Water-wind-solar complementary optimization scheduling method and system considering new energy extreme output event

Abstract

The invention discloses a water-wind-solar complementary optimization scheduling method and system considering new energy extreme output events. The method comprises the following steps: constructing a new energy extreme output event library; a cascade hydropower dispatching risk assessment model oriented to the new energy extreme output event is constructed, the economy and reliability of cascade hydropower dispatching under different extreme events and water level states are quantitatively assessed, and weak links of system dispatching operation under the new energy extreme output event are identified; the method comprises the following steps: establishing a cascade hydropower multi-target optimization scheduling model giving consideration to the generating capacity and the scheduling risk, solving a non-inferior scheduling scheme set, selecting a compromise scheduling scheme giving consideration to economy and robustness by adopting a weight method, and adjusting a scheduling plan in a rolling manner according to real-time hydrological forecast and extreme event early warning. And the adjustment capability of cascade hydropower on the new energy extreme output event is improved. According to the method, the reliability of the water-wind-light complementary system under the new energy extreme event is improved, and technical support can be provided for formulating a system scheduling scheme for defending continuous multi-day extreme output of new energy.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of clean energy system scheduling, in particular to a water-wind-solar complementary optimization scheduling method and system considering new energy extreme output events. BACKGROUND

[0002] New energy output has a high dependence on weather conditions. A water-wind-solar complementary system not only has to deal with regular fluctuations in wind and solar power, but also may face extreme disturbances caused by small probability events such as heat waves and cold waves. Traditional water-wind-solar complementary scheduling research focuses on seasonal or short-term fluctuations of water, wind and solar resources, and does not consider the cumulative impact of medium-scale (week / decade) new energy extreme output events, which may result in insufficient regulation capacity of reservoirs at key nodes, causing power shortages or water abandonment risks. Therefore, how to improve the adaptability of the system under extreme events has become a core problem that needs to be broken through for the scientific scheduling of water-wind-solar complementary systems.

[0003] In recent years, the content of water-wind-solar complementary scheduling under new energy extreme output events has become a hot research topic in the academic field, and a series of fruitful research results have been achieved. For example, Peng Yumin considers the pumping balance constraint of the upper reservoir water level operation control of pumped storage power stations, builds an optimization scheduling model of power systems containing pumped storage, and proposes emergency scheduling strategies for extreme weather events such as high temperature and sunny weather, low temperature and cold wave, and typhoon passage; Chen Cong et al. build a medium-term complementary scheduling model of water, wind and solar power with the minimum total operation cost as the target, and propose water-wind-solar complementary response strategies under extreme conditions from the aspects of key water level control and water and electricity storage loss tolerance coefficient.

[0004] However, existing researches mostly focus on the scheduling problem of water-wind-solar complementary system under extreme events at a single time scale, without considering the improvement of medium-term flexible regulation capacity of water and electricity by optimizing long-term water level, and there are few researches on water-wind-solar medium and long-term complementary scheduling and multi-day extreme output of new energy, which need to be further explored and improved. SUMMARY

[0005] The purpose of the present application is to provide a water-wind-solar complementary optimization scheduling method and system considering new energy extreme output events to improve the response capability of water-wind-solar complementary system under new energy extreme output events.

[0006] Technical scheme: The water-wind-solar complementary optimization scheduling method considering new energy extreme output events provided by the present application comprises the following steps:

[0007] For the water-wind-solar complementary system with independent section transmission, long sequence data of new energy output is obtained, extreme new energy output events are defined, the occurrence frequency, duration and fluctuation intensity characteristics of wind power and photovoltaic extreme high output or extreme low output events are extracted, and an extreme new energy output event library is constructed;

[0008] A water-wind-solar complementary system dispatching risk assessment model for extreme new energy output events is constructed, including a water-wind-solar complementary system medium and long term generation plan compilation model with the maximum system power generation as the target and a water-wind-solar complementary system dispatching simulation model under extreme new energy output events;

[0009] According to different extreme new energy output events and reservoir water level states, dispatching scenarios are set, wherein, based on the event type, duration and fluctuation intensity, the extreme event parameters are selected from the typical extreme output curve in the extreme new energy output event library; considering the economy and reliability, a water-wind-solar complementary system dispatching risk evaluation index for extreme new energy output events is established, including cascade reservoir water abandonment risk and system power shortage risk; based on the water-wind-solar complementary system dispatching risk assessment model, the dispatching process of the complementary system under different dispatching scenarios is simulated: first, the new energy output prediction result is input into the water-wind-solar complementary system medium and long term generation plan compilation model, the operation process of the water-wind-solar power station is optimized to compile the water-wind-solar complementary system generation plan within ten days; then, the new energy extreme output scenario is substituted into the water-wind-solar complementary system dispatching simulation model under extreme new energy output events, the water power station dispatching process is updated according to the wind-solar extreme output process, and the weak link of the water-wind-solar complementary system dispatching operation under extreme new energy output events is identified;

[0010] For the weak link of the water-wind-solar complementary system dispatching operation, a water-wind-solar complementary multi-objective optimization dispatching model considering power generation and dispatching risk is established, the dispatching targets include the maximum system power generation and the minimum dispatching risk, and a double-layer optimization framework is used for solution: the outer layer takes the key node water level as the decision variable, and the multi-objective cuckoo algorithm is used to speed up the optimization; the inner layer uses the dynamic programming algorithm to optimize the system dispatching plan, simulates the system operation process under extreme scenarios, and feeds back the power generation and dispatching risk to the outer layer, and iterates until the maximum iteration number, to obtain a non-inferior dispatching scheme set, and a compromise dispatching scheme is selected as the optimal dispatching scheme based on the weight method.

[0011] Further, the definition method of the extreme new energy output event is that based on the long sequence historical output data of the new energy, considering the seasonal difference of the new energy output, the daily average output corresponding to the 90% quantile is set as the high threshold and the daily average output corresponding to the 10% quantile is set as the low threshold in different seasons; when the daily average output of the new energy is greater than or equal to the high threshold for 3 days or more in a row, it is defined as an extreme high output event of the new energy, and when the daily average output of the new energy is less than or equal to the low threshold for 3 days or more in a row, it is defined as an extreme low output event of the new energy.

[0012] The frequency of new energy extreme output event is the number of new energy extreme high output or extreme low output events in the statistical period;

[0013] The fluctuation intensity of new energy extreme output event is the maximum deviation from the monthly average output during the occurrence of new energy extreme high output or extreme low output event.

[0014] Further, the objective function of the long-term generation plan compilation model of the water, wind and light complementary system is:

[0015] ;

[0016] Among them, is the long-term generation of the water, wind and light complementary system, , and are the number of weeks, the number of days in a week and the number of cascade hydropower stations in the dispatching period, , and are the week number, day number in a week and cascade hydropower station number in the dispatching period, , and are the planned output of the th cascade hydropower station, wind power and photovoltaic in the th week, , and are the planned output of the th cascade hydropower station, wind power and photovoltaic in the th week on the th day, , are the lengths of long-term and medium-term dispatching periods.

[0017] Further, the objective function of the water, wind and light complementary system dispatching simulation model under new energy extreme output event is:

[0018] ;

[0019] Among them, is the power shortage of the water, wind and light complementary system, and are the number of periods and the number of cascade hydropower stations in the dispatching period; is the planned output of the water, wind and light complementary system in the th period, is the actual output of the th cascade hydropower station in the th period under new energy extreme event, ​are the extreme outputs of wind power and photovoltaic power stations in the time period, is the duration of the medium-term scheduling period.

[0020] Furthermore, according to the characteristics of new energy extreme output events, different continuous days and fluctuation intensities of new energy extreme high output and extreme low output events are set respectively to obtain new energy extreme output scenarios;

[0021] According to the typical water level states of the leading hydropower station reservoir, four scheduling periods, namely the reservoir drawdown period, the pre-flood dry period, the water storage period, and the end of the flood season, are selected as the analysis periods;

[0022] The risk of cascade reservoir water abandonment is characterized by the cumulative abandoned electricity; the system power shortage risk is characterized by the cumulative missing electricity, the maximum power shortage depth, and the cumulative power shortage duration;

[0023] By comparing the scheduling risk indicators of the water-wind-solar complementary system under different scheduling scenarios, the weak links in the scheduling operation of the complementary system under new energy extreme output events are identified.

[0024] Furthermore, the objective function of the scheduling objective of the water-wind-solar complementary multi-objective optimal scheduling model that takes into account both power generation and scheduling risk is:

[0025] (1) The maximum power generation of the water-wind-solar complementary system:

[0026] ;

[0027] Among them, is the annual power generation of the water-wind-solar complementary system, and are the number of ten-day periods in the scheduling period and the number of cascade hydropower stations respectively, is the planned output of the -th hydropower station in the ten-day period, , are the planned outputs of the wind power and photovoltaic power stations in the ten-day period respectively, is the duration of the long-term scheduling period, in ten-day periods;

[0028] (2) The minimum scheduling risk of the water-wind-solar complementary system:

[0029] ;​​​​​​​​​​​​​​the water abandonment risk of the system under the new energy extreme output event, the power shortage risk of the system under the new energy extreme output event, the output coefficient of the nth level hydropower station, the water abandonment flow of the nth level hydropower station in the mth time period, the net water head of the nth level hydropower station in the mth time period, the planned output of the complementary system in the mth time period, the actual output of the nth level hydropower station in the mth time period under the new energy extreme event, , the extreme output of the wind power station and the photovoltaic power station in the mth time period, the time length of the medium-term scheduling time period.

[0033] Further, in the water-wind-solar complementary multi-objective optimization scheduling model considering power generation and scheduling risk, the outer layer is specifically:

[0034] (1) set the number of bird nests , dimension , archive size , search domain upper and lower bounds and , maximum iteration number , and let the iteration number ;

[0035] (2) initialize the bird nest position , regard the key node water level sequence of dimension as a bird nest, and the position of the nth bird nest in the mth dimension is: wherein, , , ; input the key node water level information contained in the bird nest into the inner model, calculate the annual power generation of the water-wind-solar complementary system by the water-wind-solar complementary system medium and long-term power generation planning model , and evaluate the scheduling risk of the key node water-wind-solar complementary system by the water-wind-solar complementary system scheduling simulation model under the new energy extreme output event ;

[0036] (3) select non-dominated solutions by non-dominated sorting to obtain the current Pareto optimal solution;

[0037] (4) Update the location of the nest using the Levy principle. Among them, the step size is adaptively adjusted based on the exponential distribution. The annual power generation was calculated using a model for compiling a medium- and long-term power generation plan for a hydro-wind-solar hybrid system. A simulation model of hydro-wind-solar hybrid system scheduling under extreme power output events of new energy sources was used to assess the scheduling risks of the hybrid system at key nodes. ;

[0038] (5) Merge the previous generation of bird nests With the new generation of bird nests Non-dominated solutions are obtained through non-dominated sorting and stored in an external archive set. When the total number of individuals exceeds the size of the external archive set, the external archive set is maintained by calculating crowding distance, and some individuals with smaller crowding distances are deleted to generate a new generation of nests. ;

[0039] (6) According to the formula for linear decrease; ,in, For the first The probability of discovering evolutionary generations. To maximize the probability of discovery, To maximize the probability of discovery, For the current generation, Calculate the dynamic discovery probability for the maximum number of iterations of the algorithm. For the bird's nest Each solution is given a random number between 0 and 1 and is then combined with... By comparing, the new location of the bird's nest can be obtained. The annual power generation was calculated using a model for compiling a medium- and long-term power generation plan for a hydro-wind-solar hybrid system. A simulation model of hydro-wind-solar hybrid system scheduling under extreme power output events of new energy sources was used to assess the scheduling risks of the hybrid system at key nodes. ;

[0040] (7) Repeat step (5) to obtain the Pareto optimal solution;

[0041] (8) Order Determine whether the iteration exit condition is met; if it is met, output the Pareto optimal solution and end the iteration; otherwise, proceed to step (4) and continue iterating.

[0042] Furthermore, in the solution method of the multi-objective optimization scheduling model for hydro-wind-solar hybrid power generation that takes into account both power generation and scheduling risks, the inner layer specifically includes:

[0043] (1) According to the key node, the scheduling period is divided into three sub-scheduling periods of early year-flood season, pre-flood season, post-flood season, and the ten-day water level process in the scheduling period of cascade hydropower stations is solved in turn, wherein, for the light hydropower station with annual regulation capacity, a dynamic programming algorithm is used for water level optimization, and the remaining daily regulation power station is operated according to fixed water level control;

[0044] (2) Generate complete water level process in a year, calculate annual power generation through a long-term power generation planning model of the water-wind-solar complementary system ;

[0045] (3) Select the pre-flood season and post-flood season key period, and use a dynamic programming algorithm to develop a daily power generation plan of the complementary system;

[0046] (4) Substitute the extreme low output event of new energy in the pre-flood period and the extreme high output event of new energy in the post-flood period, simulate the actual operation process of the complementary system by using a simulation model of the water-wind-solar complementary system under the extreme output event of new energy, and evaluate the scheduling risk of the complementary system at the key node .

[0047] The method described in the application corresponds to a system, comprising:

[0048] A new energy extreme output event library construction unit is configured to obtain historical long sequence data of new energy output for a water-wind-solar complementary system independently connected to a power transmission and delivery section, define an extreme output event of new energy, extract the occurrence frequency, duration and fluctuation intensity characteristics of extreme high output or extreme low output events of wind power and photovoltaic power, and construct a new energy extreme output event library;

[0049] A risk assessment model construction unit is configured to construct a water-wind-solar complementary system scheduling risk assessment model facing the extreme output event of new energy, including a water-wind-solar complementary system long-term power generation planning model with the maximum system power generation as the target and a simulation model of the water-wind-solar complementary system under the extreme output event of new energy;

[0050] The weak link identification unit is used for setting a dispatching scenario according to different new energy extreme output events and reservoir water level states, wherein a typical extreme output curve is selected from a new energy extreme output event library based on extreme event parameters of an event type, a duration and a fluctuation intensity; a water-wind-solar complementary system dispatching risk evaluation index facing new energy extreme output events is established by considering economy and reliability, including a cascade reservoir water abandonment risk and a system power shortage risk; a water-wind-solar complementary system dispatching risk evaluation model is used to simulate a dispatching process of the complementary system under different dispatching scenarios: firstly, a new energy output prediction result is input into a water-wind-solar complementary system medium-long term power generation plan compilation model, an operation process of each power station of the water-wind-solar complementary system is optimized to compile a water-wind-solar complementary system ten-day power generation plan; then, the new energy extreme output scenario is substituted into a water-wind-solar complementary system dispatching simulation model under new energy extreme output events, a water power station dispatching process is updated according to a wind-solar extreme output process, and a weak link of the water-wind-solar complementary system dispatching operation under new energy extreme output events is identified;

[0051] The optimization dispatching model construction and solution unit is used for establishing a water-wind-solar complementary multi-objective optimization dispatching model considering power generation and dispatching risk for the weak link of the water-wind-solar complementary system dispatching operation, dispatching targets include maximum system power generation and minimum dispatching risk, a double-layer optimization framework is used for solution: an outer layer takes a key node water level as a decision variable, a multi-objective cuckoo algorithm is used to accelerate the optimization speed; an inner layer uses a dynamic programming algorithm to optimize a dispatching plan of the system, simulates a system operation process under an extreme scenario, and feeds back power generation and dispatching risk to the outer layer, and the cycle is iterated until a maximum iteration number, a non-inferior dispatching scheme set is obtained, and a compromise dispatching scheme is selected as an optimal dispatching scheme based on a weight method.

[0052] An electronic device for storing and executing the method, the device comprising:

[0053] A memory storing executable program codes;

[0054] A processor coupled with the memory;

[0055] The processor invokes the executable program codes stored in the memory to execute the steps of the dispatching method.

[0056] Beneficial effects: Compared with the prior art, the significant technical effects of the present application are: (1) The water-wind-solar complementary system scheduling risk assessment model for new energy extreme output events is constructed, the scheduling process of the complementary system under different scheduling scenarios is simulated, the economic efficiency and reliability of the complementary system operation are quantified, the weak link of the complementary system operation under new energy extreme output events can be identified, and the response ability of the system to extreme events is improved; (2) The traditional water-wind-solar complementary system scheduling under extreme events does not consider improving the adjustment ability of the system to respond to new energy extreme output events by optimizing the key node water level, the present application integrates the new energy extreme output event into the decision framework of the key water level control of the reservoir, establishes a water-wind-solar complementary multi-objective optimization scheduling model considering power generation and scheduling risk, and improves the adjustment ability of the cascade hydropower to new energy extreme output events, which can provide a reference for formulating the scheduling scheme to prevent new energy extreme output. BRIEF DESCRIPTION OF DRAWINGS

[0057] Figure 1 is a method flowchart of the present application;

[0058] Figure 2 is a schematic diagram of a new energy sustained extreme output event;

[0059] Figure 3 is a solution flowchart of the cascade hydropower multi-objective optimization model considering power generation and scheduling risk. DETAILED DESCRIPTION

[0060] The present application will be further described below in combination with the drawings and specific embodiments. The following embodiments are only used to more clearly illustrate the technical solutions of the present application, and cannot be used to limit the protection scope of the present application.

[0061] As shown in Figure 1 , the water-wind-solar complementary optimization scheduling method considering new energy extreme output events comprises the following steps:

[0062] S1, for the water-wind-solar complementary system independently connected to the power transmission and delivery section, obtaining the historical long sequence data of new energy (wind power, photovoltaic) output, defining new energy extreme output events, extracting the occurrence frequency, duration and fluctuation intensity characteristics of wind power and photovoltaic extreme high output or extreme low output events, and constructing a new energy extreme output event library;

[0063] Figure 2The application discloses a new energy extreme output event definition method, and is based on historical long sequence data of new energy (wind power and photovoltaic) output, considers seasonal differences of new energy output, and sets daily average output corresponding to 90% quantile as a high threshold value and sets daily average output corresponding to 10% quantile as a low threshold value in different seasons; when daily average output of 3 days or more is greater than or equal to the high threshold value, a new energy extreme high output event is defined (see formula (1)), and when daily average output of 3 days or more is less than or equal to the low threshold value, a new energy extreme low output event is defined (see formula (2)).

[0064] (1)

[0065] (2)

[0066] wherein, is a new energy output unit value; is a day index; , are respectively a new energy extreme high output event and a new energy extreme low output event; , are start and end time of continuous extreme output; is a duration, and is set as 3d; , are respectively a high threshold value and a low threshold value.

[0067] A new energy extreme output event occurrence frequency is a number of new energy extreme high output or extreme low output events occurring in a statistical period;

[0068] A new energy extreme output event fluctuation intensity is a maximum offset amount of new energy extreme high output or extreme low output event occurrence relative to monthly average output.

[0069] (3)

[0070] wherein, is a new energy extreme event fluctuation intensity; is new energy multi-year monthly average output; is an offset amount of new energy output relative to monthly average output.

[0071] S2, a water-wind-solar complementary system dispatching risk assessment model facing the new energy extreme output event is constructed, including a water-wind-solar complementary system medium and long term generation plan compilation model with a maximum system power generation as an objective and a water-wind-solar complementary system dispatching simulation model under the new energy extreme output event;

[0072] An objective function of the water-wind-solar complementary system medium and long term generation plan compilation model is:

[0073] (4)

[0074] wherein, is the long-term generation of the water-wind-solar complementary system, , and are the number of weeks, the number of days in a week and the number of cascade hydropower stations in the dispatch period, respectively, , and are the week number, the day number in a week and the cascade hydropower station number in the dispatch period, respectively, , and are the planned output of the cascade hydropower station, wind power and solar power in the week, , and are the planned output of the cascade hydropower station, wind power and solar power in the week on the day, , are the long-term and medium-term dispatch period lengths, respectively. The constraints include water-wind-solar power (maximum, minimum) output constraints, transmission channel constraints and water power system constraints.

[0075] The water-wind-solar complementary system dispatch simulation model under new energy extreme output events simulates the cascade hydropower dispatching process under new energy extreme output events, mainly through the cascade hydropower to compensate for the deviation between the planned output and the extreme output of new energy, to evaluate the adjustment capacity of cascade hydropower to respond to new energy extreme output events. The objective of the model is to minimize the deviation between the total output and the planned output of the water-wind-solar complementary system under new energy extreme output events, and the objective function expression is:

[0076] (5)

[0077] wherein, is the power shortage of the water-wind-solar complementary system, MW·h; and are the number of periods in the dispatch period and the number of cascade hydropower stations, respectively; is the planned output of the water-wind-solar complementary system in the period, MW; is the actual output of the cascade hydropower station in the period under new energy extreme events, MW; , are the extreme output of wind power and solar power in the period, MW; is the length of the mid-term scheduling period, d.

[0078] S3, setting a scheduling scenario according to different new energy extreme output events and reservoir water level states, wherein a typical extreme output curve is selected from a new energy extreme output event library based on event type, duration, fluctuation intensity and other extreme event parameters; a water-wind-solar complementary system scheduling risk evaluation index facing new energy extreme output events is established considering economy and reliability, including cascade reservoir water loss risk and system power shortage risk; a water-wind-solar complementary system scheduling risk evaluation model is used to simulate the scheduling process of the complementary system under different scheduling scenarios, and identify the weak link of the water-wind-solar complementary system scheduling operation under new energy extreme output events;

[0079] According to the characteristics of new energy extreme output events, different duration and fluctuation intensity of new energy extreme high output and extreme low output events are set respectively to obtain new energy extreme output scenarios;

[0080] According to the typical water level state of the leading hydropower station reservoir, four scheduling periods of reservoir drawdown period, pre-flood dry period, storage period and post-flood period are selected as the analysis period.

[0081] From the aspects of economy and reliability, a water-wind-solar complementary system scheduling risk evaluation index facing new energy extreme output events is established; including cascade reservoir water loss risk and system power shortage risk, the cascade reservoir water loss risk is represented by cumulative water loss power, and the system power shortage risk is represented by cumulative power loss, maximum power loss depth and cumulative power loss time:

[0082] (1) Cumulative water loss power:

[0083] (6)

[0084] Wherein, is the water loss risk of the system under new energy extreme output events, MW·h; is the output coefficient of the th cascade hydropower station; is the water loss flow of the th cascade hydropower station in the th period, m³ / s; is the net water head of the th cascade hydropower station in the th period, m; is the length of the mid-term scheduling period, d.

[0085] (2) Cumulative power loss:

[0086] (7)

[0087] Wherein, The power shortage risk of the system under the new energy extreme output event is MW·h; The actual output of the water-wind-solar complementary system at the first period is MW. The actual output of the water-wind-solar complementary system at the first period is MW. The actual output of the water-wind-solar complementary system at the first period is MW. The extreme output of the wind power station at the first period is MW. The extreme output of the photovoltaic power station at the first period is MW.

[0088] The medium-term scheduling period is d.

[0089] (8)

[0090] The maximum power shortage depth is MW. The cumulative power shortage duration is days.

[0091]

[0092] (9)

[0093] (10) The cumulative power shortage duration is days.

[0094] The system power shortage indication sequence is. The water-wind-solar complementary system scheduling risk assessment model is used to simulate the scheduling process of the complementary system under different scheduling scenarios: first, the new energy output prediction result is input into the water-wind-solar complementary system medium-long term generation plan compilation model, the operation process of the water-wind-solar power stations is optimized to compile the water-wind-solar complementary system generation plan within ten days; then, the new energy extreme output scenario is substituted into the water-wind-solar complementary system scheduling simulation model under the new energy extreme output event, the water power station scheduling process is updated according to the wind-solar extreme output process, and the weak link of the complementary system scheduling operation under the new energy extreme output event is identified by comparing the scheduling risk indicators of the complementary system under different scheduling scenarios.

[0095] S4, for the weak link of the water-wind-solar complementary system scheduling operation, a water-wind-solar complementary multi-objective optimization scheduling model considering generation capacity and scheduling risk is established, the scheduling targets include maximum system generation capacity and minimum scheduling risk, a multi-objective cuckoo algorithm is used to solve the non-inferior scheduling scheme set, a compromise scheduling scheme is selected based on the weight method, and the regulation capacity of the cascade hydropower station to the new energy extreme output event is improved.

[0096]

[0097] ​The new energy extreme event is included in the key water level control decision of the reservoir, and a multi-objective optimization model of cascade hydropower is constructed, which considers the power generation and scheduling risk. The objective function of the model is:

[0098] (1) The maximum power generation of water-wind-solar complementary system;

[0099] (11)

[0100] where, is the annual power generation of water-wind-solar complementary system, and are the number of weeks and the number of cascade hydropower stations in the scheduling period, is the planned output of the th cascade hydropower station in the th week, , are the planned output of wind and photovoltaic power stations in the th week, is the length of the long-term scheduling period;

[0101] (2) The minimum scheduling risk of water-wind-solar complementary system;

[0102] (12)

[0103] (13)

[0104] (14)

[0105] where, is the scheduling risk of water-wind-solar complementary system, MW·h; is the water abandonment risk of the system under the new energy extreme output event, MW·h; is the power shortage risk of the system under the new energy extreme output event, MW·h; is the output coefficient of the th cascade hydropower station; is the water abandonment flow of the th cascade hydropower station in the th period, m³ / s; is the net water head of the th cascade hydropower station in the th period, m; is the planned output of water-wind-solar complementary system in the th period, MW; is the actual output of the th cascade hydropower station in the th period under the new energy extreme event, MW; , are the actual output of wind and photovoltaic power stations in the Extreme output during a given period, MW; This refers to the duration of the intermediate scheduling period.

[0106] The constraints are:

[0107] (1) Water balance constraint:

[0108] (15)

[0109] in, , The first The first-class hydroelectric power station Reservoir capacity at the beginning and end of the time period, m 3 ; For the first The first-class hydroelectric power station Average inbound flow rate over a period of time, m 3 / s; For the first The first-class hydroelectric power station Average outflow from the reservoir during the time period, m 3 / s.

[0110] (2) Water level and reservoir capacity constraints:

[0111] (16)

[0112] (17)

[0113] (18)

[0114] (19)

[0115] in, For the first The first-class hydroelectric power station The water level above the dam at the end of the time period, in meters; For the first The water level and reservoir capacity function of a hydroelectric power station; and The first The initial and final reservoir water levels of the hydropower station, in meters; and The first The initial and final water levels of a hydropower station are given in meters. For long-term scales, the initial and final water levels are the specified beginning and end water levels. For medium-term scales, the water level boundaries of the time period given by the long-term dispatch are the initial and final water levels. and The first The lower and upper limits of the water level of a Class I hydropower station, in meters; and the lower and upper limits of reservoir capacity of the nth stage hydropower station, m 3 .

[0116] (3) Tailwater level-discharge and discharge constraint:

[0117] (20)

[0118] (21)

[0119] (22)

[0120] wherein, the average generation discharge of the nth stage hydropower station in the th time period, m 3 / s; the lower and upper limits of generation discharge of the nth stage hydropower station, m 3 / s; the lower and upper limits of outflow of the nth stage hydropower station, m 3 / s; the tailwater level of the nth stage hydropower station in the th time period, m; represents the tailwater level-discharge function of the nth stage hydropower station.

[0121] (4) Head constraint:

[0122] (23)

[0123] wherein, the net head of the nth stage hydropower station in the th time period, m.

[0124] (5) Output constraint:

[0125] (24)

[0126] wherein, the planned output of the nth stage hydropower station in the th time period, MW; the upper limit of output of the nth stage hydropower station, i.e., the installed capacity of the nth stage hydropower station, MW; the lower limit of output of the nth ​​The lower limit of the output of a Class A hydroelectric power station, in MW.

[0127] (6) Hydraulic connection

[0128] (25)

[0129] in, For the first The first-class hydroelectric power station Average outbound flow rate over a period of time, m 3 / s; In the first Time period Level 1 hydropower station to the first The flow rate between hydropower stations, m 3 / s.

[0130] Figure 3 This provides a solution approach for the multi-objective optimization model of hydro-wind-solar hybrid power generation that balances power generation and dispatch risk.

[0131] This model is a two-layer optimization model that coordinates power generation and scheduling risks. The outer layer involves water level control at key nodes, which is currently solved using intelligent optimization algorithms or combinatorial ergonomic methods. The inner layer is essentially a hydropower, wind power, and solar power scheduling problem, commonly solved using intelligent optimization algorithms and dynamic programming algorithms. Intelligent optimization algorithms are highly adaptable and computationally efficient, but they are prone to generating infeasible solutions under complex constraints, leading to convergence to an infeasible Pareto front. In contrast, dynamic programming algorithms can accurately find the optimal solution within the feasible region, effectively avoiding infeasible solutions and ensuring global optimality. However, its computational complexity increases sharply with dimensionality, resulting in relatively low computational efficiency. To improve the search efficiency of the outer layer while ensuring the feasibility of the inner layer's results, this invention employs a solution strategy combining an outer-layer multi-objective cuckoo algorithm with an inner-layer dynamic programming algorithm, improving computational efficiency while obtaining a more reliable Pareto solution set.

[0132] The outer layer uses the key node water level (reservoir capacity) as the decision variable and accelerates the optimization speed through a multi-objective cuckoo algorithm. The inner layer uses a dynamic programming algorithm to optimize the operation process of each power station, formulates the power generation plan of the cascade hydropower stations within a ten-day period, and follows the principle of "determining water supply based on electricity demand" under extreme power output events of new energy sources. It calculates the scheduling risk of the hydro-wind-solar hybrid system at key time nodes by compensating for the deviation between the planned output and the extreme output of new energy sources through cascade hydropower. The solution steps are as follows:

[0133] 1. Outer layer:

[0134] (1) Set the number of bird nests Dimensions Archive size , search domain upper bound and , maximum iteration number , let iteration number ;

[0135] (2) initialize bird nest position , take the key node water level composition dimension sequence as a bird nest, for the bird nest in the dimension position: , wherein , , ; input the key node water level information contained in the bird nest into the inner model, calculate the annual power generation of the water-wind-solar complementary system through the long-term power generation planning model of the water-wind-solar complementary system , and evaluate the dispatching risk of the key node water-wind-solar complementary system under the extreme output event of new energy through the water-wind-solar complementary system dispatching simulation model ;

[0136] (3) select non-dominated solutions through non-dominated sorting to obtain the current Pareto optimal solution;

[0137] (4) update the position of the bird nest through the Levy flight principle , wherein the step length is adaptively adjusted based on the exponential distribution ; calculate the annual power generation through the long-term power generation planning model of the water-wind-solar complementary system , and evaluate the dispatching risk of the key node complementary system under the extreme output event of new energy through the water-wind-solar complementary system dispatching simulation model ;

[0138] (5) merge the last generation bird nest and the new generation bird nest , obtain non-dominated solutions through non-dominated sorting and store them in the external archive set, when the total number of individuals exceeds the size of the external archive set, maintain the external archive set by calculating the crowding distance, delete some individuals with small crowding distance, and generate the new generation bird nest ;

[0139] (6) according to the formula of linearly decreasing , wherein is the discovery probability of the evolution generation, is the maximum discovery probability, is the maximum discovery probability, is the current evolution generation, is the maximum iteration number of the algorithm, calculate the dynamic discovery probability , and the bird nest Each solution is randomly given an arbitrary number between 0 and 1 and compared with The new bird nest position is obtained by comparison ; The annual power generation is calculated by the long-term power generation planning model of the water, wind and light complementary system , and the key node complementary system scheduling risk is evaluated by using the water, wind and light complementary system scheduling simulation model under the extreme output event of new energy .

[0140] (7) Repeat step (5) to obtain the Pareto optimal solution.

[0141] (8) Let , judge whether the iteration exit condition is met; if met, output the Pareto optimal solution and end, otherwise, turn to step (4) and continue iteration.

[0142] 2, inner layer:

[0143] (1) According to the key node, the scheduling period is divided into three sub-scheduling periods of early year-flood season, flood season-end of flood season and end of year, and the ten-day water level process in the scheduling period of cascade hydropower station is solved in sequence, wherein, for the light station with annual regulation capacity, dynamic programming algorithm is adopted for water level optimization, and the rest of the daily regulation station is controlled according to fixed water level;

[0144] (2) The complete water level process in the year is generated, and the annual power generation is calculated by the long-term power generation planning model of the water, wind and light complementary system .

[0145] (3) The dynamic programming algorithm is adopted to formulate the daily power generation plan of the complementary system in the pre-flood season and the end of flood season.

[0146] (4) The extreme low output event of new energy is substituted in the pre-flood season, and the extreme high output event of new energy is substituted in the end of flood season, and the actual operation process of the complementary system is simulated by using the water, wind and light complementary system scheduling simulation model under the extreme output event of new energy, and the key node complementary system scheduling risk is evaluated .

[0147] An embodiment of the present application also provides a water, wind and light complementary optimization scheduling system considering extreme output events of new energy, comprising:

[0148] A new energy extreme output event library construction unit is used for obtaining the historical long sequence data of new energy output for the water, wind and light complementary system independently transmitted by the section, defining the extreme output event of new energy, extracting the occurrence frequency, duration and fluctuation intensity characteristics of the extreme high output or extreme low output event of wind power and photovoltaic, and constructing the new energy extreme output event library.

[0149] The risk assessment model construction unit is configured to construct a water-wind-solar complementary system scheduling risk assessment model for new energy extreme output events, including a water-wind-solar complementary system medium and long-term generation plan compilation model with the maximum system power generation as the target and a water-wind-solar complementary system scheduling simulation model under new energy extreme output events;

[0150] The weak link identification unit is configured to set a scheduling scenario according to different new energy extreme output events and reservoir water level states, wherein a typical extreme output curve is selected from a new energy extreme output event library based on extreme event parameters such as event type, duration and fluctuation intensity; an economic and reliable water-wind-solar complementary system scheduling risk evaluation index for new energy extreme output events is established, including cascade reservoir water loss risk and system power shortage risk; and a scheduling process of the complementary system under different scheduling scenarios is simulated based on the water-wind-solar complementary system scheduling risk assessment model: firstly, new energy output prediction results are input into the water-wind-solar complementary system medium and long-term generation plan compilation model, and the operation process of each power station of the water-wind-solar complementary system is optimized to compile a water-wind-solar complementary system generation plan within a ten-day period; and then, the new energy extreme output scenario is substituted into the water-wind-solar complementary system scheduling simulation model under new energy extreme output events, the water power station scheduling process is updated according to the wind-solar extreme output process, and the weak link of the water-wind-solar complementary system scheduling operation under new energy extreme output events is identified.

[0151] The optimization scheduling model construction and solution unit is configured to establish a water-wind-solar complementary multi-objective optimization scheduling model considering power generation and scheduling risk for the weak link of the water-wind-solar complementary system scheduling operation, the scheduling targets include maximum system power generation and minimum scheduling risk, and a double-layer optimization framework is used for solution: the outer layer takes the key node water level as a decision variable, and a multi-objective cuckoo algorithm is used to accelerate the optimization speed; the inner layer uses a dynamic programming algorithm to optimize the scheduling plan of the system, simulates the operation process of the system under extreme scenarios, and feeds back the power generation and scheduling risk to the outer layer, and the cycle iteration is performed until the maximum iteration number, a non-inferior scheduling scheme set is obtained, and a compromise scheduling scheme is selected as the optimal scheduling scheme based on a weight method.

[0152] An embodiment of the present application also provides an electronic device, which comprises:

[0153] a memory storing executable program codes;

[0154] a processor coupled with the memory;

[0155] The processor invokes the executable program codes stored in the memory to execute the steps of the scheduling method.

[0156] The application takes HP1-HP2-HP3 of certain cascade hydropower stations as an example, and applies the above scheduling operation model and method to carry out analysis. HP1 hydropower station is the leading station on the main stream of the basin, and has strong regulation performance. The two downstream daily regulation hydropower stations mainly undertake the tasks of power generation, peak regulation, frequency regulation and the like. The basin is rich in wind energy and solar energy resources, and has good development conditions. At present, 750MW photovoltaic power station has been built, and 270MW wind power station and 3720MW photovoltaic power station are planned to be built. The parameter settings of each power station are shown in Table 1.

[0157] Table 1 Basic parameter table of cascade hydropower stations

[0158] By applying the above water-wind-solar complementary optimization scheduling technology considering extreme output events of new energy, it is concluded that the water-wind-solar system can effectively respond to extreme output events of new energy in most time periods, but the flexible regulation capacity of the system is obviously insufficient when the water level is near the dead water level before the flood season and near the normal storage level at the end of the flood season. The water-wind-solar complementary multi-objective optimization scheduling model considering power generation and scheduling risk can significantly reduce the scheduling risk at the cost of small power loss. The risk levels of wet years, normal years and dry years are reduced by 19.60%, 69.29% and 46.72% respectively, while the corresponding power generation is reduced by only 0.13%, 0.12% and 0.18% respectively.

Claims

1. A water-wind-solar complementary optimization scheduling method considering new energy extreme output events, characterized in that, The method comprises the following steps: For the water-wind-solar complementary system of independent section power transmission, long sequence data of new energy output is obtained, extreme new energy output events are defined, the occurrence frequency, duration and fluctuation intensity characteristics of wind power and photovoltaic extreme high output or extreme low output events are extracted, and an extreme new energy output event library is constructed; A water-wind-solar complementary system dispatching risk assessment model for extreme new energy output events is constructed, including a water-wind-solar complementary system medium and long term generation plan compilation model with the maximum system power generation as the target and a water-wind-solar complementary system dispatching simulation model under extreme new energy output events; According to different extreme new energy output events and reservoir water level states, dispatching scenarios are set, wherein, based on the event type, duration and fluctuation intensity, the extreme event parameters are selected from the typical extreme output curve in the extreme new energy output event library; considering the economy and reliability, a water-wind-solar complementary system dispatching risk evaluation index for extreme new energy output events is established, including the cascade reservoir water abandonment risk and the system power shortage risk; based on the water-wind-solar complementary system dispatching risk assessment model, the dispatching process of the complementary system in different dispatching scenarios is simulated: first, the new energy output prediction result is input into the water-wind-solar complementary system medium and long term generation plan compilation model, the operation process of the water-wind-solar power station is optimized to compile the water-wind-solar complementary system generation plan within ten days; then, the extreme new energy output scenario is substituted into the water-wind-solar complementary system dispatching simulation model under extreme new energy output events, the water power station dispatching process is updated according to the wind-solar extreme output process, and the weak link of the water-wind-solar complementary system dispatching operation under extreme new energy output events is identified; For the weak link of the water-wind-solar complementary system dispatching operation, a water-wind-solar complementary multi-objective optimization dispatching model considering power generation and dispatching risk is established, the dispatching targets include the maximum system power generation and the minimum dispatching risk, and a double-layer optimization framework is used for solving: the outer layer takes the key node water level as the decision variable, and the multi-objective cuckoo algorithm is used to speed up the optimization speed; the inner layer adopts the dynamic programming algorithm to optimize the dispatching plan of the system, simulates the operation process of the system under extreme scenarios, and feeds back the power generation and dispatching risk to the outer layer, and iterates until the maximum iteration number, to obtain a non-inferior dispatching scheme set, and a compromise dispatching scheme is selected as the optimal dispatching scheme based on the weight method.

2. The water, wind and light complementary optimization scheduling method considering new energy extreme output event according to claim 1, characterized in that, The definition method of the extreme new energy output event is that based on the long sequence historical output data of the new energy, the seasonal difference of the new energy output is considered, the daily average output corresponding to the 90% quantile is set as the high threshold value and the daily average output corresponding to the 10% quantile is set as the low threshold value in different seasons; when the daily average output of the new energy is greater than or equal to the high threshold value for 3 days or more, the new energy extreme high output event is defined; when the daily average output of the new energy is less than or equal to the low threshold value for 3 days or more, the new energy extreme low output event is defined; The occurrence frequency of the extreme new energy output event is the number of times of the occurrence of the new energy extreme high output or extreme low output event in the statistical period; The fluctuation intensity of the extreme new energy output event is the maximum deviation from the monthly average during the occurrence of the new energy extreme high output or extreme low output event.

3. The water, wind and light complementary optimization scheduling method considering new energy extreme output event according to claim 1, characterized in that, The objective function of the long-term generation scheduling model of the water-wind-solar complementary system is: ； wherein, is the long-term generation of the water-wind-solar hybrid system, , and are the number of weeks, the number of days in a week and the number of cascade hydropower stations in the dispatch period, respectively, , and are the week number, the day number in a week and the cascade hydropower station number in the dispatch period, respectively, , and are the planned output of the cascade hydropower station, the wind power and the solar power in the week, respectively, , and are the planned output of the cascade hydropower station, the wind power and the solar power in the week on the day, respectively, , are the long-term and medium-term dispatch period lengths, respectively.

4. The water, wind and light complementary optimization scheduling method considering new energy extreme output event according to claim 1, characterized in that, The objective function of the water-wind-solar complementary system scheduling simulation model under the new energy extreme output event is: ； wherein, is the power shortage of the water-wind-solar complementary system, and are the number of time periods and the number of cascade hydropower stations in the dispatch period, respectively; is the planned output of the water-wind-solar complementary system in the time period, is the actual output of the cascade hydropower station in the time period under the new energy extreme event, , are the extreme outputs of the wind power station and the photovoltaic power station in the time period, respectively, is the length of the time period in the medium-term dispatch.

5. The water-wind-solar complementary optimization scheduling method considering new energy extreme output events according to claim 1, characterized in that, According to the characteristics of new energy extreme output events, different durations and fluctuation intensities of new energy extreme high output and extreme low output events are set respectively to obtain new energy extreme output scenarios; According to the typical water level state of the leading hydropower station reservoir, four dispatching periods, namely, the drawdown period, the pre-flood dry period, the storage period and the post-flood period, are selected as the analysis period; The risk of cascade hydropower station water abandonment is represented by cumulative water abandonment; the risk of system power shortage is represented by cumulative power shortage, maximum power shortage depth and cumulative power shortage duration; By comparing the dispatching risk indicators of the water-wind-solar complementary system under different dispatching scenarios, the weak link of the complementary system dispatching operation under new energy extreme output events is identified.

6. The water, wind and light complementary optimization scheduling method considering new energy extreme output event according to claim 1, characterized in that, The objective function of the dispatching target of the water-wind-solar complementary multi-objective optimization scheduling model considering generation and dispatching risk is: (1) Maximum generation of water-wind-solar complementary system: ； wherein, is the annual energy production of the water-wind-solar hybrid system, and are the number of weeks and the number of cascade hydropower stations in the dispatch period, respectively, is the planned output of the th cascade hydropower station in the th week, , are the planned outputs of the wind power station and the solar power station in the th week, respectively, is the length of the long-term dispatch period. (2) Minimum dispatching risk of water-wind-solar complementary system: ； ； ； in, To mitigate the scheduling risks of the hydro-wind-solar hybrid system, To mitigate the risk of water wastage in the event of extreme power output from new energy sources, To mitigate the risk of power shortages when the system encounters extreme power output events from new energy sources, For the first The output coefficient of a hydroelectric power station. For the first The first-class hydroelectric power station Water discharge rate during the period For the first The first-class hydroelectric power station The water head during the period, For complementary systems in the first Planned output during the time period For the first time under extreme events of new energy The first-class hydroelectric power station Actual output during the time period , Wind power and photovoltaic power stations were respectively in the 1st Extreme output during a certain period of time This refers to the duration of the intermediate scheduling period.

7. The water, wind and light complementary optimization scheduling method considering new energy extreme output event according to claim 1, characterized in that, In the solving method of the water-wind-solar complementary multi-objective optimization scheduling model considering generation and dispatching risk, the outer layer is: (1) Set the number of bird nests , dimension , archive set size , search domain lower bound and , maximum iteration number , let the iteration number ; (2) initialization of the bird nest position , the key node water level sequence is regarded as a bird nest, and the position of the first bird nest in the first dimension is initialized as ; the position of the first bird nest in the first dimension is initialized as ; the position of the first bird nest in the first dimension is initialized as ; the position of the first bird nest in the first dimension is initialized as ; the position of the first bird nest in the first dimension is initialized as ; the position of the first bird nest in the first dimension is initialized as ; the position of the first bird nest in the first dimension is initialized as ; the key node water level information contained in the bird nest is input into an inner model, the annual power generation of the water-wind-solar complementary system is calculated through a long-term power generation planning model of the water-wind-solar complementary system , and a water-wind-solar complementary system dispatching simulation model under new energy extreme output events is adopted to evaluate the dispatching risk of the key node water-wind-solar complementary system ; (3) Select non-dominated solutions by non-dominated sorting to obtain the current Pareto optimal solution; (4) updating the location of the bird's nest by the Levy flight principle wherein the step size is adaptively adjusted based on an exponential distribution ; calculating the annual power generation by a long-term power generation planning model of the water, wind and light complementary system ; evaluating the complementary system dispatching risk of key nodes by a water, wind and light complementary system dispatching simulation model under extreme output events of new energy ; (5) merge the previous generation of bird nests with the new generation of bird nests , obtain non-dominated solutions in the external archive by non-dominated sorting, when the total number of individuals exceeds the size of the external archive, maintain the external archive by calculating the crowding distance, delete some individuals with smaller crowding distance, and generate the new generation of bird nests ; (6) According to the formula of linearly decreasing; wherein, is the first evolutional number of discovery probability, is the maximum discovery probability, is the maximum discovery probability, is the current evolutional number, is the maximum iteration number of the algorithm, and the dynamic discovery probability is calculated , the bird nest Each solution is randomly given an arbitrary number between 0 and 1 and compared with to obtain a new bird nest position ; Calculate the annual power generation by the long-term power generation planning model of the water, wind and light complementary system , and evaluate the complementary system scheduling risk of key nodes by using the water, wind and light complementary system scheduling simulation model under the extreme output event of new energy ; (7) Repeat step (5) to obtain the Pareto optimal solution; (8) Let , determine whether the iteration exit condition is satisfied; if so, output the Pareto optimal solution and end, otherwise, go to step (4) and continue iteration. 8.The method of claim 1, wherein, In the solving method of the water-wind-solar complementary multi-objective optimization scheduling model considering generation and dispatching risk, the inner layer is: (1) According to the key nodes, the dispatching period is divided into three sub-dispatching periods: early in the year, before the flood season, and after the flood season, and the ten-day water level process in the dispatching period of cascade hydropower stations is solved, wherein, for the light-illumination power station with annual regulation capacity, dynamic programming algorithm is adopted for water level optimization, and the remaining daily regulation power station is controlled according to fixed water level; (2) Generate complete annual water level process, calculate annual power generation through long-term power generation planning model of water and wind complementary system ; (3) Select the key period before and after the flood season, and use dynamic programming algorithm to develop daily generation plan of the complementary system; (4) In the pre-flood period, extreme low output events of new energy are substituted, and in the post-flood period, extreme high output events of new energy are substituted. The actual operation process of the complementary system is simulated by using the simulation model of the complementary system dispatching under the extreme output events of new energy, and the dispatching risk of the complementary system at the key node is evaluated .

9. A water-wind-solar complementary optimization scheduling system considering new energy extreme output events, characterized in that, It includes: The new energy extreme output event library construction unit is used for the water-wind-solar complementary system with independent section power transmission, obtains the historical long sequence data of new energy output, defines the new energy extreme output event, extracts the occurrence frequency, duration and fluctuation intensity characteristics of wind power and photovoltaic extreme high output or extreme low output event, and constructs the new energy extreme output event library; The risk assessment model construction unit is used for constructing the water-wind-solar complementary system dispatching risk assessment model facing new energy extreme output events, including the long-term generation scheduling model of the water-wind-solar complementary system with the maximum system generation as the target and the water-wind-solar complementary system scheduling simulation model under new energy extreme output events; The weak link identification unit is configured to set a dispatching scenario according to different new energy extreme output events and reservoir water level states, wherein a typical extreme output curve is selected from a new energy extreme output event library based on extreme event parameters of event types, durations and fluctuation intensities; economic and reliable dispatching risk evaluation indexes of the water-wind-solar complementary system for the new energy extreme output events are established, including cascade reservoir water abandonment risk and system power shortage risk; and a water-wind-solar complementary system dispatching risk evaluation model is used to simulate the dispatching process of the complementary system under different dispatching scenarios: firstly, the new energy output prediction result is input into a long-term generation plan compilation model of the water-wind-solar complementary system, the operation process of each power station of the water-wind-solar complementary system is optimized to compile a generation plan of the water-wind-solar complementary system within a ten-day period; and then, the new energy extreme output scenario is substituted into the water-wind-solar complementary system dispatching simulation model under the new energy extreme output event, the water power station dispatching process is updated according to the wind-solar extreme output process, and the weak link of the water-wind-solar complementary system dispatching operation under the new energy extreme output event is identified. The optimization dispatching model construction and solution unit is configured to establish a water-wind-solar complementary multi-objective optimization dispatching model considering generation capacity and dispatching risk for the weak link of the water-wind-solar complementary system dispatching operation, the dispatching objectives include maximum system generation capacity and minimum dispatching risk, and a double-layer optimization framework is used for solution: the outer layer takes the key node water level as a decision variable, and a multi-objective cuckoo algorithm is used to accelerate the optimization speed; the inner layer uses a dynamic programming algorithm to optimize the dispatching plan of the system, simulates the operation process of the system under the extreme scenario, and feeds back the generation capacity and the dispatching risk to the outer layer, and the cycle iteration is performed until the maximum iteration number, a non-inferior dispatching scheme set is obtained, and a compromise dispatching scheme is selected as the optimal dispatching scheme based on a weight method.

10. An electronic device, comprising: The device comprises: a memory storing executable program codes; a processor coupled with the memory; the processor invokes the executable program codes stored in the memory to execute the steps of the dispatching method in any one of claims 1-8.

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