Water-wind-light multi-energy complementary multi-cycle nested optimization modeling solving method

Abstract

The invention belongs to the technical field of modeling and solving of water-wind-light multi-energy complementary production simulation, and relates to a water-wind-light multi-energy complementary multi-cycle nested optimization modeling and solving method. Firstly, on the premise that the time continuity of long-period hour-by-hour simulation results is not damaged, original continuous multi-year hour simulation is decomposed into a year simulation model, a month simulation model and an hour simulation model according to the coupling relation between hydropower simulation results under different time resolutions. Secondly, based on a parallelogram two-dimensional interpolation method, under the condition that 0-1 integer variables are not introduced, linear approximation modeling is conducted on the hydroelectric nonlinear function, and all decomposed models are converted into linear programming models capable of being solved rapidly; running simulation is carried out in a rolling mode according to the time sequence, and a simulation result correction mechanism is added to feed back and update unreasonable simulation results layer by layer so as to guarantee solvability of the large-scale long-time-sequence running simulation model. According to the method, simulation support is provided for more reasonably planning the new energy capacity which is in complementary operation with the hydropower station.

Description

Technical Field

[0001] The present invention belongs to the technical field of modeling and solving of water, wind and solar multi-energy complementary production simulation, and relates to a multi-period nested optimization modeling and solving method for water, wind and solar multi-energy complementary production. Background Art

[0002] To achieve the goal of decarbonizing the power system, renewable energy is rapidly developing globally. Wind and photovoltaic power generation introduce high variability and uncertainty into the power system, necessitating greater flexibility to ensure a strict, real-time balance between power supply and demand. Hydropower is a clean and economical technology that can compensate for the volatility of wind and solar power, offering advantages such as rapid start-up, rapid ramp-up, and reservoir storage. The complementary operation of wind and photovoltaic power plants with existing hydropower stations has become a key approach in the planning of large-scale renewable energy power plants.

[0003] Given the variability of wind and solar resources, runoff, and the complex, nonlinear operational characteristics of hydropower plants, determining the renewable energy capacity required to complement hydropower plants presents a real challenge. Wind and solar resources and runoff exhibit significant interannual variability, making traditional operational simulation methods based on typical days or years incapable of assessing the long-term performance of wind-solar hybrid systems. However, performing hourly simulations over multiple years significantly increases the complexity of the solution.

[0004] First, the large number of decision variables in hydropower systems leads to the significant large-scale, high-dimensional nature of hydropower operation simulations. Second, hydropower station characteristic curves (such as water level-reservoir capacity curves and NHQ curves) are mostly nonlinear, making traditional linearization methods difficult to handle large numbers of complex non-convex functions. Furthermore, hydropower has multi-stage dynamic characteristics. For a single hydropower station, it strictly meets the timing constraints of water balance. For cascade hydropower stations, the temporal hydraulic connections between upstream and downstream stations must also be considered. Currently, dynamic programming, nonlinear programming, and linear programming are widely used in hydropower systems. However, most of these methods require the calculation and storage of all potential state combinations and their indicator values. Due to the combined effects of factors such as the number of hydropower stations, the number of time period states, and the number of decisions, the computational and storage requirements of the algorithms grow nonlinearly with system scale, significantly increasing the execution overhead and resulting in a serious problem of the curse of dimensionality. Various artificial intelligence algorithms (such as genetic algorithms, ant colony algorithms, particle swarm algorithms, etc.) have the advantages of simplicity, implicit parallelism, high applicability, and strong robustness. They are almost not troubled by the curse of dimensionality. However, the quality of artificial intelligence algorithm solutions is easily affected by the operation operators and related parameter settings, and the premature convergence phenomenon is prominent, and the rationality, stability and feasibility of the results cannot be strictly guaranteed.

[0005] In short, hydropower system operation is characterized by high dimensionality, strong nonlinearity, multiple computational stages, tight spatiotemporal coupling, and complex scheduling requirements, resulting in a significant computational burden. The operational constraints imposed by the integration of renewable energy sources further exacerbate the difficulty of solving mathematical optimization problems. For simulations of hydropower, wind, and solar power hybrid operations spanning multiple years and with hourly resolution, modeling, solving, and efficiently solving these problems are pressing challenges. Summary of the Invention

[0006] The present invention combines a multi-resolution soft connection framework with a linearized modeling method to construct a multi-period nested optimization modeling and solution method for the complementary operation of hydropower, wind, and solar power, providing simulation support for more rationally determining the capacity of renewable energy sources that complement hydropower stations. First, without destroying the temporal continuity of long-term hourly simulation results, the original continuous multi-year hourly simulation is decomposed into a series of smaller-scale annual, monthly, and hourly simulation models based on the coupling relationship between hydropower simulation results at different time resolutions. Second, based on the parallelogram two-dimensional interpolation method, linear approximation modeling is performed on hydropower nonlinear functions without introducing 0-1 integer variables, and all decomposed models are converted into linear programming models that can be quickly solved. The operation simulation is carried out in a rolling chronological order, and a simulation result correction mechanism is added to feedback and update unreasonable simulation results layer by layer to ensure the solvability of large-scale long-term operation simulation models. The present invention provides simulation support for more rationally determining the capacity of renewable energy sources that complement hydropower stations.

[0007] Technical solution of the present invention:

[0008] A multi-cycle nested optimization modeling and solution method for water, wind and solar multi-energy complementarity, the steps are as follows:

[0009] Step (1): Initial calculation conditions.

[0010] Historical observation data: including 12 consecutive years of hourly natural runoff data, hourly wind speed data at wind turbine hub height, hourly solar radiation intensity data, and hourly air temperature data;

[0011] Basic data of hydropower stations: including installed capacity data, upper and lower limits of reservoir capacity, upper and lower limits of power generation flow, initial and final reservoir capacity, and transmission channel capacity;

[0012] Wind turbine characteristic data: including installation hub height, rated power, rated wind speed, cut-in wind speed and cut-out wind speed;

[0013] PV module data: including rated power, solar radiation intensity and PV panel temperature under standard test conditions, temperature coefficient and PV panel temperature under normal operating conditions.

[0014] Step (2): Construct a multi-year operation simulation model for water, wind and solar power with a multi-year cycle and a year as the step length.

[0015] Objective function 1: Minimize the sum of the deviations between the annual power generation of the hydro-wind-solar hybrid system and the mean over many years.

[0016]

[0017] Where: is the period of running the simulation model over many years; It is a hydroelectric power station Output during the time period; The photovoltaic power station is Output during the time period; The wind power station is Output during the period.

[0018] Objective function 2: Maximize the total power generation of the hydro-wind-solar complementary system over many years.

[0019]

[0020] Where: yes The total number of hours in the period.

[0021] Constraints include: hydropower, wind and solar power station output function constraints, reservoir capacity upper and lower limit constraints, outflow upper and lower limit constraints, power generation flow upper and lower limit constraints, hydropower output upper and lower limit constraints, initial and final reservoir capacity constraints, water balance constraints, channel capacity constraints, etc. The mathematical expression of the constraint model is as follows:

[0022] Hydropower station output expression:

[0023] Average storage capacity expression:

[0024] Photovoltaic power station output expression:

[0025] Wind power station output expression:

[0026] Water balance constraints:

[0027] Storage capacity constraints:

[0028] Outbound flow constraints:

[0029] Power generation flow constraints:

[0030] Hydropower station output constraints:

[0031] Initial and final storage capacity constraints:

[0032] Channel constraints:

[0033] Photovoltaic output conversion formula:

[0034] Wind power output conversion formula:

[0035] In the above formula, is the hydropower station output function; It is a hydroelectric power station Average storage capacity at the time; It is a hydroelectric power station Power generation flow at the moment; It is a hydroelectric power station Storage capacity at a given moment; is the capacity of the PV power station; Is the photovoltaic power station built in Output per unit installed capacity at any moment; is the capacity of the wind power station; Is the wind power station built in Output per unit installed capacity at any moment; and The hydropower stations are Inflow and outflow at each moment; and The hydropower stations are The minimum and maximum allowable storage capacity at the time; and They are the minimum allowed outbound flow rate and the maximum allowed outbound flow rate; and They are the minimum and maximum permissible power generation flow rates of the hydropower station; and are the minimum and maximum allowable outputs of the hydropower station; and are the initial and final storage capacities of the hydropower station during the simulation period; It is the maximum capacity of the transmission channel of the hydropower station; and are the solar radiation intensity and photovoltaic panel temperature under standard test conditions, respectively; PV panel temperature under normal operating conditions; is the temperature coefficient; 、 and The photovoltaic power station is PV panel operating temperature, air temperature and solar radiation intensity during the period; 、 and are the cut-in wind speed, rated wind speed and cut-out wind speed of the fan respectively; For wind power stations Hub height wind speed for the time period.

[0036] Step (3): Construct a water, wind and light yearly operation simulation model with a year as the cycle and a month as the step length.

[0037] Objective function 1: Minimize the sum of the deviations between the power generation of the hydro-wind-solar complementary system in different months and the average value.

[0038]

[0039] Where, , representing the annual running simulation period.

[0040] Objective function 2: Maximize the total annual power generation of the hydro-wind-solar complementary system.

[0041]

[0042] Where, yes The hours of the period.

[0043] The constraints include the output function constraint of the hydropower station, wind power station, and solar power station, the upper and lower limit constraints of the reservoir capacity, the upper and lower limit constraints of the outflow, the upper and lower limit constraints of the power generation flow, the upper and lower limit constraints of the hydropower output, the initial and final reservoir capacity constraints, the water balance constraint, and the channel capacity constraint. The remaining constraints are the same as those in step (2), except that the reservoir water balance constraint is replaced by the following formula:

[0044]

[0045] If the annual operation simulation model cannot be solved, it is necessary to modify the end-of-year reservoir capacity boundary and replace the initial and final capacity constraints with:

[0046]

[0047] in, It is the percentage of allowable storage capacity deviation.

[0048] In order to reduce the deviation between the year-end storage capacity correction and the year-end planned storage capacity, set the allowable storage capacity deviation percentage in ascending order. Percentage of allowable storage capacity deviation Set to 0.05%, 0.1%, 0.5%, 1%, 5%, 10%, 20%, 30%, and 40% respectively.

[0049] Step (4): Construct a water, wind and light monthly operation simulation model with a monthly cycle and hourly step length.

[0050] Objective function 1: The deviation between the reservoir capacity at the end of the month and the reservoir capacity boundary delivered by the annual operation simulation model is minimized.

[0051]

[0052] Where: Represents the simulation period for running the simulation monthly.

[0053] Objective function 2: The total monthly power generation of the water-wind-solar complementary system is maximized.

[0054]

[0055] Where: ,represent The hours of the period.

[0056] The constraints include the output function constraint of the hydropower station, wind power station, and solar power station, the upper and lower limit constraints of the reservoir capacity, the upper and lower limit constraints of the outflow, the upper and lower limit constraints of the power generation flow, the upper and lower limit constraints of the hydropower output, the initial and final reservoir capacity constraints, the water balance constraint, and the channel capacity constraint. The remaining constraints are the same as those in step (2), except that the reservoir water balance constraint is replaced by the following formula:

[0057]

[0058] The final reservoir capacity constraint is converted into the objective function, and the initial capacity constraint is retained:

[0059]

[0060] Step (5): Add the water-wind-solar complementary operation mode constraint to the monthly operation simulation model.

[0061] Through analysis of actual operational data, we clustered and abstracted three operational modes for hydropower, wind-solar hybrid systems: a bimodal mode (mostly occurring in January-May and November-December); a unimodal mode (mostly occurring in June and October); and a smooth mode (mostly occurring in July, August, and September). The details are as follows:

[0062] ① Bimodal model

[0063]

[0064] Where: The complementary system The maximum output of the day; is the peak regulation parameter; Representative Year the number of days in the month; Right now The remainder when divided by 24 represents the time of day.

[0065] ②Single peak mode

[0066]

[0067] ③Smooth mode

[0068]

[0069] Step (6): Run the simulation strategy based on the multi-time-scale nested rolling correction of the soft connection framework.

[0070] Based on the coupling relationship between hydropower simulation results at different time resolutions, without destroying the temporal continuity of long-period hourly simulation results, the original multi-year hourly resolution operation simulation is decomposed into a series of smaller-scale annual, monthly and hourly resolution models for rolling simulation. The upper layer transfers the operation boundary to the lower layer, and the lower layer feeds back the correction results to the upper layer.

[0071] Specifically, the multi-year simulation model simulates all remaining years with a yearly step size, sending the hydropower station's annual reservoir capacity boundaries to the annual simulation model. The annual simulation model simulates with a yearly step size and a monthly step size. Using the reservoir capacity boundaries obtained from the multi-year simulation model as input, a rolling simulation begins from the initial time period of the multi-year simulation model. Then, the monthly reservoir capacity boundaries are sent to the monthly simulation model. The monthly simulation model simulates with a hourly step size and a monthly cycle, using the reservoir capacity boundaries obtained from the annual simulation model and rolling simulation begins from the first time period of the annual simulation model. The end-of-month reservoir capacity boundaries obtained from the monthly simulation are fed back to the annual simulation model once a month to serve as the initial reservoir capacity for the remaining months of the year. That is, if the monthly operation simulation can satisfy the monthly reservoir storage capacity boundary of the hydropower station transferred from the annual operation simulation model, the monthly operation simulation of the next month will be carried out directly; otherwise, the revised reservoir storage capacity boundary at the end of the month will be returned to the annual operation simulation model of the current year as the starting reservoir storage capacity boundary for the current year. The annual operation simulation of the remaining months of the current year will be rerun, and the revised reservoir storage capacity boundary of the remaining months will be re-transferred to the monthly operation simulation model. Similarly, if the annual operation simulation of the remaining months cannot satisfy the annual reservoir storage capacity boundary obtained from the multi-year operation simulation model, the revised reservoir storage capacity boundary at the end of the year will be returned to the multi-year operation simulation model as the initial reservoir storage capacity boundary for the remaining years, and the multi-year operation simulation will be rerun. The corrected reservoir storage capacity boundary of the remaining years will be re-transferred to the annual operation simulation model. It should be noted that if the end-of-month reservoir capacity correction is made in November, the corrected reservoir capacity will be returned to the annual operation simulation model, and there is no need to conduct annual operation simulation in the remaining months of the year; if it occurs in December, the corrected reservoir capacity will be directly fed back to the multi-year operation simulation model.

[0072] Step (7): Using the parallelogram two-dimensional interpolation method, without introducing 0-1 integer variables, the nonlinear output function of hydropower is linearly approximated and modeled, and all the decomposed models are converted into linear programming models that can be quickly solved.

[0073] According to the historical actual operation data of the hydropower station, the nonlinear hydropower station output function is fitted into the form of a two-variable quadratic polynomial.

[0074]

[0075] Where: The hydropower station output function is a two-variable quadratic function Parameters.

[0076] Based on the parallelogram two-dimensional interpolation method, without introducing 0-1 integer variables, the nonlinear hydropower output function in the multi-year simulation model, annual simulation model, and monthly simulation model is linearly approximated and modeled. The linearized expression is as follows:

[0077]

[0078]

[0079]

[0080]

[0081]

[0082]

[0083]

[0084]

[0085]

[0086]

[0087]

[0088] For the sake of clarity, the following description omits subscripts. First, the hydropower output function is discretized into a non-orthogonal grid defined by parallelograms; represents the discrete gradient of power generation flow, represents the vertices of the parallelogram, and The power generation flow is Liedi The sampling value and storage capacity of the row The sampling value of the row, and .also and represents the vertex index, , , and They represent the number of horizontal and vertical division intervals of the parallelogram network, and represents the linear auxiliary variable. The first formula determines the adjacent The distances between them are equal, so in the sixth formula That means adjacent The distance between them does not need to be expressed by other parameters.

[0089] Step (8): With the help of the linear programming solver Gurobi, the simulation model of steps (2)(3)(4) after linearization by the parallelogram interpolation method in step (7) is solved according to the operation simulation strategy proposed in step (6), and the generated wind power and photovoltaic installed capacity to be optimized are input into the simulation model of steps (2)(3)(4), and the specific operation conditions of the water-wind-solar complementary system such as hydropower output, photovoltaic output, wind power output, hydropower station reservoir capacity change, and hydropower station power generation flow are solved with multi-year hourly resolution.

[0090] Beneficial effects of the present invention: Under the premise of not destroying the temporal continuity of the long-period hourly simulation results, the present invention decomposes the original continuous multi-year hourly simulation into a series of smaller-scale annual, monthly and hourly simulation models according to the coupling relationship between the hydropower simulation results at different time resolutions. Secondly, based on the parallelogram two-dimensional interpolation method, the hydropower nonlinear function is linearly approximated and modeled without introducing 0-1 integer variables, and all the decomposed models are converted into linear programming models that can be quickly solved. The operation simulation is carried out in a rolling manner in chronological order, and a simulation result correction mechanism is added to feedback and update unreasonable simulation results layer by layer to ensure the solvability of large-scale long-time series operation simulation models. The present invention solves the challenge of large-scale nonlinear solution of multi-year operation simulation of water, wind and solar power, and provides simulation support for more reasonable determination of the capacity of new energy sources that complement the operation of hydropower stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0091] Figure 1 This is a schematic diagram of a multi-time-scale nested rolling correction operation simulation strategy based on a soft connection framework;

[0092] Figure 2 This is a schematic diagram of the water, wind and solar complementary operation mode;

[0093] Figure 3 This is a schematic diagram of the distribution of sampling points for the linearization of the power generation function;

[0094] Figure 4 It is the overall solution framework diagram of the method of the present invention. DETAILED DESCRIPTION

[0095] The present invention will be further described below with reference to the accompanying drawings and implementation examples.

[0096] The overall process of the present invention is as follows Figure 4 shown.

[0097] This example uses the Guangzhao Hydropower Station, located in the Beipanjiang River Basin of Guizhou Province, China, as a case study to test the present invention. Located in the middle reaches of the Beipanjiang River in Guizhou Province, China, the Guangzhao Hydropower Station is the leading hydropower station in the Beipanjiang River Basin. Clean hydropower generated by the Guangzhao Hydropower Station is transmitted to Guangdong Province, China, via the Xing'an DC interconnection line. To effectively utilize wind and solar resources and fully leverage the flexibility of hydropower, a water-wind-solar complementary operation base is currently being planned at the Guangzhao Hydropower Station. Wind and photovoltaic power stations will operate in conjunction with the existing hydropower station, serving as a hybrid power source to complete a complementary power generation plan. After grid connection, power will be transmitted to the Guangdong power grid via the existing Xing'an DC interconnection line. Because the water-wind-solar complementary system is still under construction, the optimal capacity of wind and photovoltaic power remains controversial. Due to significant interannual variability in wind and solar resources and runoff, multi-year operation simulations of the water-wind-solar system are necessary to provide a simulation basis for wind and solar capacity planning. Therefore, this example uses the water-wind-solar complementary operation system of the Guangzhao Hydropower Station as a simulation case study and conducts multi-year continuous operation simulations to provide simulation support for wind and solar capacity planning.

[0098] Step (1): Initial calculation conditions.

[0099] Historical observation data: Hourly natural runoff data for the Beipanjiang Guangzhao Power Station from 2008 to 2019 was collected from the power grid company. Hourly wind speed data, solar radiation intensity data, and air temperature data for the Beipanjiang Guangzhao Power Station from 2008 to 2019 were downloaded from the Copernicus Climate DataStore of the European Centre for Medium-Range Weather Forecasts (ERA5).

[0100] Basic data of the hydropower station: The relevant data of the Guangzhao Hydropower Station comes from the Beipanjiang River Basin Centralized Control Center, including installed capacity data, upper and lower limits of reservoir capacity, upper and lower limits of power generation flow, initial and final reservoir capacity, and transmission channel capacity;

[0101] Wind turbine characteristic data: including hub height, rated power, rated wind speed, cut-in wind speed, and cut-out wind speed, from the ERA5 official website (Copernicus Climate Data Store of the European Centre for Medium-Range Weather Forecasts);

[0102] PV module data: including rated power, solar radiation intensity and PV panel temperature under standard test conditions, temperature coefficient, and PV panel temperature under normal operating conditions, from the ERA5 official website (Copernicus Climate Data Store of the European Centre for Medium-Range Weather Forecasts).

[0103] Step (2): Construct a multi-year operation simulation model for water, wind and solar power with a multi-year cycle and a year as the step length.

[0104] Objective function 1: Minimize the sum of the deviations between the annual power generation of the hydro-wind-solar hybrid system and the mean over many years.

[0105]

[0106] Where: is the period of running the simulation model over many years; It is a hydroelectric power station Output during the time period; The photovoltaic power station is Output during the time period; The wind power station is Output during the period.

[0107] Objective function 2: Maximize the total power generation of the hydro-wind-solar complementary system over many years.

[0108]

[0109] Where: yes The total number of hours in the period.

[0110] Constraints include: hydropower, wind and solar power station output function constraints, reservoir capacity upper and lower limit constraints, outflow upper and lower limit constraints, power generation flow upper and lower limit constraints, hydropower output upper and lower limit constraints, initial and final reservoir capacity constraints, water balance constraints, channel capacity constraints, etc. The mathematical expression of the constraint model is as follows:

[0111] Hydropower station output expression:

[0112] Average storage capacity expression:

[0113] Photovoltaic power station output expression:

[0114] Wind power station output expression:

[0115] Water balance constraints:

[0116] Storage capacity constraints:

[0117] Outbound flow constraints:

[0118] Power generation flow constraints:

[0119] Hydropower station output constraints:

[0120] Initial and final storage capacity constraints:

[0121] Channel constraints:

[0122] Photovoltaic output conversion formula:

[0123] Wind power output conversion formula:

[0124] In the above formula, is the hydropower station output function; It is a hydroelectric power station Average storage capacity at the time; It is a hydroelectric power station Power generation flow at the moment; It is a hydroelectric power station Storage capacity at a given moment; is the capacity of the PV power station; Is the photovoltaic power station built in Output per unit installed capacity at any moment; is the capacity of the wind power station; Is the wind power station built in Output per unit installed capacity at any moment; and The hydropower stations are Inflow and outflow at each moment; and The hydropower stations are The minimum and maximum allowable storage capacity at the time; and They are the minimum allowed outbound flow rate and the maximum allowed outbound flow rate; and They are the minimum and maximum permissible power generation flow rates of the hydropower station; and are the minimum and maximum allowable outputs of the hydropower station; and are the initial and final storage capacities of the hydropower station during the simulation period; It is the maximum capacity of the transmission channel of the hydropower station; and are the solar radiation intensity and photovoltaic panel temperature under standard test conditions, respectively; PV panel temperature under normal operating conditions; is the temperature coefficient; 、 and The photovoltaic power station is PV panel operating temperature, air temperature and solar radiation intensity during the period; 、 and are the cut-in wind speed, rated wind speed and cut-out wind speed of the fan respectively; For wind power stations Hub height wind speed for the time period.

[0125] Step (3): Construct a water, wind and light yearly operation simulation model with a year as the cycle and a month as the step length.

[0126] Objective function 1: Minimize the sum of the deviations between the power generation of the hydro-wind-solar complementary system in different months and the average value.

[0127]

[0128] Where, , representing the annual running simulation period.

[0129] Objective function 2: Maximize the total annual power generation of the hydro-wind-solar complementary system.

[0130]

[0131] Where, yes The hours of the period.

[0132] The constraints include the output function constraint of the hydropower station, wind power station, and solar power station, the upper and lower limit constraints of the reservoir capacity, the upper and lower limit constraints of the outflow, the upper and lower limit constraints of the power generation flow, the upper and lower limit constraints of the hydropower output, the initial and final reservoir capacity constraints, the water balance constraint, and the channel capacity constraint. The remaining constraints are the same as those in step (2), except that the reservoir water balance constraint is replaced by the following formula:

[0133]

[0134] If the annual operation simulation model cannot be solved, it is necessary to modify the end-of-year reservoir capacity boundary and replace the initial and final capacity constraints with:

[0135]

[0136] in, It is the percentage of allowable storage capacity deviation.

[0137] In order to reduce the deviation between the year-end storage capacity correction and the year-end planned storage capacity, set the allowable storage capacity deviation percentage in ascending order. Percentage of allowable storage capacity deviation Set to 0.05%, 0.1%, 0.5%, 1%, 5%, 10%, 20%, 30%, and 40% respectively.

[0138] Step (4): Construct a water, wind and light monthly operation simulation model with a monthly cycle and hourly step length.

[0139] Objective function 1: The deviation between the reservoir capacity at the end of the month and the reservoir capacity boundary delivered by the annual operation simulation model is minimized.

[0140]

[0141] Where: Represents the simulation period for running the simulation monthly.

[0142] Objective function 2: The total monthly power generation of the water-wind-solar complementary system is maximized.

[0143]

[0144] Where: ,represent The hours of the period.

[0145] The constraints include the output function constraint of the hydropower station, wind power station, and solar power station, the upper and lower limit constraints of the reservoir capacity, the upper and lower limit constraints of the outflow, the upper and lower limit constraints of the power generation flow, the upper and lower limit constraints of the hydropower output, the initial and final reservoir capacity constraints, the water balance constraint, and the channel capacity constraint. The remaining constraints are the same as those in step (2), except that the reservoir water balance constraint is replaced by the following formula:

[0146]

[0147] The final reservoir capacity constraint is converted into the objective function, and the initial capacity constraint is retained:

[0148]

[0149] Step (5): Add the water-wind-solar complementary operation mode constraint to the monthly operation simulation model.

[0150] Constraints on the water-wind-solar complementary operation modes Through the analysis of actual operation data, three water-wind-solar complementary operation modes were clustered and abstracted: bimodal mode (mainly occurring in January-May and November-December); unimodal mode (mostly occurring in June and October); and smooth mode (mostly occurring in July, August and September).

[0151] Schematic diagram of water-wind-solar complementary operation mode Figure 2 shown.

[0152] ① Bimodal model

[0153]

[0154] Where: The complementary system The maximum output of the day; is the peak regulation parameter; Representative Year the number of days in the month; Right now The remainder after dividing by 24 represents the time of day, e.g. ,but , which represents the first moment of the next day.

[0155] ②Single peak mode

[0156]

[0157] ③Smooth mode

[0158]

[0159] Step (6): Run the simulation strategy based on the multi-time-scale nested rolling correction of the soft connection framework.

[0160] Based on the coupling relationship between hydropower simulation results at different time resolutions, without destroying the temporal continuity of long-term hourly simulation results, the original multi-year hourly resolution operation simulation is decomposed into a series of smaller-scale annual, monthly, and hourly resolution models for rolling simulation. The upper layer transfers the operation boundary to the lower layer, and the lower layer feeds back the correction results to the upper layer. Figure 1 shown.

[0161] Specifically, the multi-year simulation model simulates all remaining years with a yearly step size, sending the hydropower station's annual reservoir capacity boundaries to the annual simulation model. The annual simulation model simulates with a yearly step size and a monthly step size. Using the reservoir capacity boundaries obtained from the multi-year simulation model as input, a rolling simulation begins from the initial time period of the multi-year simulation model. Then, the monthly reservoir capacity boundaries are sent to the monthly simulation model. The monthly simulation model simulates with a hourly step size and a monthly cycle, using the reservoir capacity boundaries obtained from the annual simulation model and rolling simulation begins from the first time period of the annual simulation model. The end-of-month reservoir capacity boundaries obtained from the monthly simulation are fed back to the annual simulation model once a month to serve as the initial reservoir capacity for the remaining months of the year. That is, if the monthly operation simulation can satisfy the monthly reservoir storage capacity boundary of the hydropower station transferred from the annual operation simulation model, the monthly operation simulation of the next month will be carried out directly; otherwise, the revised reservoir storage capacity boundary at the end of the month will be returned to the annual operation simulation model of the current year as the starting reservoir storage capacity boundary for the current year. The annual operation simulation of the remaining months of the current year will be rerun, and the revised reservoir storage capacity boundary of the remaining months will be re-transferred to the monthly operation simulation model. Similarly, if the annual operation simulation of the remaining months cannot satisfy the annual reservoir storage capacity boundary obtained from the multi-year operation simulation model, the revised reservoir storage capacity boundary at the end of the year will be returned to the multi-year operation simulation model as the initial reservoir storage capacity boundary for the remaining years, and the multi-year operation simulation will be rerun. The corrected reservoir storage capacity boundary of the remaining years will be re-transferred to the annual operation simulation model. It should be noted that if the end-of-month reservoir capacity correction is made in November, the corrected reservoir capacity will be returned to the annual operation simulation model, and there is no need to conduct annual operation simulation in the remaining months of the year; if it occurs in December, the corrected reservoir capacity will be directly fed back to the multi-year operation simulation model.

[0162] Step (7): Using the parallelogram two-dimensional interpolation method, without introducing 0-1 integer variables, the nonlinear output function of hydropower is linearly approximated and modeled, and all the decomposed models are converted into linear programming models that can be quickly solved.

[0163] According to the historical actual operation data of the hydropower station, the nonlinear hydropower station output function is fitted into the form of a two-variable quadratic polynomial.

[0164]

[0165] Where: The hydropower station output function is a two-variable quadratic function Parameters.

[0166] Based on the parallelogram two-dimensional interpolation method, without introducing 0-1 integer variables, the nonlinear hydropower output function in the multi-year simulation model, annual simulation model, and monthly simulation model is linearly approximated and modeled. The linearized expression is as follows:

[0167]

[0168]

[0169]

[0170]

[0171]

[0172]

[0173]

[0174]

[0175]

[0176]

[0177]

[0178] For the sake of clarity, the following description omits subscripts. First, the hydropower output function is discretized into a non-orthogonal grid defined by parallelograms; represents the discrete gradient of power generation flow, represents the vertices of the parallelogram, and The power generation flow is Liedi The sampling value and storage capacity of the row The sampling value of the row, and .also and represents the vertex index, , , and Respectively represent the number of horizontal division intervals of the parallelogram network ( =4) and the number of vertical division intervals ( =3), and represents the linear auxiliary variable. The first formula determines the adjacent The distances between them are equal, so in the sixth formula That means adjacent The distance between them does not need to be expressed by other parameters. In this example, the linearized sampling point distribution of the power generation function is as follows: Figure 3 As shown, , .

[0179] Step (8): With the help of the linear programming solver Gurobi, the linear programming model after parallelogram interpolation linearization in steps (2), (3), and (4) is solved according to the operation simulation strategy proposed in step (6). 10 groups of photovoltaic capacity and wind power capacity combinations are randomly generated within the allowable range, and are respectively input into the hourly hydro-wind-solar operation simulation model with variable time scale decomposition for 12 consecutive years, and the solution results and solution time are recorded. As a comparison, a traditional hourly hydro-wind-solar operation simulation model without variable time scale decomposition for 12 consecutive years is established to explore the performance of the operation simulation technology proposed in the present invention in terms of solution accuracy and efficiency. The traditional hourly operation simulation model without variable time scale decomposition for 12 consecutive years takes the maximum total hydro-wind-solar power generation for many years as the objective function, and at the same time sets the relevant constraints in the above steps, inputs the same wind-solar resource and runoff data as the above steps, and also performs parallelogram interpolation linear approximation modeling on the hydropower nonlinear function. The above 10 groups of photovoltaic capacity and wind power capacity combinations are respectively input into the traditional model for continuous 12-year hourly hydro-wind-solar complementary operation simulation, and the solution results and solution time are recorded. The solution results are shown in Table 1.

[0180] Table 1 Comparison of solutions of decomposed model (present invention) and undecomposed model

[0181]

[0182] As can be seen from Table 1, compared with the traditional hourly water-wind-solar operation simulation model for 12 consecutive years without variable time scale decomposition, the hourly water-wind-solar operation simulation model for 12 consecutive years with variable time scale decomposition can reduce the average solution time from 4103s to 616s. Since the new energy capacity planning of the water-wind-solar complementary operation base in the basin requires multiple multi-year operation simulations under different hypothetical scenarios of new energy capacity, the advantage of this operation simulation model in solution efficiency will be significantly expanded. At the same time, the average similarity of the solution results of the two is as high as 99.66%. Therefore, the water-wind-solar multi-energy complementary multi-period nested optimization modeling solution technology proposed in the present invention achieves a significant improvement in solution efficiency while taking into account the solution accuracy.

Claims

1. A multi-period nested optimization modeling and solving method for water, wind and solar multi-energy complementarity, characterized in that: Here are the steps: Step (1): initial calculation conditions; Including historical observation data, basic data of hydropower stations, wind turbine characteristic data and photovoltaic module data; Step (2): construct a multi-year operation simulation model for water, wind and solar power with a multi-year cycle and a year as the step length; Objective function 1: Minimize the sum of the deviations between the annual power generation of the hydro-wind-solar hybrid system and the mean value; Where: It is a cycle of running simulation models over many years; The hydroelectric station is Output during the time period; The photovoltaic power station is Output during the time period; The wind farm is Output during the time period; Objective function 2: Maximize the total power generation of the water-wind-solar hybrid system over many years; Where: yes Total number of hours in the session; The constraints include: output function constraints of hydropower stations, wind power stations, and photovoltaic stations, upper and lower limits of storage capacity, upper and lower limits of outflow, upper and lower limits of power generation flow, upper and lower limits of hydropower output, initial and final storage capacity constraints, water balance constraints, and channel capacity constraints; the mathematical expression of the constraint model is as follows: Hydropower station output expression: Average storage capacity expression: Photovoltaic power station output expression: Wind power station output expression: Water balance constraints: Storage capacity constraints: Outbound traffic constraints: Power generation flow constraints: Hydropower station output constraints: Initial and final storage capacity constraints: Channel constraints: Photovoltaic output conversion formula: Wind power output conversion formula: In the above formula, is the hydropower station output function; The hydroelectric station is Average storage capacity at the time; The hydroelectric station is The power generation flow at the moment; The hydroelectric station is Storage capacity at the time; is the capacity of the PV plant; The photovoltaic power station has been built in Output per unit installed capacity at any moment; is the capacity of the wind farm; Is the wind power station built in Output per unit installed capacity at any moment; and The hydropower stations are Inflow and outflow at each time; and The hydropower stations are The minimum and maximum allowable storage capacity at the time; and They are the minimum and maximum allowable outbound flow rates respectively; and They are the minimum and maximum permissible power generation flow of the hydropower station; and They are the minimum and maximum allowable output of the hydropower station; and are the initial and final storage capacities of the hydropower station during the simulation period; It is the maximum capacity of the transmission channel of the hydropower station; and are the solar radiation intensity and photovoltaic panel temperature under standard test conditions, respectively; PV panel temperature under normal operating conditions; is the temperature coefficient; , and The photovoltaic power station is PV panel operating temperature, air temperature and solar radiation intensity during the period; , and They are the cut-in wind speed, rated wind speed and cut-out wind speed of the fan respectively; For wind power plants Hub height wind speed for the time period; Step (3): construct a water, wind and light year operation simulation model with a year as the cycle and a month as the step length; Objective function 1: Minimize the sum of the deviations between the power generation of the hydro-wind-solar hybrid system in different months and the average value; In the formula, , represents the cycle of annual operation simulation; Objective function 2: Maximize the total annual power generation of the water-wind-solar complementary system; In the formula, yes The number of hours in the session; Among the constraints, except that the reservoir water balance constraint is replaced by the following formula, the remaining constraints are the same as those in step (2): If the annual operation simulation model cannot be solved, it is necessary to modify the reservoir capacity boundary at the end of the year and replace the initial and final capacity constraints with: in, is the percentage of allowable storage capacity deviation; Step (4): construct a water, wind and light monthly operation simulation model with a monthly cycle and an hourly step length; Objective function 1: The deviation between the reservoir capacity at the end of the month and the reservoir capacity boundary delivered by the annual operation simulation model is minimized; Where: represents the simulation period of the monthly operation simulation; Objective function 2: The total monthly power generation of the water-wind-solar hybrid system is maximized; Where: ,represent The number of hours in the session; Among the constraints, except that the reservoir water balance constraint is replaced by the following formula, the remaining constraints are the same as those in step (2): The final reservoir capacity constraint is transformed into the objective function, and the initial reservoir capacity constraint is retained: Step (5): adding the water-wind-solar complementary operation mode constraints to the monthly operation simulation model; Through the analysis of actual operation data, three water-wind-solar complementary operation modes are clustered and abstracted: bimodal mode, unimodal mode and smooth mode; the details are as follows: ① Bimodal model Where: The complementary system is The maximum output of the sky; is the peak regulation parameter; Representative Year The number of days in the month; Right now The remainder when divided by 24 represents the time of day; ②Single peak mode ③Smooth mode Step (6): Run simulation strategy based on multi-time scale nested rolling correction of soft connection framework; According to the coupling relationship between the hydropower simulation results at different time resolutions, without destroying the temporal continuity of the long-term hourly simulation results, the original continuous multi-year hourly resolution operation simulation is decomposed into a series of smaller-scale annual, monthly and hourly resolution models for rolling simulation. The upper layer transfers the operation boundary to the lower layer, and the lower layer feeds back the correction results to the upper layer. Step (7): using the parallelogram two-dimensional interpolation method, without introducing 0-1 integer variables, linear approximation modeling is performed on the hydropower nonlinear output function, and all decomposed models are converted into fast-solving linear programming models; According to the historical actual operation data of the hydropower station, the nonlinear hydropower station output function is fitted into the form of a two-variable quadratic polynomial; Where: It is a bivariate quadratic function of the hydropower station output function Parameters; Based on the parallelogram two-dimensional interpolation method, without introducing 0-1 integer variables, the nonlinear output function of hydropower in the multi-year simulation model, annual simulation model, and monthly simulation model is linearly approximated and modeled; the linearized expression is as follows: Firstly, the hydropower output function is discretized into a non-orthogonal grid defined by parallelograms; represents the discrete gradient of the power generation flow, represents the vertices of the parallelogram, and The power generation flow is Ledi The sampling value and storage capacity of the row The sample value of the row, and ;also and represents the vertex index, , , and They represent the number of horizontal and vertical divisions of the parallelogram network, respectively. and represents linear auxiliary variables; Step (8): With the help of the linear programming solver Gurobi, the simulation model of steps (2)(3)(4) after linearization by the parallelogram interpolation method in step (7) is solved according to the operation simulation strategy proposed in step (6), and the generated wind power and photovoltaic installed capacity to be optimized are input into the simulation model of steps (2)(3)(4), so as to obtain the specific operation status of the hydropower output, photovoltaic output, wind power output, hydropower station reservoir capacity change, and hydropower station power generation flow of the water-wind-solar complementary system with multi-year hourly resolution.

2. According to claim 1, a multi-period nested optimization modeling solution method for water-wind-solar multi-energy complementarity is characterized in that: In step (1): Historical observation data: including hourly natural runoff data for 12 consecutive years, hourly wind speed data at the height of the wind turbine hub, hourly solar radiation intensity data, and hourly air temperature data; Basic data of hydropower stations: including installed capacity data, upper and lower limits of reservoir capacity, upper and lower limits of power generation flow, initial and final reservoir capacity, and transmission channel capacity; Wind turbine characteristic data: including installation hub height, rated power, rated wind speed, cut-in wind speed and cut-out wind speed; PV module data: including rated power, solar radiation intensity and PV panel temperature under standard test conditions, temperature coefficient and PV panel temperature under normal working conditions.

3. The method for solving the multi-period nested optimization modeling of water-wind-solar multi-energy complementarity according to claim 1 is characterized in that: Step (6) is as follows: the multi-year operation simulation model uses all remaining years as a cycle and a year as a step length to perform simulation, and sends the annual reservoir capacity boundary of the hydropower station to the annual operation simulation model; the annual operation simulation model uses a year as a cycle and a month as a step length to perform simulation; using the reservoir capacity boundary obtained by the multi-year operation simulation model as input, a rolling simulation is started from the initial period of the multi-year operation simulation model; then, the monthly reservoir capacity boundary is sent to the monthly operation simulation model; the monthly operation simulation model uses an hourly step length and a month as a cycle, and uses the reservoir capacity boundary obtained by the annual operation simulation model to start a rolling simulation from the first time period of the annual operation simulation model; the reservoir capacity boundary at the end of the month obtained by the monthly operation simulation is fed back to the annual operation simulation once a month as the initial reservoir capacity for the remaining months of the year; if the monthly operation simulation can meet the monthly reservoir capacity boundary of the hydropower station transmitted from the annual operation simulation model, then Directly carry out the monthly operation simulation for the next month; otherwise, the revised reservoir capacity boundary at the end of the month will be returned to the annual operation simulation model of the current year as the starting reservoir capacity boundary for the current year, and the annual operation simulation for the remaining months of the current year will be re-run, and the revised reservoir capacity boundary for the remaining months will be re-transferred to the monthly operation simulation model; similarly, if the annual operation simulation for the remaining months cannot satisfy the annual reservoir capacity boundary obtained from the multi-year operation simulation model, the revised reservoir capacity boundary at the end of the year will be returned to the multi-year operation simulation model as the initial reservoir capacity boundary for the remaining years, and the multi-year operation simulation will be re-run; the corrected reservoir capacity boundary for the remaining years will be re-transferred to the annual operation simulation model; it should be noted that if the end-of-month reservoir capacity correction is carried out in November, the revised reservoir capacity will be returned to the annual operation simulation model, and there is no need to carry out annual operation simulation for the remaining months of the year; If it occurs in December, the revised reservoir capacity will be directly fed back into the multi-year operational simulation model.

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