CN115659792A - Multi-period multi-scene SCUC decoupling method, system, equipment and storage medium - Google Patents
Multi-period multi-scene SCUC decoupling method, system, equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a multi-period multi-scene SCUC decoupling method, a system, equipment and a storage medium, wherein the method comprises the following steps: establishing a multi-period multi-scene SCUC model according to the uncertainty of hydropower; acquiring the parameters of the cascade hydropower station, establishing an original cascade hydropower system, and equivalently using the original cascade hydropower system as a hydropower single-station system; dividing a multi-time-period multi-scene SCUC model based on a multi-time-period decoupling mechanism, and establishing SCUC sub-problems at different time periods; dividing a multi-time-interval multi-scene SCUC model based on a multi-scene decoupling mechanism, and establishing SCUC sub-problems of different scenes according to climbing coupling constraints; and performing parallel calculation according to a target cascade analysis algorithm, and solving the SCUC sub-problems in different time periods and the SCUC sub-problems in different scenes. By considering a multi-period and multi-scene decoupling mechanism, the decoupling and accelerated calculation of the SCUC problem are realized through parallel calculation, and the model solving time is effectively reduced within an acceptable confidence coefficient.
Description
Technical Field
The invention belongs to the technical field of new energy and energy conservation, and relates to a multi-period multi-scene SCUC decoupling method, system, equipment and storage medium.
Background
The water and electricity has the advantages of flexible adjustment and environmental protection, and can effectively relieve the problem of environmental pollution while the traditional energy supply is short. However, hydroelectric power generation has fluctuation and randomness, and in addition to the limitation of the storage capacity of the hydropower station, the calculation load of the safety-constrained unit (SCUC) of the power system is increased, so that the calculation cannot be completed within a specified time to obtain a corresponding optimization result. Therefore, how to accelerate the calculation of the multi-period multi-scenario SCUC of the hydroelectric generating set accessing the power system becomes a hot problem in the academic world. The problem of dividing the SCUC problem into small ones according to geographical regions is a feasible solution to the above problem, but such a solution does not consider time period decoupling and scene decoupling, and the calculation time is still long.
Disclosure of Invention
The invention aims to solve the problem that how to perform multi-time-interval and multi-scene decoupling aiming at the SCUC problem of considering water and electricity uncertainty so as to reduce the solving time length is not considered in the prior art, and provides a multi-time-interval and multi-scene SCUC decoupling method, system, equipment and storage medium.
In order to achieve the purpose, the invention adopts the following technical scheme to realize the purpose:
a multi-period multi-scene SCUC decoupling method comprises the following steps:
establishing a multi-period multi-scene SCUC model according to the uncertainty of hydropower;
acquiring cascade hydropower parameters by a multi-period multi-scene SCUC model, establishing an original cascade hydropower system, and equating the original cascade hydropower system to a hydropower single-station system;
dividing a multi-time-interval multi-scene SCUC model based on a multi-time-interval decoupling mechanism, and establishing SCUC sub-problems at different time intervals;
dividing a multi-time-interval multi-scene SCUC model based on a multi-scene decoupling mechanism, and establishing SCUC sub-problems of different scenes according to climbing coupling constraints;
and performing parallel calculation according to a target cascade analysis algorithm, and solving the SCUC sub-problems in different time periods and the SCUC sub-problems in different scenes.
The invention is further improved in that:
the establishment of the multi-period and multi-scene SCUC model is specifically represented as follows:
taking the minimization of the operation cost of the power system as an objective function:
wherein x is n The method comprises the following steps of (1) setting variable groups in an SCUC model, wherein the variable groups comprise thermal power unit output, hydroelectric power station drainage, hydroelectric power station water overflow and unit start-stop state variables; n is a random scene number; n is the total number of random scenes; pi n Representing the probability of a random scene n, and n =1; t is a time interval number; t is the total time interval; y is the number of the thermal power generating unit; y is the total number of the thermal power generating units; subscript b denotes the reference scene; f. of n (p t,y,n ,z t,y,n ) Representing the operating cost of the power system under a random scene n; f. of b (p t,y,n ,z t,y,n ) Representing the operating cost of the power system under the reference scene b; p is a radical of t,y,n The method comprises the following steps of (1) outputting power of a thermal power generating unit y in a random scene n at a time t; z is a radical of t,y,n The method comprises the following steps that a unit starting and stopping state of a thermal power unit y in a random scene n of a time t is represented, wherein 1 represents starting, and 0 represents stopping; p is a radical of t,y,b The output of the thermal power generating unit y in a time t reference scene b is obtained; z is a radical of t,y,b The method comprises the following steps that a unit starting and stopping state of a thermal power unit y in a time t reference scene b is shown, wherein 1 represents starting, and 0 represents stopping;
the constraint conditions are as follows:
wherein h is n (x n )/h b (x b ) The method comprises the following steps of power balance and cascade hydropower station storage capacity balance equality constraint; g n (x n )/g b (x b ) The method comprises inequality constraints such as thermal power unit output limit, hydroelectric power unit output limit, cascade hydropower storage capacity limit, thermal power unit climbing constraint and the like;
the up/down hill climbing of the thermal power generating unit y is limited.
The original step hydroelectric system model is specifically expressed as follows:
maximizing the gain of the cascade hydroelectric system as an objective function:
wherein the superscript O represents the original step hydroelectric system; lambda [ alpha ] t Is the electricity price for time period t; i is the hydropower station number; i is o The number of hydropower stations of the original step hydropower model; k is the number of the linear segment of the hydropower station output performance curve; k is the number of the linear sections of the output performance curve of the hydropower station;
the output performance coefficient of the k-th linearized segment of the hydropower station i;
is the water yield of the hydropower station i in the kth section of the time period t; lambda [ alpha ] F Predicting the electricity price in the future;
is the reservoir capacity of the hydropower station i in time period T;
the parameters are binary parameters, when the hydropower station j is a downstream power station of the hydropower station i, the parameters are marked as 1, otherwise, the parameters are 0;
is the future predicted output performance coefficient of the hydropower station j;
and (4) reservoir capacity balance constraint:
wherein,
the overflow amount of the hydropower station i in the time period t is shown;
the input flow of the hydropower station i in a time period t;
an upstream set of hydroelectric power stations i;
initial storage capacity constraint:
wherein,
is the percentage of the initial reservoir capacity of the hydropower station i to the total reservoir capacity;
the maximum reservoir capacity of the hydropower station i;
and (3) limiting and restricting the water yield:
wherein,
is the minimum water output of the hydropower station i in the k section;
the maximum water yield of the hydropower station i in the kth section;
and (4) constraint of storage capacity limitation:
wherein,
is the minimum reservoir capacity of the hydropower station i;
and (4) overflow amount limiting and restricting:
wherein,
is the minimum overflow of the hydropower station i;
is the maximum overflow of the hydropower station i.
The original step hydropower system is equivalent to a hydropower single station system through a double-layer optimization model, and the method is specifically represented as follows:
minimizing the income difference between the original step hydroelectric system and the hydroelectric single-station system as the objective function of the upper layer model:
the constraint conditions of the upper layer model are as follows:
limiting the output performance coefficient of the hydropower single station system:
and (3) limiting the future predicted output performance coefficient of the hydropower single station system:
the objective function of the underlying model is:
the constraint conditions of the lower layer model are as follows:
converting the lower layer model into an equivalent constraint condition by adopting a KKT condition and a large M method to obtain an equivalent single-layer model;
the method comprises the following steps of calculating parameters of a hydropower single station system according to initial water energy of an original step hydropower system, wherein the initial water energy of the original step hydropower system is as follows:
wherein E is O The initial water energy of the original step hydroelectric system is obtained;
the output performance coefficient of the 1 st linearized segment of the hydropower single station system is as follows:
wherein,
is the output performance curve of the 1 st linearized segment of the equivalent single station system; r is i The total inflow of the hydropower station i;
the initial storage capacity of the hydropower single station system is as follows:
wherein,
is the initial storage capacity of the equivalent single-station system.
The method comprises the steps of dividing a multi-period multi-scene SCUC model based on a multi-period decoupling mechanism, and establishing SCUC sub-problems in different periods, wherein the SCUC sub-problems in different periods are specifically expressed as follows:
assume a differenceThe SCUC model of the scene is divided into three time periods, and the corresponding time periods are c - C and c + The corresponding SCUC subproblems are respectively SP c- ，SP c And SP c+ ；
SCUC sub-problem SP c- The corresponding time interval is 1 to t c- +1, the objective function is:
the constraint conditions are as follows:
h c- (x c- )＝0&g c- (x c- )≤0,t＝{1,...,t c- +1}
SCUC sub-problem SP c The corresponding time period is t c- +1 to t c +1, the objective function is:
the constraint conditions are as follows:
h c (x c )＝0&g c (x c )≤0,t＝{t c- +1,...,t c +1}
SCUC sub-problem SP c+ The corresponding time period is t c +1 to T, the objective function is:
the constraint conditions are as follows:
h c+ (x c+ )＝0&g c+ (x c+ )≤0,t＝{t c +1,...,t c+ }
wherein x is c- 、x c And x c+ Are sub-problems SP, respectively c- 、SP c And SP c+ The variable group comprises the output of a thermal power generating unit, the output of a hydroelectric generating unit, the water displacement of a hydropower station, the water overflow amount of the hydropower station and the start-stop state variable of the unit.
The method comprises the following steps of dividing a multi-period multi-scene SCUC model based on a multi-scene decoupling mechanism, and establishing SCUC subproblems of different scenes according to climbing coupling constraint, wherein the SCUC subproblems specifically comprise the following steps: penalty items are added into the objective functions of the random scene and the reference scene of the hydropower single station system, consistency constraint is added, and therefore the same thermal power generating unit in different random scenes and the thermal power generating unit corresponding to the reference scene meet climbing constraint in each time interval, and the climbing constraint is expressed as follows:
the output of a reference scene in the random scene meets the output limit of the thermal power generating unit, and is specifically represented as follows:
the consistency constraint added in the random scene and the reference scene is specifically expressed as:
wherein,
and the maximum climbing rate up/down of the thermal power generating unit y is represented.
When the target cascade analysis algorithm is used for parallel calculation, a coordinator is introduced to carry out coordination optimization on the SCUC subproblems, and each subproblem is solved in parallel until a convergence condition is met; the initial value of the target cascade analysis is obtained through a supervised BP neural network.
A multi-period multi-scenario SCUC decoupling system comprises the following modules:
the model establishing module is used for establishing a multi-period multi-scene SCUC model according to the hydropower uncertainty;
the system equivalent module is used for acquiring the cascade hydropower parameters through a multi-period multi-scene SCUC model, establishing an original cascade hydropower system and equating the original cascade hydropower system to a hydropower single-station system;
the first decoupling module is used for dividing a multi-period multi-scene SCUC model based on a multi-period decoupling mechanism and establishing SCUC subproblems at different periods;
the second decoupling module is used for dividing a multi-time-interval multi-scene SCUC model based on a multi-scene decoupling mechanism and establishing SCUC subproblems of different scenes according to climbing coupling constraint;
and the data analysis processing module is used for performing parallel calculation according to a target cascade analysis algorithm and solving the SCUC sub-problems in different time periods and the SCUC sub-problems in different scenes.
An apparatus comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method of any one of the preceding claims when executing the computer program.
A computer-readable storage medium, having stored thereon a computer program which, when being executed by a processor, carries out the steps of the method according to any of the preceding claims.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a multi-period multi-scene SCUC decoupling method, which comprises the steps of establishing a multi-period multi-scene SCUC model according to hydropower uncertainty, obtaining an equivalent hydropower single-station system, obtaining a multi-period subproblem and a multi-scene subproblem by considering a multi-period and multi-scene decoupling mechanism, achieving decoupling and accelerated calculation of the SCUC problem through parallel calculation, ensuring that the solving time of the multi-period multi-scene SCUC model of a hydropower unit accessed to an electric power system is effectively reduced within acceptable confidence.
Furthermore, a data initialization method is established through a supervised BP neural network, consistency constraint is ignored, each subproblem is solved in parallel, a corresponding result is obtained, a result close to an optimal solution is used as an initial value of target cascade analysis, and the convergence speed of subproblem parallel calculation is increased.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention, and therefore should not be considered as limiting the scope, and those skilled in the art can also obtain other related drawings based on the drawings without inventive efforts.
FIG. 1 is a flow chart of a multi-period multi-scenario safety constraint unit combination decoupling method of the invention;
FIG. 2 is a schematic diagram of a multi-period multi-scenario safety constraint unit combined decoupling system module according to the present invention;
FIG. 3 is a graph of a hydropower station output performance linearization used in the present invention;
in fig. 4, (a) is an original hydroelectric system topological diagram, and (b) is an equivalent hydroelectric single-station system topological diagram;
FIG. 5 is a schematic illustration of time period decoupling according to the present invention;
FIG. 6 is a schematic diagram of the S-BPNN used in the present invention;
FIG. 7 is a flow chart of the ATC algorithm proposed by the present invention;
FIG. 8 is a graph of equivalent single station system capacity variation using a conventional centralized algorithm;
FIG. 9 is an equivalent single-station system library capacity variation curve obtained using a parallel solution algorithm.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
In the description of the embodiments of the present invention, it should be noted that if the terms "upper", "lower", "horizontal", "inner", etc. are used for indicating the orientation or positional relationship based on the orientation or positional relationship shown in the drawings or the orientation or positional relationship which is usually arranged when the product of the present invention is used, the description is merely for convenience and simplicity, and the indication or suggestion that the referred device or element must have a specific orientation, be constructed and operated in a specific orientation, and thus, cannot be understood as limiting the present invention. Furthermore, the terms "first," "second," and the like are used merely to distinguish one description from another, and are not to be construed as indicating or implying relative importance.
Furthermore, the term "horizontal", if present, does not mean that the component is required to be absolutely horizontal, but may be slightly inclined. For example, "horizontal" merely means that the direction is more horizontal than "vertical" and does not mean that the structure must be perfectly horizontal, but may be slightly inclined.
In the description of the embodiments of the present invention, it should be further noted that unless otherwise explicitly stated or limited, the terms "disposed," "mounted," "connected," and "connected" should be interpreted broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
The invention is described in further detail below with reference to the accompanying drawings:
referring to fig. 1, it is a flowchart of a multi-period multi-scenario safety constraint unit combination decoupling method of the present invention, and specifically includes the following steps:
s1, establishing a multi-period multi-scene SCUC model according to the uncertainty of water and electricity.
And (4) considering the uncertainty of hydropower, and establishing a multi-period and multi-scene SCUC model. And establishing a multi-period multi-scene SCUC model considering the uncertainty of hydropower.
The objective function for minimizing the operating cost of the power system is as follows:
wherein x is n The method comprises the following steps of (1) providing a variable group in an SCUC model, wherein the variable group comprises thermal power unit output, hydroelectric power unit output, hydropower station displacement, hydropower station overflow and unit start-stop state variables; n is a random scene number; n is the total number of random scenes; pi n Representing the probability of a random scene n, and ∑ pi n =1; t is a time interval number; t is the total time interval; y is the number of the thermal power generating unit; y is the total number of the thermal power generating units; subscript b denotes the reference scene; f. of n (p t,y,n ,z t,y,n ) Representing the operating cost of the power system under a random scene n; f. of b (p t,y,n ,z t,y,n ) Representing the operating cost of the power system under the reference scene b; p is a radical of t,y,n The method comprises the following steps of (1) outputting power of a thermal power generating unit y in a random scene n at a time t; z is a radical of t,y,n The method comprises the following steps that a unit starting and stopping state of a thermal power unit y in a random scene n of a time t is represented, wherein 1 represents starting, and 0 represents stopping; p is a radical of t,y,b The output of the thermal power generating unit y in a time t reference scene b is obtained; z is a radical of t,y,b Set for thermal power generating unit y under time period t reference scene bStart stop state, where 1 represents on and 0 represents off.
The constraint conditions are as follows:
wherein h is n (x n )/h b (x b ) Constraints including power balance, cascade hydropower station reservoir capacity balance, and the like; g n (x n )/g b (x b ) The method comprises inequality constraints such as thermal power unit output limit, hydroelectric power unit output limit, cascade hydropower storage capacity limit, thermal power unit climbing constraint and the like;
and the up/down climbing limit of the thermal power generating unit y is obtained.
S2, acquiring the cascade hydropower parameters by the multi-period multi-scene SCUC model, establishing an original cascade hydropower system, and enabling the original cascade hydropower system to be equivalent to a hydropower single-station system.
And (3) considering the cascade hydroelectric parameters contained in the SCUC model, and determining the parameters of the equivalent hydroelectric single-station system by a double-layer optimization modeling method. Establishing an original step hydroelectric system model, wherein the model is influenced by the following factors: the method comprises the following steps of topological structure among trapezoidal hydropower stations, output performance curve of the hydropower stations, random input flow, reservoir capacity constraint of the hydropower stations, initial reservoir capacity value of the hydropower stations and overflow capacity of reservoirs of the hydropower stations. Referring to fig. 3, the hydropower station output performance curve is linearized. Referring to fig. 4, the original hydroelectric system is equivalent to a single-station system through a double-layer optimization modeling method.
The objective function of the cascade hydroelectric benefit maximization model is as follows:
wherein the superscript O represents the original step hydroelectric system; lambda [ alpha ] t Is the electricity price for time period t; i is the hydropower station number; i is o The number of hydropower stations of the original step hydropower model; k is the number of the linear segment of the hydropower station output performance curve; k is the number of sections of linearization of the output performance curve of the hydropower station;
is the output performance coefficient of the k-th linearized segment of the hydropower station i;
is the water yield of the hydropower station i in the kth section of the time period t; lambda [ alpha ] F Predicting the electricity price in the future;
is the reservoir capacity of the hydropower station i in time period T;
the parameters are binary parameters, when the hydropower station j is a downstream power station of the hydropower station i, the parameters are marked as 1, otherwise, the parameters are 0;
is the future predicted output performance coefficient of the hydropower station j.
And (4) reservoir capacity balance constraint:
wherein,
the overflow amount of the hydropower station i in the time period t is shown;
the input flow of the hydropower station i in a time period t is shown;
a set of upstream hydropower stations being the hydropower station i.
Initial storage capacity constraint:
wherein,
is the percentage of the initial reservoir capacity of the hydropower station i to the total reservoir capacity;
the maximum reservoir capacity of the hydropower station i.
And (3) limiting and restricting the water yield:
wherein
Is the minimum water output of the hydropower station i in the k section;
is the maximum water output of the hydropower station i in the k-th section.
And (4) constraint of storage capacity limitation:
wherein,
is the minimum reservoir capacity of the hydropower station i.
And (4) overflow amount limiting and restricting:
wherein,
is the minimum overflow of the hydropower station i;
is the maximum overflow of the hydropower station i.
And establishing a double-layer optimization model to obtain parameters of the equivalent single-station model. Modeling with the goal of minimizing the income difference between the original cascade hydroelectric system and the equivalent single-station system, wherein the objective function is as follows:
where the superscript E denotes an equivalent single station system.
The constraints of the upper layer model are as follows.
Limiting the output performance coefficient of the equivalent single station system hydropower station:
and (3) limiting the future predicted output performance coefficient of the equivalent single-station system hydropower station:
the objective function of the underlying model is:
the constraint conditions are as follows:
converting the lower layer model into equivalent constraint conditions by using a KKT (Karush-Kuhn-Tucker) condition and a large M method to obtain an equivalent single layer model, wherein the equivalent constraint conditions of the lower layer model are as follows:
wherein,
and
is a lagrange multiplier;
and
is a boolean variable; m 3 To M 8 Is a sufficiently large number.
In order to accelerate the calculation of the equivalent single-layer model, part of parameters of the equivalent single-station system are calculated by utilizing the initial hydraulic energy of the original trapezoidal hydropower system. The initial hydraulic energy calculation formula of the original step hydroelectric system is as follows:
wherein E is O Is the initial water energy of the original step hydroelectric system.
The output performance coefficient of the 1 st linearization segment of the equivalent single station system is as follows:
wherein,
is the output performance curve of the 1 st linearized segment of the equivalent single station system; r is i Is the total inflow of the hydropower station i.
The initial storage capacity of the equivalent single-station system is as follows:
wherein,
is the initial storage capacity of the equivalent single-station system.
And S3, dividing a multi-time-period multi-scene SCUC model based on a multi-time-period decoupling mechanism, and establishing SCUC sub-problems in different time periods.
The SCUC sub-problem divided according to time intervals is established based on a multi-time interval decoupling mechanism so as to carry out time interval decoupling. The SCUC models of different scenes are divided into different time intervals according to time, corresponding SCUC sub-problems are modeled aiming at the different time intervals, in order to guarantee the feasibility of the SCUC problem solution, a penalty item is added into a target function of the sub-problems, and consistency constraint is added into the sub-models. Referring to fig. 5, without loss of generality, it is assumed that the SCUC models of different scenes are divided into 3 time periods, and the corresponding time periods are respectively denoted as c - C and c + The corresponding SCUC sub-problems are respectively marked as SP c- ，SP c And SP c+ . Sub problem SP c- The corresponding time interval is 1 to t c- +1, with an objective function of:
wherein x is c- Is the sub-problem SP c- The variable group comprises the output of a thermal power generating unit, the output of a hydroelectric generating unit, the water displacement of a hydropower station, the water overflow amount of the hydropower station, the start-stop state variable of the unit and the like.
The constraint conditions are as follows:
h c- (x c- )＝0&g c- (x c- )≤0,t＝{1,...,t c- +1}
sub problem SP c The corresponding time period is t c- +1 to t c +1, with an objective function of:
the constraint conditions are as follows:
h c (x c )＝0&g c (x c )≤0,t＝{t c- +1,...,t c +1}
sub problem SP c+ The corresponding time period is t c +1 to T, with an objective function of:
the constraint conditions are as follows:
h c+ (x c+ )＝0&g c+ (x c+ )≤0,t＝{t c +1,...,t c+ }
the subscript (,) is introduced to indicate the coupling variables found from the left model. Introducing the concept of a coupling period, wherein when the period is divided into three segments, let the coupling period be t a ＝t c- +1 and t b ＝t c +1。
To ensure the feasibility of the SCUC problem solution, the following consistency constraints are added to the constraints of the sub-problem.
The consistency constraint of the thermal power generating unit is as follows:
the consistency constraint of the hydropower station storage capacity is as follows:
to model the minimum on/off time of a thermal power generating unit between two consecutive subintervals, a pair of on/off variables is used
The pair of variables represents the thermal power generating unit y in the sub-period c - For the duration of the last time period of time. Minimum remaining on/off duration that thermal power unit y needs to meet in sub-period c
Comprises the following steps:
wherein,
and the shortest starting/stopping time length of the thermal power generating unit y is shown.
Sub-problem SP c In which a stop indication mark is set
The value of the time is equal to the first shutdown time t of the thermal power generating unit y in the sub-period c. And modeling the time t of the first shutdown of the thermal power generating unit y in the sub-period c by using a large M method. From submodel SP c Calculated sub-period c of thermal power generating unit n - The minimum remaining boot time required to be satisfied in (1) is:
wherein, M 1 Is a sufficiently large number.
In the same way, the sub-problem SP c Calculated sub-period c of thermal power generating unit y - The minimum remaining downtime required to be met in (1) is:
wherein M is 2 Is a sufficiently large number.
Y-in-sub-problem SP of thermal power generating unit c- And sub problem SP c The minimum on/off consistency constraint that the boundary needs to satisfy is:
y-in-sub-problem SP of thermal power generating unit c And sub-problem SP c+ The minimum on/off consistency constraint that the boundary needs to satisfy is:
and S4, dividing a multi-period multi-scene SCUC model based on a multi-scene decoupling mechanism, and establishing SCUC sub-problems of different scenes according to climbing coupling constraints.
And establishing an SCUC sub-problem according to climbing coupling constraint based on a multi-scene decoupling mechanism so as to perform scene decoupling. For the sake of brevity, the random scene n and the reference scene b with the equivalent single-station system are taken as examples. The multi-scene decoupling mechanism adopts a strategy similar to the multi-period decoupling mechanism, namely a penalty item is added into an objective function of a random scene n and a reference scene b, and consistency constraint is added into a model. The power system needs to keep power balance all the time, and under the condition that load or water and electricity output fluctuates, the thermal power generating unit needs to adjust output power within a specific time to meet the power balance condition. This means that, in each time period t, the same thermal power generating unit y in different random scenes n needs to satisfy the following hill climbing constraint with the corresponding thermal power generating unit y in the reference scene b, and the constraint needs to be added into the random scene n:
wherein,
and the maximum climbing rate up/down the slope of the thermal power generating unit y is represented.
The output of the reference scene calculated in the random scene n also meets the output limit of the thermal power generating unit:
the consistency constraint to be added in the random scene n and the reference scene b is as follows:
and S5, performing parallel calculation according to a target cascade analysis algorithm, and solving the SCUC sub-problems in different time periods and the SCUC sub-problems in different scenes.
And establishing a data initialization method based on the supervised BP neural network so as to accelerate the convergence speed of the parallel computation of the subproblems.
Referring to fig. 6, the process of the supervised BP neural network is mainly divided into two phases. The first stage is that the signal propagates backwards from the input layer to the hidden layer and finally to the output layer; the second stage is the propagation of the error from the output layer onwards to the hidden layer and finally to the input layer. The weight and bias of the data are adjusted in turn as it propagates backwards. The goal is to roughly estimate the expected pool capacity of the equivalent single-site system at the end of a particular period based on the initial pool capacity, incoming flow and load demand at the beginning of the particular period. The following activation functions are used to ensure that the model is available in both linear and non-linear cases:
where a is the input data.
And (4) taking 70% of the acquired historical data set for model learning, 15% of the acquired historical data set for model verification and 15% of the acquired historical data set for model effect testing.
And initializing other variables, such as the power generation amount, the start-stop state of the thermal power generating unit and the like. And neglecting the consistency constraint, and solving each subproblem in parallel to obtain a corresponding result. These results may be infeasible solutions, but close to optimal solutions, due to the interconnection without consistency constraints, and therefore, may serve as initial values for the target cascade analysis.
Referring to fig. 7, the objective cascade analysis algorithm is used to perform parallel computation of the sub-problems, and the basic principle is to decompose the whole optimization problem into different sub-problems, and introduce a coordinator to perform coordination optimization on the shared variables of the sub-problems, so that the sub-problems can be solved in parallel until the convergence condition is satisfied. And each sub-problem optimizes the model thereof, and introduces a penalty term to ensure that the shared variables of each sub-problem tend to be consistent. An augmented lagrange function is used as a penalty term to penalize behaviors that violate the consistency constraint. The shared variable group e in the coordinator is denoted as a target variable group, and the shared variable γ in each subproblem is denoted as a response variable group.
Sub-problem SP for random scene n c- The objective function is re-modeled as:
wherein h is the iteration number of the target cascade analysis algorithm; target variable group e h Obtaining from the coordinator; zeta h Is a set of lagrange multiplier vectors; ρ is a penalty factor.
Sub-problem SP for random scene n c The objective function is re-modeled as:
sub-problem SP for random scene n c+ The objective function is re-modeled as:
similarly, the child of reference scene bQuestion SP c- The objective function is re-modeled as:
sub-problem SP of reference scene b c The objective function is re-modeled as:
sub-problem SP of reference scene b c+ The objective function is re-modeled as:
the update of the target variable set in the coordinator is:
e h ＝arg min(ζ) T (e h -γ h )+ρ||e h -γ h || 2
when the coordinator receives the response variable group gamma from each subproblem h Then, solving the mathematical model of the coordinator can obtain an updated target variable group e h 。
The update of the set of lagrange multiplier vectors is:
ζ h ＝ζ h-1 +2ρ 2 |e h -γ h |
the convergence conditions are as follows:
|e h -γ h |≤ε
where ε is the convergence accuracy.
And finally, the calculation efficiency of the multi-period multi-scene SCUC problem considering the uncertainty of hydropower is improved by solving the optimization problem in parallel.
Referring to fig. 2, a schematic diagram of a multi-period multi-scenario SCUC decoupling system module in the present invention specifically includes the following modules:
the model establishing module is used for establishing a multi-period multi-scene SCUC model according to the hydropower uncertainty;
the system equivalent module is used for acquiring the cascade hydropower parameters through a multi-period multi-scene SCUC model, establishing an original cascade hydropower system and equating the original cascade hydropower system to a hydropower single-station system;
the first decoupling module is used for dividing a multi-period multi-scene SCUC model based on a multi-period decoupling mechanism and establishing SCUC subproblems at different periods;
the second decoupling module is used for dividing a multi-time-interval multi-scene SCUC model based on a multi-scene decoupling mechanism and establishing SCUC subproblems of different scenes according to climbing coupling constraint;
and the data analysis processing module is used for performing parallel calculation according to a target cascade analysis algorithm and solving the SCUC sub-problems in different time periods and the SCUC sub-problems in different scenes.
Referring to fig. 8-9, the equivalent single-station system storage capacity variation curves obtained by using the conventional centralized algorithm and the parallel solution algorithm are respectively shown, and by observing the two curves, the storage capacity curve obtained by the parallel solution algorithm is similar to the storage capacity curve obtained by the conventional centralized algorithm, which illustrates the effectiveness of the parallel solution algorithm provided by the present invention.
Referring to tables 1 to 3, the solving time lengths of different algorithms are respectively set as the upper solving limit of 5 hours, and when the solving time exceeds 5 hours, the situation is considered to be unsolvable. With the increase of the number of scenes, the time for solving the SCUC problem with the original hydro-electric system by using the traditional centralized algorithm is longest, and the time for solving the SCUC problem with the equivalent single-station system by using the parallel solving algorithm is shortest. When the number of scenes is increased to 15, solving the SCUC problem with the original hydroelectric system by using a traditional centralized algorithm is not solvable; when the number of scenes is increased to 25, the SCUC problem with the equivalent single-station system is solved by using the traditional centralized algorithm. Compared with the traditional centralized algorithm, the parallel solving algorithm has short solving time, but the utilization rate of a CPU and an RAM is increased in the calculation process along with the increase of the number of scenes, and the solving time of the parallel solving algorithm is slightly increased.
TABLE 1 solving SCUC model with original hydroelectric system using traditional centralized algorithm
| Number of |
5 | 10 | 15 | 20 | 25 |
| Solution time | 2.44h | 4.78h | - | - | - |
TABLE 2 SCUC model with equivalent single-station system solved using traditional centralized algorithm
| Number of |
5 | 10 | 15 | 20 | 25 |
| Time to solution | 2653s | 1.74h | 2.93h | 4.23h | - |
TABLE 3 SCUC model with equivalent single-station system solved using parallel solving algorithm
| Number of |
5 | 10 | 15 | 20 | 25 |
| Time to solution | 43.2s | 47.7s | 51.3s | 53.4s | 55.8s |
Therefore, compared with the traditional centralized algorithm, the parallel solving algorithm provided by the invention can ensure the effectiveness of the result on the basis of obviously reducing the solving time of the multi-period multi-scene SCUC model.
An embodiment of the present invention provides a terminal device. The terminal device of this embodiment includes: a processor, a memory, and a computer program stored in the memory and executable on the processor. And when the processor executes the computer program, the steps in the combined decoupling method embodiment of each safety constraint unit are realized. Alternatively, the processor implements the functions of the modules/units in the above device embodiments when executing the computer program.
The computer program may be partitioned into one or more modules/units that are stored in the memory and executed by the processor to implement the invention.
The safety constraint unit combined decoupling device/terminal equipment can be computing equipment such as a desktop computer, a notebook computer, a palm computer and a cloud server. The safety restraint unit combined decoupling device/terminal equipment can comprise, but is not limited to, a processor and a memory.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc.
The memory can be used for storing the computer program and/or the module, and the processor realizes various functions of the safety restraint unit combined decoupling device/terminal equipment by running or executing the computer program and/or the module stored in the memory and calling data stored in the memory.
The module/unit integrated with the decoupling device/terminal device of the safety restraint unit combination can be stored in a computer readable storage medium if the module/unit is realized in the form of a software functional unit and is sold or used as an independent product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer-readable medium may contain suitable additions or subtractions depending on the requirements of legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer-readable media may not include electrical carrier signals or telecommunication signals in accordance with legislation and patent practice.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and various modifications and changes will occur to those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A multi-period multi-scene SCUC decoupling method is characterized by comprising the following steps:
establishing a multi-period multi-scene SCUC model according to the uncertainty of hydropower;
acquiring cascade hydropower parameters by a multi-period multi-scene SCUC model, establishing an original cascade hydropower system, and equating the original cascade hydropower system to a hydropower single-station system;
dividing a multi-time-period multi-scene SCUC model based on a multi-time-period decoupling mechanism, and establishing SCUC sub-problems at different time periods;
dividing a multi-time-interval multi-scene SCUC model based on a multi-scene decoupling mechanism, and establishing SCUC sub-problems of different scenes according to climbing coupling constraints;
and performing parallel calculation according to a target cascade analysis algorithm, and solving the SCUC sub-problems in different time periods and the SCUC sub-problems in different scenes.
2. The method of claim 1, wherein the establishing the multi-period multi-scenario SCUC model is specifically represented as:
taking the minimization of the operation cost of the power system as an objective function:
wherein x is n The method comprises the following steps of (1) providing a variable group in an SCUC model, wherein the variable group comprises thermal power unit output, hydroelectric power station drainage, hydroelectric power station water overflow and unit start-stop state variables; n is a random scene number; n is the total number of random scenes; pi n Representing the probability of a random scene n, and ∑ pi n =1; t is a time interval number; t is the total time interval; y is the number of the thermal power generating unit; y is the total number of the thermal power generating units; subscript b denotes the reference scene; f. of n (p t,y,n ,z t,y,n ) Representing the operating cost of the power system under a random scene n; f. of b (p t,y,n ,z t,y,n ) Representing the operating cost of the power system under the reference scene b; p is a radical of t,y,n The method comprises the following steps of (1) outputting power of a thermal power generating unit y in a random scene n at a time t; z is a radical of formula t,y,n The method comprises the following steps that a unit starting and stopping state of a thermal power unit y in a random scene n of a time t is represented, wherein 1 represents starting, and 0 represents stopping; p is a radical of t,y,b As thermal powerThe output of the unit y in the time t reference scene b; z is a radical of t,y,b The method comprises the following steps that a unit starting and stopping state of a thermal power unit y in a time t reference scene b is shown, wherein 1 represents starting, and 0 represents stopping;
the constraint conditions are as follows:
wherein h is n (x n )/h b (x b ) The method comprises the following steps of power balance and cascade hydropower station storage capacity balance equality constraint; g is a radical of formula n (x n )/g b (x b ) The method comprises inequality constraints such as thermal power unit output limit, hydroelectric power unit output limit, cascade hydropower storage capacity limit, thermal power unit climbing constraint and the like;
the up/down hill climbing of the thermal power generating unit y is limited.
3. The multi-period multi-scenario SCUC decoupling method of claim 1, wherein the original cascade hydroelectric system model is specifically expressed as:
taking the gain maximization of the cascade hydroelectric system as an objective function:
wherein the superscript O represents the original step hydroelectric system; lambda t Is the electricity price for time period t; i is the hydropower station number; i is o The number of hydropower stations of the original step hydropower model; k is the number of the linear segment of the hydropower station output performance curve; k is the number of sections of linearization of the output performance curve of the hydropower station;
is the output performance coefficient of the k-th linearized segment of the hydropower station i;
is the water yield of the hydropower station i in the kth section of the time period t; lambda [ alpha ] F Predicting the electricity price in the future;
is the reservoir capacity of the hydropower station i in time period T;
the parameters are binary parameters, when the hydropower station j is a downstream power station of the hydropower station i, the parameters are marked as 1, otherwise, the parameters are 0;
is the future predicted output performance coefficient of the hydropower station j;
and (4) reservoir capacity balance constraint:
wherein,
the overflow amount of the hydropower station i in the time period t is obtained;
the input flow of the hydropower station i in a time period t is shown;
upstream hydropower station set for hydropower station iCombining;
initial storage capacity constraint:
wherein,
is the percentage of the initial reservoir capacity of the hydropower station i to the total reservoir capacity;
the maximum reservoir capacity of the hydropower station i;
and (3) limiting and restricting the water yield:
wherein,
is the minimum water output of the hydropower station i in the k section;
is the maximum water output of the hydropower station i in the kth section;
and (4) constraint of storage capacity limitation:
wherein,
is the minimum reservoir capacity of the hydropower station i;
and (4) overflow amount limiting and restricting:
wherein,
is the minimum overflow of the hydropower station i;
is the maximum overflow of the hydropower station i.
4. The multi-period multi-scenario SCUC decoupling method of claim 1, wherein the original cascade hydroelectric system is equivalent to a hydroelectric single-station system through a double-layer optimization model, which is specifically represented as:
minimizing the income difference between the original cascade hydroelectric system and the hydroelectric single-station system as an objective function of an upper layer model:
the constraint conditions of the upper layer model are as follows:
limiting the output performance coefficient of the hydropower single station system:
and (3) limiting the future predicted output performance coefficient of the hydropower single station system:
the objective function of the underlying model is:
the constraint conditions of the lower layer model are as follows:
converting the lower layer model into an equivalent constraint condition by adopting a KKT condition and a large M method to obtain an equivalent single-layer model;
the method comprises the following steps of calculating parameters of a hydropower single station system according to initial water energy of an original step hydropower system, wherein the initial water energy of the original step hydropower system is as follows:
wherein E is O The initial water energy of the original step hydroelectric system;
the output performance coefficient of the 1 st linearization segment of the hydropower single station system is as follows:
wherein,
is the output performance curve of the 1 st linearized segment of the equivalent single station system; r is i The total inflow of the hydropower station i;
the initial storage capacity of the hydropower single station system is as follows:
wherein,
is the initial storage capacity of the equivalent single-station system.
5. The method of claim 1, wherein the multi-period and multi-scenario SCUC decoupling mechanism is based on dividing a multi-period and multi-scenario SCUC model, and building SCUC sub-problems at different periods, wherein the SCUC sub-problems at different periods are specifically expressed as:
the SCUC model of different scenes is divided into three time periods, and the corresponding time periods are c - C and c + The corresponding SCUC subproblems are respectively SP c- ，SP c And SP c+ ；
SCUC sub-problem SP c- The corresponding time interval is 1 to t c- +1, the objective function is:
the constraint conditions are as follows:
h c- (x c- )＝0&g c- (x c- )≤0,t＝{1,...,t c- +1}
SCUC sub-problem SP c The corresponding time period is t c- +1 to t c +1, the objective function is:
the constraint conditions are as follows:
h c (x c )＝0&g c (x c )≤0,t＝{t c- +1,...,t c +1}
SCUC sub-problem SP c+ The corresponding time period is t c +1 to T, the objective function is:
the constraint conditions are as follows:
h c+ (x c+ )＝0&g c+ (x c+ )≤0,t＝{t c +1,...,t c+ }
wherein x is c- 、x c And x c+ Are sub-problems SP, respectively c- 、SP c And SP c+ The variable group in (1) comprises output of a thermal power generating unit, output of a hydroelectric generating unit, water displacement of a hydropower station, water overflow of the hydropower station and start-stop state variables of the unit.
6. The method according to claim 1, wherein the multi-time-interval multi-scene SCUC decoupling method is characterized in that a multi-time-interval multi-scene SCUC model is divided based on a multi-scene decoupling mechanism, and SCUC subproblems of different scenes based on climbing coupling constraints are established, and specifically: penalty items are added into the objective functions of the random scene and the reference scene of the hydropower single station system, consistency constraint is added, and therefore the same thermal power generating unit in different random scenes and the thermal power generating unit corresponding to the reference scene meet climbing constraint in each time interval, and the climbing constraint is expressed as follows:
the output of a reference scene in the random scene meets the output limit of the thermal power generating unit, and is specifically represented as follows:
the consistency constraint added in the random scene and the reference scene is specifically expressed as:
wherein,
and the maximum climbing rate up/down of the thermal power generating unit y is represented.
7. The multi-period multi-scenario SCUC decoupling method of claim 1, wherein when the target cascade analysis algorithm performs parallel computation, a coordinator is introduced to perform coordination optimization on SCUC subproblems, and each subproblem is solved in parallel until a convergence condition is satisfied; the initial value of the target cascade analysis is obtained through a supervised BP neural network.
8. A multisession and multiscreen SCUC decoupling system is characterized by comprising the following modules:
the model establishing module is used for establishing a multi-period multi-scene SCUC model according to the hydropower uncertainty;
the system equivalent module is used for acquiring the cascade hydropower parameters through a multi-period multi-scene SCUC model, establishing an original cascade hydropower system and equating the original cascade hydropower system to a hydropower single-station system;
the first decoupling module is used for dividing a multi-time-interval multi-scene SCUC model based on a multi-time-interval decoupling mechanism and establishing SCUC subproblems at different time intervals;
the second decoupling module is used for dividing a multi-time-interval multi-scene SCUC model based on a multi-scene decoupling mechanism and establishing SCUC subproblems of different scenes according to climbing coupling constraint;
and the data analysis processing module is used for performing parallel calculation according to a target cascade analysis algorithm and solving the SCUC sub-problems in different time periods and the SCUC sub-problems in different scenes.
9. An apparatus comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1-7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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| CN116470509A (en) * | 2023-03-16 | 2023-07-21 | 国网湖北省电力有限公司经济技术研究院 | Large-scale safety constraint unit combination acceleration method for improving model simplicity |
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| CN116454890A (en) * | 2023-04-20 | 2023-07-18 | 中国南方电网有限责任公司 | Combined control method, device and equipment for unit based on SCUC model |
| CN116454890B (en) * | 2023-04-20 | 2024-02-06 | 中国南方电网有限责任公司 | Combined control method, device and equipment for unit based on SCUC model |
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