Sensitivity analysis and evaluation method and device for economic and technical parameters of water-wind-solar hybrid system

Abstract

The present invention discloses a method and device for analyzing and evaluating the sensitivity of economic and technical parameters of a hydropower-wind-solar hybrid system. The method comprises the following steps: first, constructing a wind-solar curtailment rate function, fitting a curtailment loss function by considering short-term curtailment characteristics, and establishing a medium- and long-term optimization scheduling model to simulate hydropower output and deduce the amount of wind and solar curtailment, thereby solving the wind-solar curtailment rate function; then, constructing a cost-benefit model for the wind-solar hybrid system, and analytically solving the optimal installed capacity based on the solved wind-solar curtailment rate function; and finally, analytically evaluating the sensitivity of various economic and technical parameters to the optimal installed capacity and the net lifecycle benefits of the wind-solar hybrid system. This method quantifies the relationship between economic and technical parameters and the optimal installed capacity and the net lifecycle benefits of the wind-solar hybrid system, and rationally evaluates the sensitivity of different economic and technical parameters, providing technical support for the installation planning of hydropower-wind-solar hybrid power stations in river basins.

Description

Technical Field

[0001] The present invention relates to the field of energy planning technology, and in particular to a method and device for analyzing and evaluating the sensitivity of economic and technical parameters of a water-wind-solar complementary system. Background Art

[0002] The hydropower, wind-solar, and multi-energy complementary system leverages the energy storage and regulation capabilities of hydropower, as well as the natural intra-day and intra-year complementarity between wind and solar power, effectively overcoming the challenges of transmitting and absorbing wind and solar power within a river basin. Installed capacity, a key parameter in a complementary system, is directly related to the safety and economic efficiency of system operation. Under-configuring capacity results in underutilized resources and waste, while over-configuring capacity prevents the absorption of excess wind and solar power, increasing system investment costs. Therefore, it is essential to rationally configure the system's installed capacity and quantify the impact of installed capacity and economic and technical parameters on the system's net benefits.

[0003] At present, most studies on the capacity configuration of hydro-wind-solar complementary systems use numerical simulation methods. The calculation process is complex and it is difficult to quantify the relationship between economic and technical parameters such as on-grid electricity price and installation cost and the optimal installed capacity and economic benefits of the complementary system. Summary of the Invention

[0004] In view of the limitation that existing methods mostly use numerical simulation methods and lack analytical methods to accurately characterize the relationship between economic and technical parameters, optimal installed capacity and the net benefits of wind-solar complementary systems throughout their life cycle, the present invention provides a method and device for analytical evaluation of the sensitivity of economic and technical parameters of water-wind-solar complementary systems, quantifies the relationship between economic and technical parameters and optimal installed capacity and the net benefits of wind-solar complementary systems throughout their life cycle, and reasonably evaluates the sensitivity of different economic and technical parameters to the optimal installed capacity and the net benefits of wind-solar complementary systems throughout their life cycle, so that decision makers can make corresponding adjustment strategies based on future changes in economic and technical parameters. The proposed method can provide technical support for the installed capacity planning of water-wind-solar complementary power stations in the basin.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] The sensitivity analysis and evaluation method of economic and technical parameters of the hydro-wind-solar hybrid system includes the following steps:

[0007] Step 1: Construct a wind and solar curtailment rate function;

[0008] Step 2: Considering the short-term curtailment characteristics, a curtailment loss function is fitted, and a medium- and long-term optimization scheduling model is established to simulate the average hydropower output in each period. The average hydropower output in each period is substituted into the curtailment loss function to derive the amount of wind and solar curtailment in each period under different installed capacities.

[0009] Step 3: Using the wind and solar curtailment and wind and solar power generation in each period under different installed capacities, solve the wind and solar curtailment rate function;

[0010] Step 4: Construct a cost-benefit model for the entire life cycle of the wind-solar hybrid system, and analytically solve the optimal installed capacity based on the solved wind-solar curtailment rate function;

[0011] Step 5: Analyze and evaluate the sensitivity of the optimal installed capacity and the net benefits of the wind-solar hybrid system over its entire life cycle to various economic and technical parameters.

[0012] Furthermore, in step 1, within a certain installed capacity range, it is assumed that the average wind power curtailment rate over many years is linearly related to the wind power installed capacity, and the average photovoltaic power curtailment rate over many years is linearly related to the photovoltaic installed capacity:

[0013]

[0014] Where: γ w , γ s are the average wind power curtailment rate and photovoltaic power curtailment rate over the years; I w , I s They are wind power installed capacity and photovoltaic installed capacity, both in MW; I min , I max are the lower and upper limits of installed capacity, both in MW; solve the coefficients k1, k2, c1, and c2 in the formula through steps 2 to 3.

[0015] Furthermore, in step 3, the formula for solving the multi-year average wind and solar curtailment rate is:

[0016]

[0017] Where: Wind power installed capacity is I w The amount of power abandoned and generated in each time period, both in kW·h; The photovoltaic installed capacity is I s The amount of power curtailment and power generation in each time period are both in kW·h; I is the dispatch period;

[0018] The wind and solar power curtailment rate function is fitted based on the multi-year average wind and solar power curtailment rate and the corresponding installed capacity.

[0019] Furthermore, in step 4, since the hydropower station has been built and put into operation, the cost-benefit analysis only considers the costs and benefits related to the wind-solar hybrid system. The constructed cost-benefit model takes the maximum net benefit of the wind-solar hybrid system over its entire life cycle as the objective function:

[0020]

[0021] Where: NRws is the net benefit of the wind-solar complementary system over the entire life cycle, in yuan; Y is the operating life of the wind power station and photovoltaic power station, in years; B w 、B s are the on-grid electricity prices for wind power and photovoltaic power, both in RMB / kWh; C in,w 、C in,s are the installed costs of wind power stations and photovoltaic power stations, both in RMB / kW; C om,w 、C on,s are the operating costs of wind power stations and photovoltaic power stations, both in RMB / MWh; I w , I s are the installed capacity of wind power and photovoltaic power, both in MW; P w 、P s are wind power and photovoltaic power generation, both in kW·h; d r is the discount rate;

[0022] Based on the solved wind-solar curtailment rate function, the net benefit of the wind-solar complementary system over the entire life cycle is: w and photovoltaic installations I s is a quadratic function of the independent variable, which is used to calculate I w and I s The optimal wind power installed capacity and the optimal photovoltaic installed capacity can be solved by partial derivatives:

[0023]

[0024]

[0025] Where: is the multi-year discount factor; For the optimal wind power installed capacity, The optimal photovoltaic installed capacity is expressed in MW.

[0026] In step 5, the economic and technical parameters in the installed capacity planning include the wind and solar grid electricity price, wind and solar installed capacity cost, wind and solar operation and maintenance cost, and multi-year discount factor. Formula (6) is expanded analytically for each parameter at the original optimal installed capacity value to obtain the sensitivity analytical formulas (7)-(11) of the optimal installed capacity and the net benefit of the wind and solar complementary system over the entire life cycle to each parameter, thereby quantifying the impact of each economic and technical parameter on the optimal installed capacity and the net benefit of the wind and solar complementary system over the entire life cycle when the original plan is changed;

[0027] The sensitivity of wind power grid price to the optimal wind power installed capacity and the sensitivity of photovoltaic grid price to the optimal photovoltaic installed capacity are:

[0028]

[0029] Where: ΔBw , ΔB s are the changes in the on-grid electricity prices of wind power and photovoltaic power, both in yuan / kWh; are the changes in the optimal wind power installed capacity and the optimal photovoltaic installed capacity, both in MW;

[0030] The sensitivity of wind power installation cost to the optimal wind power installation and the sensitivity of photovoltaic installation cost to the optimal photovoltaic installation are:

[0031]

[0032] Where: ΔC in,w , ΔC in,s They are the changes in wind power and photovoltaic installation costs, both in yuan / kW;

[0033] The sensitivity of wind power operation and maintenance costs to the optimal wind power installed capacity and the sensitivity of photovoltaic operation and maintenance costs to the optimal photovoltaic installed capacity are:

[0034]

[0035] Where: ΔC om,w , ΔC om,s are the changes in wind power and photovoltaic operation and maintenance costs, both in yuan / MWh; the sensitivity of the multi-year discount factor to the optimal wind power installed capacity and the optimal photovoltaic installed capacity is:

[0036]

[0037] 5Where: Δa is the change in the multi-year discount factor;

[0038] The maximum net benefit of the wind-solar hybrid system over its entire life cycle is:

[0039]

[0040] The sensitivity analysis and evaluation device for economic and technical parameters of the water-wind-solar hybrid system includes:

[0041] Wind and solar power curtailment rate function construction module, used to construct wind and solar power curtailment rate function;

[0042] The wind and solar curtailment calculation module is used to fit the curtailment loss function by considering short-term curtailment characteristics, establish a medium- and long-term optimization scheduling model to simulate the average hydropower output in each period, and substitute the average hydropower output in each period into the curtailment loss function to derive the wind and solar curtailment in each period under different installed capacities.

[0043] The wind and solar curtailment rate function solving module is used to solve the wind and solar curtailment rate function by using the wind and solar curtailment amount and wind and solar power generation in each period under different installed capacities;

[0044] The optimal installed capacity solution module is used to build a cost-benefit model for the entire life cycle of the wind-solar hybrid system. Based on the solved wind-solar curtailment rate function, the optimal installed capacity is analytically solved.

[0045] The economic and technical parameter sensitivity evaluation module is used to analyze and evaluate the sensitivity of the optimal installed capacity and the net benefits of the wind-solar complementary system over its entire life cycle to various economic and technical parameters.

[0046] A device for analyzing and evaluating the sensitivity of economic and technical parameters of a water-wind-solar complementary system includes a processor and a memory for storing a computer program that can be run on the processor. When the processor is used to run the computer program, it executes the steps of the method for analyzing and evaluating the sensitivity of economic and technical parameters of a water-wind-solar complementary system described in any one of the above items.

[0047] A computer storage medium having a computer program stored therein, characterized in that when the computer program is executed by a processor, the steps of the above-mentioned method for analyzing and evaluating the sensitivity of economic and technical parameters of a water-wind-solar complementary system are implemented.

[0048] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0049] The method and device for analyzing and evaluating the sensitivity of economic and technical parameters of a water-wind-solar complementary system provided by the present invention introduce a wind-solar power curtailment rate function to analyze and optimize the configuration of wind-solar installed capacity. Compared with traditional numerical simulation methods, it can directly quantify the relationship between economic and technical parameters, installed capacity and the net benefit of the wind-solar complementary system over the entire life cycle, and reasonably evaluate the sensitivity of various economic and technical parameters in the planning, so that decision makers can make corresponding adjustment strategies according to future changes in various economic and technical parameters. The proposed method can provide technical support for the installed capacity planning of water-wind-solar complementary power stations in the basin. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The accompanying drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0051] Figure 1 This is a flow chart for implementing the sensitivity analysis and evaluation method of the economic and technical parameters of the water-wind-solar complementary system of the present invention.

[0052] Figure 2 Schematic diagram of the sensitivity analysis and evaluation method of the economic and technical parameters of the water-wind-solar complementary system of the present invention.

[0053] FIG3( a ) is a curve showing the relationship between the optimal installed capacity and the on-grid electricity price according to an embodiment of the present invention.

[0054] FIG3( b ) is a curve showing the relationship between the optimal installed capacity and the installed capacity cost according to an embodiment of the present invention.

[0055] FIG3( c ) is a curve showing the relationship between the optimal installation and operation and maintenance costs according to an embodiment of the present invention.

[0056] FIG3( d ) is a curve showing the relationship between the optimal installed capacity and the multi-year discount factor according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present invention.

[0058] The present invention provides a sensitivity analysis and evaluation method for economic and technical parameters of a water-wind-solar complementary system. Figure 1 and Figure 2 As shown, the following steps are included:

[0059] Step 1: Construct a wind and solar curtailment rate function;

[0060] Step 2: Consider the short-term curtailment characteristics to fit the curtailment loss function, establish a medium- and long-term optimization scheduling model to simulate the average hydropower output in each period, substitute the average hydropower output in each period into the curtailment loss function to derive the amount of wind and solar curtailment in each period under different installed capacities;

[0061] Step 3: Using the wind and solar curtailment and wind and solar power generation in each period under different installed capacities, solve the wind and solar curtailment rate function;

[0062] Step 4: Construct a cost-benefit model for the entire life cycle of the wind-solar hybrid system, and analytically solve the optimal installed capacity based on the solved wind-solar curtailment rate function;

[0063] Step 5: Analyze and evaluate the sensitivity of the optimal installed capacity and the net benefits of the wind-solar hybrid system over its entire life cycle to various economic and technical parameters.

[0064] The present invention provides a sensitivity analysis and evaluation method for economic and technical parameters of a water-wind-solar complementary system, which introduces a wind-solar power curtailment rate function to analyze and optimize the configuration of wind-solar installed capacity. Compared with traditional digital simulation methods, the method can directly quantify the relationship between economic and technical parameters, installed capacity and the net benefit of the wind-solar complementary system over the entire life cycle, and reasonably analyze the sensitivity of various economic and technical parameters to the optimal installed capacity, so that decision makers can make corresponding adjustment strategies according to changes in various economic and technical parameters, thereby providing a new method for configuring the installed capacity of a water-wind-solar complementary system in a river basin.

[0065] In this invention, in step 1, similar to traditional hydropower station curtailment, curtailment occurs when the output of the wind-solar hybrid system exceeds the system's required load or exceeds the grid's transmission capacity. As wind and solar power output increases with installed capacity, the wind and photovoltaic curtailment rates will also increase over time given limited hydropower resources.

[0066] Therefore, it is assumed that within a certain installed capacity range, the average wind power curtailment rate over many years is linearly related to wind power installed capacity, and the average photovoltaic power curtailment rate over many years is linearly related to photovoltaic installed capacity:

[0067]

[0068] Where: γ w , γ s are the average wind power curtailment rate and photovoltaic power curtailment rate over the years; I w , I s They are wind power installed capacity and photovoltaic installed capacity, both in MW; I min , I max are the lower and upper limits of installed capacity, respectively, all in MW; k1, k2, c1, c2 are coefficients.

[0069] The present invention constructs a medium- and long-term optimization scheduling model considering short-term power curtailment characteristics to solve k1, k2, c1, and c2 as coefficients. Specifically, the coefficients in formula (1) are solved through steps 2 to 3.

[0070] In order to solve the coefficients k1, k2, c1, and c2 in the formula, a medium- and long-term deterministic optimization scheduling model considering short-term power curtailment characteristics was constructed.

[0071] In the present invention, in step 2, the short-term power curtailment characteristics take into account two basic power curtailment scenarios during the flood season and the non-flood season based on the daily and annual characteristics of the hydropower, wind and solar power output, thereby constructing a flood season power curtailment loss model and a non-flood season power curtailment loss model, and obtaining the corresponding power curtailment loss function under different installed capacities during the flood season and the non-flood season, which can characterize the relationship between the average hydropower output and the wind and solar power curtailment rate over a long period of time, and then solve the wind and solar power curtailment amount in each period.

[0072] The daily output characteristics of hydropower, wind power, and solar power are as follows: hydropower is relatively stable throughout the day; wind power is higher at night and lower during the day; photovoltaic power is higher at noon and zero at night. The annual characteristics are as follows: hydropower output is much higher during the flood season than during the off-flood season; wind power is higher in winter and lower in summer; photovoltaic power is higher in spring and summer and lower in autumn and winter.

[0073] There are two basic curtailment scenarios:

[0074] (a) During the flood season, when the reservoir water level reaches its upper limit, the power generation flow is the inflow flow. To avoid water abandonment, the hydropower output cannot be reduced. In order to meet the balance between the output of the hydro-wind-solar hybrid system and the load demand, the excess wind and solar power must be abandoned;

[0075] (b) When there is adjustable reservoir capacity in the non-flood season and the hydropower output drops to the minimum, the output of the water-wind-solar complementary system is still greater than the load demand. At this time, the excess wind and solar power must be discarded.

[0076] In this invention, in order to simulate the medium- and long-term hydropower output, a medium- and long-term optimization scheduling model is established with the maximum net power generation of the water-wind-solar complementary system over many years as the objective function:

[0077]

[0078] Where: is the penalty function; EP is the total power generation of the hydro-wind-solar hybrid system in the dispatch period I, in units of 100 million kW·h; P i hp is the average output of the hydropower unit during the i-th period, in MW; P i ws (x,y) is the average output of wind power installed capacity x and photovoltaic installed capacity y in time period i, in MW; To ensure the output of complementary power stations, the unit is MW; ΔT i The length of the medium- to long-term scheduling period is in months;

[0079] The various constraints considered include water balance constraints, reservoir capacity constraints, power generation flow constraints, and boundary condition constraints. Dynamic programming is used to solve the medium- and long-term optimization scheduling model. The decision variable is set as the water level, and the average hydropower output in each time period is obtained. After the average hydropower output in each time period is substituted into the corresponding power curtailment loss function under different installed capacities during the flood season and non-flood season, the wind and solar power curtailment amount in the corresponding time period can be obtained.

[0080] In the present invention, in step 3, the formula for solving the multi-year average wind and solar power curtailment rate is:

[0081]

[0082] Where: Wind power installed capacity is I w The amount of power abandoned and generated in each time period, both in kW·h; The photovoltaic installed capacity is I s The amount of power curtailment and power generation in each time period are both in kW·h; I is the dispatch period;

[0083] The wind and solar power curtailment rate function is fitted based on the multi-year average wind and solar power curtailment rate and the corresponding installed capacity.

[0084] In the present invention, in step 4, considering that the hydropower station has been built and put into operation, the hydropower income does not affect the wind and solar power installation. The cost-benefit analysis only considers the costs and benefits related to the wind and solar complementary system. Therefore, an optimization model with the maximum net benefit of the wind and solar complementary system throughout its life cycle as the objective function is constructed:

[0085]

[0086] Where: NRws is the net benefit of the wind-solar complementary system over the entire life cycle, in yuan; Y is the operating life of the wind power station and photovoltaic power station, in years; B w 、B s are the on-grid electricity prices for wind power and photovoltaic power, both in RMB / kWh; C in,w 、C in,s are the installed costs of wind power stations and photovoltaic power stations, both in RMB / kW; C om,w 、C on,s are the operating costs of wind power stations and photovoltaic power stations, both in RMB / MWh; I w , I s are the installed capacity of wind power and photovoltaic power, both in MW; P w 、P s are wind power and photovoltaic power generation, both in kW·h; d r is the discount rate.

[0087] In the present invention, the optimal installed capacity includes the optimal wind power installed capacity and the optimal photovoltaic installed capacity. Based on the solved wind and solar power abandonment rate function, the cost-benefit model is converted into the optimal wind power installed capacity. w and photovoltaic installations I s The quadratic function (5) of the independent variable is w and photovoltaic installations I s The maximum value can be obtained by taking partial derivatives respectively as shown in formula (6).

[0088]

[0089]

[0090] Where: is the multi-year discount factor, For the optimal wind power installed capacity, The optimal photovoltaic installed capacity is expressed in MW.

[0091] In the present invention, in step 5, the economic and technical parameters in the installed capacity planning include the wind and solar grid-connected electricity price, wind and solar installed capacity cost, wind and solar operation and maintenance cost, and multi-year discount factor. Formula (6) is analytically expanded on each parameter at the original optimal installed capacity value to obtain the sensitivity analytical formulas (7)-(11) of the optimal installed capacity and the net benefit of the wind and solar complementary system over the entire life cycle to each parameter, thereby quantifying the impact of each economic and technical parameter on the optimal installed capacity and the net benefit of the wind and solar complementary system over the entire life cycle when the original plan is changed.

[0092] The sensitivity of wind power grid price to the optimal wind power installed capacity and the sensitivity of photovoltaic grid price to the optimal photovoltaic installed capacity are:

[0093]

[0094] Where: ΔB w , ΔB s is the change in the on-grid electricity price of wind power and photovoltaic power, both in yuan / kWh; are the changes in the optimal wind power installed capacity and the optimal photovoltaic installed capacity, both in MW;

[0095] The sensitivity of wind power installation cost to the optimal wind power installation and the sensitivity of photovoltaic installation cost to the optimal photovoltaic installation are:

[0096]

[0097] Where: ΔC in,w , ΔC in,s is the change in the installed cost of wind power and photovoltaic power, both in yuan / kW;

[0098] The sensitivity of wind power operation and maintenance costs to the optimal wind power installed capacity and the sensitivity of photovoltaic operation and maintenance costs to the optimal photovoltaic installed capacity are:

[0099]

[0100] Where: ΔC om,w , ΔC om,s is the change in wind power and photovoltaic operation and maintenance costs, both in yuan / MWh;

[0101]

[0102] Where: Δa is the change in the multi-year discount factor;

[0103] The maximum net benefit of the wind-solar hybrid system over its entire life cycle is:

[0104]

[0105] This can effectively evaluate the economic and technical parameters that are most sensitive and least sensitive to the optimal installed capacity and the net benefits of the wind-solar complementary system over its entire life cycle, allowing decision makers to make corresponding adjustment strategies based on future parameter changes.

[0106] In one embodiment of the present invention, the Ertan Hydropower Station and its surrounding area in the Yalong River basin were used as an example. Data from 1980-2010, including 31 years of runoff inflow, hourly radiation from the Yanbian Hydropower Station, temperature, and wind speed, were used as research data. The installed capacity plan was set with a lower limit of 100 MW and an upper limit of 2000 MW. A medium- and long-term optimal scheduling model was used to solve the wind and solar curtailment rate function, as shown in the following equation.

[0107]

[0108] The wind and solar curtailment rate function has a good linear fit (R² = 0.944), indicating that within the installed capacity threshold, the assumption of a linear relationship between wind and solar curtailment rate and the corresponding installed capacity holds true over the long term. Using typical yearly data to test the curtailment rate function, the results are: RMSE = 0.032, and Pearson correlation coefficient CC = 0.962, indicating that the curtailment rate function is reasonable.

[0109] The planning parameters and economic and technical parameters in the cost-benefit model are shown in Table 1.

[0110] Table 1 Planning parameters and economic and technical parameters of wind and solar power stations

[0111]

[0112]

[0113] The analytical planning method was used to determine the optimal installed capacity. The results compared favorably with the numerical simulation optimization method, with an error of 3.27%. The results also showed reasonableness when compared with the "Planning for the Yalong River Basin Hydropower, Wind-Solar Complementary Clean Energy Base," demonstrating the effectiveness and feasibility of the analytical method. A comparison of the three results is shown in Table 2.

[0114] Table 2 Comparison of optimal installed capacity

[0115]

[0116] Figure 3 plots the relationship between the optimal installed capacity and each economic and technical parameter within a range near the original values ​​of the economic and technical parameters. To quantitatively compare the sensitivity of different economic and technical parameters to installed capacity, each parameter was given the same 1% change relative to the original value. The results are shown in Table 3.

[0117] Table 3 Sensitivity analysis results

[0118]

[0119]

[0120] Regarding installed capacity, the most sensitive parameter is the on-grid tariff, while the least sensitive parameter is operation and maintenance costs. For wind power installations, the former is approximately 3.81 times more sensitive than the latter, and for photovoltaic installations, the former is approximately 3.06 times more sensitive than the latter. However, the sensitivity of installed capacity and the multi-year discount factor are similar, with each contributing inversely to installed capacity. Regarding the net benefits of complementary systems, the parameters are ranked in order of sensitivity: on-grid tariff, multi-year discount factor, installed capacity cost, and operation and maintenance costs. The sensitivity of the on-grid tariff is approximately 4.44 times that of the operation and maintenance costs.

[0121] The present invention also provides a device for analyzing and evaluating the sensitivity of economic and technical parameters of a water-wind-solar complementary system, comprising:

[0122] Wind and solar power curtailment rate function construction module, used to construct wind and solar power curtailment rate function;

[0123] The wind and solar curtailment calculation module is used to fit the curtailment loss function by considering short-term curtailment characteristics, establish a medium- and long-term optimization scheduling model to simulate the average hydropower output in each period, and substitute the average hydropower output in each period into the curtailment loss function to derive the wind and solar curtailment in each period under different installed capacities.

[0124] The wind and solar curtailment rate function solving module is used to solve the wind and solar curtailment rate function by using the wind and solar curtailment amount and wind and solar power generation in each period under different installed capacities;

[0125] The optimal installed capacity solution module is used to build a cost-benefit model for the entire life cycle of the wind-solar hybrid system. Based on the solved wind-solar curtailment rate function, the optimal installed capacity is analytically solved.

[0126] The economic and technical parameter sensitivity evaluation module is used to analyze and evaluate the sensitivity of the optimal installed capacity and the net benefits of the wind-solar complementary system over its entire life cycle to various economic and technical parameters.

[0127] The present invention also provides a device for analyzing and evaluating the sensitivity of economic and technical parameters of a water-wind-solar complementary system, comprising a processor and a memory for storing a computer program that can be run on the processor. When the processor is used to run the computer program, it executes the steps of the method for analyzing and evaluating the sensitivity of economic and technical parameters of a water-wind-solar complementary system as described in any one of the above items.

[0128] The memory in the embodiments of the present invention is used to store various types of data to support the operation of the device for analyzing and evaluating the sensitivity of economic and technical parameters of a hydro-wind-solar hybrid system. Examples of such data include any computer program for operating the device for analyzing and evaluating the sensitivity of economic and technical parameters of a hydro-wind-solar hybrid system.

[0129] The method for analyzing and evaluating the sensitivity of economic and technical parameters of a hydro-wind-solar hybrid system disclosed in the embodiments of the present invention can be applied to or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the method for analyzing and evaluating the sensitivity of economic and technical parameters of a hydro-wind-solar hybrid system can be completed by hardware integrated logic circuits or software instructions within the processor. The processor may be a general-purpose processor, a digital signal processor (DSP), or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, etc. The processor can implement or execute the various methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of the present invention can be directly implemented and executed by a hardware decoding processor or by a combination of hardware and software modules within the decoding processor. The software module may be located in a storage medium located in a memory. The processor reads information from the memory and, in conjunction with its hardware, completes the steps of the method for analyzing and evaluating the sensitivity of economic and technical parameters of a hydro-wind-solar hybrid system provided in the embodiments of the present invention.

[0130] In an exemplary embodiment, the sensitivity analysis and evaluation device for economic and technical parameters of a water-wind-solar complementary system can be implemented by one or more application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), FPGAs, general-purpose processors, controllers, microcontrollers (MCUs), microprocessors, or other electronic components to execute the aforementioned method.

[0131] It is understood that the memory can be volatile memory or non-volatile memory, or can include both volatile and non-volatile memory. Among them, the non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disk, or compact disc read-only memory (CD-ROM); magnetic surface memory can be magnetic disk memory or tape memory. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of RAM are available, such as static random access memory (SRAM), synchronous static random access memory (SSRAM), dynamic random access memory (DRAM), synchronous dynamic random access memory (SDRAM), double data rate synchronous dynamic random access memory (DDRSDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link dynamic random access memory (SLDRAM), and direct rambus random access memory (DRRAM). The memory described in the embodiments of the present invention is intended to include, but is not limited to, these and any other suitable types of memory.

[0132] The present invention also provides a computer storage medium, in which a computer program is stored. The computer storage medium is characterized in that when the computer program is executed by a processor, the steps of the sensitivity analysis and evaluation method of economic and technical parameters of the water-wind-solar complementary system described in any one of the above items are implemented.

[0133] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A sensitivity analysis and evaluation method for economic and technical parameters of a water-wind-solar hybrid system, characterized by: The following steps are involved: Step 1: Construct a wind and solar curtailment rate function; Step 2: Considering the short-term curtailment characteristics, a curtailment loss function is fitted, and a medium- and long-term optimization scheduling model is established to simulate the average hydropower output in each period. The average hydropower output in each period is substituted into the curtailment loss function to derive the amount of wind and solar curtailment in each period under different installed capacities. Step 3: Using the wind and solar curtailment and wind and solar power generation in each period under different installed capacities, solve the wind and solar curtailment rate function; Step 4: Construct a cost-benefit model for the entire life cycle of the wind-solar hybrid system. Based on the solved wind-solar curtailment rate function, analytically solve the optimal installed capacity. Since the hydropower station has already been built and put into operation, the cost-benefit analysis only considers the costs and benefits related to the wind-solar hybrid system. The constructed cost-benefit model takes maximizing the net benefit of the wind-solar hybrid system throughout its entire life cycle as its objective function: Where: NR ws is the net benefit of the wind-solar complementary system over the entire life cycle, in yuan; Y is the operating life of the wind power station and photovoltaic power station, in years; B w 、B s are the on-grid electricity prices for wind power and photovoltaic power, both in RMB / kWh; C in,w 、C in,s are the installed costs of wind power stations and photovoltaic power stations, both in RMB / kW; C om,w 、C on,s are the operating costs of wind power stations and photovoltaic power stations, both in RMB / MWh; I w , I s are the installed capacities of wind power and photovoltaic power, both in MW; P w 、P s are wind power and photovoltaic power generation, both in kW·h; d r is the discount rate; Based on the solved wind-solar curtailment rate function, the net benefit of the wind-solar complementary system over the entire life cycle is: w and photovoltaic installations I s is a quadratic function of the independent variable, which is used to calculate I w and I s The optimal wind power installed capacity and the optimal photovoltaic installed capacity can be solved by partial derivatives: Where: is the multi-year discount factor; For the optimal wind power installed capacity, is the optimal photovoltaic installed capacity, all in MW; Step 5: Analyze and evaluate the sensitivity of the optimal installed capacity and the net benefits of the wind-solar hybrid system over its entire life cycle to various economic and technical parameters.

2. The method for analyzing the sensitivity of economic and technical parameters of a water-wind-solar hybrid system according to claim 1 is characterized by: In step 1, within a certain installed capacity range, it is assumed that the average wind power curtailment rate over many years is linearly related to the wind power installed capacity, and the average photovoltaic power curtailment rate over many years is linearly related to the photovoltaic installed capacity: Where: γ w , γ s are the average wind power curtailment rate and photovoltaic power curtailment rate over the years; I w , I s They are wind power installed capacity and photovoltaic installed capacity, both in MW; I min , I max They are the lower and upper limits of installed capacity, both in MW; Solve the coefficients k1, k2, c1, and c2 in the formula through steps 2 and 3.

3. The method for analyzing the sensitivity of economic and technical parameters of a water-wind-solar hybrid system according to claim 1 is characterized by: In step 3, the formula for solving the multi-year average wind and solar curtailment rate is: Where: Wind power installed capacity is I w The amount of power abandoned and generated in each time period, both in kW·h; The photovoltaic installed capacity is I s The amount of power curtailment and power generation in each time period are both in kW·h; I is the dispatch period; The wind and solar power curtailment rate function is fitted based on the multi-year average wind and solar power curtailment rate and the corresponding installed capacity.

4. The method for analyzing the sensitivity of economic and technical parameters of a water-wind-solar hybrid system according to claim 1 is characterized by: In step 5, the economic and technical parameters in the installed capacity planning include the wind and solar grid electricity price, wind and solar installed capacity cost, wind and solar operation and maintenance cost, and multi-year discount factor. Formula (6) is expanded analytically for each parameter at the original optimal installed capacity value to obtain the sensitivity analytical formulas (7)-(11) of the optimal installed capacity and the net benefit of the wind and solar complementary system over the entire life cycle to each parameter, thereby quantifying the impact of each economic and technical parameter on the optimal installed capacity and the net benefit of the wind and solar complementary system over the entire life cycle when the original plan is changed; The sensitivity of wind power grid price to the optimal wind power installed capacity and the sensitivity of photovoltaic grid price to the optimal photovoltaic installed capacity are: Where: ΔB w , ΔB s are the changes in the on-grid electricity prices of wind power and photovoltaic power, both in yuan / kWh; are the changes in the optimal wind power installed capacity and the optimal photovoltaic installed capacity, both in MW; The sensitivity of wind power installation cost to the optimal wind power installation and the sensitivity of photovoltaic installation cost to the optimal photovoltaic installation are: Where: ΔC in,w , ΔC in,s They are the changes in wind power and photovoltaic installation costs, both in yuan / kW; The sensitivity of wind power operation and maintenance costs to the optimal wind power installed capacity and the sensitivity of photovoltaic operation and maintenance costs to the optimal photovoltaic installed capacity are: Where: ΔC om,w , ΔC om,s They are the changes in wind power and photovoltaic operation and maintenance costs, both in yuan / MWh; The sensitivity of the multi-year discount factor to the optimal wind power installed capacity and the optimal photovoltaic installed capacity is: Where: Δa is the change in the multi-year discount factor; The maximum net benefit of the wind-solar hybrid system over its entire life cycle is:

5. A device for analyzing and evaluating the sensitivity of economic and technical parameters of a water-wind-solar complementary system, characterized in that: include: Wind and solar power curtailment rate function construction module, used to construct wind and solar power curtailment rate function; The wind and solar curtailment calculation module is used to fit the curtailment loss function by considering short-term curtailment characteristics, establish a medium- and long-term optimization scheduling model to simulate the average hydropower output in each period, and substitute the average hydropower output in each period into the curtailment loss function to derive the wind and solar curtailment in each period under different installed capacities. The wind and solar curtailment rate function solving module is used to solve the wind and solar curtailment rate function by using the wind and solar curtailment amount and wind and solar power generation in each period under different installed capacities; The optimal installed capacity solution module is used to build a cost-benefit model for the entire life cycle of the wind-solar hybrid system. Based on the solved wind-solar curtailment rate function, the optimal installed capacity is analytically solved. Economic and technical parameter sensitivity evaluation module, used to analyze and evaluate the sensitivity of the optimal installed capacity and the net benefits of the wind-solar hybrid system over its entire life cycle to various economic and technical parameters; The device for analyzing and evaluating the sensitivity of economic and technical parameters of a water-wind-solar complementary system executes the steps of the method for analyzing and evaluating the sensitivity of economic and technical parameters of a water-wind-solar complementary system as described in any one of claims 1 to 4.

6. Equipment for analyzing and evaluating the sensitivity of economic and technical parameters of water-wind-solar hybrid systems, characterized by: The system comprises a processor and a memory for storing a computer program that can be run on the processor. When the processor is used to run the computer program, the system executes the steps of the method for analyzing and evaluating the sensitivity of economic and technical parameters of a water-wind-solar complementary system as described in any one of claims 1 to 4.

7. A computer storage medium, characterized in that: The computer storage medium stores a computer program, which is characterized in that when the computer program is executed by the processor, it implements the steps of the method for analyzing and evaluating the sensitivity of economic and technical parameters of the water-wind-solar complementary system as described in any one of claims 1 to 4.

Images