Method, system and device for compiling power generation plan for water-wind-solar complementary system

Abstract

This invention discloses a method, system, and equipment for compiling power generation plans for a hydro-wind-solar hybrid system, involving the intersection of renewable energy utilization and reservoir scheduling. The method includes: a first stage, which uses a multi-dimensional synchronous back-substitution reduction algorithm to determine typical scenarios and their probabilities based on a set of uncertainties in runoff, wind power, and photovoltaic output each month; a second stage, using the first stage as boundary conditions and with the objectives of minimizing operational risk, optimizing peak-shaving performance, and maximizing power generation and residual energy storage of the hydro-wind-solar hybrid system, constructing a risk-benefit equilibrium multi-objective scheduling model for hydro-wind-solar multi-energy complementarity according to required constraints; solving the model using a non-dominated sorting differential evolution algorithm and selecting equilibrium solutions using a multi-attribute decision method to obtain the typical daily power generation plan for the hydro-wind-solar hybrid system each month, considering system uncertainties. This invention provides a basis for the scientific formulation of power transmission curves for hydro-wind-solar hybrid systems.

Description

Technical Field

[0001] This invention relates to the intersection of renewable energy utilization and reservoir management, and particularly to a method, system and equipment for compiling a power generation plan for a hydro-wind-solar hybrid system. Background Technology

[0002] Clean energy sources, represented by hydropower, wind power, and solar power, have experienced rapid development globally and are considered the mainstay of future energy systems due to their low-carbon, environmentally friendly, and renewable advantages. Wind and solar energy are the most promising new energy sources for large-scale development, but both are susceptible to meteorological factors, resulting in highly random and unpredictable power output. Furthermore, regions rich in clean and renewable resources often have inconsistent spatial distribution with economically developed areas with high electricity demand, necessitating ultra-high-voltage transmission lines for power transmission and requiring stable transmission power to minimize transmission losses. Developing a reasonable power generation plan for hydro-wind-solar hybrid systems can further improve the utilization efficiency of hydropower, wind power, and solar energy resources, better meeting the load demands of the receiving-end power grid.

[0003] In existing technologies, the characterization process of multidimensional uncertainties (runoff and wind and solar power output) of hydro-wind-solar hybrid systems is simplified, and the representative scenarios and their occurrence probabilities obtained are not accurate enough, resulting in deviations in the formulated power generation plans. Summary of the Invention

[0004] This invention provides a method, system, and equipment for compiling a power generation plan for a hydro-wind-solar hybrid system, which can solve the problems in the prior art, such as inaccurate characterization of uncertainties in runoff, wind, and solar output, and incomplete consideration of the scheduling model of the hydro-wind-solar hybrid system.

[0005] This invention provides a method for compiling a power generation plan for a hydro-wind-solar hybrid system, comprising the following steps:

[0006] A set of uncertainties regarding runoff, wind power, and solar power output in a hydro-wind-solar hybrid system is obtained for each month. This set of uncertainties represents curves indicating changes and fluctuations in runoff / power generation over a period of time. A risk-benefit equilibrium multi-objective scheduling model for hydro-wind-solar hybrid systems is constructed using a two-layer nested optimization framework. The outer layer of the two-layer nested optimization framework uses system output stability constraints to maintain stable power generation, based on the monthly set of uncertainties in runoff, wind power, and solar power output, to obtain the power transmission plan curve for the hydro-wind-solar hybrid system. The inner layer of the two-layer nested optimization framework uses the output of the outer layer as boundary conditions and aims to minimize the operational risk of the hydro-wind-solar hybrid system, optimize peak-shaving performance, and maximize power generation and residual energy storage. A standard function is used to construct a multi-objective scheduling model for the water-wind-solar multi-energy complementary system, taking into account constraints such as transmission stability, transmission capacity, water balance, reservoir characteristics, water level, water level fluctuation between adjacent time periods, hydropower output, downstream flow, and medium- and long-term scheduling operation boundary. The model is then used to input the monthly set of uncertainties in runoff, wind power, and solar power output into the multi-objective scheduling model. A non-dominated sorting differential evolution algorithm is employed to optimize the model, obtaining the monthly power generation plan. A multi-attribute decision-making method is used to screen for equilibrium solutions, resulting in the typical daily power generation plan for the water-wind-solar complementary system considering uncertainties each month.

[0007] Furthermore, the specific steps for obtaining the set of uncertain scenarios for runoff, wind power, and photovoltaic output in the hydro-wind-solar hybrid system each month include: constructing a three-dimensional synchronous back-substitution reduction algorithm for the hydro-wind-solar hybrid system based on the set of uncertain scenarios for runoff, wind power, and photovoltaic output, and simultaneously screening typical scenarios for runoff, wind power, and photovoltaic output each month to characterize their uncertainty.

[0008] Furthermore, the specific steps for obtaining the monthly typical daily power generation plan of the hydro-wind-solar hybrid system considering uncertainties include: obtaining the monthly Pareto solution set as the monthly power generation plan through the multi-objective scheduling model of risk-benefit balance of hydro-wind-solar multi-energy complementarity; and selecting a compromise complementary system transmission scheme from the monthly Pareto solution set using a fuzzy decision-making method based on normalized membership values, with the solution having a larger normalized membership degree being the compromise solution.

[0009] Furthermore, the power generation plan assesses risk using risk indicators such as expected curtailment rate and expected load shedding.

[0010] This invention provides a system for compiling a power generation plan for a hydro-wind-solar hybrid system, comprising:

[0011] The typical scenario acquisition module is used to acquire the set of uncertain scenarios for runoff, wind power, and photovoltaic output in the hydro-wind-solar hybrid system each month. This set of uncertain scenarios represents curves showing changes and fluctuations in runoff / power generation over a period of time. The model construction module is used to construct a risk-benefit equilibrium multi-objective scheduling model for hydro-wind-solar hybrid systems using a two-layer nested optimization framework. The outer layer of the two-layer nested optimization framework obtains the power transmission plan curve for the hydro-wind-solar hybrid system by using system output stability constraints to maintain stable power generation, based on the set of uncertain scenarios for runoff, wind power, and photovoltaic output each month. The inner layer of the two-layer nested optimization framework uses the output of the outer layer as boundary conditions to minimize the operational risk of the hydro-wind-solar hybrid system, optimize peak-shaving performance, and balance power generation and residual energy storage. Using the maximum as the objective function, and with constraints such as transmission stability constraints, transmission capacity limits, water balance constraints, reservoir characteristic constraints, water level constraints, water level fluctuation constraints between adjacent time periods, hydropower output constraints, downstream flow constraints, and medium- and long-term scheduling operation boundary constraints, a multi-objective scheduling model for water-wind-solar multi-energy complementarity risk-benefit equilibrium is constructed. A power generation planning module is used to input the monthly set of uncertainties in runoff, wind power, and photovoltaic output into the multi-objective scheduling model. A non-dominated sorting differential evolution algorithm is used to optimize the multi-objective scheduling model, obtaining the monthly power generation plan. A multi-attribute decision method is then used to screen the equilibrium solution, resulting in the typical daily power generation plan for the water-wind-solar complementary system considering uncertainties each month.

[0012] This invention provides a computer device, including a memory and a processor; the memory stores a computer program, and the processor executes the computer program to implement the above-described method for compiling a power generation plan for a hydro-wind-solar hybrid system.

[0013] This invention provides a method, system, and equipment for developing a power generation plan for a hydro-wind-solar hybrid system. Compared with the prior art, its advantages are as follows:

[0014] A risk-benefit equilibrium multi-objective scheduling model for hydro-wind-solar multi-energy complementarity is constructed using a two-layer nested optimization framework. The outer layer of the framework, based on the monthly set of uncertainties in runoff, wind power, and solar power output, uses system output stability as a constraint to maintain stable power generation, thus obtaining the power transmission plan curve for the transmission channel. The inner layer of the framework, with the first stage as the boundary condition, uses the minimization of operational risk, optimization of peak-shaving performance, and maximization of power generation and surplus energy storage of the hydro-wind-solar complementary system as the objective function, and incorporates constraints such as transmission stability, transmission capacity limitations, water balance constraints, reservoir characteristic constraints, and water level constraints. Constraints such as water level fluctuations in adjacent time periods, hydropower output, downstream flow, and medium- to long-term scheduling boundary constraints are used to construct a multi-objective scheduling model for the risk-benefit equilibrium of hydro-wind-solar multi-energy complementarity. The set of uncertain scenarios for runoff, wind power, and solar power output each month is input into the multi-objective scheduling model. A non-dominated sorting differential evolution algorithm is used to optimize the multi-objective scheduling model, obtaining the monthly power generation plan. A multi-attribute decision-making method is then used to screen for equilibrium solutions, resulting in a typical daily power generation plan for the hydro-wind-solar complementary system considering uncertainties each month.

[0015] Among them, the construction of the multi-energy complementary risk-benefit balance multi-objective scheduling model is based on the set of uncertain scenarios of runoff, wind power and photovoltaic output each month. Then, the typical daily power generation plan of the multi-energy complementary system considering uncertainty is obtained through the multi-energy complementary risk-benefit balance multi-objective scheduling model, realizing the preparation of power generation plan considering the uncertainty of runoff and wind and solar output. Attached Figure Description

[0016] Figure 1 A flowchart of a method for compiling a power generation plan for a hydro-wind-solar hybrid system provided by an embodiment of the present invention;

[0017] Figure 2 Detailed constraints of the risk-benefit balance multi-objective scheduling model for multi-energy complementarity of water, wind and solar power provided in this embodiment of the invention;

[0018] Figure 3 A schematic diagram of the intelligent algorithm provided in this embodiment of the invention;

[0019] Figure 4 A flowchart of risk indicators for power generation planning provided in this embodiment of the invention. Detailed Implementation

[0020] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0021] See Figures 1-4 This invention provides a method for compiling a power generation plan for a hydro-wind-solar hybrid system, comprising the following steps:

[0022] Step 1: Obtain the set of uncertain scenarios for runoff, wind power, and photovoltaic output in the hydro-wind-solar hybrid system for each month. The set of uncertain scenarios is a curve representing the changes and fluctuations in runoff / power generation over a period of time.

[0023] Step 2: Construct a multi-objective scheduling model for the water-wind-solar multi-energy complementarity risk-benefit equilibrium using a double-layer nested optimization framework. The outer layer of the double-layer nested optimization framework is: based on the set of uncertain scenarios for runoff, wind power, and photovoltaic output each month, the system output stability that keeps power generation stable is used as a constraint to obtain the power transmission plan curve of the transmission channel. The inner layer of the double-layer nested optimization framework is: with the first stage as the boundary condition, the objective function is to minimize the operational risk of the water-wind-solar complementary system, optimize peak-shaving performance, and maximize power generation and surplus energy storage. The constraints are transmission stability, transmission capacity, water balance, reservoir characteristics, water level, water level fluctuation in adjacent time periods, hydropower output, downstream flow, and medium- and long-term scheduling operation boundary constraints. This completes the construction of the multi-objective scheduling model for the water-wind-solar multi-energy complementarity risk-benefit equilibrium.

[0024] Step 3: Input the set of uncertain scenarios for runoff, wind power, and photovoltaic output for each month into the multi-objective scheduling model for risk-benefit equilibrium of water-wind-solar multi-energy complementarity. Use the non-dominated sorting differential evolution algorithm to optimize the multi-objective scheduling model for risk-benefit equilibrium of water-wind-solar multi-energy complementarity, obtain the power generation plan for each month, and use the multi-attribute decision method to screen the equilibrium solution to obtain the typical daily power generation plan for water-wind-solar complementary system considering uncertainties each month.

[0025] First, considering the uncertainties in water, wind, and solar power output, a three-dimensional synchronous regression reduction algorithm is constructed to simultaneously screen typical scenarios for runoff and wind and solar power output in each month in order to characterize their uncertainties.

[0026] Based on the uncertainty of wind and solar power output, a three-dimensional synchronous back-substitution reduction algorithm for hydro-wind-solar complementary systems is constructed. This method regards the concurrent runoff and wind and solar power output processes as a set scenario. Based on the minimum probability distance of the set scenario, the set scenario is eliminated one by one, and finally representative set scenarios and their corresponding occurrence probabilities are obtained.

[0027] II. Based on a double-layer nested optimization framework, a risk-benefit balanced multi-objective scheduling model for multi-energy complementarity of water, wind and solar power is constructed.

[0028] The outer layer, constrained by the stability of system output, optimizes the power transmission plan curve of the transmission channel to achieve optimal peak-shaving performance. The calculation formula is as follows:

[0029]

[0030] This serves as the boundary condition; the inner layer uses the maximization of the complementary system's power generation and residual energy storage, and the minimization of the complementary system's risk as the model optimization objectives, calculated as follows:

[0031] The complementary system has the largest power generation and residual energy storage:

[0032] f2 = max(RE + RA)

[0033]

[0034] Complementary systems have the lowest operational risk:

[0035]

[0036] In the formula: L n Let be the load of the receiving end of the nth transmission channel; std(x) is the standard deviation of the variable x, used to characterize the volatility of the remaining load; D n The transmission power curve for the nth transmission channel of the complementary system is defined; NT represents the number of transmission channels in the complementary system. RE represents the total average daily power generation of the cascade reservoirs; RA represents the total residual storage capacity of the cascade reservoirs; A m Let γ be the total energy storage of the m-th reservoir; γ is the specific gravity of water; The available water volume of each reservoir; For water head. R c,n R represents the expected curtailment rate of the nth power transmission curve under multiple scenarios; s,n Let n be the expected load factor of the nth power supply curve under multiple scenarios.

[0037] Based on a double-layer nested optimization framework, a risk-benefit equilibrium multi-objective scheduling model for multi-energy complementarity (hydro, wind, and solar) is constructed. Besides minimizing the operational risk of the complementary system in objective 3, the risk indicators for the power generation plan obtained by solving this model also include:

[0038] (1) Expected curtailment rate:

[0039]

[0040] (2) Expected load loss:

[0041]

[0042] In the formula: R c p represents the expected curtailment rate under multiple scenarios; I represents the total number of scenarios; i represents the scenario number; p i Represents the probability of representative scenario i; For the actual output of wind, solar, and hydropower in representative scenario i; D t For the load value of the established transmission power curve during time period t, R l This represents the expected load shedding rate under multiple scenarios.

[0043] Third, a two-dimensional encoding strategy is adopted to efficiently solve the optimization model.

[0044] Based on a two-dimensional coding strategy, the non-dominated sorting differential evolution algorithm (NSDE) is used to optimize the transmission power P. s Given the time node tc of the power transmission change, the mathematical expression of the solution is as follows:

[0045] solution = [P1,...,P] S ,tc1,...,tc S-1 ]

[0046] In the formula, S represents the number of steps in the tiered daily power generation plan.

[0047] Fourth, a multi-attribute decision-making method is used to screen the solution set for equilibrium solutions, thereby obtaining a daily power generation plan that balances power generation, peak shaving, and risk.

[0048] The advantage of this invention is that it takes into account the uncertainties of runoff and wind and solar power output, while also taking into account the stability requirements of ultra-high voltage power transmission. It can formulate a power generation plan with controllable risks and that meets the requirements of power transmission stability to guide the actual operation of the hydro-wind-solar hybrid system.

[0049] This invention provides a system for compiling a power generation plan for a hydro-wind-solar hybrid system, comprising:

[0050] The typical scenario acquisition module is used to acquire the set of uncertain scenarios for runoff, wind power and photovoltaic output in the hydro-wind-solar hybrid system each month. The set of uncertain scenarios is a curve representing the changes and fluctuations in runoff / power generation over a period of time.

[0051] The model building module is used to construct a risk-benefit balanced multi-objective scheduling model for hydro-wind-solar multi-energy complementarity using a double-layer nested optimization framework. The outer layer of the double-layer nested optimization framework obtains the power transmission plan curve of the hydro-wind-solar complementary system by using system output stability constraints to maintain stable power generation, based on the set of uncertainties in runoff, wind power, and photovoltaic output each month. The inner layer of the double-layer nested optimization framework uses the output of the outer layer as boundary conditions, with the objective functions of minimizing the operational risk of the hydro-wind-solar complementary system, optimizing peak-shaving performance, and maximizing power generation and residual energy storage. Constraints include power transmission stability constraints, power transmission capacity limitations, water balance constraints, reservoir characteristic constraints, water level constraints, water level fluctuation constraints between adjacent time periods, hydropower output constraints, downstream flow constraints, and medium- and long-term scheduling operation boundary constraints.

[0052] The power generation planning module is used to input the set of uncertain scenarios of runoff, wind power and photovoltaic output for each month into the risk-benefit balance multi-objective scheduling model of water-wind-solar multi-energy complementarity. The non-dominated sorting differential evolution algorithm is used to optimize the risk-benefit balance multi-objective scheduling model of water-wind-solar multi-energy complementarity to obtain the power generation plan for each month. The multi-attribute decision method is used to screen the equilibrium solution to obtain the typical daily power generation plan of the water-wind-solar complementary system considering uncertainties each month.

[0053] This invention provides a computer device, including: a memory and a processor; the memory stores a computer program, and the processor executes the computer program to implement the steps of a method for compiling a power generation plan for a hydro-wind-solar hybrid system.

[0054] A specific example is as follows:

[0055] 1. A three-dimensional synchronous back-generation reduction algorithm model is constructed. Scene reduction technology is used to obtain a representative set of runoff and wind / solar combined output scenes and their probabilities to describe the uncertainty of water, wind, and solar output in each month. Synchronous back-generation reduction employs a deletion method. Based on scene similarity, one scene is deleted from the scene set each time, and the probability of the deleted scene is added to its most similar scene to ensure that the sum of the probabilities of all scenes is 1, until the number of remaining scenes reaches a preset value. The specific steps are as follows:

[0056] (1) Traverse and calculate all scenario pairs (X) in J. j ,Y j Z j Euclidean distance between:

[0057]

[0058] (2) Calculate each scenario pair (X) j ,Y j Z jThe probability distance between them:

[0059] P D (X j ,Y j Z j ) = P r (X j )·D T (X j ,Y j Z j (4)

[0060] (3) Select the scenario pair with the smallest probability distance (X j ,Y j Z j ):

[0061] P D (X k ,Y k Z k )=min P D (X j ,Y j Z j (5)

[0062] (4) Delete scenarios and update the scenario set:

[0063] J = J - {X} j ,Y j Z j},DL=DL+{X j ,Y j Z j} (6)

[0064] (5) Update the remaining scenario probabilities:

[0065] P r (X k′ ,Y k′ Z k′ ) = P r (X k′ ,Y k′ Z k′ )+P r (X k ,Y k Z k (7)

[0066] (6) Repeat steps (1) to (5) until the number of scenarios included in J reaches the preset number.

[0067] 2. Based on a double-layer nested optimization framework, a multi-objective scheduling model for water, wind and solar multi-energy complementarity risk-benefit balance is constructed.

[0068] The outer layer, constrained by the stability of system output, optimizes the power transmission plan curve of the transmission channel to minimize the fluctuation of residual load, as follows:

[0069]

[0070] In the formula: L n Let be the load of the receiving end of the nth transmission channel; std(x) is the standard deviation of the variable x, used to characterize the volatility of the remaining load; D n NT represents the transmission power curve of the nth external transmission channel of the complementary system; NT is the number of external transmission channels of the complementary system.

[0071] The inner layer uses the optimization objectives of maximizing the power generation and residual energy storage of the complementary system while minimizing operational risk, as follows:

[0072] The complementary system has the largest power generation and residual energy storage:

[0073] f2 = max(RE + RA) (9)

[0074]

[0075] Where: RE is the total average daily power generation of the cascade reservoirs; RA is the total energy storage of the cascade reservoirs during their remaining lifespan; A m Let γ be the total energy storage of the m-th reservoir; γ is the specific gravity of water; The available water volume of each reservoir; For water head.

[0076] Complementary systems have the lowest operational risk:

[0077]

[0078] In the formula: R c,n R represents the expected curtailment rate of the nth power transmission curve under multiple scenarios; s,n Let n be the expected load factor of the nth power supply curve under multiple scenarios.

[0079] The constraints considered in the established multi-energy complementary risk-benefit equilibrium multi-objective scheduling model for water, wind, and solar power include:

[0080] (1) Transmission stability constraints

[0081] Ultra-high voltage (UHV) transmission lines require a smooth, stepped power transmission curve.

[0082] D t =P s ,t=tc s-1 +1,tc s-1 +2,…,tc s (13)

[0083] In the formula: P s Let be the output power of the complementary system during the s-th stable operating period; tc is the time point at which the output of the stepped transmission curve changes. The daily power generation plan can be expressed as:

[0084]

[0085] In the formula: S is the number of steps in the power transmission curve.

[0086] (2) Transmission capacity limitation

[0087] Tc min ≤D t ≤Tc max (15)

[0088] In the formula: Tc max and Tc min These represent the maximum and minimum transmission power of the power transmission channel, respectively.

[0089] (3) Water balance constraint

[0090]

[0091] In the formula: m is the number of the cascade reservoir; V t,m and V t+1,m These represent the initial and final reservoir capacities of reservoir m at time t; QI t,m and QO t,m These represent the inflow and outflow of reservoir m during time period t; QIt t,m:m+1 lag(m,m+1) represents the inflow rate between reservoir m and reservoir m+1; lag(m,m+1) represents the flow lag time between reservoir m and reservoir m+1.

[0092] (4) Reservoir characteristic constraints

[0093]

[0094] In the formula: Z t,m and These represent the water levels in front of and behind the dam of reservoir m at the end of time period t, respectively. and These represent the water level-storage capacity relationship and the discharge-tailwater level relationship of reservoir m, respectively.

[0095] (5) Water level constraint

[0096]

[0097] In the formula: and and represent the upper and lower limits of the reservoir water level at the end of time period t, respectively.

[0098] (6) Constraints on water level fluctuations in adjacent time periods

[0099]

[0100] In the formula: This is the limit on the maximum water level fluctuation between adjacent time periods.

[0101] (7) Hydropower output constraints

[0102]

[0103] In the formula: Let m be the power output of hydropower station m during time period t; and These represent the upper and lower limits of the power output of power station m during time period t. The calculation formula is as follows:

[0104]

[0105] In the formula: k m Let m be the power output coefficient of the hydropower station. ΔH represents the power generation flow rate of reservoir m during time period t; t,m Let ΔH be the head of reservoir m at time t. t,m The calculation formula is as follows:

[0106]

[0107] In the formula: and These represent the average water level and tailwater level of reservoir m during time period t, respectively; h t,m Let m be the head loss of reservoir m during time period t.

[0108] (8) Downflow constraint

[0109]

[0110] In the formula: and These represent the upper and lower limits of the discharge flow from reservoir m during time period t.

[0111] (9) Boundary constraints of medium- and long-term scheduling and operation

[0112]

[0113] In the formula: Δq represents the average monthly inflow into reservoir m; min and Δq max The upper and lower limits of the monthly average outflow variation are acceptable to the medium- and long-term dispatch needs of hydropower stations.

[0114] Apart from minimizing the operational risk of the complementary system in objective 3, the risk indicators for the power generation plan obtained by solving this model also include:

[0115] (1) Expected curtailment rate:

[0116]

[0117] (2) Expected load loss:

[0118]

[0119] In the formula: R c p represents the expected curtailment rate under multiple scenarios; I represents the total number of scenarios; i represents the scenario number; p i Represents the probability of representative scenario i; For the actual output of wind, solar, and hydropower in representative scenario i; D t For the load value of the established transmission power curve during time period t, R l This represents the expected load shedding rate under multiple scenarios.

[0120] 4. A two-dimensional encoding strategy is adopted to efficiently solve the optimization model:

[0121] Intelligent algorithms (such as genetic algorithms and cuckoo algorithms) are used to determine the output power of the water-wind-solar hybrid system at different times.

[0122] Intelligent algorithms are used to determine the power output ([P1,…,P) of the hydro-wind-solar hybrid system during each stable operating phase. S The time points of output change ([tc1,…,tc)) S-1 When [the solution is obtained], the encoding method of the intelligent algorithm solution (individual) can be expressed by the following formula:

[0123] solution = [P1, ..., P S ,tc1,…,tc S-1 (28)

[0124] 5. A multi-attribute decision-making method is used to screen for equilibrium solutions, resulting in a daily power generation plan that balances power generation, peak shaving, and risk:

[0125] A fuzzy decision-making method based on normalized membership values ​​is used to select a compromise complementary power transmission scheme in the Pareto solution set for each month. The specific method is as follows:

[0126] When the optimization objective is to obtain the minimum value of the function, the membership degree of the solution set is calculated as follows:

[0127]

[0128] Where: μ j Let be the membership value of the j-th objective function. and These are the maximum and minimum values ​​of the j-th objective function in the Pareto solution set, respectively.

[0129] When the optimization objective is to obtain the maximum value of the function, the membership degree of the solution set is calculated as follows:

[0130]

[0131] For any Pareto solution k, the normalized membership value χ k The calculation formula is as follows:

[0132]

[0133] In the formula: NS is the number of solutions in the Pareto solution set; NF is the number of objective functions in the model. The solution with the largest normalized membership degree is considered the optimal compromise solution.

[0134] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A method for compiling a power generation plan for a hydro-wind-solar hybrid system, characterized in that, Includes the following steps: Obtain the set of uncertain scenarios for runoff, wind power, and photovoltaic output in the hydro-wind-solar hybrid system for each month. The set of uncertain scenarios is a curve representing the changes and fluctuations in runoff / power generation over a period of time. A risk-benefit balanced multi-objective scheduling model for multi-energy complementarity of water, wind and solar power is constructed using a double-nested optimization framework. The outer layer of the double-layer nested optimization framework: based on the set of uncertain scenarios for runoff, wind power, and photovoltaic output each month, the system output stability constraint that keeps power generation stable is used to obtain the power transmission plan curve of the hydro-wind-solar hybrid system. The inner layer of the double-layer nested optimization framework: taking the output of the outer layer as the boundary condition, taking the minimum operation risk, the best peak-shaving performance, and the maximum power generation and surplus energy storage of the hydro-wind-solar complementary system as the objective function, and taking the transmission stability constraint, transmission capacity limit, water balance constraint, reservoir characteristic constraint, water level constraint, water level fluctuation constraint between adjacent time periods, hydropower output constraint, downstream flow constraint, and medium- and long-term dispatch operation boundary constraint as the constraint conditions, constructs a multi-objective dispatch model for risk-benefit balance of hydro-wind-solar multi-energy complementarity. The set of uncertain scenarios for runoff, wind power, and photovoltaic output each month is input into the risk-benefit balance multi-objective scheduling model of water-wind-solar multi-energy complementarity. The non-dominated sorting differential evolution algorithm is used to optimize the risk-benefit balance multi-objective scheduling model of water-wind-solar multi-energy complementarity to obtain the power generation plan for each month. The equilibrium solution is screened using a multi-attribute decision method to obtain the typical daily power generation plan of the water-wind-solar complementary system considering uncertainties each month. The aforementioned use of a double-layer nested optimization framework to construct a risk-benefit balanced multi-objective scheduling model for multi-energy complementarity of water, wind, and solar power specifically includes: The outer layer, constrained by the stability of system output, optimizes the power transmission plan curve of the transmission channel to achieve optimal peak-shaving performance. The calculation formula is as follows: This serves as the boundary condition; the inner layer uses the maximization of the complementary system's power generation and residual energy storage, and the minimization of the complementary system's risk as the model optimization objectives, calculated as follows: The complementary system has the largest power generation and residual energy storage: f2 = max(RE + RA) Complementary systems have the lowest operational risk: In the formula: L n Let be the load of the receiving end of the nth transmission channel; std(x) is the standard deviation of the variable x, used to characterize the volatility of the remaining load; D n The transmission power curve for the nth transmission channel of the complementary system is defined; NT is the number of transmission channels in the complementary system; RE is the total average daily power generation of the cascade reservoirs; RA is the total residual storage of the cascade reservoirs; A m Let γ be the total energy storage of the m-th reservoir; γ is the specific gravity of water; The available water volume of each reservoir; For water head; R c,n R represents the expected curtailment rate of the nth power transmission curve under multiple scenarios; s,n Let be the expected load factor of the nth power transmission curve under multiple scenarios.

2. The method for formulating a power generation plan for a hydro-wind-solar hybrid system as described in claim 1, characterized in that, The specific steps for obtaining the set of uncertain scenarios for runoff, wind power, and photovoltaic output in the hydro-wind-solar hybrid system each month include: Based on the set of uncertain scenarios for runoff, wind power, and solar power output, a three-dimensional synchronous back-substitution reduction algorithm for hydro-wind-solar hybrid systems is constructed. Typical scenarios are screened for runoff and wind power and solar power output for each month to characterize their uncertainty.

3. The method for formulating a power generation plan for a hydro-wind-solar hybrid system as described in claim 1, characterized in that, The specific steps for obtaining the typical daily monthly power generation plan of the hydro-wind-solar hybrid system, taking into account uncertainties, include: The monthly Pareto solution set is obtained through a multi-energy complementary risk-benefit balance multi-objective scheduling model of water, wind and solar power as the monthly power generation plan; A fuzzy decision-making method based on normalized membership values ​​is adopted to select a compromise power generation plan from the Pareto solution set each month. The solution with the larger normalized membership value is the compromise solution.

4. The method for formulating a power generation plan for a hydro-wind-solar hybrid system as described in claim 1, characterized in that, The power generation plan assesses risk using risk indicators such as expected curtailment rate and expected load shedding.

5. A system for compiling a power generation plan for a hydro-wind-solar hybrid system, characterized in that, include: The typical scenario acquisition module is used to acquire the set of uncertain scenarios for runoff, wind power and photovoltaic output in the hydro-wind-solar hybrid system each month. The set of uncertain scenarios is a curve representing the changes and fluctuations in runoff / power generation over a period of time. The model building module is used to construct a risk-benefit balanced multi-objective scheduling model for hydro-wind-solar multi-energy complementarity using a double-layer nested optimization framework. The outer layer of the double-layer nested optimization framework obtains the power transmission plan curve of the hydro-wind-solar complementary system by using system output stability constraints to maintain stable power generation, based on the set of uncertainties in runoff, wind power, and photovoltaic output each month. The inner layer of the double-layer nested optimization framework uses the output of the outer layer as boundary conditions, with the objective functions of minimizing the operational risk of the hydro-wind-solar complementary system, optimizing peak-shaving performance, and maximizing power generation and residual energy storage. Constraints include power transmission stability constraints, power transmission capacity limitations, water balance constraints, reservoir characteristic constraints, water level constraints, water level fluctuation constraints between adjacent time periods, hydropower output constraints, downstream flow constraints, and medium- and long-term scheduling operation boundary constraints. The power generation planning module is used to input the set of uncertain scenarios of runoff, wind power and photovoltaic output for each month into the risk-benefit balance multi-objective scheduling model of water-wind-solar multi-energy complementarity. The non-dominated sorting differential evolution algorithm is used to optimize the risk-benefit balance multi-objective scheduling model of water-wind-solar multi-energy complementarity to obtain the power generation plan for each month. The multi-attribute decision method is used to screen the equilibrium solution to obtain the typical daily power generation plan of water-wind-solar complementary system considering uncertainty. The aforementioned use of a double-layer nested optimization framework to construct a risk-benefit balanced multi-objective scheduling model for multi-energy complementarity of water, wind, and solar power specifically includes: The outer layer, constrained by the stability of system output, optimizes the power transmission plan curve of the transmission channel to achieve optimal peak-shaving performance. The calculation formula is as follows: This serves as the boundary condition; the inner layer uses the maximization of the complementary system's power generation and residual energy storage, and the minimization of the complementary system's risk as the model optimization objectives, calculated as follows: The complementary system has the largest power generation and residual energy storage: f2 = max(RE + RA) Complementary systems have the lowest operational risk: In the formula: L n Let be the load of the receiving end of the nth transmission channel; std(x) is the standard deviation of the variable x, used to characterize the volatility of the remaining load; D n The transmission power curve for the nth transmission channel of the complementary system is defined; NT is the number of transmission channels in the complementary system; RE is the total average daily power generation of the cascade reservoirs; RA is the total residual storage of the cascade reservoirs; A m Let γ be the total energy storage of the m-th reservoir; γ is the specific gravity of water; The available water volume of each reservoir; For water head; R c,n R represents the expected curtailment rate of the nth power transmission curve under multiple scenarios; s,n Let be the expected load factor of the nth power transmission curve under multiple scenarios.

6. A computer device, comprising: Memory and processor; The memory stores a computer program, characterized in that when the processor executes the computer program, it implements a method for compiling a power generation plan for a hydro-wind-solar hybrid system according to any one of claims 1 to 4.

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