Wind-light-cascade hydropower station optimal economic dispatching operation method and system considering lowest charge loss risk, and medium

Abstract

The invention relates to a wind-light-cascade hydropower station optimal economic dispatching operation method and system considering the lowest charge loss risk and a medium, and the method comprises the steps: firstly, obtaining real-time data, inputting the real-time data to a wind-light-cascade hydropower station, namely, a wind-light-hydropower cooperation system model; the model takes the maximum generating capacity income and the minimum charge risk of the wind-light-cascade hydropower station as targets and constraint conditions; and finally, outputting an optimal scheduling parameter. Aiming at power generation of the cascade hydropower station, the influence of wind power and photoelectric new energy power generation and flood control in the flood season on the cascade hydropower station for water storage and power generation is considered, optimization solution is performed by means of an NSGA-II algorithm, and finally a reasonable planning scheme is obtained, so that the cascade hydropower station can efficiently utilize water resources in good time, and the water resource utilization rate is improved. The new energy power generation strong period is avoided, meanwhile, the abandoned water amount of the hydropower station can be increased, and the economic benefits of the cascade hydropower station can be optimized by reasonably planning the optimized and planned flow scheme of the cascade hydropower station.

Description

Technical Field

[0001] The present invention relates to an optimal economic dispatching operation method, system and medium for a wind-solar-cascade hydropower station with the lowest risk of lost charge, in particular considering the risk of lost charge caused by the uncertainty of wind and solar power generation in the system. Background Art

[0002] With the transformation of the global energy structure and the demand for sustainable development, the development and utilization of renewable energy have been increasingly emphasized. As the main renewable energy sources, wind energy and solar energy have the advantages of being clean and renewable. However, their power generation has significant intermittency, volatility and randomness, and the curtailment rate is relatively high. To effectively solve this problem, an economic benefit optimization method for coordinated power generation of a wind-solar-cascade hydropower station has emerged. By combining wind power, photovoltaic power and hydropower operation, taking advantage of the natural complementarity between energies, the flexibility of hydropower and the strong energy storage potential of reservoirs, a multi-energy complementary system is formed, thereby enhancing the economy of the combined dispatching operation of wind-solar-cascade.

[0003] Many studies have been carried out on the comprehensive dispatching optimization of wind, solar and hydropower at home and abroad. In terms of the description of the uncertain output of wind and solar power generation and the dispatching method, some studies have coupled the uncertainty of wind and solar power output, proposed a method for quantifying the flexibility requirements of wind and solar power generation and the short-term dispatching method of water-wind-solar complementarity. By establishing a flexibility evaluation model, the impacts of different water inflow conditions, new energy access ratios, wind and solar installed capacities, hydropower unit characteristics and different peak shaving control requirements on flexibility have been analyzed, and a comparative analysis with other methods has been carried out. In terms of technical and economic feasibility, for a certain wind-solar-hydro clean energy base, the technical and economic feasibility of its liquid compressed air energy storage (LCHES) has been analyzed, a refined optimization simulation model has been developed, the operation conditions of each hour of the whole year have been simulated, the ability of LCHES to reduce energy losses and store the excess electricity during the low electricity price period of the power grid has been evaluated, as well as its economic feasibility, and the sensitivities of different typical years, various economic parameters and the overall efficiency of the pumping station to the evaluation results have been analyzed.

[0004] However, the models constructed for the wind-solar-cascade hydropower station in the above studies are difficult to describe the curtailment and load loss risks induced by the short-term random volatility of wind and solar, and the optimization results may be difficult to coordinate the consumption of new energy and the comprehensive utilization of basin water resources. Therefore, the present invention proposes an optimal economic operation strategy for a wind-solar-cascade hydropower station system with the lowest risk of lost charge. By taking the form of the flow rate of the cascade hydropower station as the only variable, a water-determined power mode is proposed to solve the problem of disharmony between "water dispatching" and "power dispatching", thereby realizing the coordinated complementarity between wind and solar power generation and hydropower, and achieving the goal of optimizing the operation efficiency and economic benefits of the power system. Summary of the Invention

[0005] The present invention is mainly solved by the following technical solutions: Optimal economic dispatching operation method for wind-solar-cascade hydropower stations considering the lowest risk of lost charge, characterized by obtaining real-time data, where the real-time data includes: is the number of hydropower stations in the system; is the total dispatching period; is the total load; The generation flow of the i-th hydropower station in the t-th time period ; The inflow of the i-th hydropower station in the t-th time period ; The sectional flow between the (i - 1)-th reservoir and the i-th reservoir in the t-th time period ; The downstream discharge of the (i - 1)-th reservoir at time t ; The maximum discharge of the i-th hydropower station ; The minimum discharge of the i-th hydropower station ; Water level amplitude constraint ; The average generating head of the i-th power station at time t ; The maximum water level of the i-th hydropower station in the t-th time period ; The minimum water level of the i-th hydropower station in the t-th time period ; The water storage of the i-th hydropower station at the end of the t-th time period ; The water storage of the i-th hydropower station at the beginning of the t-th time period ; The minimum water storage of the i-th hydropower station ; The maximum water storage of the i-th hydropower station ; The grid-connected electricity price in time period t ; The generation plan in time period t ; The maximum generating capacity of hydropower station i ; The minimum generating capacity of hydropower station i ; The total installed capacity of hydropower stations ; The installed capacity of photovoltaic power ; The installed capacity of wind power ; Input real-time data into the wind-solar-cascade hydropower station, i.e., the wind-solar hydropower collaborative system model, where the wind-solar-cascade hydropower station, i.e., the wind-solar hydropower collaborative system model, aims at and is constrained by the maximum power generation revenue and the lowest charge risk of the wind-solar-cascade hydropower station; the lowest charge risk includes the lowest charge loss risk of the cascade hydropower station, the lowest charge loss risk of wind power and photovoltaic power, and the lowest charge loss risk of the power grid. Output the optimal scheduling parameters, and the optimal output parameters include: The total economic benefit of the wind-solar-cascade hydropower station system in time period t ; The hydropower output of power station i in time period t ; The photovoltaic power output in time period t ; The wind power output in time period t ; The water discharge of the i-th hydropower station in the cascade hydropower station at the t-th moment ; The curtailment rate of wind and light in time period t ; The load shedding rate of wind and light in time period t ; The remaining load of the power grid .

[0006] Preferably, the wind-solar-cascade hydropower station, i.e., the wind-solar hydropower collaborative system model is as follows: ; In the formula, Z is the total economic benefit of the wind-solar-cascade hydropower station system during the scheduling period, T is the total scheduling time period, n is the number of units of the cascade hydropower station, is the hydropower output of power station i in time period t, , are respectively the photovoltaic power output and the wind power output in time period t, is the grid-connected electricity price in time period t.

[0007] Preferably, the lowest charge loss risk of the cascade hydropower station is based on the following formula: ; In the formula, is the water discharge of the i-th hydropower station in the cascade hydropower station at the t-th moment, is the weight coefficient of the importance of water discharge of hydropower stations at different levels.

[0008] Preferably, the lowest charge loss risk of wind power and photovoltaic power is based on the following formula: ; ; In the formula, , are the photovoltaic power output and wind power output in the t period; , are the curtailment rate of wind and light and the load shedding rate in the t period; is the difference between the total output and the generation plan in the t period; is the new energy curtailment penalty coefficient in the t period.

[0009] Preferably, the risk of grid charge loss is the lowest based on the following formula ; In the formula, is the standard deviation of the variable x, , , are the remaining grid load, the mean value of the remaining grid load, and the grid load, respectively.

[0010] Preferably, the constraint conditions of the wind-solar-cascade hydropower station model include: Water balance constraint ; ; In the formula, is the water storage volume of the i-th hydropower station at the end of the t-th time period; is the water storage volume of the i-th hydropower station at the beginning of the t-th time period; is the inflow of the i-th hydropower station in the t-th time period; is the power generation flow of the i-th hydropower station in the t-th time period; is the water discharge flow of the i-th hydropower station in the t-th time period; is the conversion coefficient (usually 1); is the sectional flow between the (i-1)-th reservoir and the i-th reservoir in the t-th time period; is the reservoir release flow of the (i-1)-th reservoir in the time period t.

[0011] Water storage constraint ; In the formula, is the minimum water storage volume of the i-th hydropower station, is the maximum water storage volume of the i-th hydropower station.

[0012] Release flow constraint ; In the formula, , are the maximum and minimum values of the water discharge of the i-th hydropower station, respectively.

[0013] Upper limit constraint of power generation plan ; In the formula, , , respectively represent the total installed capacity of the hydropower station, the installed capacity of photovoltaic power, and the installed capacity of wind power.

[0014] Power generation load balance ; In the formula, is the power generation plan for time period t, is the total load.

[0015] Output constraints of hydropower stations at all levels ; In the formula, , are the maximum and minimum power generation outputs of hydropower station i respectively.

[0016] Water level constraint ; ; ; In the formula, is the average water head of hydropower station i in time period t, is the discharge of the previous hydropower station in time period t, , are the maximum and minimum values of the water level of the i-th hydropower station in the t-th time period respectively, is the water level amplitude constraint.

[0017] Preferably, based on the NSGA-II algorithm, the optimal economic benefit of the wind-solar-cascade hydropower station is solved. By introducing non-dominated sorting and crowding degree calculation on the basis of the genetic algorithm, multiple objectives in the multi-objective optimization problem are balanced, and the optimal solution is obtained or the maximum number of convergence times is reached, and the optimal economic benefit solution of the wind-solar-storage coordinated cascade hydropower station is output.

[0018] According to the optimal economic dispatching operation system of the wind-solar-cascade hydropower station considering the lowest charge loss risk described in claim 1, it is characterized in that it includes The first module is configured to obtain real-time data, and the real-time data includes: is the number of hydropower stations in the system; is the total dispatching time period; is the total load; The power generation flow rate of the i-th hydropower station in the t-th time period ; The inflow rate of the i-th hydropower station in the t-th time period ; The sectional flow rate between the (i - 1)-th reservoir and the i-th reservoir in the t-th time period ; The discharge flow rate of the (i - 1)-th reservoir in the t-th time period ; The maximum value of the water discharge of the i-th hydropower station ; The minimum value of the water discharge of the i-th hydropower station ; Water level amplitude constraint ; The average power generation head of the i-th power station in the t-th time period ; The maximum value of the water level of the i-th hydropower station in the t-th time period ; The minimum value of the water level of the i-th hydropower station in the t-th time period ; The water storage volume of the i-th hydropower station at the end of the t-th time period ; The water storage volume of the i-th hydropower station at the beginning of the t-th time period ; The minimum water storage volume of the i-th hydropower station ; The maximum water storage volume of the i-th hydropower station ; The grid connection electricity price in the time period t ; The power generation plan in the time period t ; The maximum power generation output of the hydropower station i ; The minimum power generation output of the hydropower station i ; The total installed capacity of the hydropower stations ; The installed capacity of the photovoltaic power ; The installed capacity of the wind power ; The second module is configured to input real-time data into the wind-solar-cascade hydropower station, i.e., the wind-solar-hydroelectricity collaborative system model, and the wind-solar-cascade hydropower station, i.e., the wind-solar-hydroelectricity collaborative system model aims at maximizing the power generation revenue and minimizing the charge risk of the wind-solar-cascade hydropower station; minimizing the charge risk includes minimizing the charge loss risk of the cascade hydropower station, minimizing the charge loss risk of the wind power and photovoltaic power, and minimizing the charge loss risk of the power grid; The third module is configured to output optimal scheduling parameters, and the optimal output parameters include: The total economic benefit of the wind-solar-cascade hydropower station system in period t ; The hydropower output of power station i in period t ; The photovoltaic output in period t ; The wind power output in period t ; The water discharge of the i-th hydropower station in the cascade hydropower station at the t-th moment ; The curtailment rate of wind and light in period t ; The load shedding rate of wind and light in period t ; The remaining load of the power grid .

[0019] A computer medium, characterized in that it includes a computer program, and the computer program stores the above method steps.

[0020] Therefore, the present invention has the following advantages: For the power generation of cascade hydropower stations, considering the influence of new energy power generation such as wind power and photovoltaic power and flood control during the flood season on cascade hydropower stations due to their dual functions of water storage and power generation, the NSGA-II algorithm is used for optimization and solution, and finally a reasonable planning scheme is obtained, enabling cascade hydropower stations to utilize water resources timely and efficiently, avoiding the strong period of new energy power generation while also reducing the water discharge of hydropower stations, and through reasonable planning of the flow scheme for the optimal planning of cascade hydropower stations, their economic benefits can be optimized. Brief Description of the Drawings

[0021] Figure 1 Flow chart of NSGA-II algorithm solution Detailed Embodiment

[0022] The present invention proposes an optimal economic operation strategy for a wind-solar-cascade hydropower station considering the lowest risk of lost charge. For the planning of the power generation of cascade hydropower stations, and according to the optimization scheme proposed by the present invention, the flow of the power station is regulated to achieve the purposes of improving power generation efficiency, increasing power generation, enhancing economic benefits, reasonably allocating water resources, reducing the amount of lost new energy charge and the remaining charge of the power grid, and considering the influence of new energy power generation such as wind power and photovoltaic power in the power grid on the power generation of cascade hydropower stations, and the optimal scheme is obtained through the NSGA-II algorithm.

[0023] The above problems of the present invention are mainly solved by the following technical solutions: Step 1: The present invention constructs a wind-solar-cascade hydropower station, i.e., a wind-solar-hydropower coordinated system model, with the goal of maximizing the economic benefits of the system and minimizing the risk of charge loss. When the time-of-use electricity price is not considered, the maximum power generation target can reflect its economic benefits, so taking this as a target can well reflect the system's regulation and application of various energy sources; the target of minimizing the risk of charge loss is refined into three aspects: the minimum risk of charge loss for cascade hydropower stations, the minimum risk of charge loss for wind power and photovoltaic power generation, and the minimum risk of charge loss for power grids. Taking this as a target can reflect the utilization rate of energy during the regulation period and improve resource utilization. The following is the objective function of the model and its constraints: The overall objective function is a linear combination of four sub-objective functions: ; In the formula, the objective function E is the maximum economic benefit of wind-solar-cascade hydropower station, where ~ is the weight coefficient of the objective function, and its value depends on the importance of the sub-objective function. The relationship between the weight coefficients is as follows: ; Objective function 1: Maximum revenue from wind-solar-cascade hydropower station power generation ; In the formula, is the output coefficient of the i-th power station in the cascade hydropower station; is the power generation flow of the ith power station in period t; is the average power generation head of the ith power station in period t; is the time span of each period; T is the number of periods; N is the number of hydropower stations in the system; , is the photovoltaic power output and wind power output during period t; is the average benchmark grid-connected electricity price in period i (i=1, 2, ···, T).

[0024] Objective function 2: The risk of charge loss in cascade hydropower stations is the lowest ; In the formula, is the amount of water abandoned by the i-th hydropower station in the cascade hydropower station at the tth moment, It is the weight coefficient of the importance of water abandonment of hydropower stations of different levels.

[0025] Objective function 3: The risk of wind power and photovoltaic power loss is the lowest ; ; ; In the formula, , are the photovoltaic power output and wind power output in period t; and are the curtailment rates of wind and light and the load shedding rate in period t; is the difference between the total output and the generation plan in period t; is the curtailment penalty coefficient for new energy in period t.

[0026] Objective function four: The lowest risk of grid charge loss ; In the formula, is the standard deviation of variable x, and , are the remaining grid load, the mean value of the remaining grid load, and the grid load respectively.

[0027] The constraint conditions of the wind-solar-cascade hydropower station model are as follows: Water balance constraint ; ; In the formula, is the water storage volume of the i-th hydropower station at the end of the t-th time period; is the water storage volume of the i-th hydropower station at the beginning of the t-th time period; is the inflow of the i-th hydropower station in the t-th time period; is the power generation flow of the i-th hydropower station in the t-th time period; is the water discharge flow of the i-th hydropower station in the t-th time period; is the conversion coefficient (usually 1); is the sectional flow between the (i-1)-th reservoir and the i-th reservoir in the t-th time period; is the reservoir discharge flow of the (i-1)-th reservoir in the t-th time period.

[0028] Water storage volume constraint ; In the formula, is the minimum water storage volume of the i-th hydropower station, is the maximum water storage volume of the i-th hydropower station.

[0029] Discharge flow constraint ; In the formula, and are the maximum and minimum values of the water discharge of the i-th hydropower station respectively.

[0030] Generation plan upper limit constraint ; In the formula, , , respectively represent the total installed capacity of the hydropower station, the installed capacity of photovoltaic power, and the installed capacity of wind power.

[0031] Power generation load balance ; In the formula, is the power generation plan for time period t, is the total load.

[0032] Output constraints of hydropower stations at all levels ; In the formula, , are the maximum and minimum power generation outputs of hydropower station i respectively.

[0033] Water level constraint ; ; ; In the formula, is the average head of hydropower station i at time period t, is the discharge of the previous hydropower station at time period t, , are the maximum and minimum values of the water level of the i-th hydropower station in the t-th time period respectively, is the water level amplitude constraint.

[0034] Step 2: Solve the optimal economic benefit of the wind-solar-cascade hydropower station based on the NSGA-II algorithm. In terms of the coordinated scheduling of the wind-solar-cascade hydropower station, by optimizing and regulating the power generation flow in a certain time period and controlling the water head to be optimal. According to this scheme, for a cascade hydropower station with M power stations, coordinated scheduling is carried out in three aspects: wind power, photovoltaic power, and water storage. The constraint conditions are complex and the calculation amount is large. Therefore, this scheme uses the NSGA-II algorithm to solve this scheme. The NSGA-II algorithm is introduced as follows: The NSGA-II algorithm balances multiple objectives in multi-objective optimization problems by introducing non-dominated sorting and crowding degree calculation on the basis of the genetic algorithm. Its characteristics are as follows: (1) Fast non-dominated sorting: The NSGA-II algorithm can calculate the domination count of each individual in the population, where represents the number of individuals that dominate individual i. If If \(r_i = 0\), then individual \(i\) belongs to the first non - dominated layer. After removing and deleting all individuals in the first non - dominated layer from the population, recalculate the domination count for each individual and repeat the removal and deletion operations until all individuals are assigned to a certain non - dominated layer. The formula for the \(k\) - th non - dominated layer is expressed as: ; In the formula, is the set of individuals in the population.

[0035] (2) Crowding distance calculation: In each non - dominated layer, calculate the crowding distance of individuals to measure the distribution density of individuals in the objective function space. For individual \(i\) in the \(k\) - th non - dominated front, its crowding distance is calculated by the formula: ; In the formula, is the value of individual \(i\) on the \(m\) - th objective function, and are the values of the adjacent individuals of individual \(i\) on the \(m\) - th objective function respectively.

[0036] (3) Crossover and mutation: The NSGA - Ⅱ algorithm uses the simulated binary crossover operator. The formula for the individuals in the \((k + 1)\) - th generation is as follows: ; In the formula, is the crossover probability; is the uniform distribution factor. The mutated individual is calculated as follows: ; In the formula, is the mutation step size, which is usually determined by the mutation distribution index .

[0037] The NSGA - II algorithm is combined with this scheme, and the specific solution steps are as follows: (1) Initialize the population: Randomly generate a group of individuals (flow combinations) as the initial population, and each individual represents a possible flow allocation scheme. Assume that the population size is \(N\), and each individual contains \(n\) flow values ( ), and their specific values need to meet the real - world constraint conditions.

[0038] (2) Select the population: For each individual in the initial population, calculate its fitness value according to the objective function and constraint conditions. For individuals that meet the constraint conditions, calculate their objective function values such as power generation benefits and water discharge losses; for individuals that do not meet the constraint conditions, a large penalty value can be assigned to make them at a disadvantage in the selection process, and then combine non - dominated sorting and crowding distance to determine the selection probability of individuals.

[0039] (3) Crossover and Mutation: For the two selected parent individuals and , determine whether to perform crossover through the crossover probability . If crossover is performed, for each flow variable , generate a random number , and then calculate the new individual according to the following formula: ; or In the formula, is the distribution index, used to control the distribution characteristics of the crossover. Perform mutation operation on the individuals after crossover, and use the mutation probability to determine whether to perform mutation. If mutation is performed, the new individual can be calculated according to the following formula: ; ; ; In the formula, is the mutation distribution index, used to control the degree of mutation.

[0040] (4) Repeat the above steps (2) to (3) until the optimal solution is obtained or the maximum convergence times are reached, and output the economic benefit optimization solution of the wind-solar-storage coordinated gradient hydropower station.

[0041] The present invention constructs an economic model of a wind-solar-cascade hydropower station, and the economic benefit function type of its power generation model is as follows: ; In the formula, Z is the total economic benefit of the wind-solar-cascade hydropower station system during the scheduling period, T is the total scheduling period, n is the number of units of the cascade hydropower station, is the hydropower output of power station i in period t, , are the photovoltaic power output and wind power output in period t respectively, is the grid-connected electricity price in period t.

[0042] Among them, the wind power output model is as follows: ; In the formula, is the wind energy utilization coefficient, is the air density, is the wind wheel radius of the wind farm, , , , They are the cut-in wind speed, cut-out wind speed, rated wind speed, and average wind speed respectively.

[0043] Among them, the photovoltaic power output model is as follows: ; In the formula, , are the actual power and rated power of the photovoltaic cell respectively, is the average value of the surface solar irradiance in the area at time period t, is the power temperature coefficient of the photovoltaic cell, is the temperature of the photovoltaic cell during operation.

[0044] The present invention proposes a judgment criterion for a wind-solar-hydroelectricity collaborative system with the lowest risk of charge loss. The risk of charge loss is mainly in three aspects. The first is reflected in the risk of abandoning electricity caused by excessive wind and solar power generation. When the actual power generation of wind power and photovoltaic power is greater than the power generation plan, it is necessary to reduce the hydraulic power generation of cascade hydropower stations to ensure that the total power generation of the system meets the actual load requirements. If the actual power generation of wind and solar is much greater than the power generation plan so that the hydropower cannot reasonably regulate the power generation of the system to be stable at the actual load requirements, then there will be a risk of abandoning wind and solar power at this time. Its expression is as follows: ; In the formula, is the abandoned electricity volume in time period t, is the difference between the actual power generation and the planned power generation. In this formula, the actual power generation needs to be greater than the planned power generation.

[0045] The second is reflected in the risk of charge loss caused by too little wind and solar power generation. When the actual power generation of wind power and photovoltaic power is less than the power generation plan, it is necessary to increase the hydraulic power generation of cascade hydropower stations to ensure that the total power generation of the system meets the actual load requirements. If the actual power generation of wind and solar is much less than the power generation plan so that the hydropower cannot reasonably regulate the power generation of the system to meet the actual load requirements, then there will be a risk of charge loss in the system at this time. Its expression is as follows: ; In the formula, is the abandoned electricity volume in time period t, is the difference between the actual power generation and the planned power generation. In this formula, the actual power generation needs to be greater than the planned power generation, is the planned power generation volume.

[0046] The third is reflected in the risk of abandoned water caused by the cascade hydropower station being unable to effectively utilize the inflow during the regulation of the system. Its expression is as follows: ; In the formula, is the abandoned water volume in time period t, , , The inflow rate, power generation rate, and storable rate in time period t, respectively, is the spillage coefficient.

[0047] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, system, or computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of the present application can be implemented using various computer languages. For example, object-oriented programming languages such as Java and interpreted scripting languages such as JavaScript.

[0048] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, as well as the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0049] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0050] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one flow or multiple flows and / or blocks Figure 1 one block or multiple blocks.

[0051] Although the preferred embodiments of the present application have been described, additional changes and modifications can be made to these embodiments by those skilled in the art once they learn the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments as well as all changes and modifications that fall within the scope of the present application.

[0052] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application is also intended to include these modifications and variations.

Claims

1. Optimal economic dispatching operation method for wind-solar-step hydropower stations considering the lowest risk of lost charge, characterized in that, Including obtaining real-time data, the real-time data includes: is the number of hydropower stations in the system; is the total scheduling period; is the total load; The power generation flow rate of the i-th hydropower station in the t-th time period ; Inflow of the i-th hydropower station during the t-th time period ; Interval flow between the (i - 1)-th reservoir and the i-th reservoir during the t-th period ; The reservoir discharge of the (i - 1)-th reservoir at time period t ; The maximum value of the water discharge of the i-th hydropower station ; The minimum value of the water discharge of the i-th hydropower station ; Water level amplitude constraint ; Average generating head of the i-th power station during period t ; The maximum water level of the $i$-th hydropower station in the $t$-th time period ; The minimum water level of the $i$-th hydropower station in the $t$-th time period ; The water storage volume of the i-th hydropower station at the end of the t-th time period ; The water storage volume of the i-th hydropower station at the beginning of the t-th time period ; The minimum water storage capacity of the i-th hydropower station ; The maximum water storage capacity of the i-th hydropower station ; Grid connection price during period t ; Power generation plan for period t ; The maximum power generation output of Hydropower Station i ; The minimum power generation output of Hydropower Station i ; Total installed capacity of the hydropower station ; Optical power installation scale ; Wind power installed capacity ; Inputting the real-time data into a wind-solar-cascade hydropower station, i.e., a wind-solar-hydroelectricity collaborative system model, where the wind-solar-cascade hydropower station, i.e., the wind-solar-hydroelectricity collaborative system model, aims at maximizing the power generation revenue of the wind-solar-cascade hydropower station and minimizing the charge risk, as well as the constraint conditions; the minimum charge risk includes the minimum charge loss risk of the cascade hydropower station, the minimum charge loss risk of wind power and photovoltaic power, and the minimum charge loss risk of the power grid; Outputting optimal scheduling parameters, the optimal output parameters include: Total economic benefit of the wind-solar-cascade hydropower system during period t ; Hydropower output of power station i during period t ; Photovoltaic output during period t ; Wind power output during period t ; The water abandonment volume of the i-th hydropower station in the cascade hydropower station at the t-th moment ; Wind and solar curtailment rates during period t ; Loss of load rate of wind and light during period t ; Residual load of the power grid .

2. The optimal economic dispatching operation method of a wind-solar-cascade hydropower station considering the lowest risk of lost charge according to claim 1, characterized in that, The wind-solar-cascade hydropower station, i.e., the wind-solar-hydroelectricity collaborative system model is as follows: ; Wherein, Z is the total economic benefit of the wind-solar-cascade hydropower station system during the scheduling period, T is the total scheduling period, n is the number of units of the cascade hydropower station, is the hydropower output of power station i in period t, , are the photovoltaic power output and wind power output in period t respectively, is the grid-connected electricity price in period t.

3. The optimal economic dispatching operation method for a wind-solar-cascade hydropower station considering the lowest risk of charge loss according to claim 1, characterized in that The minimum charge loss risk of the cascade hydropower station is based on the following formula: ； wherein, is the water abandonment volume of the i-th hydropower station in the cascade hydropower station at the t-th moment, is the weight coefficient of the importance of water abandonment of hydropower stations at different levels.

4. The optimal economic dispatching operation method for a wind-solar-cascade hydropower station considering the lowest risk of charge loss according to claim 1, characterized in that The minimum charge loss risk of wind power and photovoltaic power is based on the following formula: ； ； ； In the formula, and are the photovoltaic power output and wind power output in period t; and are the curtailment rate of wind and light and the load shedding rate in period t; is the difference between the total output and the generation plan in period t; is the curtailment penalty coefficient for new energy in period t.

5. The optimal economic dispatching operation method for a wind-solar-cascade hydropower station considering the lowest risk of charge loss according to claim 1, characterized in that, The minimum charge loss risk of the power grid is based on the following formula: ； In the formula, is the standard deviation of the variable x, , , are the residual load of the power grid, the mean value of the residual load of the power grid, and the power grid load, respectively.

6. The optimal economic dispatching operation method of a wind-solar-cascade hydropower station considering the lowest risk of charge loss according to claim 1, characterized in that, The constraint conditions of the wind-solar-cascade hydropower station model include: (1) Water volume balance constraint ； ； Wherein, is the water storage volume of the i-th hydropower station at the end of the t-th time period; is the water storage volume of the i-th hydropower station at the beginning of the t-th time period; is the inflow of the i-th hydropower station in the t-th time period; is the power generation flow of the i-th hydropower station in the t-th time period; is the water discharge flow of the i-th hydropower station in the t-th time period; is the conversion coefficient (usually 1); is the sectional flow between the (i - 1)-th reservoir and the i-th reservoir in the t-th time period; is the reservoir release flow of the (i - 1)-th reservoir in the time period t; (2) Water storage capacity constraint ； In the formula, is the minimum water storage capacity of the i-th hydropower station, is the maximum water storage capacity of the i-th hydropower station; (3) Downstream discharge constraint ； wherein, and are respectively the maximum value and the minimum value of the water discharge of the i-th hydropower station; (4) Upper limit constraint of power generation plan ； In the formula, , , respectively represent the total installed capacity of the hydropower station, the installed capacity of the photovoltaic power, and the installed capacity of the wind power; (5) Power generation load balance ； wherein, is the power generation plan for time period t, is the total load; (6) Output constraint of each-level hydropower station ； wherein, , are respectively the maximum and minimum power generation outputs of hydropower station i; (7) Water level constraint ； ； ； Wherein, is the average head of Hydropower Station i in period t, is the discharge of the previous hydropower station in period t, , are respectively the maximum and minimum water levels of the ith hydropower station in the tth time period, is the water level amplitude constraint.

7. The optimal economic dispatching operation method for a wind-solar-cascade hydropower station considering the lowest risk of charge loss according to claim 1, characterized in that, Based on the NSGA-II algorithm for solving the optimal economic benefit of the wind-solar-cascade hydropower station, by introducing non-dominated sorting and crowding degree calculation on the basis of the genetic algorithm, to balance multiple objectives in the multi-objective optimization problem, obtain the optimal solution or reach the maximum number of convergence times, and output the optimized solution of the economic benefit of the wind-solar energy storage coordinated gradient hydropower station.

8. The optimal economic dispatching operation system of a wind-solar-cascade hydropower station considering the lowest risk of lost charge according to claim 1, characterized in that, Including: The first module is configured to obtain real-time data, the real-time data includes: is the number of hydropower stations in the system; is the total scheduling period; is the total load; The power generation flow rate of the $i$-th hydropower station in the $t$-th time period ; Inflow of the $i$-th hydropower station during the $t$-th time period ; Interval flow between the (i - 1)-th reservoir and the i-th reservoir during the t-th period ; The reservoir release flow of the (i - 1)-th reservoir at time period t ; The maximum value of the discharge of the i-th hydropower station ; The minimum value of the discharge of the i-th hydropower station ; Water level amplitude constraint ; Average generating head of the i-th power station during period t ; The maximum water level of the i-th hydropower station in the t-th time period ; The minimum water level of the $i$-th hydropower station in the $t$-th time period ; The water storage volume of the i-th hydropower station at the end of the t-th time period ; Initial water storage volume of the i-th hydropower station at the beginning of the t-th time period ; The minimum water storage capacity of the i-th hydropower station ; The maximum water storage capacity of the i-th hydropower station ; Grid connection price during period t ; Power generation plan for period t ; The maximum power generation output of Hydropower Station i ; Minimum generating output of Hydropower Station i ; Total installed capacity of the hydropower station ; Optical power installation scale ; Wind power installed capacity ; The second module is configured to input the real-time data into a wind-solar-cascade hydropower station, i.e., a wind-solar-hydroelectricity collaborative system model, where the wind-solar-cascade hydropower station, i.e., the wind-solar-hydroelectricity collaborative system model, aims at maximizing the power generation revenue of the wind-solar-cascade hydropower station and minimizing the charge risk, as well as the constraint conditions; the minimum charge risk includes the minimum charge loss risk of the cascade hydropower station, the minimum charge loss risk of wind power and photovoltaic power, and the minimum charge loss risk of the power grid; The third module is configured to output optimal scheduling parameters, the optimal output parameters include: Total economic benefit of the wind-solar cascade hydropower system during period t ; Hydropower output of power station i during period t ; Photovoltaic output during period t ; Wind power output during period t ; The water abandonment volume of the i-th hydropower station in a cascade hydropower station at the t-th moment ; Wind and solar curtailment rates during period t ; Loss of load rate of wind and light in period t ; Grid residual load .

9. A computer medium, characterized in that, Including a computer program, the computer program stores the method steps recited in any one of claims 1 to 7.

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