CN118036925A - Multi-target random optimization annual ladder-crossing hydropower station optimal scheduling method and system - Google Patents
Multi-target random optimization annual ladder-crossing hydropower station optimal scheduling method and system Download PDFInfo
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Abstract
The invention discloses a multi-target random optimization cross-year step hydropower station optimal scheduling method and a multi-target random optimization cross-year step hydropower station optimal scheduling system, which relate to the technical field of hydropower station optimal scheduling and comprise the steps of establishing a step hydropower station system frame and analyzing the operation coupling effect of the step hydropower station system; setting two types of optimization targets based on hydropower station requirements, and establishing a membership function of the two types of optimization targets; constructing a coupling optimization scheduling model of the stepped hydropower station system through the coupling effect and the membership function of the coupling effect, and calculating model coupling constraint; and analyzing the uncertainty influence of the water inlet side of the step hydropower station system, and carrying out hydropower station optimal scheduling. The method improves the stability and efficiency of power supply by establishing the corresponding membership function; optimizing the hydropower resource allocation of the whole system by constructing a coupling optimization scheduling model; by carrying out hydropower station optimal scheduling, taking uncertainty of a water supply side into consideration, and introducing a random optimization method, the hydropower station can better cope with the uncertainty, and the robustness of the system is improved.
Description
Technical Field
The invention relates to the technical field of hydropower station optimal scheduling, in particular to a multi-objective random optimization annual ladder-crossing hydropower station optimal scheduling method and system.
Background
The coupling effect of the existence time and the space of each hydropower station in the ladder hydropower station belongs to a complex planning problem, and has important significance in researching multi-objective optimization scheduling of the ladder hydropower station under the influence of different time and space water.
The previous researches are usually focused on economic dispatch problems of a ladder hydropower station, few researches focus on the uncertainty influence of a coupling mechanism model and an incoming water side, one significant disadvantage of the current hydropower station research field is that, although a great deal of researches focus on the economic dispatch problems of the ladder hydropower station, the influence of the coupling mechanism model and the incoming water side uncertainty is generally ignored, the limitation of the research method is mainly that the modeling and analysis of the coupling relation and the energy conversion process between complex hydropower stations are insufficient, the uncertainty of the water quantity and the speed change of the incoming hydropower station is lack of effective prediction and management, in addition, the environmental influence, the water resource sustainability and the social responsibility are ignored due to excessive economic benefit, the comprehensive operation of the hydropower station is limited, the limitation of the prior art in the aspect of long-term or short-term hydrologic change prediction in the context of coping with extreme weather events and climate change is present, in discussing the impact of coupling mechanism models, which involve interactions and dependencies between hydropower stations, and overall efficiency, and of the uncertainty of the incoming water side, we need to recognize how one site's operation affects the performance of the other site and the allocation of water resources, which requires us to understand and model these relationships more precisely to optimize the overall system's performance, while the uncertainty of the incoming water side, including the effects of seasonal changes, extreme weather events and long term weather changes, constitutes a significant challenge to the hydropower station's operating strategy, which requires more advanced predictive models and flexible management strategies to ensure that efficient operation of the plant is maintained under unstable environmental conditions, therefore, the comprehensive and accurate understanding of these complex factors is of great importance for the optimization of hydropower station scheduling.
Disclosure of Invention
The present invention has been made in view of the above-described problems.
Therefore, the technical problems solved by the invention are as follows: the existing hydropower station optimal scheduling method has the problems of low reliability, low precision and low efficiency, and how to consider the economic scheduling problem of the stepped hydropower station and the uncertainty influence of a coupling mechanism model and a water supply side, and solves the problem of multi-objective lower-stepped hydropower station optimal scheduling.
In order to solve the technical problems, the invention provides the following technical scheme: a multi-objective random optimization annual-crossing ladder hydropower station optimal scheduling method comprises the steps of establishing a ladder hydropower station system frame and analyzing the operation coupling effect of the ladder hydropower station system; setting two types of optimization targets based on hydropower station requirements, and establishing a membership function of the two types of optimization targets; constructing a coupling optimization scheduling model of the stepped hydropower station system through the coupling effect and the membership function of the coupling effect, and calculating model coupling constraint; and analyzing the uncertainty influence of the water inlet side of the step hydropower station system, and carrying out hydropower station optimal scheduling.
As a preferable scheme of the multi-objective random optimization cross-annual ladder hydropower station optimal scheduling method, the invention comprises the following steps: the step hydropower station system frame establishment comprises the step hydropower station system frame establishment based on the main source hydropower station, the main water outlet side hydropower station, the main line hydropower station group and the branch line hydropower station group.
As a preferable scheme of the multi-objective random optimization cross-annual ladder hydropower station optimal scheduling method, the invention comprises the following steps: the coupling effect comprises a coupling relation between hydropower stations in the stepped hydropower station system, wherein the coupling relation comprises time coupling and space coupling; the time coupling is that reservoirs are contained in different hydropower stations in different time periods, the reservoirs have a water storage effect, and water flow of the hydropower stations has time delay; the space coupling is that different hydropower stations are coupled through water inlet, water outlet and water inlet in the same period, the hydropower stations are in cascade connection with each other to perform joint optimization operation, and a reservoir in each hydropower station is used for temporarily storing or discharging water in the stepped hydropower station to treat the delay effect of water supply among the different hydropower stations; when the water yield and the water storage capacity of the hydropower station are regulated, the regulation standard is regulated based on reservoir capacity, power generation capacity, local requirements and environmental influence, and in a single flow area, the hydropower stations coordinate with each other, and the water quantity of the upstream hydropower station is regulated to control the power generation capacity of the downstream power station.
As a preferable scheme of the multi-objective random optimization cross-annual ladder hydropower station optimal scheduling method, the invention comprises the following steps: the two set optimization targets comprise a first supply sufficiency target and a second peak regulation target, wherein the two set optimization targets comprise a total residual load quantity and residual load peak-valley difference are analyzed through two indexes of supply sufficiency and peak regulation capability based on hydropower station requirements.
The objective-supply sufficiency objective function is expressed as:
Wherein min F 1 represents the total minimum of the residual load of the system, T is the total scale of the optimal scheduling time, T takes a value of one day, I is the total quantity of the hydropower station, P L,t is the load demand of the hydropower station in the period T in one day, and P i,t is the net water yield of the hydropower station I in the period T.
The target second peak shaver capability objective function is expressed as:
Where min F 2 represents that the system residual load peak-valley difference is minimized.
As a preferable scheme of the multi-objective random optimization cross-annual ladder hydropower station optimal scheduling method, the invention comprises the following steps: establishing two membership functions, namely, the number of the first targets is higher than that of the second targets, carrying out equalization treatment on the optimization relation between the first targets and the second targets, normalizing the orders of magnitude of the two targets to be in the same range by adopting the membership functions, setting membership boundaries of the two targets, and representing the membership boundaries as follows:
Wherein, And/>For maximum value boundary and minimum value boundary after object membership degreeAndThe worst case of maxF 1 being the maximum value boundary and the minimum value boundary after membership of the second target comprises the worst water resource supply sufficiency, and the worst case of maxF 2 being the second target comprises the worst peak regulation capability of the stepped hydropower station, namely/>And/>And taking 0.9 and 1.2 as the maximum adjustment coefficient and the minimum adjustment coefficient of the target I, wherein the adjustment coefficient of the target II is the same as the treatment process of the target I.
Based on the membership boundary, a membership function of two objective functions is constructed, expressed as:
Wherein u (F 1) is the membership function of the target one and u (F 2) is the membership function of the target two.
As a preferable scheme of the multi-objective random optimization cross-annual ladder hydropower station optimal scheduling method, the invention comprises the following steps: the step hydropower station system coupling optimization scheduling model is constructed and model coupling constraint is calculated by a coupling effect and a membership function of the coupling effect and the membership function, and the step hydropower station system coupling optimization scheduling model is constructed and expressed as:
min F＝λ1u(F1)+λ2u(F2)
Wherein lambda 1 and lambda 2 are weight factors of a first target and a second target, and the weights are positively correlated with the importance of the objective function; the model coupling constraint comprises the output limit of the hydroelectric generating set, the reservoir capacity coupling relation, the reservoir capacity limit, the water delay time and the generating set power flow limit; the total output of the water motor group at any moment does not exceed the load of the system, expressed as:
for each hydropower station, there are upper and lower limits on the output of the hydroelectric generating set, expressed as:
Wherein, And/>Respectively representing the upper limit and the lower limit of the i output of the hydroelectric generating set; each hydroelectric generating set has output power limitation in the power generation process, the power generation process involves water resources, the water resources are limited by flow in the flowing process, and the constraint of the generating flow limitation of the generating set is expressed as follows:
Wherein, And/>Respectively representing the upper limit and the lower limit of the power generation flow of the hydroelectric generating set i; the constraints on the total output of each hydropower station are expressed as:
Wherein, And/>Respectively representing an upper limit and a lower limit of the Y output of the hydropower station; the constraints of the reservoirs in each hydropower station comprise coupling constraints of each period of the reservoirs, upper and lower limit constraints of the reservoir capacity and head and tail capacity constraints of the reservoirs, and are expressed as follows:
VY,t＝VY,t-1+(RY,t-QY,t)Δt
VY,0＝VY,in
VY,T＝VY,out
wherein V Y,t represents the capacity of the Y reservoir of the hydropower station, And/>The upper limit and the lower limit of the capacity of the hydropower station Y reservoir are respectively represented, V Y,0 and V Y,T respectively represent the capacity of the hydropower station Y reservoir at the initial time and the capacity of the hydropower station Y reservoir at the final time, and V Y,in and V Y,out respectively represent the capacity set value of the hydropower station Y reservoir at the initial time and the capacity set value of the hydropower station Y reservoir at the final time; the downward leakage flow is constrained, expressed as:
wherein Q Y,t represents the let-down flow of the hydropower station Y, And/>Respectively representing the upper limit and the lower limit of the discharging flow of the hydropower station Y, and s Y,t represents the discharging flow of the hydropower station Y.
As a preferable scheme of the multi-objective random optimization cross-annual ladder hydropower station optimal scheduling method, the invention comprises the following steps: the hydropower station optimizing scheduling comprises the step hydropower station system water supply side uncertainty influence, the normal distribution is used as probability distribution for carrying out calculation analysis, and the hydropower station optimizing scheduling is carried out.
The example comprises four operation scenes, wherein the first operation scene comprises the optimization of the first objective function, and the objective function is only set to be minimum in the total amount of residual load.
The second scene comprises the step of carrying out second optimization, wherein the objective function is only set to be the minimum residual load peak-valley difference.
The third scene comprises the steps of carrying out multi-objective optimization under the combination of the first objective and the second objective, and setting the comprehensive optimization mode of the two objectives.
And the fourth scene comprises adding uncertainty optimization of the water inlet side on a third scene multi-objective optimization framework, and performing random optimization.
The invention further aims to provide a multi-objective random optimization cross-year step hydropower station optimal scheduling system which can construct a step hydropower station system coupling optimal scheduling model and calculate model coupling constraint through coupling effect and membership functions of the multi-objective random optimization cross-year step hydropower station optimal scheduling system, and solves the problem that the existing hydropower station optimal scheduling has low accuracy.
As a preferable scheme of the multi-objective random optimization cross-annual ladder hydropower station optimization scheduling system, the invention comprises the following steps: the system comprises a coupling analysis module, an optimization target setting module, a function analysis module and an optimization scheduling module; the coupling analysis module is used for establishing a stepped hydropower station system frame and analyzing the operation coupling effect of the stepped hydropower station system; the optimization target setting module is used for setting two types of optimization targets based on hydropower station requirements and establishing membership functions of the two types of optimization targets; the function analysis module is used for constructing a coupling optimization scheduling model of the stepped hydropower station system and calculating model coupling constraint through the coupling effect and the membership function of the coupling effect and the membership function; and the optimal scheduling module is used for analyzing the uncertainty influence of the water inlet side of the step hydropower station system and performing hydropower station optimal scheduling.
A computer device comprising a memory and a processor, the memory storing a computer program, the processor executing the computer program being the steps of implementing a multi-objective random optimization cross-annual ladder hydropower station optimization scheduling method.
A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of a multi-objective random optimization cross-annual ladder hydropower station optimization scheduling method.
The invention has the beneficial effects that: the multi-objective random optimization cross-year step hydropower station optimization scheduling method provided by the invention realizes comprehensive understanding of complex coupling relations among hydropower stations by constructing a system frame comprising a water inlet source, a main water outlet side, a main line and a branch line hydropower station group, improves the utilization rate of water resources, and better understands and predicts the mutual influence among hydropower stations; by setting two optimization targets of supply sufficiency and peak regulation capability and establishing corresponding membership functions, optimization among multiple targets is balanced, so that the supply stability of a whole system can be met, peak clipping and valley filling on a load side can be realized, and the stability and efficiency of power supply are improved; the coupling optimization scheduling model of the stepped hydropower station system is constructed, the model coupling constraint is calculated, the coordinated operation among hydropower stations is facilitated, and the hydropower resource allocation of the whole system is optimized; by analyzing the uncertainty influence of the water inlet side of the hydropower station system of the step, carrying out hydropower station optimization scheduling, considering the uncertainty of the water inlet side, increasing the complexity of the prediction and scheduling of the hydropower station system, and introducing a random optimization method, the hydropower station can better cope with the uncertainty, and the robustness and the adaptability of the system are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is an overall flowchart of a multi-objective random optimization cross-year ladder hydropower station optimization scheduling method according to a first embodiment of the invention.
Fig. 2 is a ladder hydropower station framework diagram of a multi-objective random optimization cross-year ladder hydropower station optimal scheduling method according to a second embodiment of the invention.
Fig. 3 is a pattern 1 hydropower station drainage flow chart of a multi-objective random optimization cross-year ladder hydropower station optimization scheduling method according to a second embodiment of the invention.
Fig. 4 is a model 1 hydropower station power generation summary diagram of a multi-objective random optimization cross-year-ladder hydropower station optimal scheduling method according to a second embodiment of the invention.
Fig. 5 is a graph of optimizing the reservoir capacity of a hydropower station in mode 1 of a multi-objective random optimization cross-year-ladder hydropower station optimizing and dispatching method according to a second embodiment of the invention.
Fig. 6 is a graph comparing the original load of the mode 1 system and the residual load after optimization of the multi-objective random optimization cross-year ladder hydropower station optimization scheduling method according to the second embodiment of the invention.
Fig. 7 is a pattern 2 hydropower station drainage flow chart of a multi-objective random optimization cross-year ladder hydropower station optimal scheduling method according to a second embodiment of the invention.
Fig. 8 is a schematic diagram of a power generation overview of a mode 2 hydropower station of a multi-objective random optimization cross-year ladder hydropower station optimization scheduling method according to a second embodiment of the invention.
Fig. 9 is a graph of optimizing the reservoir capacity of a mode 2 hydropower station according to a multi-objective random optimization cross-year-ladder hydropower station optimizing and dispatching method according to a second embodiment of the invention.
Fig. 10 is a graph comparing the original load of the mode 2 system and the residual load after optimization of the multi-objective random optimization cross-annual ladder hydropower station optimization scheduling method according to the second embodiment of the invention.
Fig. 11 is a pattern 3 hydropower station drainage flow chart of a multi-objective random optimization cross-year ladder hydropower station optimization scheduling method according to a second embodiment of the invention.
Fig. 12 is a model 3 hydropower station power generation summary diagram of a multi-objective random optimization cross-year ladder hydropower station optimal scheduling method according to a second embodiment of the invention.
Fig. 13 is a graph of optimizing reservoir capacity of a mode 3 hydropower station according to a multi-objective random optimization cross-year-ladder hydropower station optimizing scheduling method according to a second embodiment of the invention.
Fig. 14 is a graph comparing the original load of the mode 3 system and the residual load after optimization of the multi-objective random optimization cross-annual ladder hydropower station optimization scheduling method according to the second embodiment of the invention.
Fig. 15 is a pattern 4 hydropower station drainage flow chart of a multi-objective random optimization cross-year ladder hydropower station optimal scheduling method according to a second embodiment of the invention.
Fig. 16 is a model 4 hydropower station power generation summary diagram of a multi-objective random optimization cross-year ladder hydropower station optimal scheduling method according to a second embodiment of the invention.
Fig. 17 is a graph of optimizing reservoir capacity of a model 4 hydropower station according to a multi-objective random optimization cross-year-ladder hydropower station optimizing scheduling method according to a second embodiment of the invention.
Fig. 18 is a graph comparing the original load and the optimized residual load of the mode 4 system of the multi-objective random optimization cross-annual ladder hydropower station optimizing and scheduling method according to the second embodiment of the invention.
Fig. 19 is an optimized solution set diagram of two targets obtained by adjusting weight factors of a multi-target random optimization cross-annual ladder hydropower station optimized scheduling method according to a second embodiment of the invention.
Fig. 20 is an overall flowchart of a multi-objective random optimization cross-year ladder hydropower station optimization scheduling system according to a third embodiment of the invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
Example 1
Referring to fig. 1, for one embodiment of the present invention, there is provided a multi-objective random optimization cross-annual ladder hydropower station optimization scheduling method, including:
S1: and establishing a stepped hydropower station system frame and analyzing the operation coupling effect of the stepped hydropower station system.
Furthermore, establishing the stepped hydropower station system frame comprises establishing the stepped hydropower station system frame based on the main source hydropower station, the main water outlet hydropower station, the main line hydropower station group and the branch line hydropower station group.
It should be noted that the coupling effect includes a coupling relationship between hydropower stations in the stepped hydropower station system, and the coupling relationship includes time coupling and space coupling; the time coupling is that reservoirs are contained in different hydropower stations in different time periods, the reservoirs have a water storage effect, and water flow of the hydropower stations has time delay; the space coupling is that different hydropower stations are coupled through water inlet, water outlet and water inlet in the same period, the hydropower stations are in cascade connection with each other to perform joint optimization operation, and a reservoir in each hydropower station is used for temporarily storing or discharging water in the stepped hydropower station to treat the delay effect of water supply among the different hydropower stations; when the water yield and the water storage capacity of the hydropower station are regulated, the regulation standard is regulated based on reservoir capacity, power generation capacity, local requirements and environmental influence, and in a single flow area, the hydropower stations coordinate with each other, and the water quantity of the upstream hydropower station is regulated to control the power generation capacity of the downstream power station.
It should also be noted that, there is a coupling relationship between water inflow and water outflow between hydropower stations, and there is a time delay effect, each hydropower station needs to flexibly allocate respective water outflow, water storage, etc., the core of the optimal scheduling of hydropower stations is to comprehensively consider allocation standards, linkage relationships between hydropower stations, and the necessity of flexible allocation, allocation standards are usually based on reservoir capacity, power generation capacity, and multiple factors such as local demand and environmental impact, and are adjusted along with the change of season, weather and power grid demand, in one flow area, multiple hydropower stations need to coordinate with each other, the water volume adjustment of the upstream hydropower station directly affects the power generation capacity of the downstream hydropower station, this linkage relationship is critical to the efficiency and reliability of the whole system, flexible allocation, that is, the capability of fast responding to load change and maintaining the stability of the power grid, is the key of improving energy efficiency and reducing environmental impact, implementing this goal needs advanced prediction technology, real-time data analysis and comprehensive management policy, meanwhile, scheduling of hydropower stations should also consider environmental and social factors, ensure sustainable management and environmental protection of water resources, balance economic benefit and system and local healthy development, multiple water supply systems are coupled with the same time period, water supply systems need to the same time delay effect is not to be fully coupled with the water storage system, hydropower stations need to implement the same time, and water storage system has a full time-delay effect is not to be coupled with the same time period, and the same time period is fully used by the water storage system, and the same time is not needed to implement the water supply system is fully coupled with the water supply system, and has a sufficient time effect is sufficient time frame is sufficient to have a sufficient time of water supply by the same time, the hydropower stations are cascaded with each other, so that the water discarding can be reduced, the utilization rate of water resources is fully improved, reservoirs with certain capacity are arranged in each hydropower station in the step hydropower stations, the reservoirs can temporarily store water or discharge water, and the time delay effect of water delivery among different hydropower stations can be relieved.
S2: based on hydropower station requirements, two types of optimization targets are set, and a membership function of the two types of optimization targets is established.
Further, setting two types of optimization targets comprises analyzing total residual load quantity and residual load peak-valley difference through two indexes of supply sufficiency and peak regulation capacity based on hydropower station requirements, setting two types of optimization targets, wherein the supply sufficiency is the first target, and the peak regulation capacity is the second target.
The objective-supply sufficiency objective function is expressed as:
Wherein min F 1 represents the total minimum of the residual load of the system, T is the total scale of the optimal scheduling time, T takes a value of one day, I is the total quantity of the hydropower station, P L,t is the load demand of the hydropower station in the period T in one day, and P i,t is the net water yield of the hydropower station I in the period T.
The target second peak shaver capability objective function is expressed as:
Where min F 2 represents that the system residual load peak-valley difference is minimized.
It should be noted that, establishing the membership functions of the two targets includes that the number of the first target is higher than that of the second target, performing equalization processing on the optimization relationship between the first target and the second target, normalizing the orders of magnitude of the two targets to the same range by adopting the membership functions, setting membership boundaries of the two target functions, and representing as:
Wherein, And/>For maximum value boundary and minimum value boundary after object membership degreeAndThe worst case of maxF 1 being the maximum value boundary and the minimum value boundary after membership of the second target comprises the worst water resource supply sufficiency, and the worst case of maxF 2 being the second target comprises the worst peak regulation capability of the stepped hydropower station, namely/>And/>And taking 0.9 and 1.2 as the maximum adjustment coefficient and the minimum adjustment coefficient of the target I, wherein the adjustment coefficient of the target II is the same as the treatment process of the target I.
Based on the membership boundary, a membership function of two objective functions is constructed, expressed as:
Wherein u (F 1) is the membership function of the target one and u (F 2) is the membership function of the target two.
It should also be noted that for a step hydropower station problem, two factors are typically considered: the water resource utilization rate is as high as possible, the water resource is utilized to realize peak clipping and valley filling, namely the peak regulation capability is fully exerted, the burden is reduced for a large power grid, on the basis of the membership function model, the values of the two optimization objective functions can be limited in the range of 0-1, the relationship between the two optimization objective functions is well balanced, the quantification and optimization of the hydropower station operation objective are realized by setting the two optimization objectives of the supply sufficiency and the peak regulation capability and establishing the corresponding membership function, and the step ensures that the hydropower station can furthest reduce the residual load and peak valley difference while meeting the power demand, and improves the stability and efficiency of power supply.
S3: and constructing a coupling optimization scheduling model of the stepped hydropower station system through the coupling effect and the membership function of the coupling effect, and calculating model coupling constraint.
Further, constructing the coupling optimization scheduling model of the stepped hydropower station system and calculating the model coupling constraint comprises constructing the coupling optimization scheduling model of the stepped hydropower station system through a coupling effect and a membership function of the coupling effect and the membership function, wherein the coupling optimization scheduling model is expressed as:
min F＝λ1u(F1)+λ2u(F2)
Wherein lambda 1 and lambda 2 are weight factors of a first target and a second target, and the weights are positively correlated with the importance of the objective function.
It should be noted that the model coupling constraint includes a hydroelectric generating set output limit, a reservoir capacity coupling relationship, a reservoir capacity limit, a water delay time, and a generating set generating flow limit.
The total output of the water motor group at any moment does not exceed the load of the system, expressed as:
for each hydropower station, there are upper and lower limits on the output of the hydroelectric generating set, expressed as:
Wherein, And/>The upper limit and the lower limit of the output of the hydroelectric generating set i are respectively shown.
Each hydroelectric generating set has output power limitation in the power generation process, the power generation process involves water resources, the water resources are limited by flow in the flowing process, and the constraint of the generating flow limitation of the generating set is expressed as follows:
Wherein, And/>The upper limit and the lower limit of the power generation flow rate of the hydroelectric generating set i are respectively shown.
The constraints on the total output of each hydropower station are expressed as:
Wherein, And/>Respectively representing the upper limit and the lower limit of the Y output of the hydropower station.
The constraints of the reservoirs in each hydropower station comprise coupling constraints of each period of the reservoirs, upper and lower limit constraints of the reservoir capacity and head and tail capacity constraints of the reservoirs, and are expressed as follows:
VY,t＝VY,t-1+(RY,t-QY,t)Δt
VY,0＝VY,in
VY,T＝VY,out
wherein V Y,t represents the capacity of the Y reservoir of the hydropower station, And/>The upper limit and the lower limit of the capacity of the hydropower station Y reservoir are respectively represented, V Y,0 and V Y,T are respectively represented by the capacity of the hydropower station Y reservoir at the initial time and the capacity of the hydropower station Y reservoir at the final time, and V Y,in and V Y,out are respectively represented by the capacity set value of the hydropower station Y reservoir at the initial time and the capacity set value of the hydropower station Y reservoir at the final time.
The downward leakage flow is constrained, expressed as:
wherein Q Y,t represents the let-down flow of the hydropower station Y, And/>Respectively representing the upper limit and the lower limit of the discharging flow of the hydropower station Y, and s Y,t represents the discharging flow of the hydropower station Y.
It should be further noted that, because the hydroelectric generating set plays a role in assisting adjustment in the whole power system, the system is mainly powered by thermal power generation, therefore, the total output of the hydroelectric generating set group at any moment cannot exceed the load of the system, and the output of each hydroelectric generating set has an upper limit and a lower limit inside each hydropower station, which are similar to the thermal power generating set, unlike the thermal power generating set, the hydroelectric generating set has almost no constraint limit on the climbing rate in the running process.
S4: and analyzing the uncertainty influence of the water inlet side of the step hydropower station system, and carrying out hydropower station optimal scheduling.
Furthermore, the optimal scheduling of the hydropower station comprises the steps of performing an example analysis on the uncertainty influence of the water inlet side of the stepped hydropower station system by taking normal distribution as probability distribution, and performing the optimal scheduling of the hydropower station; the calculation example comprises four operation scenes, wherein the first operation scene comprises the optimization of a first target, and the target function is only set to be the minimum total residual load; the second scene comprises the steps of performing second optimization, wherein an objective function is only set to be the minimum residual load peak-valley difference; the third scene comprises the steps of performing multi-objective optimization under the combination of the first objective and the second objective, and setting a comprehensive optimization mode of the two objectives; and the fourth scene comprises adding uncertainty optimization of the water inlet side on a third scene multi-objective optimization framework, and performing random optimization.
It should be noted that, taking the uncertainty influence of the incoming water side into consideration, the normal distribution is taken as the probability distribution to carry out the example analysis, which shows that the provided model not only can well complete the stability of the supply, but also can reduce the peak-valley difference of the load, and can realize the optimal scheduling under the environment of the incoming water uncertainty.
Example 2
Referring to fig. 2-19, for one embodiment of the present invention, a multi-objective random optimization cross-annual ladder hydropower station optimization scheduling method is provided, and in order to verify the beneficial effects of the present invention, scientific demonstration is performed through economic benefit calculation and simulation experiments.
Firstly, in order to analyze the feasibility of the model provided by the invention, four operation scenes are established, namely scene 1: target 1 optimization, i.e. the objective function is set only to minimum total residual load, scenario 2: target 2 optimization, i.e. the objective function is set only to minimum residual load peak-valley difference, scenario 3: target 1 and target 2 are comprehensively considered to be multi-target optimization, namely, a comprehensive optimization mode of weighing and considering two targets is adopted, and scene 4: consider the uncertainty optimization of the incoming water side, i.e., stochastic optimization, on the scenario 3 multi-objective optimization framework.
Referring to FIG. 2, the coupling relation of all hydropower stations in the stepped hydropower station is 10 hydropower stations, and the upper and lower limits of the output force of the hydropower unit in each period are 15MW and 0MW respectively; the initial reservoir capacity of the reservoir is 210.12 x 10 5 cubic meters, the final reservoir capacity of the reservoir is 210.06 x 10 5 cubic meters, the minimum maximum reservoir capacity of the reservoir is 38.5 x 10 5 cubic meters and 252.2 x 10 5 cubic meters respectively, the minimum and maximum downward discharge flow amounts of the reservoir are 200 and 3000 cubic meters per second respectively, and the number of hydroelectric units in each hydropower station is as follows in sequence: 3. 3, 4, 3, 5, 4, 3, 2.
The peak-valley differences between the total residual load and the residual load of the system under the four scenes are shown in table 1.
Table 1 total residual load and peak-to-valley difference for the system
From reference to table 1: the total residual load of the system in the scene 1 is optimized to be minimum and is 15781.86MW, but the peak-valley difference of the residual load is still larger and is 643.65MW, and the water supply stability of the system in the scene is fully ensured, but the peak regulation capability of the system is not exerted; likewise, for the optimization of scenario 2, the optimization results of the total amount of the residual load and the peak-to-valley difference of the residual load of the system are 16088.15MW and 255.65MW respectively, which indicate that the peak regulation capability of the system is fully mobilized in the scenario, but the optimization in the aspect of water supply is ignored; the optimization results of the total residual load and the peak-valley difference of the residual load in the scene 3 are 15781.86MW and 350.00MW respectively, which shows that the multi-objective optimization in the scene can balance two objectives, and the water supply capacity and the peak regulation capacity of the system are comprehensively considered; in scenario 4, the optimization results of the total residual load and the peak-valley difference of the residual load are 15788.06MW and 259.45MW respectively, and compared with scenario 3, the situation that the uncertainty of incoming water considered in scenario 4 brings challenges to the water supply capacity of the stepped hydropower station, and causes the increase fluctuation of the total residual load of the system, and the stepped hydropower station in the area needs to consider the guarantee of the water supply capacity in a key way under the scenario.
Referring to fig. 3-18, the system original load and the optimized residual load are compared, and the total output, the discharging flow and the reservoir capacity of the hydroelectric generating set are optimized curves under four scenes.
Referring to fig. 3 to 6: the comparison of the original load and the optimized residual load shows that in the scene 1, the water supply optimizing quantity of the stepped hydropower station in each period is different, because in the scene, the system optimizing target is that the total system residual load is minimum, the stepped hydropower station is required to optimize the water yield of each period in different periods, the water yield of each period is related to the water yield by the interactive water yield of the coupled hydropower station, so that the optimizing value of the load reducing quantity of each period is different, the total power generated by the hydropower station corresponds to each period of the load reducing quantity optimizing curve one by one, the validity of the result is shown, the hydropower station reservoir capacity optimizing curve can analyze, the initial and final capacity requirements exist in the hydropower station reservoir capacity, the capacity change shows that the hydropower station reservoir has the phenomena of water storage and water generation, and the optimizing trend is approximately from 7:00 to 13: interval 00 exhibits a significant rise, while at 13:00 to 17: the 00 interval shows obvious decline, and the other intervals have small-range floating and belong to the coupling reason of the water motor group in the stepped hydropower station.
As in the case of scene 1 analysis, see fig. 7 to 10: according to the original load and the optimized residual load curve, the peak-valley difference of the residual load curve is optimized to be minimum, and according to the original load curve characteristic analysis and optimization process, the load between two peak section intervals is supplied with the output force as much as possible, so that the peak-valley difference is reduced, the peak-peak regulation capability of the stepped hydropower station is utilized, and according to the total output force of the hydropower unit, the following steps 6:00 to 15: interval 00, 19:00 to 21: the high output exists in the 00 interval, and is consistent with the analysis, and the high output is obtained according to a reservoir capacity optimization curve, and the high output is 0:00 to 6: the 00 interval system reservoir is always storing water, and at 6:00 to 12: the 00 interval system reservoir is water-producing because from 6:00 is faced to the first peak load section of the system, and the reservoir needs to be matched with a hydroelectric generating set for water generation.
Referring to fig. 11 to 14: according to the comparison of the original load and the optimized residual load curve, the peak-valley difference of the residual load curve is larger than that of the scene 2, and the peak reference surface is higher, but the load is 'reduced in depth' in a plurality of time periods in the middle, because the stepped hydropower station needs to balance the optimization of two objective functions in the scene, and the 'reduced in depth' section has certain water supply capacity although the peak regulation capacity is sacrificed. And (3) comparing the total output and reservoir capacity optimization curve of the hydroelectric generating set, wherein the floating frequency of the scene 1 and the scene 2 is higher compared with that of the curve, and the reason is that the stepped hydropower station searches for an optimization result of 'deep water supply' interval.
Referring to fig. 15 to 18: the analysis of the scene 4 is similar to the scene 3, and belongs to the category of multi-objective optimization, except that the uncertainty factor of the water inflow is involved in the scene 4, and is not repeated.
In order to illustrate the robustness of the multi-objective membership optimization model established by the invention, the weight factors in the multi-objective optimization model are respectively changed, and the optimization solution sets of two objectives are obtained through optimization, and refer to FIG. 19.
Referring to fig. 19: when the optimization targets 1 are reduced, the optimization targets 2 are increased, the process is nonlinear, the model is proved to have no optimal solution but an optimal solution set, meanwhile, the orders of magnitude of the two target optimization values are different, the change of the two target optimization values can be effectively changed by changing the weight factors, and the robustness of the multi-target optimization of the membership function is verified, so that the invention has creativity.
Example 3
Referring to fig. 20, for one embodiment of the present invention, a multi-objective random optimization cross-annual ladder hydropower station optimization scheduling system is provided, which includes a coupling analysis module, an optimization objective setting module, a function analysis module, and an optimization scheduling module.
The coupling analysis module is used for establishing a stepped hydropower station system frame and analyzing the operation coupling effect of the stepped hydropower station system; the optimization target setting module is used for setting two types of optimization targets based on hydropower station requirements and establishing membership functions of the two types of optimization targets; the function analysis module is used for constructing a coupling optimization scheduling model of the stepped hydropower station system and calculating model coupling constraint through the coupling effect and the membership function of the coupling effect and the membership function; the optimal scheduling module is used for analyzing the uncertainty influence of the water inlet side of the step hydropower station system and performing hydropower station optimal scheduling.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium may even be paper or other suitable medium upon which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like. It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (10)
1. The multi-objective random optimization annual ladder-crossing hydropower station optimization scheduling method is characterized by comprising the following steps of:
establishing a stepped hydropower station system frame, and analyzing the operation coupling effect of the stepped hydropower station system;
setting two types of optimization targets based on hydropower station requirements, and establishing a membership function of the two types of optimization targets;
constructing a coupling optimization scheduling model of the stepped hydropower station system through the coupling effect and the membership function of the coupling effect, and calculating model coupling constraint;
And analyzing the uncertainty influence of the water inlet side of the step hydropower station system, and carrying out hydropower station optimal scheduling.
2. The multi-objective random optimization cross-annual ladder hydropower station optimization scheduling method according to claim 1, wherein the method comprises the following steps: the step hydropower station system frame establishment comprises the step hydropower station system frame establishment based on the main source hydropower station, the main water outlet side hydropower station, the main line hydropower station group and the branch line hydropower station group.
3. The multi-objective random optimization cross-annual ladder hydropower station optimization scheduling method as claimed in claim 2, wherein: the coupling effect comprises a coupling relation between hydropower stations in the stepped hydropower station system, wherein the coupling relation comprises time coupling and space coupling;
the time coupling is that reservoirs are contained in different hydropower stations in different time periods, the reservoirs have a water storage effect, and water flow of the hydropower stations has time delay;
The space coupling is that different hydropower stations are coupled through water inlet, water outlet and water inlet in the same period, the hydropower stations are in cascade connection with each other to perform joint optimization operation, and a reservoir in each hydropower station is used for temporarily storing or discharging water in the stepped hydropower station to treat the delay effect of water supply among the different hydropower stations;
When the water yield and the water storage capacity of the hydropower station are regulated, the regulation standard is regulated based on reservoir capacity, power generation capacity, local requirements and environmental influence, and in a single flow area, the hydropower stations coordinate with each other, and the water quantity of the upstream hydropower station is regulated to control the power generation capacity of the downstream power station.
4. The multi-objective random optimization cross-year ladder hydropower station optimization scheduling method according to claim 3, the method is characterized in that: the setting of two types of optimization targets comprises the steps of based on hydropower station requirements, providing two indexes of sufficiency and peak regulation capacity, analyzing total residual load quantity and residual load peak-valley difference, setting two types of optimization targets, wherein the supply sufficiency is a first target, and the peak regulation capacity is a second target;
the objective-supply sufficiency objective function is expressed as:
Wherein min F 1 represents that the total amount of the residual load of the system is minimized, T is the total scale of the optimal scheduling time, T takes a value of one day, I is the total amount of the hydropower station, P L,t is the load demand of the hydropower station in a period T in one day, and P i,t is the net water generation amount of the hydropower station I in the period T;
the target second peak shaver capability objective function is expressed as:
Where min F 2 represents that the system residual load peak-valley difference is minimized.
5. The multi-objective random optimization cross-annual ladder hydropower station optimization scheduling method according to claim 4, wherein the method comprises the following steps: establishing two membership functions, namely, the number of the first targets is higher than that of the second targets, carrying out equalization treatment on the optimization relation between the first targets and the second targets, normalizing the orders of magnitude of the two targets to be in the same range by adopting the membership functions, setting membership boundaries of the two targets, and representing the membership boundaries as follows:
Wherein, And/>For maximum value boundary and minimum value boundary after object membership degreeAnd/>The worst case of maxF 1 being the maximum value boundary and the minimum value boundary after membership of the second target comprises the worst water resource supply sufficiency, and the worst case of maxF 2 being the second target comprises the worst peak regulation capability of the stepped hydropower station, namely/>AndTaking 0.9 and 1.2 as the adjustment coefficient of the maximum value and the adjustment coefficient of the minimum value of the target I, wherein the adjustment coefficient of the target II is the same as the treatment process of the target I;
Based on the membership boundary, a membership function of two objective functions is constructed, expressed as:
Wherein u (F 1) is the membership function of the target one and u (F 2) is the membership function of the target two.
6. The multi-objective random optimization cross-annual ladder hydropower station optimization scheduling method according to claim 5, wherein the method comprises the following steps: the step hydropower station system coupling optimization scheduling model is constructed and model coupling constraint is calculated by a coupling effect and a membership function of the coupling effect and the membership function, and the step hydropower station system coupling optimization scheduling model is constructed and expressed as:
min F＝λ1u(F1)+λ2u(F2)
Wherein lambda 1 and lambda 2 are weight factors of a first target and a second target, and the weights are positively correlated with the importance of the objective function;
The model coupling constraint comprises the output limit of the hydroelectric generating set, the reservoir capacity coupling relation, the reservoir capacity limit, the water delay time and the generating set power flow limit;
The total output of the water motor group at any moment does not exceed the load of the system, expressed as:
for each hydropower station, there are upper and lower limits on the output of the hydroelectric generating set, expressed as:
Wherein, And/>Respectively representing the upper limit and the lower limit of the i output of the hydroelectric generating set;
Each hydroelectric generating set has output power limitation in the power generation process, the power generation process involves water resources, the water resources are limited by flow in the flowing process, and the constraint of the generating flow limitation of the generating set is expressed as follows:
Wherein, And/>Respectively representing the upper limit and the lower limit of the power generation flow of the hydroelectric generating set i;
the constraints on the total output of each hydropower station are expressed as:
Wherein, And/>Respectively representing an upper limit and a lower limit of the Y output of the hydropower station;
The constraints of the reservoirs in each hydropower station comprise coupling constraints of each period of the reservoirs, upper and lower limit constraints of the reservoir capacity and head and tail capacity constraints of the reservoirs, and are expressed as follows:
VY,t＝VY,t-1+(RY,t-QY,t)Δt
VY,0＝VY,in
VY,T＝VY,out
wherein V Y,t represents the capacity of the Y reservoir of the hydropower station, And/>The upper limit and the lower limit of the capacity of the hydropower station Y reservoir are respectively represented, V Y,0 and V Y,T respectively represent the capacity of the hydropower station Y reservoir at the initial time and the capacity of the hydropower station Y reservoir at the final time, and V Y,in and V Y,out respectively represent the capacity set value of the hydropower station Y reservoir at the initial time and the capacity set value of the hydropower station Y reservoir at the final time;
the downward leakage flow is constrained, expressed as:
wherein Q Y,t represents the let-down flow of the hydropower station Y, And/>Respectively representing the upper limit and the lower limit of the discharging flow of the hydropower station Y, and s Y,t represents the discharging flow of the hydropower station Y.
7. The multi-objective random optimization cross-annual ladder hydropower station optimization scheduling method according to claim 6, wherein: the step hydropower station system water supply side uncertainty influence is subjected to hydropower station optimization scheduling, normal distribution is used as probability distribution to carry out calculation analysis, and hydropower station optimization scheduling is carried out;
the calculation example comprises four operation scenes, wherein the first operation scene comprises the optimization of a first target, and the target function is only set to be the minimum total residual load;
The second scene comprises the steps of performing second optimization, wherein an objective function is only set to be the minimum residual load peak-valley difference;
the third scene comprises the steps of performing multi-objective optimization under the combination of the first objective and the second objective, and setting a comprehensive optimization mode of the two objectives;
And the fourth scene comprises adding uncertainty optimization of the water inlet side on a third scene multi-objective optimization framework, and performing random optimization.
8. A system employing the multi-objective random optimization cross-annual ladder hydropower station optimization scheduling method according to any one of claims 1-7, characterized in that: the system comprises a coupling analysis module, an optimization target setting module, a function analysis module and an optimization scheduling module;
The coupling analysis module is used for establishing a stepped hydropower station system frame and analyzing the operation coupling effect of the stepped hydropower station system;
The optimization target setting module is used for setting two types of optimization targets based on hydropower station requirements and establishing membership functions of the two types of optimization targets;
The function analysis module is used for constructing a coupling optimization scheduling model of the stepped hydropower station system and calculating model coupling constraint through the coupling effect and the membership function of the coupling effect and the membership function;
and the optimal scheduling module is used for analyzing the uncertainty influence of the water inlet side of the step hydropower station system and performing hydropower station optimal scheduling.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the multi-objective random optimization trans-annual step hydropower station optimization scheduling method of any one of claims 1 to 7.
10. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor implements the steps of the multi-objective random optimization trans-annual step hydropower station optimization scheduling method of any one of claims 1 to 7.
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