is used to acquire the load forecast value of the system and the operating parameter forecast value of each unit, wherein the units include new energy units and hydropower units; The model building module 602 is used to construct a hydropower spatiotemporal matrix model of the system based on the topology and water flow parameters between nodes in the basin involved in the system. Each node represents a hydropower station within the basin, which includes several small and conventional hydropower stations. The system's load forecast and the predicted operating parameters of each unit are substituted into a deterministic model for solution to obtain the baseline value of the system's optimization objective. The deterministic model consists of the hydropower spatiotemporal matrix model, a pre-defined objective function of the system scheduling model, and constraints. The objective function is designed to maximize power generation and minimize total fluctuations. The strategy generation module 603 is used to construct the IGDT scheduling model of the system based on the information gap decision theory, according to the uncertainty model of the system, the benchmark value and the deterministic model, and solve the IGDT scheduling model to obtain the output plan and optimization target of each unit in the system. The uncertainty model represents the uncertainty and fluctuation of hydropower output and system load in the system.

[0100] The new energy power system dispatching device containing small hydropower proposed in this invention constructs a hydropower spatiotemporal matrix model of the system to consider the topological structure and water flow state between various small hydropower stations and conventional hydropower stations in the basin when making decisions, thereby improving the accuracy and reliability of dispatching results and avoiding decision-making errors caused by model simplification. Secondly, by applying information gap decision theory, the impact of uncertainty on dispatching results can be quantified, and dispatching strategies under different risks and effects can be provided to decision-makers, ensuring the reliability and economy of the final dispatching scheme.

[0101] Figure 7 An internal structural diagram of a computer device according to one embodiment of the present invention is shown. This computer device can specifically be a terminal or a system. Figure 7 As shown, the computer device includes a processor, memory, and network interface connected via a system bus. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and may also store a computer program. When executed by the processor, this computer program causes the processor to perform the steps in the above-described method embodiments. The internal memory may also store a computer program, which, when executed by the processor, causes the processor to perform the steps in the above-described method embodiments. Those skilled in the art will understand that... Figure 7 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0102] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps in the above method embodiments.

[0103] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, causes the processor to perform the steps in the above method embodiments.

[0104] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by a computer program instructing relevant hardware, and the program can be stored in a non-volatile computer readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiment methods. Any reference to memory, storage, database or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0105] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0106] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A method for dispatching a new energy power system including small hydropower, characterized in that, The method includes: Obtain the load forecast value of the system and the operating parameter forecast value of each unit, wherein the units include new energy units and hydropower units; Based on the topology and water flow parameters between nodes in the basin involved in the system, a hydropower spatiotemporal matrix model of the system is constructed, wherein a node represents a hydropower station in the basin, and the basin includes several small hydropower stations and conventional hydropower stations. The load forecast value of the system and the operating parameter forecast value of each unit are substituted into the deterministic model for solution to obtain the benchmark value of the system optimization objective. The deterministic model consists of the hydropower spatiotemporal matrix model, the objective function of the preset system scheduling model and the constraints. The objective function is designed with the goal of maximizing power generation and minimizing total fluctuation. Based on the information gap decision theory, an IGDT scheduling model for the system is constructed according to the uncertainty model, the benchmark value, and the deterministic model. The IGDT scheduling model is then solved to obtain the output plan and optimization target of each unit in the system. The uncertainty model characterizes the uncertainty and volatility of hydropower output and system load in the system.

2. The method as described in claim 1, characterized in that, The construction of the hydropower spatiotemporal matrix model of the system, based on the topology and water flow parameters between nodes in the watershed involved in the system, specifically includes: Based on the topology and water flow parameters between nodes in the basin, a water flow time delay model between upstream and downstream hydropower stations is constructed. Based on the water flow time delay model of the upstream and downstream hydropower stations and the topology between the hydropower stations, a hydropower spatiotemporal matrix model of the system is constructed.

3. The method as described in claim 2, characterized in that, The water flow parameters include at least the water flow velocity and the flow rate; The step of constructing a water flow time delay model between upstream and downstream hydropower stations based on the topology and water flow parameters between nodes within the watershed specifically includes: The historical flow velocity and runoff between the target upstream node and the target downstream node are obtained, wherein the target upstream node and the target downstream node are any pair of upstream and downstream nodes within the watershed; Based on the Big M method and piecewise linearization, a flow time delay model between the upstream and downstream nodes is constructed according to the historical flow velocity and flow rate between the upstream and downstream nodes.

4. The method as described in claim 2, characterized in that, The hydropower spatiotemporal matrix model consists of a basin node matrix, a basin correlation matrix, a water flow time delay matrix, and a basin allocation matrix; The watershed node matrix includes the watershed node types; The watershed correlation matrix includes the water flow direction of the watershed nodes; The water flow time delay matrix includes the water flow time delay magnitude of the watershed nodes; The watershed allocation matrix contains the proportion of water flow allocation to watershed nodes.

5. The method as described in claim 1, characterized in that, The uncertainty model includes the following expression: In the formula, The summation uncertainty radius of the system is given by [the given information]. , These are the uncertain radii of the hydropower station's output and the system load, respectively. , These are the uncertainty coefficients for the hydropower station's output and the system load, respectively. This represents the uncertain set of power output of the i-th hydropower station at time t. Let be the actual output of the i-th hydropower station at time t. Let be the predicted output of the i-th hydropower station at time t, and be the actual output of each hydropower station at time t. The absolute deviation from the corresponding predicted output value shall not exceed the uncertain radius of the hydropower station's output multiplied by the magnitude of the predicted output value. This represents the uncertain set of system load at time t. for The predicted value of the system load at any given time. for The actual value of the system load at any given time.

6. The method as described in claim 1, characterized in that, The IGDT scheduling model of the system includes a robust scheduling model and an opportunistic scheduling model; The IGDT scheduling model of the system, based on the information gap decision theory and according to the uncertainty model of the system, the benchmark value, and the deterministic model, specifically includes: With the goal of minimizing the total uncertainty radius of the system, a robust scheduling model is constructed based on the uncertainty model of the system, the benchmark value, the deterministic model, and a preset robustness level factor. With the goal of maximizing the risk avoidance capability of the scheduling model within a preset optimization target range, an opportunity scheduling model is constructed based on the uncertainty model of the system, the benchmark value, the deterministic model, and the preset opportunity seeking factor.

7. The method as described in claim 1, characterized in that, The step of substituting the system's load forecast and the predicted operating parameters of each unit into a deterministic model for solution to obtain the baseline value of the system's optimization objective specifically includes: Based on the genetic algorithm and NSGA-II method, the load forecast value of the system and the operating parameter forecast value of each unit are substituted into the deterministic model to generate the Pareto optimal solution set; Based on the ideal point method, the optimal solution is found in the Pareto optimal solution set to obtain the benchmark value of the system optimization objective.

8. A dispatching device for a new energy power system including small hydropower, characterized in that, The device includes: The data acquisition module is used to acquire the load forecast value of the system and the operating parameter forecast value of each unit, wherein the units include new energy units and hydropower units; The model building module is used to construct a hydropower spatiotemporal matrix model of the system based on the topology and water flow parameters between nodes in the basin involved in the system. Each node represents a hydropower station within the basin, which includes several small and conventional hydropower stations. The system's load forecast and the predicted operating parameters of each unit are substituted into a deterministic model for solution to obtain the baseline value of the system's optimization objective. The deterministic model consists of the hydropower spatiotemporal matrix model, a pre-defined objective function for the system scheduling model, and constraints. The objective function is designed to maximize power generation and minimize total fluctuations. The strategy generation module is used to construct the IGDT scheduling model of the system based on the information gap decision theory, according to the uncertainty model of the system, the benchmark value and the deterministic model, and solve the IGDT scheduling model to obtain the output plan and optimization target of each unit in the system. The uncertainty model represents the uncertainty and fluctuation of hydropower output and system load in the system.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it causes the processor to perform the steps of the method as described in any one of claims 1 to 7.

10. A computer device, comprising a memory and a processor, characterized in that, The memory stores a computer program that, when executed by the processor, causes the processor to perform the steps of the method as described in any one of claims 1 to 7.

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