Method for planning installed capacity of watershed cascade hydro-landscape multi-energy complementary base

Abstract

This invention relates to the field of power system installed capacity planning technology, and discloses a method for installed capacity planning of a basin-level cascade hydropower-wind-solar multi-energy complementary base. The method includes: constructing a two-layer capacity planning model for the basin-level cascade hydropower-wind-solar multi-energy complementary base, comprising an inner-layer optimization scheduling model and an outer-layer capacity optimization model; using the inner-layer optimization scheduling model to perform time-series simulation analysis of wind and solar capacity schemes; using the outer-layer capacity optimization model to establish technical and economic indicators to initially screen wind and solar capacity schemes and obtain candidate schemes; using TOPSIS to evaluate the candidate schemes and determine the target wind and solar installed capacity scheme. This invention decomposes the two-layer capacity planning of the basin-level cascade hydropower-wind-solar multi-energy complementary base into inner-layer optimization scheduling and outer-layer capacity optimization, reducing the difficulty of solving the problem. The inner-layer optimization scheduling model and the outer-layer capacity optimization model collaboratively determine the capacity scheme, providing installed capacity guidance for the basin-level cascade hydropower-wind-solar multi-energy complementary base project.

Description

Technical Field

[0001] This invention relates to the field of power system installed capacity planning technology, specifically to a method for planning installed capacity for a multi-energy complementary base of watershed cascade hydropower, wind power, and solar power. Background Technology

[0002] Faced with the increasingly severe dual challenges of energy security and climate change, the massive consumption of traditional fossil fuels has led to resource depletion and triggered serious environmental pollution problems. Against this backdrop, clean energy sources, such as hydropower, wind power, and solar power, have become an important direction for energy transformation. Due to the spatial distribution differences of renewable energy in resource-rich and high-load areas, cascade hydropower-wind-solar multi-energy complementary bases in river basins often undertake the task of transmitting electricity to other regions. Therefore, how to plan the installed capacity of these bases has become a key issue. Summary of the Invention

[0003] This invention provides a method for planning the installed capacity of a multi-energy complementary base of water, wind and solar power in a river basin, in order to solve the problem of how to plan the installed capacity of such a base.

[0004] In a first aspect, the present invention provides a method for planning the installed capacity of a multi-energy complementary base of watershed, wind, and solar power, the method comprising: Based on the output characteristics of wind and solar power generation in the target basin and the overall planning, the installed capacity range and capacity step size of wind and solar power are set to generate wind and solar capacity schemes to be evaluated. A two-layer capacity planning model for a multi-energy complementary base of water, wind and solar power in a basin is constructed. The two-layer capacity planning model includes an inner-layer optimization scheduling model and an outer-layer capacity optimization model. The constraints in the inner-layer optimization scheduling model are linearized using the successive approximation method. The wind and solar capacity scheme was analyzed by time-series simulation using the inner-layer optimization scheduling model to obtain the optimization scheduling results; Using the outer capacity optimization model, technical and economic indicators are established based on the optimized scheduling results to initially screen wind and solar capacity schemes, obtain alternative schemes, and use TOPSIS to evaluate the alternative schemes to determine the target wind and solar installed capacity scheme.

[0005] This invention constructs a two-layer capacity planning model for a watershed-level cascade hydropower-wind-solar multi-energy complementary base. This model decomposes the two-layer capacity planning of the watershed-level cascade hydropower-wind-solar multi-energy complementary base into an inner-layer optimization scheduling and an outer-layer capacity optimization, reducing the difficulty of solving the problem. By using the inner-layer optimization scheduling model and the outer-layer capacity optimization model in synergy, combined with TOPSIS to evaluate alternative schemes, the target wind and solar installed capacity scheme is determined, providing installation guidance for the watershed-level cascade hydropower-wind-solar multi-energy complementary base project.

[0006] In one optional implementation, the inner-layer optimization scheduling model takes maximizing the total revenue of the watershed cascade hydropower-wind-solar multi-energy complementary base as its objective function. The constraints of the inner-layer optimization scheduling model include the hydraulic connection between upstream and downstream reservoirs, water balance constraints, upper and lower limits of reservoir capacity constraints, initial and final reservoir capacity constraints, power generation flow constraints, downstream flow constraints, upper and lower limits of hydropower station output constraints, water level-storage capacity curve, tailwater level-downflow curve, hydropower output function, power generation head constraints, wind and solar power output constraints, UHVDC transmission constraints, and power balance constraints.

[0007] This invention achieves optimal returns by using the maximization of the total returns of the watershed cascade hydro-wind-solar multi-energy complementary base as the objective function. It sets full-dimensional constraints, covering the core aspects of the watershed cascade hydro-wind-solar multi-energy complementary base, to ensure the feasibility of the scheduling results.

[0008] In one optional implementation, the water level-reservoir capacity curve, tailrace water level-discharge flow curve, hydropower output function, and UHVDC transmission constraints are nonlinear factors in the constraints of the inner-layer optimal scheduling model. The method further includes: Set basic conditions, including hydropower station reservoir capacity limit, initial reservoir capacity, final reservoir capacity, hydropower station output limit, wind and solar power output per unit value, transmission channel capacity limit, and power grid load curve; The water level-reservoir capacity curve and tailwater level-discharge curve were linearized using a piecewise linear approximation method. The hydropower output function is linearized using a successive approximation method; The state variable of the upward and downward fluctuation of the grid power of the multi-energy complementary base during a specific period is introduced as a binary variable, and the transmission constraint of the UHVDC interconnection line is linearized. The objective function and constraints of the inner-layer optimization scheduling model are constructed as a mixed-integer linear programming model. The mixed-integer linear programming model is solved using the CPLEX solver, and the output is the operation data of cascade hydropower stations and wind and solar power stations under the capacity scheme. The operation data of hydropower stations include reservoir water level, power generation flow, downstream flow, abandoned water flow, and power generation of hydropower stations in each time period. The operation data of wind and solar power stations include the actual grid-connected power and abandoned power of wind and solar power stations in each time period.

[0009] This invention provides a prerequisite for subsequent processing by setting basic conditions, linearizing the nonlinear factors in the constraints, and converting complex, nonlinear, and nonconvex models into general mixed-integer linear programming models. It breaks through the problem of parameter adaptation for different watershed energy bases and different computational boundary conditions, and uses the CPLEX solver to solve quickly, thereby improving the solution efficiency.

[0010] In one optional implementation, the hydropower output function is linearized using a successive approximation method, including: Set the relevant parameters for the successive approximation method, including the maximum number of iterations and convergence accuracy; The initial head of hydropower station for generating electricity is estimated based on the outflow of the hydropower station during the scheduling cycle. The mixed-integer linear programming model was solved using the CPLEX solver to obtain the downstream discharge flow of each hydropower station at different time periods; Calculate the new generating head for each hydroelectric power station; If the hydropower head meets any preset condition, the current optimal solution is determined as the linearized result of the hydropower output function. The preset conditions include the difference between the hydropower head between two consecutive iterations reaching the convergence accuracy and the number of iterations reaching the maximum number of iterations.

[0011] This invention uses a successive approximation method to linearize the hydropower output function, transforming this nonlinear constraint into a solvable linear constraint, thus providing data support for the accurate solution of the inner-layer optimization scheduling model.

[0012] In one alternative implementation, the method further includes: If the hydropower head does not meet the preset conditions, the number of iterations is increased, the new hydropower head is used to replace the hydropower head calculated in the previous iteration, and the process returns to the steps of using the CPLEX solver to solve the mixed integer linear programming model to obtain the downstream flow of each hydropower station at each time period, until the hydropower head meets any preset conditions.

[0013] This invention utilizes an iterative, repeated solution mechanism to gradually eliminate computational biases, achieving a dynamic balance between accuracy and efficiency.

[0014] In one optional implementation, technical and economic indicators are established based on the optimized scheduling results to initially screen wind and solar capacity schemes, obtaining alternative schemes. TOPSIS is then used to evaluate the alternative schemes to determine the target wind and solar installed capacity scheme, including: Technical and economic indicators include: electricity transmitted, cost per unit of electricity, wind and solar curtailment rate, power supply guarantee rate, and load shortage rate. Set thresholds for wind and solar curtailment rate, power supply guarantee rate, and load shortage rate. Based on the screening principle of meeting the thresholds for wind and solar curtailment rate, power supply guarantee rate, and load shortage rate, a preliminary screening of capacity schemes is conducted to obtain feasible alternative schemes. The TOPSIS method is used to construct ideal positive and ideal negative solutions, calculate the Euclidean distance between each feasible alternative and the ideal positive and ideal negative solutions, sort all feasible alternatives, and determine the target wind and solar installed capacity scheme based on the sorting results.

[0015] This invention ensures the engineering feasibility of alternative schemes by selecting technical and economic indicators and setting screening principles, and uses the TOPSIS method to determine the target wind and solar installed capacity scheme, providing decision support for capacity scheme planning.

[0016] In one alternative implementation, the method further includes: Subjective weights were calculated using the improved analytic hierarchy process, and objective weights were calculated using the CRITIC method. By combining subjective and objective weights, a comprehensive weight is obtained, and this comprehensive weight is then substituted into the TOPSIS method for optimization analysis.

[0017] This invention calculates subjective and objective weights by improving the analytic hierarchy process, and then combines the subjective and objective weights to obtain a comprehensive weight, which is then substituted into the TOPSIS method for optimization analysis, thereby making the optimization process more in line with the needs of actual engineering.

[0018] Secondly, the present invention provides an installed capacity planning device for a multi-energy complementary base of watershed cascade hydropower, wind power, and solar power, the device comprising: The configuration module is used to set the installed capacity range and capacity step size of wind power and photovoltaic power generation based on the output characteristics of wind power and photovoltaic power generation in the target watershed and the overall planning, and generate wind and solar capacity schemes to be evaluated. The model building module is used to construct a two-layer capacity planning model for the watershed cascade hydro-wind-solar multi-energy complementary base. The two-layer capacity planning model includes an inner-layer optimization scheduling model and an outer-layer capacity optimization model. The constraints in the inner-layer optimization scheduling model are linearized using the successive approximation method. The optimization scheduling module is used to perform time-series simulation analysis of wind and solar capacity schemes using the inner optimization scheduling model to obtain optimization scheduling results. The capacity scheme screening module is used to use the outer capacity optimization model to establish technical and economic indicators based on the optimized scheduling results to initially screen wind and solar capacity schemes, obtain alternative schemes, and use TOPSIS to evaluate the alternative schemes to determine the target wind and solar installed capacity scheme.

[0019] Thirdly, the present invention provides an electronic device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the above-described first aspect or any corresponding embodiment of the method for planning the installed capacity of a watershed cascade hydro-wind-solar multi-energy complementary base.

[0020] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the installed capacity planning method for a multi-energy complementary base of watershed cascade hydropower, wind power, and solar power as described in the first aspect above or any corresponding embodiment thereof. Attached Figure Description

[0021] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 This is a flowchart illustrating the installed capacity planning method for a multi-energy complementary base of watershed hydropower, wind power, and solar power according to an embodiment of the present invention. Figure 2 This is a schematic diagram of the technical route of the two-layer capacity planning model according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the overall structure of the watershed cascade hydro-wind-solar multi-energy complementary base according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the power transmission capacity of a high-voltage direct current transmission line under the free power transmission mode of the sending end according to an embodiment of the present invention; Figure 5 This is a schematic diagram showing the monthly power generation hours of wind and photovoltaic power in a multi-energy complementary base according to an embodiment of the present invention; Figure 6 This is a schematic diagram illustrating the monthly hourly average trend of wind power in a multi-energy complementary base according to an embodiment of the present invention; Figure 7 This is a schematic diagram illustrating the monthly hourly average trend of photovoltaic power in a multi-energy complementary base according to an embodiment of the present invention; Figure 8 This is a typical processing analysis diagram according to an embodiment of the present invention; Figure 9 This is a schematic diagram showing the changing trend of the curtailment rate of multi-energy complementary power bases under nine expansion schemes according to embodiments of the present invention; Figure 10 This is a schematic diagram illustrating the changing trends of wind and solar power generation at a multi-energy complementary power base under nine expansion schemes according to embodiments of the present invention. Figure 11 This is a schematic diagram illustrating the changing trend of power generation hours of a multi-energy complementary base under nine expansion schemes according to embodiments of the present invention; Figure 12 This is a schematic diagram showing the changing trends of annual power generation and annual operating hours of each hydropower station under nine expansion schemes according to embodiments of the present invention. Figure 13 This is a schematic diagram of a typical weekly simulation scheduling result according to Scheme 1 of the present invention; Figure 14This is a schematic diagram of a typical weekly simulation scheduling result according to Scheme 6 of the present invention; Figure 15 This is a schematic diagram of a typical weekly simulation scheduling result of Scheme 8 according to an embodiment of the present invention; Figure 16 This is a structural block diagram of an installed capacity planning device for a multi-energy complementary base of watershed cascade hydropower, wind and solar power according to an embodiment of the present invention; Figure 17 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0025] Currently, the inherent intermittency, randomness, and volatility of renewable energy sources such as wind and solar power pose unprecedented challenges to the safe and stable operation of power systems. Therefore, effectively integrating the regulation capacity of hydropower with the renewable characteristics of wind and solar power, and rationally planning the installed capacity of hybrid systems to achieve multi-energy complementarity and optimized operation, is crucial for improving overall system efficiency, ensuring energy supply security, and promoting large-scale consumption of renewable energy. Among related technologies, most research on the installed capacity planning of hydro-wind-solar multi-energy complementary systems focuses on off-grid or microgrid systems involving small hydropower.

[0026] Due to the spatial distribution differences between renewable energy-rich areas and high-load areas, multi-energy complementary bases in river basins often undertake the task of power transmission. Therefore, the installed capacity planning of multi-energy complementary bases should incorporate power transmission demand into the model. However, current studies considering the transmission of power from multi-energy complementary bases via UHVDC transmission lines generally only consider the impact of channel capacity on output. Furthermore, existing studies mostly rely on simulation scheduling results from multiple typical days as the basis for capacity planning, which fails to fully capture the uncertainty and seasonal fluctuations of renewable energy output and their actual impact on system operation, thus exhibiting significant limitations.

[0027] This invention provides a method for planning the installed capacity of a multi-energy complementary base of watershed cascade hydropower, wind power, and solar power, in order to optimize the installed capacity of wind power and solar power and the operation strategy of the multi-energy complementary base, thereby improving the utilization rate of clean energy and the stability of the power grid.

[0028] According to an embodiment of the present invention, an embodiment of a method for planning the installed capacity of a multi-energy complementary base of watershed cascade hydropower, wind power and solar power is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] This embodiment provides a method for planning the installed capacity of a multi-energy complementary base of watershed, wind, and solar power. Figure 1 This is a flowchart of an installed capacity planning method for a multi-energy complementary base of watershed hydropower, wind power, and solar power according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps: Step S101: Based on the output characteristics of wind power and photovoltaic power generation in the target basin and the overall planning, set the installed capacity range and capacity step size for wind power and photovoltaic power, and generate the wind and solar capacity scheme to be evaluated.

[0030] In this embodiment of the invention, the output characteristics of wind power and photovoltaic power generation serve as the resource basis for capacity planning, while comprehensive planning serves as the engineering constraint for capacity planning. By considering the output characteristics of wind power and photovoltaic power generation in the target basin and the comprehensive planning, installed capacity ranges and capacity step sizes for wind power and photovoltaic power are set. The installed capacity range defines the selectable range of wind and solar power installations, and the capacity step size is the incremental value iteratively applied within the installed capacity range. This yields the wind and solar capacity schemes to be evaluated. The set of wind and solar capacity schemes is as follows:

[0031]

[0032]

[0033] in, , These represent the sets of wind power and photovoltaic installation capacity schemes, respectively. This indicates the maximum installed capacity of wind power (unit: MW). This indicates the maximum installed capacity of photovoltaic power (in MW). Minimum installed capacity of wind power (unit: MW) This indicates the maximum and minimum installed photovoltaic capacity (in MW). , These represent the iteration step size (in MW) for wind power and photovoltaic installed capacity, respectively. Indicates the number of capacity iterations.

[0034] Step S102: Construct a two-layer capacity planning model for the watershed cascade hydro-wind-solar multi-energy complementary base. The two-layer capacity planning model includes an inner-layer optimization scheduling model and an outer-layer capacity optimization model. The constraints in the inner-layer optimization scheduling model are linearized using the successive approximation method.

[0035] In this embodiment of the invention, the two-layer capacity planning model includes an inner layer and an outer layer. The inner layer is an optimization scheduling model, and the outer layer is a capacity optimization model. The technical approach of this two-layer capacity planning model is as follows: Figure 2 As shown.

[0036] By optimizing the installed capacity of wind power and photovoltaic power and the operation strategy of multi-energy complementary bases through the synergistic optimization of inner and outer layers, the utilization rate of clean energy and the stability of multi-energy complementary base operation can be improved.

[0037] Step S103: Use the inner-layer optimization scheduling model to perform time-series simulation analysis on the wind and solar capacity scheme to obtain the optimization scheduling results.

[0038] In this embodiment of the invention, the inner-layer optimization scheduling model performs 8760h time-series simulation analysis on different wind and solar capacity schemes to obtain the optimization scheduling results, and feeds the optimization scheduling results back to the outer layer to evaluate the operational and economic effects of the wind and solar capacity schemes.

[0039] Step S104: Using the outer capacity optimization model, the technical and economic indicators are established based on the optimization scheduling results to initially screen wind and solar capacity schemes, obtain alternative schemes, and use TOPSIS to evaluate the alternative schemes to determine the target wind and solar installed capacity scheme.

[0040] In this embodiment of the invention, the outer capacity optimization model establishes economic indicators based on the optimized scheduling results output by the inner layer, performs preliminary screening of wind and solar capacity schemes, obtains feasible alternative schemes, and finally applies TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) to evaluate the alternative schemes and determine the target wind and solar installed capacity scheme as the optimal wind and solar installed capacity scheme.

[0041] The installed capacity planning method for a watershed cascade hydropower-wind-solar multi-energy complementary base provided in this embodiment constructs a two-layer capacity planning model for the watershed cascade hydropower-wind-solar multi-energy complementary base. This model decomposes the two-layer capacity planning into an inner-layer optimization scheduling and an outer-layer capacity optimization, reducing the difficulty of solving the problem. By using the inner-layer optimization scheduling model and the outer-layer capacity optimization model in conjunction with TOPSIS to evaluate alternative schemes, the target wind and solar installed capacity scheme is determined, providing installation guidance for the watershed cascade hydropower-wind-solar multi-energy complementary base project.

[0042] This embodiment provides a method for planning the installed capacity of a multi-energy complementary base of watershed, wind, and solar power. The process includes the following steps: Step S301: Based on the output characteristics of wind power and photovoltaic power generation in the target basin and the overall planning, set the installed capacity range and capacity step size for wind power and photovoltaic power, and generate the wind and solar capacity scheme to be evaluated.

[0043] Please see details Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0044] Step S302: Construct a two-layer capacity planning model for the watershed cascade hydro-wind-solar multi-energy complementary base. The two-layer capacity planning model includes an inner-layer optimization scheduling model and an outer-layer capacity optimization model. The constraints in the inner-layer optimization scheduling model are linearized using the successive approximation method.

[0045] Please see details Figure 1 Step S102 of the illustrated embodiment will not be described again here.

[0046] Step S303 uses the inner-layer optimization scheduling model to perform time-series simulation analysis on the wind and solar capacity scheme and obtains the optimized scheduling results.

[0047] Specifically, the inner-layer optimization scheduling model takes maximizing the total revenue of the watershed cascade hydropower-wind-solar multi-energy complementary base as its objective function, which is as follows:

[0048] in, The total revenue of the watershed cascade hydropower-solar multi-energy complementary base is expressed in yuan. Indicates the total duration of the scheduling cycle (unit: h); Indicates the number of hydroelectric power stations; Indicates hydroelectric power station In the Power generation during a given time period (unit: MW); , These represent wind power and photovoltaic power in the [number]th [year]. Actual on-grid power during the time period (unit: MW); , and These represent the grid connection prices for hydropower, wind power, and solar power, respectively (unit: yuan / MWh); , and They represent hydroelectric power stations. Wind farms and photovoltaic power stations in the first Power curtailment during a given time period (unit: MW); This indicates that the multi-energy complementary base is in the [number]th [year]. Power loss during the time period (unit: MW); , , and These represent the curtailment of hydropower, wind power, and solar power, as well as the penalty for load shedding (unit: yuan / MWh); The investment and construction cost of the cascade hydropower-solar multi-energy complementary base in the basin is expressed in yuan. This represents the operation and maintenance cost of the cascade hydropower-wind-solar multi-energy complementary base in the basin (unit: yuan).

[0049] The constraints of the inner-layer optimization scheduling model include hydraulic connection between upstream and downstream reservoirs, water balance constraints, upper and lower limits of reservoir capacity constraints, initial and final reservoir capacity constraints, power generation flow constraints, discharge flow constraints, upper and lower limits of hydropower station output constraints, water level-storage capacity curve, tailwater level-discharge flow curve, hydropower output function, power generation head constraints, wind and solar power output constraints, UHVDC transmission line constraints, and power balance constraints.

[0050] Specifically as follows: (1) Hydraulic connection between upstream and downstream reservoirs:

[0051]

[0052] in, Indicates hydroelectric power station In the Inflow rate during a given time period (unit: m³ / s); Indicates hydroelectric power station In the Natural inflow runoff during the time period (unit: m³ / s); , , , They represent hydroelectric power stations. In the Discharge flow, power generation flow, wastewater discharge, and ecological flow for each time period (unit: m³ / s).

[0053] (2) Water balance constraints:

[0054] in, Indicates hydroelectric power station In the Initial storage capacity for a given period (unit: 10,000 m³) 3 ), Indicates hydroelectric power station In the Time period loss flow (unit: m³ / s).

[0055] (3) Upper and lower limits of storage capacity constraints:

[0056] in, , They represent hydroelectric power stations. Upper and lower limits of storage capacity (unit: 10,000 m³) 3 ).

[0057] (4) Reservoir capacity constraints at the beginning and end:

[0058] in, , They represent hydroelectric power stations. Initial and final reservoir capacity during the scheduling period (unit: 10,000 m³) 3 ).

[0059] (5) Power generation flow constraints:

[0060] in, , They represent hydroelectric power stations. The upper and lower limits of power generation flow (unit: m³ / s).

[0061] (6) Downflow constraint:

[0062] in, , They represent hydroelectric power stations. The upper and lower limits of the discharge flow rate (unit: m³ / s).

[0063] (7) Upper and lower limits of hydropower station output constraints:

[0064] in, , They represent hydroelectric power stations. The upper and lower limits of power generation (in MW).

[0065] (8) Water level-reservoir capacity curve:

[0066] in, Indicates hydroelectric power station In the Initial water level for the specified time period (in meters); Indicates hydroelectric power station Water level-reservoir capacity curve.

[0067] (9) Tailwater level-discharge curve:

[0068] in, Indicates hydroelectric power station In the Tailwater level for the specified time period (in meters); Indicates hydroelectric power station Tailwater level-discharge curve.

[0069] (10) Hydropower output function:

[0070] in, Indicates hydroelectric power station The output coefficient; Indicates hydroelectric power station In the Net hydropower head (in meters) for a given period.

[0071] (11) Hydropower head constraint:

[0072] in, Indicates hydroelectric power station In the Head loss over a given period (in meters).

[0073] (12) Constraints on wind and solar power output:

[0074]

[0075] in, , These represent the installed capacity (in MW) of wind farms and photovoltaic power plants, respectively. , These represent the wind farm and the photovoltaic power station in the [number]th [year]. Per-unit output value for a given time period.

[0076] (13) Transmission constraints of UHVDC interconnection lines:

[0077]

[0078]

[0079] in, This indicates that the multi-energy complementary base is in the [number]th [year]. Total grid-connected power during the time period (unit: MW); , These represent the upper and lower limits (in MW) of the ultra-high voltage direct current transmission line channel, respectively. This indicates the number of steps in the power transmission curve of the high-voltage direct current interconnection line.

[0080] (14) Power balance constraints: Under the agreed power supply curve mode, the output power of the multi-energy complementary base should be as close as possible to the load curve, and a load shortage should be generated when the power supply is insufficient.

[0081]

[0082] in, Indicates the network terminal is at the Load demand for a given time period (in MW).

[0083] Among the above constraints, there are four nonlinear factors, namely (8) water level-reservoir capacity curve, (9) tailwater level-discharge flow curve, (10) hydropower output function, and (13) UHVDC interconnection line transmission constraint.

[0084] By taking the maximization of the total revenue of the watershed cascade hydro-wind-solar multi-energy complementary base as the objective function, the optimal revenue is achieved. Full-dimensional constraints are set to cover the core links of the watershed cascade hydro-wind-solar multi-energy complementary base, ensuring the feasibility of the scheduling results.

[0085] In some alternative implementations, the method further includes: Step Sa: Set the basic conditions; Step Sb involves linearizing the water level-reservoir capacity curve and the tailwater level-discharge flow curve using a piecewise linear approximation method. Step Sc involves linearizing the hydropower output function using a successive approximation method. Step Sd introduces the state variables of the upward and downward fluctuation of the grid power of the multi-energy complementary base during a specific period as binary variables, and linearizes the transmission constraints of the UHVDC interconnection line. Step Se involves constructing the objective function and constraints of the inner-layer optimization scheduling model into a mixed-integer linear programming model, solving the mixed-integer linear programming model using the CPLEX solver, and outputting the operation data of cascade hydropower stations and wind and solar power stations under the capacity scheme.

[0086] In this embodiment of the invention, basic conditions are set, including hydropower station reservoir capacity limit, initial reservoir capacity, final reservoir capacity, hydropower station output limit, wind and solar power output per unit value, transmission channel capacity limit, and power grid load curve, to provide basic conditions for linear processing of nonlinear factors.

[0087] To ensure both accuracy and efficiency, nonlinear factors are linearized while still meeting accuracy requirements.

[0088] Since the water level in front of each hydropower station's dam is a cubic or quartic function of the reservoir's capacity, the water level-capacity relationship needs to be linearized while meeting accuracy requirements. The relationship between the tailrace water level and the outflow from each hydropower station is also non-linear and requires linearization as well. The specific processing steps are as follows: For water level-storage capacity curves and tailwater level-discharge curves, generally speaking, the reservoir capacity is a nonlinear function of the upstream water level, and the relationship between the tailwater level and the outflow is also nonlinear. Therefore, it is necessary to linearize the water level-storage capacity relationship and the tailwater level-discharge curve.

[0089] Taking the water level-reservoir capacity relationship as an example, the main steps include: reservoir i The water level in front of the dam is discrete as N For intervals:

[0090] in, , For reservoir i No. n Each water level interpolation point and its corresponding reservoir capacity.

[0091] Then the reservoir is t The water level during a given period can be expressed as:

[0092]

[0093]

[0094]

[0095] in, As an indicator variable, when the reservoir i exist tThe water level in front of the dam during that period was at the first n When there are several water level ranges ,otherwise, .

[0096] The reservoir capacity during that period is expressed as:

[0097] This achieves the linearization of the relationship between the upstream water level and the reservoir capacity. The same method is used to linearize the relationship between the tailwater level and the downstream discharge.

[0098] For the hydropower output function, the successive approximation method is used to linearize the hydropower output function.

[0099] For the transmission constraints of UHVDC tie lines, due to the highly discrete and nonlinear characteristics of the transmission constraints, a binary variable is introduced. and This transforms the original nonlinear constraints into a linearized form:

[0100]

[0101] in, This indicates the maximum number of fluctuations in grid power allowed for a multi-energy complementary base during the scheduling period; , These represent the minimum and maximum power variations (in MW) of the multi-energy complementary base, respectively. , These respectively represent the multi-energy complementary base in the 19th century. The state variable representing the upward and downward fluctuations of internet power during a given time period; This indicates the minimum stable output duration (in hours) of the multi-energy complementary base power.

[0102] The objective function and constraints of the inner-layer optimization scheduling model are constructed as a mixed-integer linear programming model. The CPLEX solver is used, and its built-in branch and bound algorithm is called to solve the mixed-integer linear programming model. The output data includes the reservoir water level, power generation flow, discharge flow, abandoned water flow, and power generation of the cascade hydropower stations under the capacity scheme at each time period, as well as the operating data of wind and solar power stations, including the actual grid-connected power and abandoned power of wind and solar power stations at each time period. These data are used by the outer-layer capacity optimization model to establish technical and economic evaluation indicators.

[0103] By setting basic conditions to provide a premise for subsequent processing, the nonlinear factors in the constraints are linearized, and the complex, nonlinear, and nonconvex model is transformed into a general mixed-integer linear programming model. This breakthrough solves the problem of parameter adaptation for different watershed energy bases and different computational boundary conditions. The CPLEX solver is used to solve the problem quickly, improving the solution efficiency.

[0104] Specifically, step Sc above includes: Step Sc1: Set the relevant parameters for the successive approximation method; Step Sc2: Estimate the initial head of hydropower station based on the outflow of hydropower station during the scheduling cycle; Step Sc3: Use the CPLEX solver to solve the mixed integer linear programming model to obtain the downstream discharge flow of each hydropower station at each time period; Step Sc4: Calculate the new generating head for each hydropower station; In step Sc5, if the hydropower head meets any preset condition, the current optimal solution is determined as the linearized result of the hydropower output function.

[0105] In this embodiment of the invention, the hydropower output function includes the power generation flow rate. With net power generation head Two unknown parameters, which are correlated with each other, make the hydropower output function a function of the power generation flow rate. With net power generation head The function is a complex nonlinear function, therefore, it needs to be linearized. The specific method is as follows: The hydropower output function is as follows: .

[0106] The hydropower output function is linearized using a successive approximation method, and the steps are as follows: Setting parameters related to the successive approximation method mainly includes the maximum number of iterations. and convergence accuracy These two parameters are the termination conditions for successive approximations, relating to the effectiveness and quality of the solution. This represents the difference in power generation head between two consecutive iterations.

[0107] The initial head of a cascade hydropower station is a crucial factor affecting the convergence speed of the successive head approximation method. The initial head is estimated based on the inflow over the entire dispatch cycle, as shown in the following formula:

[0108]

[0109]

[0110] in, Indicates hydroelectric power station Total discharge volume (in m³) during the entire scheduling cycle; and They represent hydroelectric power stations. Storage capacity (in m³) at the beginning and end of the scheduling cycle; Indicates hydroelectric power station Average discharge flow rate (in m³ / s) over the entire scheduling cycle; Indicates the estimated hydroelectric power station In the Hydropower head (in meters) for a given time period.

[0111] The mixed-integer linear programming model was solved using the CPLEX solver to obtain the downstream discharge flow of each hydropower station at different time periods.

[0112] Calculate the new generating head for each hydropower station based on the discharge flow rate at each time period.

[0113] Determine whether the successive approximation of the water head reaches the iteration termination condition. If the power generation water head meets any of the following preset conditions, terminate the calculation and determine the current optimal solution as the linearized result of the hydropower output function.

[0114] The preset conditions are as follows:

[0115]

[0116]

[0117] in, , They represent the first time. Hydropower station in the next iteration In the The new and original hydroelectric heads for the current period (unit: m).

[0118] By using the successive approximation method to linearize the hydropower output function, this nonlinear constraint is transformed into a solvable linear constraint, providing data support for the accurate solution of the inner-layer optimization scheduling model.

[0119] In some alternative implementations, the method further includes: In step Sc6, if the hydropower head does not meet the preset conditions, the number of iterations is increased, the hydropower head calculated in the previous iteration is replaced with a new hydropower head, and the process returns to the step of using the CPLEX solver to solve the mixed integer linear programming model to obtain the downstream flow of each hydropower station at each time period, until the hydropower head meets any preset conditions.

[0120] In this embodiment of the invention, if the power generation head does not meet the preset conditions, the iteration number is incremented by one, the latest power generation head is used to replace the power generation head calculated in the previous iteration, and the process returns to step Sc3 until the power generation head meets any preset conditions.

[0121] By utilizing an iterative and repeated solution mechanism, computational biases are gradually eliminated, achieving a dynamic balance between accuracy and efficiency.

[0122] Step S304: Using the outer capacity optimization model, the technical and economic indicators are established based on the optimized scheduling results to initially screen wind and solar capacity schemes, obtain alternative schemes, and use TOPSIS to evaluate the alternative schemes to determine the target wind and solar installed capacity scheme.

[0123] Specifically, step S304 above includes: Step S3041, the technical and economic indicators are: power transmission volume, unit cost per kilowatt-hour, wind and solar curtailment rate, power supply guarantee rate, and load shortage rate. Step S3042: Set the threshold for wind and solar curtailment rate, power supply guarantee rate, and load shortage rate. Based on the screening principle of meeting the threshold for wind and solar curtailment rate, power supply guarantee rate, and load shortage rate, initially screen capacity schemes and obtain feasible alternative schemes. Step S3043: Using the TOPSIS method, construct the ideal positive solution and the ideal negative solution, calculate the Euclidean distance between each feasible alternative and the ideal positive solution and the ideal negative solution, sort all feasible alternatives, and determine the target wind and solar installed capacity scheme based on the sorting results.

[0124] In this embodiment of the invention, based on the optimized scheduling results, technical and economic indicators for evaluating its performance are established, as follows: (1) Electricity transmitted to other regions (DE): that is, the total electricity transmitted to other regions by the multi-energy complementary base throughout the year, as shown in the following formula:

[0125] in, The total system output is expressed in MW. The time step is set to 1 hour.

[0126] (2) Unit Cost of Electricity (LCOE): This refers to the cost required for a multi-energy complementary base to produce 1 kWh of electricity, as shown in the following formula:

[0127] in, The initial total investment for the system is expressed in ten thousand yuan. The system operating cost is expressed in ten thousand yuan.

[0128] (3) Wind and solar curtailment rate (CR): An important indicator for measuring the consumption of new energy in multi-energy complementary bases, that is, the proportion of unusable new energy power to ideally generated power, as shown in the following formula:

[0129] in, Wind power curtailment (MW) The amount of curtailed photovoltaic power (MW) The ideal per-unit value (%) for wind power output. Wind power installed capacity (MW). The per-unit value (%) of the ideal photovoltaic output. This refers to the installed capacity of photovoltaic power (MW).

[0130] (4) Power Supply Guarantee Rate (PSGR): This is the proportion of the number of time periods in which the output of the multi-energy complementary base meets the requirements to the total number of time periods, as shown in the following formula:

[0131] in, This represents the frequency of system load deficit throughout the year. This refers to the system load deficit (MW).

[0132] (5) Load Deficiency Rate (LPSP): This is the ratio of the total load deficit to the total load demand, as shown in the following formula:

[0133] in, The required output power (MW) of the system.

[0134] The threshold for wind and solar curtailment rate is set at 15% for the above-mentioned techno-economic evaluation indicators, i.e. Set the power supply guarantee rate threshold to 90%, that is Set the load power shortage rate threshold to 10%, that is Based on the above conditions as the screening principle, all capacity schemes are initially screened, and those that meet the conditions are identified as feasible alternatives.

[0135] The TOPSIS method is used to construct ideal positive and ideal negative solutions. Then, the distance between each feasible alternative and these two ideal solutions is calculated. Finally, by calculating the Euclidean distance with the ideal positive and ideal negative solutions, all feasible alternatives are ranked, effectively quantifying the comprehensive performance of each solution and determining the optimal capacity solution.

[0136] By selecting technical and economic indicators and setting screening principles, the engineering feasibility of alternative schemes is ensured. The TOPSIS method is used to determine the target wind and solar installed capacity scheme, providing decision support for capacity scheme planning.

[0137] In some alternative implementations, the method further includes: Step S3044: Calculate subjective weights using the improved analytic hierarchy process and objective weights using the CRITIC method. Step S3045: Combine subjective weights and objective weights to obtain a comprehensive weight, and then substitute the comprehensive weight into the TOPSIS method for optimization analysis.

[0138] In this embodiment of the invention, in order to make the optimization process more in line with actual engineering needs, the improved Analytic Hierarchy Process (AHP) is used to calculate subjective weights and the CRITIC method is used to calculate objective weights. The subjective weights and objective weights are combined to obtain a comprehensive weight, and the comprehensive weight is then used in TOPSIS for optimization analysis.

[0139] The installed capacity planning method for a multi-energy complementary base of watershed cascade hydropower, wind and solar power provided in this embodiment calculates subjective and objective weights by improving the analytic hierarchy process, and then combines the subjective and objective weights to obtain a comprehensive weight, which is then substituted into the TOPSIS method for optimization analysis, thereby making the optimization process more in line with the actual engineering needs.

[0140] As one or more specific application embodiments of this invention, a certain river basin in region A has a large drop, steep slopes, and rapid currents, possessing extremely rich hydropower resources. According to the hydropower plan for this basin, the total installed capacity of five cascade hydropower stations on the main stream and tributaries is 772MW. The basin also has abundant wind and solar resources. According to current planning requirements, the maximum planned installed capacity for wind power is 364MW, and the maximum planned installed capacity for photovoltaic power is 850MW. Due to the low local load demand, the electricity generated by local wind and solar power cannot be fully absorbed. Therefore, it is necessary to transmit electricity to other provinces with higher electricity demand to ensure better absorption of wind and solar energy. This method is now applied to a multi-energy complementary hydropower, wind, and solar power base in this basin to verify its effectiveness.

[0141] The overall structure of the cascade hydro-wind-solar multi-energy complementary base in the basin is as follows: Figure 3 As shown, the power transmission capacity of the high-voltage DC transmission line in the free-sending mode is as follows: Figure 4 As shown, in this mode, the receiving-end grid does not impose specific requirements on the output curve of the multi-energy complementary base, but only restricts its upper and lower limits and fluctuation steps. The monthly generating hours of wind power and photovoltaic power are as follows: Figure 5 As shown, the monthly hourly average wind power output is as follows: Figure 6 As shown, the trend of the monthly average hourly output of wind power is as follows: Figure 7As shown in Table 1, the basic parameters of the cascade hydropower stations in this basin before expansion include installed capacity, normal water level, dead water level, reservoir capacity corresponding to normal water level and dead water level, maximum power generation flow, output coefficient, investment per unit kW of hydropower station, multi-year average flow, and sediment flushing flow.

[0142] Table 1. Basic parameters of cascade hydropower stations in the basin before capacity expansion.

[0143] Table 2 shows the basic parameters of the cascade hydropower stations in the basin after expansion, including installed capacity, normal water level, dead water level, reservoir capacity corresponding to normal water level and dead water level, maximum power generation flow, output coefficient, investment per unit kW of hydropower station, multi-year average flow, and sediment flushing flow.

[0144] Table 2 Basic parameters of cascade hydropower stations in the basin after capacity expansion

[0145] To find the optimal wind and solar power installed capacity under different hydropower scales, we set the iteration step size according to the local renewable energy scale, divided wind power and solar power into 9 and 18 different capacity levels respectively, and combined them to form 162 wind and solar capacity planning schemes, as shown in Table 3.

[0146] Table 3 Wind and Solar Capacity Planning Scheme

[0147] To select the optimal installed capacity for Power Station 4 while keeping the installed capacity of the other hydropower stations unchanged, the installed capacity of Power Station 4 is selected as 220, 240, and 260 MW, forming the following three hydropower capacity configuration scenarios: Scenario 1: Power Station 1 120 - Power Station 3 12 - Power Station 2 240 - Power Station 4 220 - Power Station 5 200; Scenario 2: Power Station 1 120 - Power Station 3 12 - Power Station 2 240 - Power Station 4 240 - Power Station 5 200; Scenario 3: Power Station 1 120 - Power Station 3 12 - Power Station 2 240 - Power Station 4 260 - Power Station 5 200.

[0148] By conducting time-series simulation scheduling of 162 wind and solar capacities under three scenarios for 8760 hours and calculating technical and economic indicators, the multi-attribute decision method (TOPSIS) was finally used to evaluate all planning schemes. The higher the score, the better the scheme, and the optimal scheme was selected in the end.

[0149] The weights of the evaluation indicators for screening principle 1 are set as follows: investment per unit of electricity, total grid-connected electricity, overall curtailment rate, renewable energy curtailment rate, and system dry season electricity ratio = 10:0:0:0:0. This means that the lower the investment per unit of electricity, the better the solution. Based on the evaluation results, the optimal wind and solar power configuration schemes for each scenario are shown in Table 4. Scenario 3 has the lowest investment per unit of electricity and is the optimal scheme; that is, it is economically optimal when the installed capacity of power plant four is selected as 260MW.

[0150] Table 4 Optimal landscape configuration schemes for various scenarios

[0151] The weights of the evaluation indicators for screening principle 2 are set as follows: investment per unit of electricity, total grid-connected electricity, overall curtailment rate, renewable energy curtailment rate, and system off-peak electricity ratio = 5:2:1:1:1. Based on the evaluation results, the optimal wind and solar power configuration schemes for each scenario are shown in Table 5.

[0152] Table 5 Optimal landscape configuration schemes for various scenarios

[0153] Further evaluation of the results under screening principle 2 showed that the power plant with an installed capacity of 260MW scored the highest and was selected as the recommended scheme. Its typical weekly output analysis is as follows. Figure 8 As shown, (a) represents the typical weekly output of the multi-energy complementary base in January, (b) in April, (c) in July, and (d) in October. Since January to April is the dry season and the initial water level is at a dead level, the overall hydropower output is low and fluctuates significantly. Wind and solar power bear the main load, and there is no power curtailment. Entering July, the water inflow is higher, and the overall hydropower output stabilizes. At this time, to reduce water curtailment, there is significant wind and solar power curtailment. In October, the water inflow decreases, but the hydropower output still remains at a high level and maintains a certain complementary relationship with the solar power output; that is, when solar power generation is high, hydropower output decreases.

[0154] To investigate the impact of hydropower expansion on the cascade hydro-wind-solar multi-energy complementary base in the basin, nine expansion schemes were proposed. A one-year, 8760-hour time-series simulation analysis was conducted on each scheme to explore its technical and economic feasibility. It should be noted that for each expansion scheme, the optimal wind and solar installed capacity was determined as the maximum available installed capacity through pre-conducted simulations. Based on the simulation results, this section will evaluate the schemes from a technical and economic perspective to select the optimal expansion scheme, and further compare and analyze the typical weekly scheduling results before and after the expansion. Table 6 shows the nine proposed hydropower expansion schemes.

[0155] Table 6 Hydropower Expansion Plan

[0156] Figure 9 The chart shows the changing trends of curtailment rates for multi-energy complementary power bases under 10 expansion schemes. (a) represents wind power curtailment rate, (b) photovoltaic curtailment rate, (c) wind and solar renewable energy curtailment rate, and (d) the overall curtailment rate of the power base. It is evident that the original hydropower capacity cannot meet peak-shaving demands, resulting in high curtailment rates for all categories. However, with the increase in hydropower capacity, the curtailment rates show a significant downward trend. The decline in wind, photovoltaic, and renewable energy curtailment rates slows down after Scheme 5 to Scheme 6, even approaching stagnation, indicating that the expansion of the power station to Scheme 5 has limited impact on the absorption capacity of wind and photovoltaic power.

[0157] Figure 10 and Figure 11 The figures show the changing trends of wind and solar power generation and generating hours at multi-energy complementary power bases under nine different expansion schemes. Figure 10 (a) represents the amount of wind power fed into the grid, and (b) represents the amount of photovoltaic power fed into the grid. Figure 11 (a) represents wind power generation hours, and (b) represents photovoltaic power generation hours. It is clear that the annual on-grid electricity and generation hours of both wind and photovoltaic power increase with the increase in installed capacity of hydropower stations. Similar to the trend in curtailment rates, the growth rate of on-grid electricity for both wind and photovoltaic power also slows down between Scheme 5 and Scheme 6. This is related to... Figure 8 The trends in the curtailment rates reflect each other, further illustrating that after Scheme 5, further increasing the installed capacity of the power plant will no longer significantly improve the absorption capacity of the base's wind and solar resources.

[0158] The trends of annual power generation and annual operating hours of each hydropower station under the nine schemes are as follows: Figure 12 As shown, (a) represents the annual power generation of each hydropower station, and (b) represents the annual operating hours of each hydropower station. Among them, the third hydropower station, located on a tributary and with no change in installed capacity, maintained its annual power generation. Overall, the annual power generation of the other four expanded hydropower stations, excluding the third station, increased significantly after the expansion. It is noteworthy that after the expansion of the fourth hydropower station, the annual power generation of the second hydropower station actually decreased. This is attributed to the fact that the expansion of the fourth hydropower station altered the runoff process between the two reservoirs and their mutual scheduling relationship.

[0159] Figure 13The following are typical weekly simulated dispatch results for Scheme 1 (pre-expansion configuration: Power Station 1 56 - Power Station 3 12 - Power Station 2 160 - Power Station 4 140 - Power Station 5 76). (a) shows the typical weekly output of the multi-energy complementary base in January, (b) in April, (c) in July, and (d) in October. During the dry season, due to insufficient inflow and the initial water level being at a dead water level, the overall output of the hydropower stations is low and fluctuates significantly, with the main load relying on wind and solar power. During this period, limited by the small channel capacity, especially during the winter when daytime wind and solar power output is high, there is significant wind and solar curtailment. In spring, daytime wind power resources are relatively scarce, resulting in a corresponding reduction in wind and solar curtailment during this period. After entering the wet season in July, the inflow increases significantly, and the overall output of the hydropower stations remains stable and at a high level. However, due to limited channel capacity, in order to prioritize the utilization of abundant hydropower resources and reduce water curtailment, the system tends to suppress wind and solar power output, resulting in significant wind and solar curtailment. While water inflow decreased in October, hydropower output remained at a high level due to the reservoir's still-high water level. During this period, solar power resources were relatively abundant while wind power resources were scarce. Combined with channel capacity limitations, this led to a larger amount of solar curtailment and a relatively smaller amount of wind curtailment.

[0160] Figure 14 The following are typical weekly simulated dispatch results for Scheme 6 (configuration after expansion: Power Station 1 120 - Power Station 3 12 - Power Station 2 240 - Power Station 4 240 - Power Station 5 200). (a) shows the typical weekly output of the multi-energy complementary base in January, (b) in April, (c) in July, and (d) in October. Compared with the analysis results of Scheme 1 (before expansion), the expansion significantly increased the system's channel capacity, resulting in a substantial improvement in the absorption capacity of wind and solar power. Specifically, the amount of wind and solar power curtailment decreased significantly in winter, and there was virtually no curtailment in spring and autumn. However, during the summer high-water season, despite the increased channel capacity, the abundant water inflow and large hydropower output meant that significant amounts of wind and solar power curtailment still occurred to ensure hydropower absorption.

[0161] Figure 15The following are typical weekly simulated dispatch results for Scheme 8 (configuration: Power Station 1 160, Power Station 3 12, Power Station 2 240, Power Station 4 300, Power Station 5 200). (a) shows the typical weekly output of the multi-energy complementary base in January, (b) in April, (c) in July, and (d) in October. Compared to previous schemes (especially Scheme 6), this scheme further increases the channel capacity of the multi-energy complementary base. This results in a more significant improvement in energy consumption: only a small amount of wind and solar curtailment exists in winter; zero wind and solar curtailment is maintained in spring and autumn; and wind and solar power are further absorbed in summer, with a reduction in curtailment compared to Scheme 6.

[0162] In summary, this method can provide a systematic and practical solution for the planning of installed capacity of multi-energy complementary bases of watershed cascade hydropower, wind power and solar power. It is of great significance for optimizing resource allocation, improving energy utilization, enhancing economic efficiency, strengthening grid stability and improving the flexibility and adaptability of multi-energy complementary bases.

[0163] This embodiment also provides an installed capacity planning device for a multi-energy complementary base of watershed cascade hydropower, wind power, and solar power. This device is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0164] This embodiment provides an installed capacity planning device for a multi-energy complementary base of watershed cascade hydropower, wind power, and solar power, such as... Figure 16 As shown, it includes: The setting module 1601 is used to set the installed capacity range and capacity step size of wind power and photovoltaic power generation based on the output characteristics of wind power and photovoltaic power generation in the target watershed and the comprehensive planning, and to generate wind and solar capacity schemes to be evaluated. The model building module 1602 is used to construct a two-layer capacity planning model for the watershed cascade hydro-wind-solar multi-energy complementary base. The two-layer capacity planning model includes an inner-layer optimization scheduling model and an outer-layer capacity optimization model. The constraints in the inner-layer optimization scheduling model are linearized using the successive approximation method. The optimization scheduling module 1603 is used to perform time-series simulation analysis of wind and solar capacity schemes using the inner-layer optimization scheduling model to obtain optimization scheduling results. The capacity scheme screening module 1604 is used to use the outer capacity optimization model to establish technical and economic indicators based on the optimized scheduling results to initially screen wind and solar capacity schemes, obtain alternative schemes, and use TOPSIS to evaluate the alternative schemes to determine the target wind and solar installed capacity scheme.

[0165] The installed capacity planning device for a multi-energy complementary hydropower, wind, and solar power base in a river basin provided in this embodiment of the invention can execute the installed capacity planning method for such bases provided in any embodiment of the invention, and possesses the corresponding functional modules and beneficial effects of the method. Further functional descriptions of the various modules and units described above are the same as in the corresponding embodiments described above, and will not be repeated here.

[0166] Figure 17 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention.

[0167] The following is a detailed reference. Figure 17 This diagram illustrates a suitable structural design for implementing an electronic device according to embodiments of the present invention. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 1701, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 1702 or a program loaded from memory 1708 into random access memory (RAM) 1703. The RAM 1703 also stores various programs and data required for the operation of the electronic device. The processor 1701, ROM 1702, and RAM 1703 are interconnected via a bus 1704. An input / output (I / O) interface 1705 is also connected to the bus 1704.

[0168] Typically, the following devices can be connected to I / O interface 1705: input devices 1706 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 1707 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 1708 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1709. Communication device 1709 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 17 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0169] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 1709, or installed from a memory 1708, or installed from a ROM 1702. When the computer program is executed by the processor 1701, it performs the functions defined in the method for planning the installed capacity of a multi-energy complementary base for watershed cascade hydropower, wind power, and solar power in the present invention.

[0170] Figure 17 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0171] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the installed capacity planning method for a multi-energy complementary base of watershed cascade hydropower, wind power, and solar power shown in the above embodiments is implemented.

[0172] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and all such modifications and variations fall within the scope defined by the appended invention.

Claims

1. A method for planning installed capacity in a multi-energy complementary base of watershed, wind, and solar power, characterized in that, The method includes: Based on the output characteristics of wind and solar power generation in the target basin and the overall planning, the installed capacity range and capacity step size of wind and solar power are set to generate wind and solar capacity schemes to be evaluated. A two-layer capacity planning model for a multi-energy complementary base of water, wind and solar power in a watershed is constructed. The two-layer capacity planning model includes an inner-layer optimization scheduling model and an outer-layer capacity optimization model. The constraints in the inner-layer optimization scheduling model are linearized using the successive approximation method. The wind and solar capacity scheme is analyzed by time-series simulation using the inner-layer optimization scheduling model to obtain the optimization scheduling results; Using the outer capacity optimization model, technical and economic indicators are established based on the optimized scheduling results to initially screen wind and solar capacity schemes, obtain alternative schemes, and use TOPSIS to evaluate the alternative schemes to determine the target wind and solar installed capacity scheme.

2. The method according to claim 1, characterized in that, The inner-layer optimization scheduling model takes maximizing the total revenue of the cascade hydropower-wind-solar multi-energy complementary base in the basin as its objective function. The constraints of the inner-layer optimization scheduling model include hydraulic connection between upstream and downstream reservoirs, water balance constraints, upper and lower limits of reservoir capacity constraints, initial and final reservoir capacity constraints, power generation flow constraints, downstream flow constraints, upper and lower limits of hydropower station output constraints, water level-storage capacity curve, tailwater level-downflow curve, hydropower output function, power generation head constraints, wind and solar power output constraints, ultra-high voltage DC transmission line constraints, and power balance constraints.

3. The method according to claim 2, characterized in that, The water level-reservoir capacity curve, the tailrace water level-discharge flow curve, the hydropower output function, and the UHVDC transmission constraint are nonlinear factors in the constraints of the inner-layer optimization scheduling model. The method also includes: The basic conditions are set, including hydropower station reservoir capacity limit, initial reservoir capacity, final reservoir capacity, hydropower station output limit, wind and solar power output per unit value, transmission channel capacity limit, and power grid load curve. The water level-reservoir capacity curve and the tailwater level-discharge flow curve are linearized using a piecewise linear approximation method. The hydropower output function is linearized using a successive approximation method; The state variable of the upward and downward fluctuation of the grid power of the multi-energy complementary base during a specific period is introduced as a binary variable, and the transmission constraint of the UHVDC interconnection line is linearized. The objective function and constraints of the inner-layer optimization scheduling model are constructed as a mixed-integer linear programming model. The mixed-integer linear programming model is solved using the CPLEX solver, and the operating data of the cascade hydropower stations and wind and solar power stations under the capacity scheme are output. The hydropower station operating data includes the reservoir water level, power generation flow, downstream flow, abandoned water flow, and power generation of the hydropower station in each time period. The wind and solar power station operating data includes the actual grid-connected power and abandoned power of the wind and solar power stations in each time period.

4. The method according to claim 3, characterized in that, The linearization of the hydropower output function using the successive approximation method includes: Set relevant parameters for the successive approximation method, including the maximum number of iterations and convergence accuracy; The initial head of hydropower station for generating electricity is estimated based on the outflow of the hydropower station during the scheduling cycle. The mixed-integer linear programming model was solved using the CPLEX solver to obtain the downstream discharge flow of each hydropower station at each time period; Calculate the new generating head for each hydroelectric power station; If the hydropower head meets any preset condition, the current optimal solution is determined as the linearized result of the hydropower output function. The preset conditions include the difference between the hydropower head and the power generation accuracy reaching the convergence accuracy between two consecutive iterations and the number of iterations reaching the maximum number of iterations.

5. The method according to claim 4, characterized in that, The method further includes: If the hydropower head does not meet the preset conditions, the number of iterations is increased, the new hydropower head is used to replace the hydropower head calculated in the previous iteration, and the process returns to the step of using the CPLEX solver to solve the mixed integer linear programming model to obtain the downstream flow of each hydropower station at each time period, until the hydropower head meets any preset conditions.

6. The method according to claim 1, characterized in that, The process of establishing technical and economic indicators based on the optimized scheduling results to initially screen wind and solar capacity schemes, obtaining alternative schemes, evaluating the alternative schemes using TOPSIS, and determining the target wind and solar installed capacity scheme includes: Technical and economic indicators include: electricity transmitted, cost per unit of electricity, wind and solar curtailment rate, power supply guarantee rate, and load shortage rate. Set thresholds for wind and solar curtailment rate, power supply guarantee rate, and load shortage rate. Based on the screening principle of meeting the thresholds for wind and solar curtailment rate, power supply guarantee rate, and load shortage rate, a preliminary screening of capacity schemes is conducted to obtain feasible alternative schemes. The TOPSIS method is used to construct ideal positive and ideal negative solutions, calculate the Euclidean distance between each feasible alternative and the ideal positive and ideal negative solutions, sort all feasible alternatives, and determine the target wind and solar installed capacity scheme based on the sorting results.

7. The method according to claim 6, characterized in that, The method further includes: Subjective weights were calculated using the improved analytic hierarchy process, and objective weights were calculated using the CRITIC method. The subjective weight and the objective weight are combined to obtain a comprehensive weight, which is then substituted into the TOPSIS method for optimization analysis.

8. A device for planning installed capacity in a multi-energy complementary base of watershed cascade hydropower, wind power, and solar power, characterized in that: The device includes: The configuration module is used to set the installed capacity range and capacity step size of wind power and photovoltaic power generation based on the output characteristics of wind power and photovoltaic power generation in the target watershed and the overall planning, and generate wind and solar capacity schemes to be evaluated. The model building module is used to construct a two-layer capacity planning model for a watershed cascade hydro-wind-solar multi-energy complementary base. The two-layer capacity planning model includes an inner-layer optimization scheduling model and an outer-layer capacity optimization model. The constraints in the inner-layer optimization scheduling model are linearized using the successive approximation method. The optimization scheduling module is used to perform time-series simulation analysis of the wind and solar capacity scheme using the inner-layer optimization scheduling model to obtain the optimization scheduling result. The capacity scheme screening module is used to use the outer capacity optimization model to establish technical and economic indicators based on the optimized scheduling results to initially screen wind and solar capacity schemes, obtain alternative schemes, evaluate the alternative schemes using TOPSIS, and determine the target wind and solar installed capacity scheme.

9. An electronic device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the installed capacity planning method for a multi-energy complementary base of watershed cascade hydropower, wind power, and solar power as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the installed capacity planning method for a multi-energy complementary base of watershed cascade hydropower, wind power, and solar power as described in any one of claims 1 to 7.

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