Water-wind-light multi-energy complementary scheduling method for coupling PID parameter optimization and hydroelectric generating set combination

Abstract

The invention discloses a water-wind-light multi-energy complementary scheduling method for coupling PID parameter optimization and hydroelectric generating set combination. The method specifically comprises the following steps: calculating a compensation output value of a hydroelectric generating set; the power grid load is known, and the difference between the power grid load and the wind power and photovoltaic output is calculated, so that the compensated output value of the hydroelectric generating set can be obtained; establishing a water-wind-light complementary power station unit combination mathematical model, and determining an objective function and constraint conditions of the model; according to the determined model objective function and constraint conditions, a double-layer nested optimization model is constructed, the outer layer adopts an optimization algorithm to optimize the number of started hydroelectric generating units and PID parameters of a water turbine governor, and the inner layer adopts a dynamic planning method to determine a load optimal distribution strategy to solve the double-layer nested optimization model under the condition that the number of started hydroelectric generating units is given. According to the water-wind-light multi-energy complementary scheduling method coupling PID parameter optimization and hydroelectric generating set combination, the problem that PID parameter optimization and hydroelectric generating set combination are not coordinated in the prior art is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of hydraulic turbine regulation, and relates to a water-wind-solar multi-energy complementary scheduling method combining coupled PID parameter optimization with a hydropower unit. Background Art

[0002] To effectively reduce carbon emissions, a large number of renewable energy sources such as wind and solar are being integrated into the power grid. However, wind and solar output is highly intermittent, volatile, and random. Direct grid integration can have a significant negative impact on the grid's peak load regulation and stable operation. Hydropower, with its advantages of fast regulation, high operational efficiency, clean and efficient operation, and flexible operation, is an ideal power source for peak load regulation and frequency regulation. Combining renewable energy sources such as wind and solar with hydropower to form a coordinated, multi-energy complementary system of water, wind, and solar can effectively promote the grid integration and absorption of wind and photovoltaic power, and reduce the impact of renewable energy integration on the power system.

[0003] However, in practical applications, the coordinated operation of multiple energy resources and the effective coordination of their complementary relationships remain a major challenge, especially in how to maintain near-optimal operating conditions for hydropower units while meeting system demand and peak-shaving capabilities. The hydropower unit combination problem is the first issue to be addressed when formulating a short-term power generation plan for a hydropower station. Secondly, in order to ensure that the hydropower units can fully play their role, the turbine speed governor must have high control performance and automation levels to adapt to the higher frequency control requirements of the power system and ensure that the units have a fast startup process and fast load adjustment. Therefore, how to optimize the solution to the hydropower unit combination problem and optimize the hydropower unit regulation operation strategy are important issues that need to be solved urgently, which are related to the economic and efficient operation of the system. Summary of the Invention

[0004] The purpose of the present invention is to provide a water-wind-solar multi-energy complementary scheduling method that couples PID parameter optimization with the combination of hydropower units, thereby solving the problem of the lack of coordination between PID parameter optimization and the combination of hydropower units in the prior art.

[0005] The technical solution adopted by the present invention is to couple PID parameter optimization with a hydropower unit combination water-wind-solar multi-energy complementary scheduling method, which specifically includes the following steps: Step 1: Calculate the compensated output value of the hydropower unit; given the grid load, calculate the difference between the grid load and the wind power and photovoltaic output to obtain the compensated output value of the hydropower unit; Step 2: Establish a mathematical model for the combination of hydropower, wind and solar power plant units, and determine the model's objective function and constraints; Step 3: Based on the model objective function and constraints determined in step 2, a two-layer nested optimization model is constructed. The outer layer uses an optimization algorithm to optimize the number of hydropower units in operation and the turbine governor PID parameters. The inner layer uses a dynamic programming method to determine the optimal load distribution strategy under a given number of units in operation. Step 4: Solve the two-layer nested optimization model in step 3.

[0006] The present invention is also characterized in that: The specific process of step 1 is: Assuming that the forecast errors of wind power and photovoltaic output obey the normal distribution, the forecast error distribution of wind power and photovoltaic output in period t is expressed by normal distribution, that is, and According to the shortest output forecast technology, the forecast error of wind power and photovoltaic output is controlled within 10%. Therefore, based on the given forecast error, the normal distribution standard deviation of the wind power forecast output value in period t at a 95% confidence level is determined. And the normal distribution standard deviation of the photovoltaic predicted output value , the formula is as follows: (1) (2) Where: 、 for The predicted output values of wind power and photovoltaic power at the moment; The theoretical output values of wind power and photovoltaic power are expressed as: (3) Where: 、 for The theoretical output of wind power and photovoltaic power in the time period; The difference between the grid load and the theoretical wind power output and photovoltaic power output calculated above is the compensation output value of the hydropower unit.

[0007] The specific process of step 2 is: According to the theoretical output of wind power and photovoltaic power and the output value that needs to be compensated for the hydropower unit calculated in step 1, in order to maximize the utilization rate of wind power and photovoltaic power, and at the same time make the hydropower unit quickly and stably reach the required output compensation value, the objective function of this model is: to minimize the average water consumption of the hydropower unit under multiple scenarios. The calculation formula is as follows: (4) The objective function of this model also includes the minimum weighted sum of the time multiplied by the absolute error integral of the speed, the overshoot and the adjustment time, which is calculated as follows: (5) .

[0008] In formula (4) and formula (5), is the average water consumption of the hydro-wind-solar hybrid power station during the entire dispatch period; is the number of generating units in the hydropower station; is the number of time periods in the scheduling period; is the number of scenarios; It is s The weighting factors of the scenarios; Indicates time t Next, n The power generation capacity of hydropower, wind power and photovoltaic units of unit No. The on / off status of the unit, which is a 0-1 variable; Unit flow rate; The scheduling period is long; is the systematic error; is the overshoot; To adjust the time; 、 、 are weights, which are 0.5, 0.3, and 0.2 respectively.

[0009] The constraints of the model in step 2 are as follows: Power Constraints: (6) Where: 、 、 Wind turbines, photovoltaic turbines, and hydropower turbines Total output at any given moment; for The grid load at the moment; Start-stop constraints: (7) Where: For the crew exist The startup state at the moment; For the crew Maximum number of starts per day; Water balance constraints: (8) Where: 、 They are the water storage capacity of the hydropower station at the end and beginning of the period respectively; 、 、 They are inflow, power generation and abandoned water flow respectively; Reservoir water storage capacity constraints: (9) Where: and are the minimum and maximum allowable water storage capacity of the hydropower station respectively; and They are Time period The minimum and maximum power generation flow of unit No. Machine flow constraints: (10) Where: and They are Time period The minimum and maximum power generation flow of unit No. Hydropower output constraints: (11) Where: and They are time The upper and lower limits of the hydropower station's output; Power balance constraints: (12) Where: For the In this scenario Photovoltaic output value during the time period; is the total load imposed on the system; Load reserve constraints: (13) Where: Hydropower Station Load reserve value for the time period; and are the upper limits of the speed at which the output of the hydropower station decreases and increases, respectively; Output lift constraints: (14) Minimum start-stop constraints: (15) (16) Where: and Minimum duration of startup and shutdown of hydropower units; Indicates the status of the unit startup process, 1 means startup, 0 means no startup; Indicates the status of the unit shutdown process.

[0010] The constraints of the model in step 2 also include: Vibration zone constraints: (17) Where: and are the lower and upper limits of the unit vibration zone respectively; Delivery capacity limitations: (18) Where: The maximum transmission power for UHVDC transmission; Power flow characteristics: (19) (20) (twenty one) (twenty two) Where: It is the relationship between the flow rate, output and water head of the unit in the power characteristic curve of the hydropower unit; is the water level-reservoir capacity relationship; is the relationship between discharge flow and tailwater level; For the Unit No. Output during the time period; , , , They are Net head for the period, water level in front of the dam, tailwater level and head loss; and for Beginning of period Reservoir capacity at the end of the period; The inputs of the above model are: grid load, theoretical output of photovoltaic and wind power obtained in step 1, and reservoir inflow; The variables optimized in the above model are: turbine governor PID parameters, unit on / off status, the compensated output value of the hydropower unit calculated in step 1, and the unit's flow rate.

[0011] The specific process of step 3 is: In the inner layer, a dynamic programming method is used to optimize the load distribution strategy. Since dynamic programming requires large calculations and is time-consuming, it is performed offline in advance. First, basic data is imported for offline pre-calculation. That is, the optimal load distribution strategy under different numbers of operating units and all water heads is calculated and stored as a lookup table. When performing nested optimization, the real-time system parameters, namely the number of operating units and the real-time water head, are obtained. By searching the pre-calculated table and performing interpolation calculations to match the nearest parameter point, the optimal load distribution strategy is output. In the outer optimization model, the parallel search capability of the optimization algorithm is used to simultaneously optimize the PID parameters of the hydropower unit combination and the turbine regulator. 、 、 When optimizing the PID parameters of the turbine governor, the weighted sum of the time absolute error of the speed, overshoot, and adjustment time is selected as the fitness function, and the response speed and stability of the turbine are taken as the optimization objectives. The function expression is: (twenty three) The number of units started in the double-layer nested optimization model is converted into the on / off status of the optimized units. After obtaining the current load demand, according to the real-time head and inflow flow of the reservoir and the list of available units and their efficiency characteristic curves, the current faulty units are eliminated and the operable units are screened to generate possible unit combinations. The total water consumption of each hydropower unit combination is calculated and its output range is checked to see whether it covers the load demand. The optimization algorithm is used for optimization. Under the two optimization objectives of minimum water consumption and minimum weighted sum of the integral of the absolute error of the speed time, the overshoot and the adjustment time, and the above constraints, the optimal unit combination of the hydropower units and the optimized PID parameters are obtained. 、 、 After determining the optimal number of units to be started, the output value of each hydropower unit can be obtained through load distribution, and the PID parameters obtained by the optimization algorithm will be 、 、 Applied to the controller of the turbine speed governor, the turbine control system obtains the feedback signal of the controlled system through the measuring element, and calculates the adjustment signal according to the difference between the feedback signal and the given signal. The adjustment signal is amplified by the actuator and drives the guide vanes of the turbine, thereby changing the power and frequency of the hydropower unit, enabling it to quickly track the grid load demand and suppress frequency fluctuations, so that the hydropower unit can quickly and stably reach the output value required by the outer optimization model.

[0012] The specific process of step 4 is: Considering hydropower as a regulator of wind and solar energy, a multi-objective optimization algorithm is used to solve the outer optimization scheduling of the double-layer nested optimization model. Finally, the optimized unit on / off status, PID parameters, output of each unit and flow through the unit are obtained.

[0013] The beneficial effects of the present invention are as follows: The water-wind-solar multi-energy complementary scheduling method of the present invention couples PID parameter optimization with the combination of hydropower units, optimizes the hydropower unit combination problem and the turbine speed governor PID parameters, has theoretical global optimality, and significantly improves the control performance of the turbine speed governor, ensuring rapid load adjustment of the unit, so that the hydropower unit can adapt to the higher frequency control requirements of the power system, effectively deal with the problem of insufficient system flexibility caused by multi-energy load fluctuations, and better ensure the rapidity and stability of the total transmission power of the multi-energy complementary system. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a flow chart of the water-wind-solar multi-energy complementary scheduling method of the present invention, which couples PID parameter optimization with the combination of hydropower units; Figure 2 It is a schematic flow diagram of step 3 in the present invention. DETAILED DESCRIPTION

[0015] The present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0016] like Figure 1 As shown, the technical solution adopted by the present invention is to couple PID parameter optimization with a water-wind-solar multi-energy complementary scheduling method of a hydropower unit combination, which specifically includes the following steps: Step 1: Calculate the compensated output value of the hydropower unit; given the grid load, calculate the difference between the grid load and the wind power and photovoltaic output to obtain the compensated output value of the hydropower unit; The specific process of step 1 is: Assuming that the forecast errors of wind power and photovoltaic output obey the normal distribution, the forecast error distribution of wind power and photovoltaic output in period t is expressed by normal distribution, that is, and According to the shortest output forecast technology, the forecast error of wind power and photovoltaic output is controlled within 10%. Therefore, based on the given forecast error, the normal distribution standard deviation of the wind power forecast output value in period t at a 95% confidence level is determined. And the normal distribution standard deviation of the photovoltaic predicted output value , the formula is as follows: (1) (2) Where: 、 for The predicted output values of wind power and photovoltaic power at the moment; The theoretical output values of wind power and photovoltaic power are expressed as: (3) Where: 、 for The theoretical output of wind power and photovoltaic power in the time period; The difference between the grid load and the theoretical wind power output and photovoltaic power output calculated above is the compensation output value of the hydropower unit.

[0017] Step 2: Establish a mathematical model for the combination of hydropower, wind and solar power plant units, and determine the model's objective function and constraints; The specific process of step 2 is: According to the theoretical output of wind power and photovoltaic power and the output value that needs to be compensated for the hydropower unit calculated in step 1, in order to maximize the utilization rate of wind power and photovoltaic power, and at the same time make the hydropower unit quickly and stably reach the required output compensation value, the objective function of this model is: to minimize the average water consumption of the hydropower unit under multiple scenarios. The calculation formula is as follows: (4) The objective function of this model also includes the minimum weighted sum of the time multiplied by the absolute error integral of the speed, the overshoot and the adjustment time, which is calculated as follows: (5) .

[0018] In formula (4) and formula (5), is the average water consumption of the hydro-wind-solar hybrid power station during the entire dispatch period; is the number of generating units in the hydropower station; is the number of time periods in the scheduling period; is the number of scenarios; It is s The weighting factors of the scenarios; Indicates time t Next, n The power generation capacity of hydropower, wind power and photovoltaic units of unit No. The power on / off status of the unit, which is a 0-1 variable; Unit flow rate; The scheduling period is long; is the systematic error; is the overshoot; To adjust the time; 、 、 are weights, which are 0.5, 0.3, and 0.2 respectively.

[0019] The constraints of the model in step 2 are as follows: Power Constraints: (6) Where: 、 、 Wind turbines, photovoltaic turbines, and hydropower turbines Total output at any given moment; for The grid load at the moment; Start-stop constraints: (7) Where: For the crew exist The startup state at the moment; For the crew Maximum number of starts per day; Water balance constraints: (8) Where: 、 They are the water storage capacity of the hydropower station at the end and beginning of the period respectively; 、 、 They are inflow, power generation and abandoned water flow respectively; Reservoir water storage capacity constraints: (9) Where: and are the minimum and maximum allowable water storage capacity of the hydropower station respectively; and They are Time period The minimum and maximum power generation flow of unit No. Machine flow constraints: (10) Where: and They are Time period The minimum and maximum power generation flow of unit No. Hydropower output constraints: (11) Where: and They are time The upper and lower limits of the hydropower station's output; Power balance constraints: (12) Where: For the In this scenario Photovoltaic output value during the time period; is the total load imposed on the system; Load reserve constraints: (13) Where: Hydropower Station Load reserve value for the time period; and are the upper limits of the speed at which the output of the hydropower station decreases and increases, respectively; Output lift constraints: (14) Minimum start-stop constraints: (15) (16) Where: and Minimum duration of startup and shutdown of hydropower units; Indicates the status of the unit startup process, 1 means startup, 0 means no startup; Indicates the status of the unit shutdown process.

[0020] The constraints of the model in step 2 also include: Vibration zone constraints: (17) Where: and are the lower and upper limits of the unit vibration zone respectively; Delivery capacity limitations: (18) Where: The maximum transmission power for UHVDC transmission; Power flow characteristics: (19) (20) (twenty one) (twenty two) Where: It is the relationship between the flow rate, output and water head of the unit in the power characteristic curve of the hydropower unit; is the water level-reservoir capacity relationship; is the relationship between discharge flow and tailwater level; For the Unit No. Output during the time period; , , , They are Net head for the period, water level in front of the dam, tailwater level and head loss; and for Beginning of period Reservoir capacity at the end of the period; The inputs of the above model are: grid load, theoretical output of photovoltaic and wind power obtained in step 1, and reservoir inflow; The variables optimized in the above model are: turbine governor PID parameters, unit on / off status, the compensated output value of the hydropower unit calculated in step 1, and the unit's flow rate.

[0021] Step 3: Based on the model objective function and constraints determined in step 2, a two-layer nested optimization model is constructed. The outer layer uses an optimization algorithm to optimize the number of hydropower units in operation and the turbine governor PID parameters. The inner layer uses a dynamic programming method to determine the optimal load distribution strategy under a given number of units in operation. The specific process of step 3 is: In the inner layer, a dynamic programming method is used to optimize the load distribution strategy. Since dynamic programming requires large calculations and is time-consuming, it is performed offline in advance. First, basic data is imported for offline pre-calculation. That is, the optimal load distribution strategy under different numbers of operating units and all water heads is calculated and stored as a lookup table. When performing nested optimization, the real-time system parameters, namely the number of operating units and the real-time water head, are obtained. By searching the pre-calculated table and performing interpolation calculations to match the nearest parameter point, the optimal load distribution strategy is output. In the outer optimization model, the parallel search capability of the optimization algorithm is used to simultaneously optimize the PID parameters of the hydropower unit combination and the turbine regulator. 、 、 In order to improve system efficiency and optimize the PID parameters of the turbine governor, the weighted sum of the time absolute error of the speed, overshoot, and adjustment time is selected as the fitness function, and the response speed and stability of the turbine are taken as the optimization objectives. The function expression is: (twenty three) The number of units started in the double-layer nested optimization model is converted into the on / off status of the optimized units. Figure 2As shown in the figure, after obtaining the current load demand, according to the real-time head and inflow flow of the reservoir and the list of available units and their efficiency characteristic curves, the current faulty units are eliminated and the operable units are screened to generate possible unit combinations. The total water consumption of each hydropower unit combination is calculated and its output range is checked to see whether it covers the load demand. The optimization algorithm is used for optimization. Under the two optimization objectives of minimum water consumption and minimum weighted sum of the absolute error integral of the speed time, overshoot and adjustment time and the above constraints, the optimal unit combination of hydropower units and the optimized PID parameters are obtained. 、 、 After determining the optimal number of units to be started, the output value of each hydropower unit can be obtained through load distribution. Output changes will cause the operating conditions of the unit to change and deviate from the design conditions. Traditional PID control is designed for specific load conditions such as design conditions. At this time, the PID control parameters are no longer applicable to the current conditions, which will seriously affect the regulation performance and unit stability, and reduce the regulation efficiency of the turbine regulation system. Therefore, it is necessary to optimize and adjust the PID control parameters to keep the unit in the optimal operating state. The PID parameters obtained by the optimization algorithm will be used. 、 、 Applied to the controller of the turbine speed governor, the turbine control system obtains the feedback signal of the controlled system through the measuring element, and calculates the adjustment signal according to the difference between the feedback signal and the given signal. The adjustment signal is amplified by the actuator and drives the guide vanes of the turbine, thereby changing the power and frequency of the hydropower unit, enabling it to quickly track the load demand of the power grid and suppress frequency fluctuations, so that the hydropower unit can quickly and stably reach the output value required by the outer optimization model, significantly shortening the adjustment time and reducing the overshoot, ensuring the efficient and stable operation of the system.

[0022] Step 4: Solve the two-layer nested optimization model in step 3.

[0023] The specific process of step 4 is as follows: considering hydropower as a regulator of wind and solar energy, its synergistic effect should be considered in many aspects during the complementary scheduling process, and a multi-objective optimization algorithm is used to solve the outer optimization scheduling of the double-layer nested optimization model. Finally, the optimized unit on / off status, PID parameters, output of each unit, and flow through the unit are obtained.

[0024] This method effectively promotes the consumption of new energy and significantly improves the regulation capacity of hydropower units, which is of great significance to ensuring the stable and efficient operation of the power grid.

[0025] Example 1 like Figure 1 As shown in FIG5 , the method for coupling PID parameter optimization with hydropower generation units to implement water-wind-solar multi-energy complementary scheduling proposed in this embodiment specifically includes the following steps: Step 1: Calculate the compensated output value of the hydropower unit; given the grid load, calculate the difference between the grid load and the wind power and photovoltaic output to obtain the compensated output value of the hydropower unit; Step 2: Establish a mathematical model for the combination of hydropower, wind and solar power plant units, and determine the model's objective function and constraints; Step 3: Based on the model objective function and constraints determined in step 2, a two-layer nested optimization model is constructed. The outer layer uses an optimization algorithm to optimize the number of hydropower units in operation and the turbine governor PID parameters. The inner layer uses a dynamic programming method to determine the optimal load distribution strategy under a given number of units in operation. Step 4: Solve the two-layer nested optimization model in step 3.

[0026] Example 2 like Figure 1 As shown, the water-wind-solar multi-energy complementary scheduling method proposed in this embodiment, which combines PID parameter optimization with hydropower units, specifically includes the following steps: The method for coupling PID parameter optimization with hydropower generation units to implement water-wind-solar multi-energy complementary scheduling proposed in this embodiment specifically includes the following steps: Step 1: Calculate the compensated output value of the hydropower unit; given the grid load, calculate the difference between the grid load and the wind power and photovoltaic output to obtain the compensated output value of the hydropower unit; The specific process of step 1 is: Assuming that the forecast errors of wind power and photovoltaic output obey the normal distribution, the forecast error distribution of wind power and photovoltaic output in period t is expressed by normal distribution, that is, and According to the shortest output forecast technology, the forecast error of wind power and photovoltaic output is controlled within 10%. Therefore, based on the given forecast error, the normal distribution standard deviation of the wind power forecast output value in period t at a 95% confidence level is determined. And the normal distribution standard deviation of the photovoltaic predicted output value , the formula is as follows: (1) (2) Where: 、 for The predicted output values of wind power and photovoltaic power at the moment; The theoretical output values of wind power and photovoltaic power are expressed as: (3) Where: 、 for The theoretical output of wind power and photovoltaic power in the time period; The difference between the grid load and the theoretical wind power output and photovoltaic power output calculated above is the compensation output value of the hydropower unit.

[0027] Step 2: Establish a mathematical model for the combination of hydropower, wind and solar power plant units, and determine the model's objective function and constraints; Step 3: Based on the model objective function and constraints determined in step 2, a two-layer nested optimization model is constructed. The outer layer uses an optimization algorithm to optimize the number of hydropower units in operation and the turbine governor PID parameters. The inner layer uses a dynamic programming method to determine the optimal load distribution strategy under a given number of units in operation. Step 4: Solve the two-layer nested optimization model in step 3.

[0028] Example 3 like Figure 1 As shown, the water-wind-solar multi-energy complementary scheduling method of coupling PID parameter optimization with hydropower units proposed in this embodiment specifically includes the following steps: The method for coupling PID parameter optimization with hydropower generation units to implement a multi-energy complementary scheduling method for wind, solar, and water power generation proposed in this embodiment specifically includes the following steps: Step 1: Calculate the compensated output value of the hydropower unit; given the grid load, calculate the difference between the grid load and the wind power and photovoltaic output to obtain the compensated output value of the hydropower unit; The specific process of step 1 is: Assuming that the forecast errors of wind power and photovoltaic output obey the normal distribution, the forecast error distribution of wind power and photovoltaic output in period t is expressed by normal distribution, that is, and According to the shortest output forecast technology, the forecast error of wind power and photovoltaic output is controlled within 10%. Therefore, based on the given forecast error, the normal distribution standard deviation of the wind power forecast output value in period t at a 95% confidence level is determined. And the normal distribution standard deviation of the photovoltaic predicted output value , the formula is as follows: (1) (2) Where: 、 for The predicted output values of wind power and photovoltaic power at the moment; The theoretical output values of wind power and photovoltaic power are expressed as: (3) Where: 、 for The theoretical output of wind power and photovoltaic power in the time period; The difference between the grid load and the theoretical wind power output and photovoltaic power output calculated above is the compensation output value of the hydropower unit.

[0029] Step 2: Establish a mathematical model for the combination of hydropower, wind and solar power plant units, and determine the model's objective function and constraints; The specific process of step 2 is: According to the theoretical output of wind power and photovoltaic power and the output value that needs to be compensated for the hydropower unit calculated in step 1, in order to maximize the utilization rate of wind power and photovoltaic power, and at the same time make the hydropower unit quickly and stably reach the required output compensation value, the objective function of this model is: to minimize the average water consumption of the hydropower unit under multiple scenarios. The calculation formula is as follows: (4) The objective function of this model also includes the minimum weighted sum of the time multiplied by the absolute error integral of the speed, the overshoot and the adjustment time, which is calculated as follows: (5) .

[0030] In formula (4) and formula (5), is the average water consumption of the hydro-wind-solar hybrid power station during the entire dispatch period; is the number of generating units in the hydropower station; is the number of time periods in the scheduling period; is the number of scenarios; It is s The weighting factors of the scenarios; Indicates time t Next, n The power generation capacity of hydropower, wind power and photovoltaic units of unit No. The on / off status of the unit, which is a 0-1 variable; Unit flow rate; The scheduling period is long; is the systematic error; is the overshoot; To adjust the time; 、 、 are weights, which are 0.5, 0.3, and 0.2 respectively.

[0031] The constraints of the model in step 2 are as follows: Power Constraints: (6) Where: 、 、 Wind turbines, photovoltaic turbines, and hydropower turbines Total output at any given moment; for The grid load at the moment; Start-stop constraints: (7) Where: For the crew exist The startup state at the moment; For the crew Maximum number of starts per day; Water balance constraints: (8) Where: 、 They are the water storage capacity of the hydropower station at the end and beginning of the period respectively; 、 、 They are inflow, power generation and abandoned water flow respectively; Reservoir water storage capacity constraints: (9) Where: and are the minimum and maximum allowable water storage capacity of the hydropower station respectively; and They are Time period The minimum and maximum power generation flow of unit No. Machine flow constraints: (10) Where: and They are Time period The minimum and maximum power generation flow of unit No. Hydropower output constraints: (11) Where: and They are time The upper and lower limits of the hydropower station's output; Power balance constraints: (12) Where: For the In this scenario Photovoltaic output value during the time period; is the total load imposed on the system; Load reserve constraints: (13) Where: Hydropower Station Load reserve value for the time period; and are the upper limits of the speed at which the output of the hydropower station decreases and increases, respectively; Output lift constraints: (14) Minimum start-stop constraints: (15) (16) Where: and Minimum duration of startup and shutdown of hydropower units; Indicates the status of the unit startup process, 1 means startup, 0 means no startup; Indicates the status of the unit shutdown process.

[0032] The constraints of the model in step 2 also include: Vibration zone constraints: (17) Where: and are the lower and upper limits of the unit vibration zone respectively; Delivery capacity limitations: (18) Where: The maximum transmission power for UHVDC transmission; Power flow characteristics: (19) (20) (twenty one) (twenty two) Where: It is the relationship between the flow rate, output and water head of the unit in the power characteristic curve of the hydropower unit; is the water level-reservoir capacity relationship; is the relationship between discharge flow and tailwater level; For the Unit No. Output during the time period; , , , They are Net head for the period, water level in front of the dam, tailwater level and head loss; and for Beginning of period Reservoir capacity at the end of the period; The inputs of the above model are: grid load, theoretical output of photovoltaic and wind power obtained in step 1, and reservoir inflow; The variables optimized in the above model are: turbine governor PID parameters, unit on / off status, the compensated output value of the hydropower unit calculated in step 1, and the unit's flow rate.

[0033] Step 3: Based on the model objective function and constraints determined in step 2, a two-layer nested optimization model is constructed. The outer layer uses an optimization algorithm to optimize the number of hydropower units in operation and the turbine governor PID parameters. The inner layer uses a dynamic programming method to determine the optimal load distribution strategy under a given number of units in operation. Step 4: Solve the two-layer nested optimization model in step 3.

[0034] Example 4 like Figure 1 and Figure 2 As shown, the water-wind-solar multi-energy complementary scheduling method of the coupled PID parameter optimization and hydropower unit combination proposed in this embodiment is based on Example 3. The specific process of step 3 is as follows: In the inner layer, a dynamic programming method is used to optimize the load distribution strategy. Since dynamic programming requires large calculations and is time-consuming, it is performed offline in advance. First, basic data is imported for offline pre-calculation. That is, the optimal load distribution strategy under different numbers of operating units and all water heads is calculated and stored as a lookup table. When performing nested optimization, the real-time system parameters, namely the number of operating units and the real-time water head, are obtained. By searching the pre-calculated table and performing interpolation calculations to match the nearest parameter point, the optimal load distribution strategy is output. In the outer optimization model, the parallel search capability of the optimization algorithm is used to simultaneously optimize the PID parameters of the hydropower unit combination and the turbine regulator. 、 、 When optimizing the PID parameters of the turbine governor, the weighted sum of the time absolute error of the speed, overshoot, and adjustment time is selected as the fitness function, and the response speed and stability of the turbine are taken as the optimization objectives. The function expression is: (twenty three).

[0035] Example 5 like Figure 1 and Figure 2As shown, the water-wind-solar multi-energy complementary scheduling method of coupling PID parameter optimization and hydropower unit combination proposed in this embodiment, based on Example 4, step three also includes: converting the number of started units of the optimized units in the double-layer nested optimization model into the on / off state of the optimized units, after obtaining the current load demand, according to the real-time head and water flow of the reservoir and the list of available units and their efficiency characteristic curves, eliminating the current faulty units and screening the operable units to generate possible unit combinations, calculating the total water consumption of each hydropower unit combination and checking whether its output range covers the load demand, using the optimization algorithm for optimization, and obtaining the optimal unit combination of hydropower units and the optimized PID parameters under the two optimization objectives of minimum water consumption and minimum weighted sum of the integral of the absolute error of the speed time, the overshoot and the adjustment time and the above constraints. 、 、 After determining the optimal number of units to be started, the output value of each hydropower unit can be obtained through load distribution, and the PID parameters obtained by the optimization algorithm will be 、 、 Applied to the controller of the turbine speed governor, the turbine control system obtains the feedback signal of the controlled system through the measuring element, and calculates the adjustment signal according to the difference between the feedback signal and the given signal. The adjustment signal is amplified by the actuator and drives the guide vanes of the turbine, thereby changing the power and frequency of the hydropower unit, enabling it to quickly track the grid load demand and suppress frequency fluctuations, so that the hydropower unit can quickly and stably reach the output value required by the outer optimization model.

[0036] Example 6 like Figure 1 and Figure 2 As shown, the water-wind-solar multi-energy complementary scheduling method of coupling PID parameter optimization and hydropower unit combination proposed in this embodiment is based on Example 5. The specific process of step 4 is as follows: Considering hydropower as a regulator of wind and solar energy, its synergistic effect should be considered in many aspects during the complementary scheduling process. A multi-objective optimization algorithm is used to solve the outer optimization scheduling of the double-layer nested optimization model, and finally the optimized unit on / off status, PID parameters, output of each unit and flow through the unit are obtained.

Claims

1. A water-wind-solar multi-energy complementary scheduling method combining PID parameter optimization with hydropower units, characterized in that: The specific steps include: Step 1: Calculate the compensated output value of the hydropower unit; given the grid load, calculate the difference between the grid load and the wind power and photovoltaic output to obtain the compensated output value of the hydropower unit; Step 2: Establish a mathematical model for the combination of hydropower, wind and solar power plant units, and determine the model's objective function and constraints; Step 3: Based on the model objective function and constraints determined in step 2, a two-layer nested optimization model is constructed. The outer layer uses an optimization algorithm to optimize the number of hydropower units in operation and the turbine governor PID parameters. The inner layer uses a dynamic programming method to determine the optimal load distribution strategy under a given number of units in operation. Step 4: Solve the two-layer nested optimization model in step 3.

2. The water-wind-solar multi-energy complementary scheduling method combining coupled PID parameter optimization and hydropower units according to claim 1 is characterized in that: The specific process of step 1 is: Assuming that the forecast errors of wind power and photovoltaic output obey the normal distribution, the forecast error distribution of wind power and photovoltaic output in period t is expressed by normal distribution, that is, and According to the shortest output forecast technology, the forecast error of wind power and photovoltaic output is controlled within 10%. Therefore, based on the given forecast error, the normal distribution standard deviation of the wind power forecast output value in period t at a 95% confidence level is determined. And the normal distribution standard deviation of the photovoltaic predicted output value , the formula is as follows: （1） （2） Where: 、 for The predicted output values of wind power and photovoltaic power at the moment; The theoretical output values of wind power and photovoltaic power are expressed as: （3） Where: 、 for The theoretical output of wind power and photovoltaic power in the time period; The difference between the grid load and the theoretical wind power output and photovoltaic power output calculated above is the compensation output value of the hydropower unit.

3. The water-wind-solar multi-energy complementary scheduling method combining coupled PID parameter optimization with hydropower units according to claim 2 is characterized in that: The specific process of step 2 is: According to the theoretical output of wind power and photovoltaic power and the output value that needs to be compensated for the hydropower unit calculated in step 1, in order to maximize the utilization rate of wind power and photovoltaic power, and at the same time make the hydropower unit quickly and stably reach the required output compensation value, the objective function of this model is: to minimize the average water consumption of the hydropower unit under multiple scenarios. The calculation formula is as follows: (4) The objective function of this model also includes the minimum weighted sum of the time multiplied by the absolute error integral of the speed, the overshoot and the adjustment time, which is calculated as follows: (5) 。 4. The water-wind-solar multi-energy complementary scheduling method combining coupled PID parameter optimization with hydropower units according to claim 3 is characterized in that: In the formula (4) and formula (5), is the average water consumption of the hydro-wind-solar hybrid power station during the entire dispatch period; is the number of generating units in the hydropower station; is the number of time periods in the scheduling period; is the number of scenarios; It is s The weighting factors of the scenarios; Indicates time t Next, n The power generation capacity of hydropower, wind power and photovoltaic units of unit No. The on / off status of the unit, which is a 0-1 variable; Unit flow rate; The scheduling period is long; is the systematic error; is the overshoot; To adjust the time; 、 、 are weights, which are 0.5, 0.3, and 0.2 respectively.

5. The water-wind-solar multi-energy complementary scheduling method combining coupled PID parameter optimization with hydropower units according to claim 4 is characterized in that: The constraints of the model in step 2 are as follows: Power Constraints: (6) Where: 、 、 Wind turbines, photovoltaic turbines, and hydropower turbines Total output at any given moment; for The grid load at the moment; Start-stop constraints: (7) Where: For the crew exist The startup state at the moment; For the crew Maximum number of starts per day; Water balance constraints: (8) Where: 、 They are the water storage capacity of the hydropower station at the end and beginning of the period respectively; 、 、 They are inflow, power generation and abandoned water flow respectively; Reservoir water storage capacity constraints: (9) Where: and are the minimum and maximum allowable water storage capacity of the hydropower station respectively; and They are Time period The minimum and maximum power generation flow of unit No. Machine flow constraints: (10) Where: and They are Time period The minimum and maximum power generation flow of unit No. Hydropower output constraints: (11) Where: and They are time The upper and lower limits of the hydropower station's output; Power balance constraints: (12) Where: For the In this scenario Photovoltaic output value during the time period; is the total load imposed on the system; Load reserve constraints: (13) Where: Hydropower Station Load reserve value for the time period; and are the upper limits of the speed at which the output of the hydropower station decreases and increases, respectively; Output lift constraints: (14) Minimum start-stop constraints: (15) (16) Where: and Minimum duration of startup and shutdown of hydropower units; Indicates the status of the unit startup process, 1 means startup, 0 means no startup; Indicates the status of the unit shutdown process.

6. The method for water-wind-solar multi-energy complementary scheduling combining coupled PID parameter optimization and hydropower units according to claim 5 is characterized in that: The constraints of the model in step 2 also include: Vibration zone constraints: (17) Where: and are the lower and upper limits of the unit vibration zone respectively; Delivery capacity limitations: (18) Where: The maximum transmission power for UHVDC transmission; Power flow characteristics: (19) (20) (21) (22) Where: It is the relationship between the flow rate, output and water head of the unit in the power characteristic curve of the hydropower unit; is the water level-reservoir capacity relationship; is the relationship between discharge flow and tailwater level; For the Unit No. Output during the time period; , , , They are Net head for the period, water level in front of the dam, tailwater level and head loss; and for Beginning of period Reservoir capacity at the end of the period; The inputs of the above model are: grid load, theoretical output of photovoltaic and wind power obtained in step 1, and reservoir inflow; The variables optimized in the above model are: turbine governor PID parameters, unit on / off status, the compensated output value of the hydropower unit calculated in step 1, and the unit's flow rate.

7. The water-wind-solar multi-energy complementary scheduling method combining coupled PID parameter optimization with hydropower units according to claim 6 is characterized in that: The specific process of step 3 is: In the inner layer, a dynamic programming method is used to optimize the load distribution strategy. Since dynamic programming requires large calculations and is time-consuming, it is performed offline in advance. First, basic data is imported for offline pre-calculation. That is, the optimal load distribution strategy under different numbers of operating units and all water heads is calculated and stored as a lookup table. When performing nested optimization, the real-time system parameters, namely the number of operating units and the real-time water head, are obtained. By searching the pre-calculated table and performing interpolation calculations to match the nearest parameter point, the optimal load distribution strategy is output. In the outer optimization model, the parallel search capability of the optimization algorithm is used to simultaneously optimize the PID parameters of the hydropower unit combination and the turbine regulator. 、 、 When optimizing the PID parameters of the turbine governor, the weighted sum of the time absolute error of the speed, overshoot, and adjustment time is selected as the fitness function, and the response speed and stability of the turbine are taken as the optimization objectives. The function expression is: (23) The number of units started in the double-layer nested optimization model is converted into the on / off status of the optimized units. After obtaining the current load demand, according to the real-time head and inflow flow of the reservoir and the list of available units and their efficiency characteristic curves, the current faulty units are eliminated and the operable units are screened to generate possible unit combinations. The total water consumption of each hydropower unit combination is calculated and its output range is checked to see whether it covers the load demand. The optimization algorithm is used for optimization. Under the two optimization objectives of minimum water consumption and minimum weighted sum of the integral of the absolute error of the speed time, the overshoot and the adjustment time, and the above constraints, the optimal unit combination of the hydropower units and the optimized PID parameters are obtained. 、 、 After determining the optimal number of units to be started, the output value of each hydropower unit can be obtained through load distribution, and the PID parameters obtained by the optimization algorithm will be 、 、 Applied to the controller of the turbine speed governor, the turbine control system obtains the feedback signal of the controlled system through the measuring element, and calculates the adjustment signal according to the difference between the feedback signal and the given signal. The adjustment signal is amplified by the actuator and drives the guide vanes of the turbine, thereby changing the power and frequency of the hydropower unit, enabling it to quickly track the grid load demand and suppress frequency fluctuations, so that the hydropower unit can quickly and stably reach the output value required by the outer optimization model.

8. The method for water-wind-solar multi-energy complementary scheduling combining coupled PID parameter optimization and hydropower units according to claim 7 is characterized in that: The specific process of step 4 is as follows: Considering hydropower as a regulator of wind and solar energy, a multi-objective optimization algorithm is used to solve the outer optimization scheduling of the double-layer nested optimization model. Finally, the optimized unit on / off status, PID parameters, output of each unit and flow through the unit are obtained.

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