Method, system and equipment for compiling power generation plan of water-wind-light complementary system

Abstract

The invention discloses a method, a system and equipment for compiling a power generation plan of a water-wind-light complementary system, and relates to the crossing field of renewable energy utilization and reservoir scheduling. Adopting a multi-dimensional synchronous back substitution reduction algorithm to determine a typical scene and an occurrence probability thereof; in the second stage, the first stage is used as a boundary condition, the minimum operation risk, the optimal peak regulation performance and the maximum power generation amount and residual energy storage of the water-wind-light complementary system are used as objective functions, and the construction of a water-wind-light multi-energy complementary risk-benefit balance multi-objective scheduling model is completed according to the required constraint condition; and solving the model by using a non-dominated sorting differential evolution algorithm, and screening an equilibrium solution by using a multi-attribute decision-making method to obtain a monthly typical daily power generation plan of the water-wind-light complementary system considering the uncertainty of the system. The method can provide a basis for scientific formulation of the power transmission curve of the water-wind-light complementary system.

Description

Technical Field

[0001] The present invention relates to the intersection of renewable energy utilization and reservoir scheduling, and in particular to a method, system and equipment for compiling a power generation plan for a water-wind-solar complementary system. Background Art

[0002] Clean energy represented by hydropower, wind power, and photovoltaics has developed rapidly around the world, and due to its low-carbon, environmentally friendly, and renewable advantages, it is considered to be the main force of the future energy system. Wind power and solar energy are the new energy sources with the greatest prospects for large-scale development, but they are easily affected by meteorological factors, and their output power has strong random fluctuations and is difficult to predict. In addition, areas rich in clean and renewable resources are often inconsistent with the spatial distribution of socioeconomically developed areas with large electricity demand, requiring ultra-high voltage transmission lines for power transmission, and requiring the transmission power to remain stable to reduce transmission losses. Formulating a reasonable power generation plan for the water-wind-solar complementary system can further improve the utilization efficiency of water, wind, and light energy resources and better meet the load requirements of the receiving power grid.

[0003] In the existing technology, the characterization process of the multi-dimensional uncertainty (runoff and wind and solar output) of the water-wind-solar complementary system is simplified, and the representative scenarios and occurrence probabilities obtained are not accurate enough, resulting in deviations in the formulated power generation plan. Summary of the invention

[0004] The embodiments of the present invention provide a method, system and device for compiling a power generation plan for a water-wind-solar complementary system, which can solve the problems in the prior art of inaccurate characterization of uncertainty in runoff and wind and solar output, and incomplete consideration of the scheduling model of the water-wind-solar complementary system.

[0005] The embodiment of the present invention provides a method for compiling a power generation plan for a water-wind-solar complementary system, comprising the following steps:

[0006] Obtain a set of uncertainty scenarios for runoff, wind power and photovoltaic output in each month of the water-wind-solar complementary system, wherein the uncertainty scenario set is a curve representing the change and fluctuation of runoff / power generation over a period of time; use a double-layer nested optimization framework to construct a multi-objective scheduling model for risk-benefit balance of water-wind-solar multi-energy complementarity; the outer layer of the double-layer nested optimization framework: based on the uncertainty scenario set of runoff, wind power and photovoltaic output in each month, use the system output stability constraint that keeps the power generation stable to obtain the power transmission plan curve of the water-wind-solar complementary system; the inner layer of the double-layer nested optimization framework: use the output of the outer layer as the boundary condition, and take the minimum operation risk, optimal peak-shaving performance, and maximum power generation and residual energy storage of the water-wind-solar complementary system as the goal The target function is constructed, and the multi-objective scheduling model of risk-benefit balance of water-wind-solar multi-energy complementarity is constructed with transmission stability constraints, transmission capacity limitations, water balance constraints, reservoir characteristics constraints, water level constraints, water level fluctuation constraints in adjacent periods, hydropower output constraints, downstream flow constraints and medium- and long-term scheduling and operation boundary constraints as constraints; the set of uncertainty scenarios of runoff, wind power and photovoltaic output each month is input into the multi-objective scheduling model of risk-benefit balance of water-wind-solar multi-energy complementarity, and the non-dominated sorting differential evolution algorithm is used to optimize the multi-objective scheduling model of risk-benefit balance of water-wind-solar multi-energy complementarity, to obtain the power generation plan for each month and use the multi-attribute decision-making method to screen the equilibrium solution, and to obtain the typical daily power generation plan of the water-wind-solar complementary system considering uncertainty.

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

[0008] Furthermore, the typical daily power generation plan for each month of the water-wind-solar complementary system taking into account uncertainty is obtained, and the specific steps include: obtaining the Pareto solution set of each month as the power generation plan of each month through the risk-benefit balance multi-objective scheduling model of water-wind-solar multi-energy complementarity; adopting a fuzzy decision-making method based on the normalized membership value to select a compromise complementary system transmission scheme from the Pareto solution set of each month, and the solution with a large normalized membership value is the compromise solution.

[0009] Furthermore, the power generation plan evaluates risk through risk indicators of expected power abandonment rate and expected load loss.

[0010] The embodiment of the present invention provides a system for compiling a power generation plan for a water-wind-solar complementary system, comprising:

[0011] A typical scenario acquisition module is used to obtain a set of uncertainty scenarios for runoff, wind power and photovoltaic output in each month of the water-wind-solar complementary system, wherein the uncertainty scenario set is a curve representing the change and fluctuation of runoff / power generation over a period of time; a model construction module is used to use a double-layer nested optimization framework to construct a multi-objective scheduling model for risk-benefit balance of water-wind-solar multi-energy complementarity; the outer layer of the double-layer nested optimization framework: based on the uncertainty scenario set of runoff, wind power and photovoltaic output in each month, the system output stability constraint that keeps the power generation stable is used to obtain the power transmission plan curve of the water-wind-solar complementary system; the inner layer of the double-layer nested optimization framework: with the output of the outer layer as the boundary condition, the water-wind-solar complementary system has the lowest operating risk, the best peak-shaving performance, the highest power generation and the lowest remaining energy storage The maximum is the objective function, and the transmission stability constraint, transmission capacity limitation, water balance constraint, reservoir characteristic constraint, water level constraint, water level fluctuation constraint in adjacent periods, hydropower output constraint, downstream flow constraint and medium- and long-term scheduling operation boundary constraint are used as constraints to construct a risk-benefit balanced multi-objective scheduling model for water-wind-solar multi-energy complementarity; the power generation plan preparation module is used to input the uncertainty scenario set of runoff, wind power and photovoltaic output each month into the risk-benefit balanced multi-objective scheduling model for water-wind-solar multi-energy complementarity, and use the non-dominated sorting differential evolution algorithm to optimize the risk-benefit balanced multi-objective scheduling model for water-wind-solar multi-energy complementarity, obtain the power generation plan for each month, and use the multi-attribute decision-making method to screen the equilibrium solution, and obtain the typical daily power generation plan for the water-wind-solar complementary system considering uncertainty.

[0012] An embodiment of the present invention provides a computer device, comprising: a memory and a processor; the memory stores a computer program, and the processor implements the above-mentioned method for compiling a power generation plan for a water-wind-solar complementary system when executing the computer program.

[0013] The embodiment of the present invention provides a method, system and device for compiling a power generation plan for a water-wind-solar complementary system. Compared with the prior art, the beneficial effects thereof are as follows:

[0014] A double-layer nested optimization framework is used to construct a multi-objective scheduling model for the risk-benefit balance of water, wind and photovoltaic multi-energy complementarity; the outer layer of the double-layer nested optimization framework uses the system output stability that keeps the power generation stable as a constraint based on the set of uncertainty scenarios of runoff, wind power and photovoltaic output each month to obtain the power transmission plan curve of the transmission channel; the inner layer of the double-layer nested optimization framework uses the first stage as the boundary condition, and takes the minimum operation risk of the water-wind-photovoltaic complementary system, the best peak-shaving performance, and the maximum power generation and remaining energy storage as the objective function, with transmission stability constraints, transmission capacity limitations, water balance constraints, reservoir characteristics constraints, water level constraints, Taking the water level fluctuation constraints, hydropower output constraints, downstream flow constraints and medium- and long-term scheduling operation boundary constraints in adjacent time periods as constraints, the construction of the risk-benefit balanced multi-objective scheduling model of water-wind-solar multi-energy complementarity is completed; the set of uncertainty scenarios of runoff, wind power and photovoltaic output for each month is input into the risk-benefit balanced multi-objective scheduling model of water-wind-solar multi-energy complementarity, and the non-dominated sorting differential evolution algorithm is used to optimize the risk-benefit balanced multi-objective scheduling model of water-wind-solar multi-energy complementarity, to obtain the power generation plan for each month and use the multi-attribute decision-making method to screen the equilibrium solution, and to obtain the typical daily power generation plan of the water-wind-solar complementary system considering uncertainty.

[0015] Among them, the construction of the risk-benefit balanced multi-objective scheduling model of water-wind-solar multi-energy complementarity is based on the set of uncertainty scenarios of runoff, wind power and photovoltaic output each month. The monthly typical daily power generation plan of the water-wind-solar complementary system considering uncertainty is obtained through the risk-benefit balanced multi-objective scheduling model of water-wind-solar multi-energy complementarity, realizing the preparation of power generation plans considering the uncertainty of runoff and wind and photovoltaic output. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flow chart of a method for compiling a power generation plan for a water-wind-solar complementary system provided by an embodiment of the present invention;

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

[0018] Figure 3 A schematic diagram of an intelligent algorithm provided by an embodiment of the present invention;

[0019] Figure 4 A risk indicator flow chart of a power generation plan provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific embodiments disclosed below.

[0021] See also Figure 1 to Figure 4 The embodiment of the present invention provides a method for compiling a power generation plan for a water-wind-solar complementary system, comprising the following steps:

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

[0023] Step 2: Use a double-layer nested optimization framework to construct a multi-objective scheduling model for risk-benefit balance of water, wind and solar energy multi-energy complementarity; the outer layer of the double-layer nested optimization framework is: based on the set of uncertainty scenarios of runoff, wind power and photovoltaic output each month, use the system output stability that keeps the power generation stable 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: taking the first stage as the boundary condition, and taking the minimum operation risk, optimal peak-shaving performance, maximum power generation and residual energy storage of the water-wind-solar complementary system as the objective function, with transmission stability constraints, transmission capacity restrictions, water balance constraints, reservoir characteristics constraints, water level constraints, water level fluctuation constraints in adjacent time periods, hydropower output constraints, downstream flow constraints and medium- and long-term scheduling operation boundary constraints as constraints, to complete the construction of a multi-objective scheduling model for risk-benefit balance of water, wind and solar energy multi-energy complementarity.

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

[0025] First, considering the uncertainty of water, wind and solar output, a three-dimensional synchronous return reduction algorithm is constructed to screen typical scenarios of runoff, wind and solar output of each month at the same time to characterize their uncertainty.

[0026] Based on the uncertainty of wind and solar output, a three-dimensional synchronous back-reduction algorithm for the water-wind-solar complementary system is constructed. This method regards the runoff and wind and solar output processes in the same period as a collective scenario, and deletes the collective scenarios one by one based on the minimum probability distance of the collective scenarios. Finally, representative collective scenarios and their corresponding occurrence probabilities are obtained.

[0027] 2. Based on a two-layer nested optimization framework, a multi-objective scheduling model with risk-benefit balance for the complementary energy of water, wind and solar power is constructed.

[0028] The outer layer takes the stability of system output as a prerequisite constraint and optimizes the transmission plan curve of the transmission channel to make the peak load performance of the transmission curve optimal. The calculation formula is:

[0029]

[0030] This is used as the boundary condition; the inner layer takes the maximum power generation and remaining energy storage of the complementary system and the minimum risk of the complementary system as the model optimization target, and the calculation formula is:

[0031] Complementary system power generation and maximum remaining energy storage:

[0032] f2=max(RE+RA)

[0033]

[0034] Complementary systems operate with minimal risk:

[0035]

[0036] Where: L n is the load of the receiving grid at the nth transmission channel; std(x) is the standard deviation of the variable x, which is used to characterize the volatility of the residual load; D n is the transmission power curve of the nth transmission channel of the complementary system; NT is the number of transmission channels of the complementary system. RE is the total daily average power generation of the cascade reservoirs; RA is the total energy storage of the cascade reservoirs during the remaining period; A m is the total energy storage of the mth reservoir; γ is the specific gravity of water; is the available water volume of each reservoir; R is the water head. c,n is the expected power curtailment rate of the nth transmission curve under multiple scenarios; R s,n is the expected load loss rate of the nth transmission curve under multiple scenarios.

[0037] Based on the double-layer nested optimization framework, a multi-objective scheduling model of risk-benefit balance of hydropower, wind power and solar power is constructed. In addition to the minimum operation risk of the complementary system in objective 3, the risk indicators of the power generation plan obtained by solving the model also include:

[0038] (1) Expected power abandonment rate:

[0039]

[0040] (2) Load loss expectation:

[0041]

[0042] Where: R c is the expected power abandonment rate under multiple scenarios; I is the total number of scenarios; i is the scenario number; p i represents the probability of the representative scenario i; is the actual output of wind, solar and hydropower in the representative scenario i; D t is the load value of the transmission power curve in period t, R l is the expected load loss rate under multiple scenarios.

[0043] 3. Use a two-dimensional coding strategy to efficiently solve the optimization model.

[0044] Based on the two-dimensional coding strategy, the non-dominated sorting differential evolution algorithm (NSDE) is used to optimize the transmission power P s The mathematical expression of the solution obtained by considering the time node tc at which the transmission power changes is as follows:

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

[0046] Where S is the number of steps in the step-type daily power generation plan.

[0047] Fourth, a multi-attribute decision-making method is used to screen the equilibrium solution from the solution set to obtain a daily power generation plan that balances power generation, peak regulation and risk.

[0048] The effect of the present invention is that it takes into account the uncertainty of runoff and wind and solar output, while taking into account the stability requirements of ultra-high voltage power transmission. It can formulate a power generation plan with controllable risks and meeting the stability requirements of power transmission to guide the actual operation of the water-wind-solar complementary system.

[0049] The embodiment of the present invention provides a system for compiling a power generation plan for a water-wind-solar complementary system, comprising:

[0050] The typical scenario acquisition module is used to obtain the uncertainty scenario set of runoff, wind power and photovoltaic output each month in the water-wind-solar complementary system. The uncertainty scenario set is a curve representing the changes and fluctuations in runoff / power generation over a period of time.

[0051] A model building module is used to use a double-layer nested optimization framework to construct a risk-benefit balanced multi-objective scheduling model for water-wind-solar multi-energy complementary systems; the outer layer of the double-layer nested optimization framework: based on the set of uncertainty scenarios of runoff, wind power and photovoltaic output each month, the system output stability constraint that keeps the power generation stable is used to obtain the power transmission plan curve of the water-wind-solar complementary system; the inner layer of the double-layer nested optimization framework: using the output of the outer layer as the boundary condition, the minimum operation risk, the best peak-shaving performance, the maximum power generation and the remaining energy storage of the water-wind-solar complementary system as the objective function, and the transmission stability constraint, transmission capacity limitation, water balance constraint, reservoir characteristic constraint, water level constraint, adjacent time period water level fluctuation constraint, hydropower output constraint, downstream flow constraint and medium- and long-term scheduling operation boundary constraint as the constraint conditions, to construct a risk-benefit balanced multi-objective scheduling model for water-wind-solar multi-energy complementary systems.

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

[0053] An embodiment of the present invention provides a computer device, including: a memory and a processor; the memory stores a computer program, and when the processor executes the computer program, the steps of a method for compiling a power generation plan for a water-wind-solar complementary system are implemented.

[0054] A specific embodiment is as follows:

[0055] 1. Construct a three-dimensional synchronous back-generation reduction algorithm model, and use scene reduction technology to obtain a representative runoff and wind and light combined output scene set and its probability to describe the uncertainty of water, wind and light output in each month. Synchronous back-generation reduction adopts the deletion method, deleting one scene from the scene set each time according to the similarity of the scene, and adding the probability of the deleted scene to the most similar scene to ensure that the sum of all scene probabilities is 1, until the number of remaining scenes reaches the preset value. The specific steps are as follows:

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

[0057]

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

[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 a set of scenario pairs 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 scenario sets:

[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 J contains a preset number of scenarios.

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

[0068] The outer layer takes the stability of system output as a prerequisite constraint and optimizes the transmission plan curve of the transmission channel to minimize the residual load fluctuation, as follows:

[0069]

[0070] Where: L n is the load of the receiving grid at the nth transmission channel; std(x) is the standard deviation of the variable x, which is used to characterize the volatility of the residual load; D n is the transmission power curve of the nth transmission channel of the complementary system; NT is the number of transmission channels of the complementary system.

[0071] The inner layer takes the maximum power generation and remaining energy storage of the complementary system and the minimum operation risk as the model optimization objectives, as follows:

[0072] Complementary system power generation and maximum remaining energy storage:

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

[0074]

[0075] Where: RE is the total daily average power generation of the cascade reservoirs; RA is the total energy storage of the cascade reservoirs during the remaining period; A m is the total energy storage of the mth reservoir; γ is the specific gravity of water; is the available water volume of each reservoir; For the water head.

[0076] Complementary systems operate with minimal risk:

[0077]

[0078] Where: R c,n is the expected power curtailment rate of the nth transmission curve under multiple scenarios; R s,n is the expected load loss rate of the nth transmission curve under multiple scenarios.

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

[0080] (1) Transmission stability constraints

[0081] UHV transmission lines require that the transmission curve must be smooth and stepped:

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

[0083] Where: P s is the output power of the complementary system in the sth stable operation period; tc is the time point when the output of the step-shaped transmission curve changes. The daily power generation plan can be expressed as:

[0084]

[0085] Where: S is the number of steps of the power transmission curve.

[0086] (2) Transmission capacity limitation

[0087] Tc min ≤D t ≤Tc max (15)

[0088] Where: Tc max and Tc min are the maximum and minimum transmission powers of the transmission channel respectively.

[0089] (3) Water balance constraints

[0090]

[0091] Where: m is the number of the cascade reservoir; V t,m and V t+1,m are the storage capacities of reservoir m at the beginning and end of period t; QI t,m and QO t,m are the inflow and outflow of reservoir m during period t; QIt t,m:m+1 is the interval water flow between reservoir m and reservoir m+1; lag(m,m+1) is the water flow lag between reservoir m and reservoir m+1.

[0092] (4) Reservoir characteristic constraints

[0093]

[0094] Where: Z t,m and are the water level before and after the dam of reservoir m at the end of period t, respectively; and They represent the water level-storage capacity relationship and the discharge-tailwater level relationship of reservoir m respectively.

[0095] (5) Water level constraint

[0096]

[0097] Where: and They respectively represent the upper and lower limits of the reservoir water level of reservoir m at the end of period t.

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

[0099]

[0100] Where: It is the maximum water level fluctuation limit between adjacent time periods.

[0101] (7) Hydropower output constraints

[0102]

[0103] Where: is the output of hydropower station m in period t; and They represent the upper and lower limits of the output of power station m in period t respectively. The calculation formula is as follows:

[0104]

[0105] Where: k m is the output coefficient of the m hydropower station; represents the power generation flow of reservoir m in period t; ΔH t,m ΔH is the hydraulic head of reservoir m during period t. t,m The calculation formula is as follows:

[0106]

[0107] Where: and are the average water level and tailwater level of reservoir m in period t; h t,m is the head loss of reservoir m during period t.

[0108] (8) Downflow flow restriction

[0109]

[0110] Where: and They represent the upper and lower limits of the discharge flow of reservoir m in period t respectively.

[0111] (9) Medium- and long-term scheduling and operation boundary constraints

[0112]

[0113] Where: represents the monthly average inflow of reservoir m; Δq min and Δq max The upper and lower limits of the monthly average discharge flow rate that can be accepted by the medium- and long-term scheduling needs of the hydropower station.

[0114] In addition to the minimum operation risk of the complementary system in objective 3, the risk indicators of the power generation plan obtained by solving the model also include:

[0115] (1) Expected power abandonment rate:

[0116]

[0117] (2) Load loss expectation:

[0118]

[0119] Where: R c is the expected power curtailment rate under multiple scenarios; I is the total number of scenarios; i is the scenario number; p i represents the probability of the representative scenario i; is the actual output of wind, solar and hydropower in the representative scenario i; D t is the load value of the transmission power curve in period t, R l is the expected load loss rate under multiple scenarios.

[0120] 4. Use two-dimensional coding strategy 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 complementary system in each period.

[0122] The intelligent algorithm is used to determine the output of the water-wind-solar hybrid system in each stable operation period ([P1,…,P S ]) The time point of output change ([tc1,…,tc S-1 ]), the encoding method of the intelligent algorithm solution (individual) can be expressed as follows:

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

[0124] 5. Use the multi-attribute decision-making method to screen the equilibrium solution and obtain the daily power generation plan with power generation, peak load regulation and risk balance:

[0125] A fuzzy decision-making method based on normalized membership value is used to select the compromise complementary system transmission scheme from the Pareto solution set of each month. The specific method is as follows:

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

[0127]

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

[0129] When the optimization goal is to obtain the maximum value of the function, the membership 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] Where: 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 is considered to be the best compromise solution.

[0134] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for compiling a power generation plan for a water-wind-solar complementary system, characterized in that: The following steps are involved: Obtain a set of uncertainty scenarios for runoff, wind power and photovoltaic output in each month of the hydro-wind-solar hybrid system, wherein the uncertainty scenario set is a curve representing the change and fluctuation of runoff / power generation over a period of time; Using a double-layer nested optimization framework, a multi-objective scheduling model with risk-benefit balance of water, wind and solar energy complementary is constructed; The outer layer of the two-layer nested optimization framework: based on the set of uncertainty scenarios of runoff, wind power and photovoltaic output each month, the system output stability constraint that keeps the power generation stable is used to obtain the power transmission plan curve of the water-wind-solar complementary system; The inner layer of the two-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, the maximum power generation and the remaining energy storage of the water-wind-solar complementary system as the objective function, taking the transmission stability constraint, the transmission capacity limitation, the water balance constraint, the reservoir characteristic constraint, the water level constraint, the water level fluctuation constraint in adjacent time periods, the hydropower output constraint, the downstream flow constraint and the medium- and long-term scheduling operation boundary constraint as the constraint conditions, constructing a multi-objective scheduling model of risk-benefit balance of water-wind-solar multi-energy complementary; The uncertainty scenario set of runoff, wind power and photovoltaic output for each month is input into the risk-benefit balanced 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 balanced multi-objective scheduling model of water-wind-solar multi-energy complementarity. The power generation plan for each month is obtained, and the multi-attribute decision-making method is used to screen the equilibrium solution to obtain the typical daily power generation plan of the water-wind-solar complementary system considering uncertainty.

2. The method for compiling a power generation plan for a water-wind-solar complementary system according to claim 1, characterized in that: The specific steps of obtaining the uncertainty scenario set of runoff, wind power and photovoltaic output in each month in the water-wind-solar complementary system include: Based on the uncertainty scenario set of runoff, wind power and photovoltaic output, a three-dimensional synchronous back-reduction algorithm for the water-wind-solar complementary system is constructed. Typical scenarios of runoff, wind power and photovoltaic output are screened simultaneously for each month to characterize their uncertainty.

3. The method for compiling a power generation plan for a water-wind-solar complementary system according to claim 1, characterized in that: The specific steps of obtaining the monthly typical daily power generation plan of the water-wind-solar complementary system considering uncertainty include: Through the risk-benefit equilibrium multi-objective scheduling model of water, wind and solar energy complementary multi-energy, the Pareto solution set of each month is obtained as the power generation plan of each month; A fuzzy decision-making method based on normalized membership value is used to select a compromise power generation plan from the Pareto solution set of each month, and the solution with a large normalized membership value is the compromise solution.

4. The method for compiling a power generation plan for a water-wind-solar complementary system according to claim 1, characterized in that: The power generation plan evaluates risk through risk indicators of expected power curtailment rate and expected load loss.

5. A system for compiling a power generation plan for a water-wind-solar complementary system, characterized in that: include: A typical scenario acquisition module is used to obtain a set of uncertainty scenarios of runoff, wind power and photovoltaic output in each month of the water-wind-solar hybrid system. The uncertainty scenario set is a curve representing the change and fluctuation of runoff / power generation over a period of time. A model building module is used to use a double-layer nested optimization framework to construct a multi-objective scheduling model for risk-benefit balance of water, wind and photovoltaic multi-energy complementarity; the outer layer of the double-layer nested optimization framework: based on the set of uncertainty scenarios of runoff, wind power and photovoltaic output each month, the system output stability constraint that keeps the power generation stable is used to obtain the power transmission plan curve of the water, wind and photovoltaic complementary system; the inner layer of the double-layer nested optimization framework: using the output of the outer layer as the boundary condition, the minimum operation risk, the best peak-shaving performance, the maximum power generation and the remaining energy storage of the water, wind and photovoltaic complementary system as the objective function, and the transmission stability constraint, transmission capacity limitation, water balance constraint, reservoir characteristic constraint, water level constraint, adjacent time period water level fluctuation constraint, hydropower output constraint, downstream flow constraint and medium- and long-term scheduling operation boundary constraint as the constraint conditions, to construct a multi-objective scheduling model for risk-benefit balance of water, wind and photovoltaic multi-energy complementarity; The power generation plan preparation module is used to input the uncertainty scenario set of runoff, wind power and photovoltaic output of each month into the risk-benefit equilibrium multi-objective scheduling model of water-wind-solar multi-energy complementarity, and optimize the risk-benefit equilibrium multi-objective scheduling model of water-wind-solar multi-energy complementarity using the non-dominated sorting differential evolution algorithm to obtain the power generation plan for each month and use the multi-attribute decision-making method to screen the equilibrium solution, so as to obtain the typical daily power generation plan of the water-wind-solar complementary system considering uncertainty.

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

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