Small hydropower station group scheduling method and system considering power station characteristics and power grid constraints

Abstract

The invention discloses a small hydropower station group scheduling method and system considering power station characteristics and power grid constraints. The method comprises the following steps: constructing a fixed constraint condition according to static information of each stage of small hydropower station group; according to runoff and wind and light energy output prediction, dynamic constraint conditions are established in combination with power grid load requirements; based on an NSGA-III model, determining a cascade small hydropower station group optimization scheduling scheme; and completing small hydropower station group scheduling according to the obtained scheduling scheme. According to the method, multiple groups of Pareto optimal scheduling schemes can be generated by taking the maximum total output and the minimum abandoned water as targets on the premise of meeting ecological flow, power grid load balance and unit operation constraints. The method can effectively improve the water energy resource utilization efficiency of small and medium-sized watersheds, enhances the regional new energy consumption capability, and adapts to the operation characteristics of small hydropower station intra-day peak scheduling and limited management personnel.

Description

Technical Field

[0001] This invention belongs to the field of hydropower station optimization technology, specifically relating to a method and system for scheduling small hydropower groups that considers power station characteristics and grid constraints. Background Technology

[0002] Hydropower, as a renewable and clean energy source, has become a key focus of energy development for various countries due to its economic benefits and development potential. Hydropower operation and scheduling is a crucial aspect of hydropower station management and operation, and its level of operation and scheduling directly impacts the overall benefits of the hydropower station.

[0003] Hydropower station operation and scheduling utilizes systems engineering theory and optimization techniques to seek optimal reservoir operation strategies and corresponding decisions, aiming to minimize water wastage while maximizing power generation while meeting the hydropower station's flood control and water resource utilization requirements. Currently widely used scheduling methods mainly include traditional optimization methods (such as linear programming and dynamic programming) and intelligent optimization algorithms (such as particle swarm optimization and artificial neural network algorithms). These technologies have been widely applied in large and medium-sized hydropower stations, and their technology is relatively mature. This is mainly because large and medium-sized hydropower stations generally have high-quality hydrological and rainfall monitoring networks, strong runoff regulation capabilities, and sufficient human resources. Therefore, mature forecasting and scheduling technologies can be effectively applied in both short-term and medium-to-long-term power station scheduling.

[0004] However, the operational characteristics of small hydropower stations in river basins differ significantly from those of large and medium-sized hydropower stations. The production and operation of small hydropower exhibit several characteristics: 1) smaller reservoir capacity, limiting operations to short-term scheduling on a daily to weekly scale; 2) a scarcity of technically skilled management personnel, with unit start-up and shutdown often depending solely on upstream water inflow. These characteristics lead to the following management approach for small hydropower stations: during the flood season, when water inflow is sufficient, they can generate electricity 24 / 7; during the non-flood season, limited regulation capacity results in each station primarily employing a peak-hour scheduling mode, with management personnel starting units during midday and evening peak hours based on water inflow levels, and shutting them down when reservoir water levels are low or during off-peak periods. This extensive management approach results in low water resource utilization efficiency in small and medium-sized river basins. Furthermore, while wind and solar renewable energy are developing rapidly, their output is significantly affected by climate; fully leveraging the regulatory role of small and medium-sized hydropower can further enhance the region's renewable energy absorption capacity. Summary of the Invention

[0005] To address the shortcomings of existing technologies, one of the objectives of this invention is to provide a small hydropower group scheduling method that considers power plant characteristics and grid constraints. This method utilizes deep learning-based runoff predictions for different intervals within the basin, regional wind and solar power output, and grid load demand predictions. It combines static information (including different characteristic water levels, number of installed units, capacity of each unit, turbine characteristic curves, water level-reservoir capacity curves, water level-discharge capacity curves, downstream flow-downstream water level curves, and downstream ecological flow demand) and dynamic information (measured water level at the dam at the start of scheduling and real-time water level at the dam during each period of scheduling) of small hydropower stations at various levels within the basin with an optimized third-generation non-dominated sorting genetic algorithm (NSGA-Ⅲ) to achieve joint optimization scheduling of small hydropower groups.

[0006] The second objective of this invention is to provide a system for implementing the small hydropower group scheduling method that considers power plant characteristics and grid constraints.

[0007] This invention provides a method for dispatching small hydropower groups considering power plant characteristics and grid constraints, comprising the following steps:

[0008] S1. Based on the static information of small hydropower groups at all levels, construct fixed constraints;

[0009] S2. Based on runoff and wind and solar energy output forecasts, and combined with grid load demand, establish dynamic constraints.

[0010] S3. Based on the NSGA-Ⅲ model, determine the optimal scheduling scheme for the cascade small hydropower group;

[0011] S4. Based on the obtained scheduling plan, complete the scheduling of the small hydropower group.

[0012] In step S1, the fixed constraints include reservoir capacity-water level constraints, power output constraints, discharge constraints, and other constraints.

[0013] The reservoir capacity-water level constraint is expressed using the following formula: For any given time t, the reservoir capacity is constrained to be no less than the minimum reservoir capacity. At the same time, it should not exceed the maximum storage capacity at the current moment. ;

[0014] The processing constraints shown are represented using the following formula: For any given time t, the output is constrained to be no less than the minimum output under the minimum safe operation of a single unit in the power plant. At the same time, it shall not exceed the installed capacity of the power station. ;

[0015] The leakage constraint is expressed using the following formula: For any given time t, the power station's discharge is constrained to be no less than the ecological flow required downstream of the power station. At the same time, it should not exceed the maximum discharge flow rate. The required ecological flow downstream of the power station The real-time discharge flow rate or the average discharge flow rate over a period of time is taken; the maximum discharge flow rate is... Take the maximum value between the maximum discharge capacity corresponding to the water level above the dam at time t and the maximum allowable discharge flow during the flood process;

[0016] The other constraints include water balance and non-negativity constraints.

[0017] Step S2 includes the following steps:

[0018] Historical monitoring data of small hydropower stations are obtained, and a prediction model for small hydropower stations is constructed based on a long short-term memory artificial neural network, expressed by the following formula: Where hist represents historical detection data; pre represents the obtained prediction period data;

[0019] Using the next 24 hours as the scheduling period, the hourly prediction results are estimated; as the scheduling period progresses by one hour, the latest hour's measured data is used as the training set to continuously correct the future prediction data.

[0020] The total output of regional thermal power plants at the current moment is used as the total output of the thermal power system for the next hour. The power output demand of small hydropower groups is calculated through the balance relationship. Then, based on the rolling correction prediction results, dynamic constraints are established.

[0021] The rolling correction logic is represented by the following formula: ;

[0022] The following formula is used to calculate the power demand of small hydropower groups based on the balance relationship. : ; To meet the total output requirements;

[0023] Step S3 includes the following steps:

[0024] A target function is established to maximize the overall treatment capacity of cascade small hydropower projects and minimize water wastage.

[0025] Optimize the method for solving the objective function;

[0026] Using predicted runoff and monitored water levels above the dam as inputs, the feasible output range for each small hydropower station in the next hour is determined based on constraints.

[0027] Step S3 is as follows:

[0028] To maximize the overall treatment capacity and minimize the wastewater discharge of cascade small hydropower projects, an objective function is established, expressed by the following formula: ;in, Provide power to various small hydropower stations; Waste water from various small hydropower plants;

[0029] The specific method for optimizing the objective function solution is as follows: set the power generation weight during the preset peak electricity consumption period, and use the weighted output demand as the small hydropower generation demand during the peak period; secondly, set the minimum operating time of the generating units.

[0030] Using predicted runoff and monitored dam water levels as inputs, a population is initialized for each power station within its feasible output range. Considering the new constraints added to the solution of the objective function, the initialized population is mutated, crossovered, and selected. Finally, multiple feasible Pareto optimal solutions are determined based on the objective function. The optimal scheduling scheme for small hydropower stations within the next day is obtained by recursively calculating hourly.

[0031] The present invention also provides a system for implementing the small hydropower group scheduling method that considers power plant characteristics and grid constraints, including a static constraint construction module, a dynamic constraint construction module, an objective function solving module, and a small hydropower group scheduling module;

[0032] The static constraint construction module constructs fixed constraints based on the static information of small hydropower groups at all levels, and uploads the data to the dynamic constraint construction module.

[0033] The dynamic constraint construction module establishes dynamic constraint conditions based on the received data, runoff and wind and solar energy output predictions, and grid load demand, and uploads the data to the objective function solution module.

[0034] The objective function solving module determines the optimal scheduling scheme for the cascade small hydropower group based on the NSGA-Ⅲ model according to the received data, and uploads the data to the small hydropower group scheduling module;

[0035] The small hydropower group scheduling module completes the scheduling of the small hydropower group based on the received data and the obtained scheduling scheme.

[0036] This invention discloses a method and system for dispatching small hydropower groups, considering power plant characteristics and grid constraints. Under the premise of satisfying ecological flow, grid load balance, and unit operation constraints, it generates multiple Pareto-optimal dispatch schemes with the objectives of maximizing overall output and minimizing water wastage. This invention can effectively improve the utilization efficiency of hydropower resources in small and medium-sized river basins, enhance the regional renewable energy absorption capacity, and adapt to the operational characteristics of small hydropower projects with limited daily peak dispatching and management personnel. Attached Figure Description

[0037] Figure 1 This is a schematic flowchart of the method of the present invention;

[0038] Figure 2 This is a schematic diagram of the system of the present invention. Detailed Implementation

[0039] This invention provides a method for scheduling small hydropower groups that considers power plant characteristics and grid constraints, the flowchart of which is shown below. Figure 1 As shown, it includes the following steps:

[0040] S1. Based on the static information of small hydropower groups at all levels, construct fixed constraints;

[0041] In step S1, the fixed constraints include reservoir capacity-water level constraints, power output constraints, discharge constraints, and other constraints.

[0042] The reservoir capacity-water level constraint is expressed using the following formula: For any given time t, the reservoir capacity is constrained to be no less than the minimum reservoir capacity. At the same time, it should not exceed the maximum storage capacity at the current moment. ;

[0043] The processing constraints shown are represented using the following formula: For any given time t, the output is constrained to be no less than the minimum output under the minimum safe operation of a single unit in the power plant. At the same time, it shall not exceed the installed capacity of the power station. ;

[0044] The leakage constraint is expressed using the following formula: For any given time t, the power station's discharge is constrained to be no less than the ecological flow required downstream of the power station. At the same time, it should not exceed the maximum discharge flow rate. The required ecological flow downstream of the power station The real-time discharge flow rate or the average discharge flow rate over a period of time is taken; the maximum discharge flow rate is... Take the maximum value between the maximum discharge capacity corresponding to the water level above the dam at time t and the maximum allowable discharge flow during the flood process;

[0045] The other constraints include water balance and non-negativity constraints.

[0046] S2. Based on runoff and wind and solar energy output forecasts, and combined with grid load demand, establish dynamic constraints.

[0047] Step S2 includes the following steps:

[0048] Historical monitoring data of small hydropower stations are obtained, and a prediction model for small hydropower stations is constructed based on a long short-term memory artificial neural network, expressed by the following formula: Where hist represents historical detection data; pre represents the obtained prediction period data;

[0049] Using the next 24 hours as the scheduling period, the hourly prediction results are estimated; as the scheduling period progresses by one hour, the latest hour's measured data is used as the training set to continuously correct the future prediction data.

[0050] The total output of regional thermal power plants at the current moment is used as the total output of the thermal power system for the next hour. The power output demand of small hydropower groups is calculated through the balance relationship. Then, based on the rolling correction prediction results, dynamic constraints are established.

[0051] The rolling correction logic is represented by the following formula: ;

[0052] The following formula is used to calculate the power demand of small hydropower groups based on the balance relationship. : ; To meet the total output requirements;

[0053] S3. Based on the NSGA-Ⅲ model, determine the optimal scheduling scheme for the cascade small hydropower group;

[0054] Step S3 includes the following steps:

[0055] A target function is established to maximize the overall treatment capacity of cascade small hydropower projects and minimize water wastage.

[0056] Optimize the method for solving the objective function;

[0057] Using predicted runoff and monitored water levels above the dam as inputs, the feasible output range for each small hydropower station in the next hour is determined based on constraints.

[0058] Step S3 is as follows:

[0059] To maximize the overall treatment capacity and minimize the wastewater discharge of cascade small hydropower projects, an objective function is established, expressed by the following formula: ;in, Provide power to various small hydropower stations; Waste water from various small hydropower plants;

[0060] The specific method for optimizing the objective function solution is as follows: set the power generation weight during the preset peak electricity consumption period, and use the weighted output demand as the small hydropower generation demand during the peak period; secondly, set the minimum operating time of the generating units.

[0061] Using predicted runoff and monitored dam water levels as inputs, a population is initialized for each power station within its feasible output range. Considering the new constraints added to the solution of the objective function, the initialized population is mutated, crossovered, and selected. Finally, multiple feasible Pareto optimal solutions are determined based on the objective function. The optimal scheduling scheme for small hydropower stations within the next day is obtained by recursively calculating hourly.

[0062] S4. Based on the obtained scheduling plan, complete the scheduling of the small hydropower group.

[0063] The present invention also provides a system for implementing the small hydropower group scheduling method that considers power plant characteristics and grid constraints, the schematic diagram of which is shown below. Figure 2 As shown, it includes a static constraint construction module, a dynamic constraint construction module, an objective function solution module, and a small hydropower group scheduling module;

[0064] The static constraint construction module constructs fixed constraints based on the static information of small hydropower groups at all levels, and uploads the data to the dynamic constraint construction module.

[0065] The dynamic constraint construction module establishes dynamic constraint conditions based on the received data, runoff and wind and solar energy output predictions, and grid load demand, and uploads the data to the objective function solution module.

[0066] The objective function solving module determines the optimal scheduling scheme for the cascade small hydropower group based on the NSGA-Ⅲ model according to the received data, and uploads the data to the small hydropower group scheduling module;

[0067] The small hydropower group scheduling module completes the scheduling of the small hydropower group based on the received data and the obtained scheduling scheme.

Claims

1. A method for dispatching small hydropower groups considering power plant characteristics and grid constraints, characterized in that, Includes the following steps: S1. Based on the static information of small hydropower groups at all levels, construct fixed constraints; S2. Based on runoff and wind and solar energy output forecasts, and combined with grid load demand, establish dynamic constraints. S3. Based on the NSGA-Ⅲ model, determine the optimal scheduling scheme for the cascade small hydropower group; S4. Based on the obtained scheduling plan, complete the scheduling of the small hydropower group.

2. The small hydropower group dispatching method considering power plant characteristics and grid constraints according to claim 1, characterized in that, In step S1, the fixed constraints include reservoir capacity-water level constraints, power output constraints, discharge constraints, and other constraints.

3. The small hydropower group dispatching method considering power station characteristics and grid constraints according to claim 2, characterized in that, The reservoir capacity-water level constraint is expressed using the following formula: For any given time t, the reservoir capacity is constrained to be no less than the minimum reservoir capacity. At the same time, it should not exceed the maximum storage capacity at the current moment. ; The processing constraints shown are represented using the following formula: For any given time t, the output is constrained to be no less than the minimum output under the minimum safe operation of a single unit in the power plant. At the same time, it shall not exceed the installed capacity of the power station. ; The leakage constraint is expressed using the following formula: For any given time t, the power station's discharge is constrained to be no less than the ecological flow required downstream of the power station. At the same time, it should not exceed the maximum discharge flow rate. ; The required ecological flow downstream of the power station The real-time discharge flow rate or the average discharge flow rate over a period of time is taken; the maximum discharge flow rate is... Take the maximum value between the maximum discharge capacity corresponding to the water level above the dam at time t and the maximum allowable discharge flow during the flood process; The other constraints include water balance and non-negativity constraints.

4. The small hydropower group dispatching method considering power station characteristics and grid constraints according to claim 1, characterized in that, Step S2 includes the following steps: Historical monitoring data of small hydropower stations are obtained, and a prediction model for small hydropower stations is constructed based on a long short-term memory artificial neural network, expressed by the following formula: Where hist represents historical detection data; pre represents the obtained prediction period data; Using the next 24 hours as the scheduling period, the hourly prediction results are estimated; as the scheduling period progresses by one hour, the latest hour's measured data is used as the training set to continuously correct the future prediction data. The total output of regional thermal power plants at the current moment is used as the total output of the thermal power system for the next hour. The power output demand of small hydropower groups is calculated through the balance relationship. Then, based on the rolling correction prediction results, dynamic constraints are established.

5. The small hydropower group dispatching method considering power station characteristics and grid constraints according to claim 4, characterized in that, The rolling correction logic is represented by the following formula: ; The following formula is used to calculate the power demand of small hydropower groups based on the balance relationship. : ; To meet the total output demand.

6. The small hydropower group dispatching method considering power plant characteristics and grid constraints according to claim 1, characterized in that, Step S3 includes the following steps: A target function is established to maximize the overall treatment capacity of cascade small hydropower projects and minimize water wastage. Optimize the method for solving the objective function; Using predicted runoff and monitored water levels above the dam as inputs, the feasible output range for each small hydropower station in the next hour is determined based on constraints.

7. The small hydropower group dispatching method considering power station characteristics and grid constraints according to claim 6, characterized in that, Step S3 is as follows: To maximize the overall treatment capacity and minimize the wastewater discharge of cascade small hydropower projects, an objective function is established, expressed by the following formula: ;in, Provide power to various small hydropower stations; Waste water from various small hydropower plants; The specific method for optimizing the objective function solution is as follows: set the power generation weight during the preset peak electricity consumption period, and use the weighted output demand as the small hydropower generation demand during the peak period; secondly, set the minimum operating time of the generating units. Using predicted runoff and monitored dam water levels as inputs, a population is initialized for each power station within its feasible output range. Considering the new constraints added to the solution of the objective function, the initialized population is mutated, crossovered, and selected. Finally, multiple feasible Pareto optimal solutions are determined based on the objective function. The optimal scheduling scheme for small hydropower stations within the next day is obtained by recursively calculating hourly.

8. A system for implementing the small hydropower group dispatching method considering power plant characteristics and grid constraints as described in any one of claims 1 to 7, characterized in that, It includes a static constraint construction module, a dynamic constraint construction module, an objective function solution module, and a small hydropower group scheduling module; The static constraint construction module constructs fixed constraints based on the static information of small hydropower groups at all levels, and uploads the data to the dynamic constraint construction module. The dynamic constraint construction module establishes dynamic constraint conditions based on the received data, runoff and wind and solar energy output predictions, and grid load demand, and uploads the data to the objective function solution module. The objective function solving module determines the optimal scheduling scheme for the cascade small hydropower group based on the NSGA-Ⅲ model according to the received data, and uploads the data to the small hydropower group scheduling module; The small hydropower group scheduling module completes the scheduling of the small hydropower group based on the received data and the obtained scheduling scheme.

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