CN108009941A - Solve the nested optimization method of water light complementation power station Optimization of Unit Commitment By Improved - Google Patents
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Abstract
The present invention provides a kind of nested optimization method for solving water light complementation power station Optimization of Unit Commitment By Improved, it is characterised in that including：Step 1：Predict photoelectricity output process, consider the uncertainty of prediction, generate a variety of photoelectricity output scenes, and calculate the corresponding probability of every kind of scene；Step 2：Water light complementation power station Unit Combination mathematical model is established, determines object function, constraints and the decision variable of the model；The optimization aim of water light complementation power station Unit Combination mathematical model is that Hydropower Unit average water consumption is minimum under Scenario, and calculating formula is：Step 3：The nested optimization method of construction, outer layer are started shooting under number of units using intelligent algorithm optimization unit start number of units, internal layer in given unit, and load shifting rate strategy is determined using dynamic programming method.
Description
Technical Field
The invention belongs to the cross field of renewable energy utilization and reservoir scheduling, and particularly relates to a nesting optimization method for solving a water-light complementary power station unit combination problem.
Technical Field
The solar photovoltaic is a non-schedulable renewable energy source, the output of the solar photovoltaic is influenced by factors such as day and night alternation, weather change, cloud layer thickness and the like, and the solar photovoltaic has obvious intermittence, volatility and randomness. The large-scale direct photovoltaic grid connection brings great pressure to the peak regulation and stable operation of the system. The method is characterized in that water-light complementary power generation is implemented, photoelectricity is transmitted to a hydropower station, the photoelectricity is compensated by utilizing the quick adjustment capacity of a hydroelectric generating set, and the photoelectricity and the hydropower are packed together to be networked, so that the method is an effective way for promoting photoelectricity absorption.
However, due to the strong randomness of the photoelectric, the current prediction method cannot accurately predict the photoelectric. When uncertain photoelectricity is accessed into the hydropower station, the hydropower scheduling decision becomes uncertain, and the difficulty of safe and economic operation of the hydropower station is further increased. The hydropower station unit combination problem is the problem to be solved firstly when a short-term power generation plan of a hydropower station is compiled. The reasonable starting and stopping scheme can further increase the economic benefit of the hydropower station or save the operation cost. Due to the complexity of the hydroelectric generating set combination problem, optimization and solution are very difficult. Especially for large hydropower stations (a large number of units and large installed capacity), when the existing optimization technology is adopted for solving, the unification of calculation precision and efficiency can not be realized: if a dynamic programming method is adopted, a global optimal solution of the problem can be obtained, but the method occupies a large number of calculation inner layers and cannot effectively process time period coupling constraints (such as minimum startup and shutdown constraints); the time interval coupling constraint can be processed by adopting an intelligent algorithm, but the time interval coupling constraint can be trapped into a local optimal solution when the optimization variables are more, and the optimization is represented as premature convergence. Therefore, the solution for researching the water-light complementary power station unit combination problem not only can provide technical support for economic operation of the water-light complementary power station unit, but also can further enrich and expand the hydropower optimization scheduling theory.
Disclosure of Invention
The invention aims to solve the problem of safe and economic operation of a water-light complementary power station, and provides a nested optimization method for solving the problem of unit combination of the water-light complementary power station.
In order to achieve the purpose, the invention adopts the following scheme:
the invention provides a nesting optimization method for solving a water-light complementary power station unit combination problem, which is characterized by comprising the following steps of: the method comprises the following steps: predicting a photoelectric output process, generating various photoelectric output scenes by considering the prediction uncertainty, and calculating the probability corresponding to each scene; step two: establishing a water-light complementary power station unit combination mathematical model, and determining a target function, a constraint condition and a decision variable of the model; the optimization target of the water-light complementary power station unit combination mathematical model is that the average water consumption of the hydroelectric generating set is minimum under multiple scenarios, and the calculation formula is as follows:in the formula: f is the average water consumption of the water-light complementary power station in the whole scheduling period; n is the number of hydropower station units; t is the number of scheduling period; s is the number of photoelectric output scenes; n, t and s are respectively the unit number, the scheduling time interval number and the photoelectric scene number; u. of n,t The on-off state (0-1 variable) of the unit is obtained;the machine flow of the machine set is measured; delta t is the scheduling period length; step three: the method comprises the steps of constructing a nested optimization method, optimizing the number of the units for starting by adopting an intelligent algorithm (such as a genetic algorithm and a cuckoo algorithm) on the outer layer, and determining the optimal load distribution strategy by adopting a dynamic planning method on the inner layer under the condition of the given number of the units for starting.
The nesting optimization method for solving the water-light complementary power station unit combination problem provided by the invention can also have the following characteristics: in the first step: firstly, a mathematical statistical method or a physical method is adopted to predict the photoelectric output process (P) of the next day t T =1, \ 8230;, T); second, the predicted contribution process is subtracted by a different prediction error (e) 1 ,e 2 ,e 3 ) A variety of scenarios can be generated; finally, it is assumed that the prediction error of the photo follows a normal distribution N (μ, σ) 2 )，By using discrete probability distribution instead of continuous probability distribution, the probability (rho) corresponding to each scene can be calculated 1 ,ρ 2 ,ρ 3 ) The calculation formula is as follows:
the nesting optimization method for solving the water-light complementary power station unit combination problem provided by the invention can also have the following characteristics: in the second step: in the formula: f. of rph The relation among the unit excess flow, output and water head in the dynamic characteristic curve of the hydroelectric generating set; f. of vz The relation between water level and reservoir capacity is formed; f. of qz The relationship of the lower discharge flow and the tail water level is adopted;the output of the nth unit in the s scene in the t time period; the water purification head, the dam front water level, the tail water level and the head loss in the t time period in the s type of scenes are respectively;andthe initial and final reservoir capacities at the t-th and t-th time periods in the s-th scenario.
The nesting optimization method for solving the water-light complementary power station unit combination problem provided by the invention can also have the following characteristics: in the second step, the constraint conditions considered by the model are as follows: water quantity balance constraint, storage capacity constraint, machine passing flow constraint and machineGroup output constraint, power balance constraint, load standby constraint, output lifting constraint, minimum start-stop constraint and vibration region constraint, in the formula: i is t The reservoir warehousing flow is the t-th time period;the total generating flow of the reservoir in the t-th time period of the s-th scene; WP t s The water discharge rate of the reservoir in the t th time period in the s th scene is obtained;andrespectively the lower limit and the upper limit of the storage capacity;andrespectively is a lower limit value and an upper limit value of the generating flow of the nth unit in the t period;andrespectively the lower limit and the upper limit of the unit output;P t s photoelectric force generation values for the t-th time period of the s-th scene; d t The total load assigned to the system; LR t A load reserve value at the t-th time period of the hydropower station; Δ p of d And Δ p u Respectively the upper limit values of the output descending speed and the output ascending speed of the hydropower station; SU n And SD n The minimum duration time of the starting and stopping states of the hydroelectric generating set; su n,t Indicating the state for the starting process of the unit (1 is starting, and 0 is non-starting); sd n,t Indicating the state for the shutdown process of the unit (1 is closed and 0 is closed);andrespectively the lower limit and the upper limit of the vibration area of the unit.
The nesting optimization method for solving the water-light complementary power station unit combination problem provided by the invention can also have the following characteristics: in the third step, considering that most of the existing hydropower stations are composed of the same units, the determined on-off state of the units in the unit combination optimization problem is converted into the determined number of the units to be started, so that the dimension reduction is realized.
The nesting optimization method for solving the water-light complementary power station unit combination problem provided by the invention can also have the following characteristics: in the third step, when the outer layer adopts an intelligent algorithm to optimize the number of the units for starting up, in order to effectively process the minimum start-up and shut-down constraints in the unit combination model and simultaneously realize dimension reduction, the encoding mode of the intelligent algorithm solution is as follows:m is more than or equal to 2 and less than T, wherein:is the solution of the intelligent algorithm; m is the number of time segments for dividing the whole scheduling period according to the number of the online units; t is the number of time segments in the whole scheduling period; x is the number of 1 ,x 2 ,…,x m-1 Respectively changing the number of the units in the whole scheduling period and the number of the previous period;are respectively 1 to x 1 ,x 1 +1～x 2 ,…,x m-1 The number of the units started in the period of +1 to T.
The nesting optimization method for solving the water-light complementary power station unit combination problem provided by the invention can also have the following characteristics: in the third step, when the inner layer adopts dynamic programming to perform optimal load distribution, the two-stage recursion equation is as follows:in the formula:as a total load ofOptimal water consumption distributed among the d units; f. of rph (p d,t ,h t ) As a load of p d,t Head of water h t Water consumption of the d-th unit;as a total load ofOptimal water consumption is allocated among d-1 units.
The nesting optimization method for solving the water-light complementary power station unit combination problem provided by the invention can also have the following characteristics: before executing the nested optimization, a dynamic programming method is adopted in advance to calculate the optimal load distribution strategy of the unit, and the optimal load distribution strategy is stored in a database; and directly calling an optimization result when the nested optimization is executed so as to improve the optimization efficiency.
Action and Effect of the invention
The nesting optimization method provided by the invention has theoretical global optimality, the number of optimized variables is small, and the minimum startup and shutdown constraint in the unit combination model can be effectively processed. In addition, uncertainty of photoelectric output prediction is considered in the optimization model, so that the scheduling scheme formulated according to the invention can still realize safe and economic operation of the water-light complementary power station under the condition of inaccurate photoelectric prediction.
Drawings
FIG. 1 is a flowchart of a nested optimization method for solving a water-light complementary power station unit combination problem in an embodiment of the present invention;
fig. 2 is a schematic diagram of an encoding mode of an intelligent algorithm during outer layer optimization.
Detailed Description
The following describes in detail a specific embodiment of the nested optimization method for solving the problem of the water-photovoltaic complementary power plant unit combination according to the present invention with reference to the accompanying drawings.
< example >
As shown in fig. 1, the nested optimization method for solving the water-photovoltaic complementary power station unit combination problem provided by this embodiment includes the following steps:
1. predicting a photoelectric output process; considering the prediction uncertainty, generating a plurality of photoelectric output scenes, and calculating the probability corresponding to each scene;
firstly, a mathematical statistical method or a physical method is adopted to predict the photoelectric output process (P) of the next day t ,t＝1,…,T)；
Second, the predicted contribution process is subtracted by a different prediction error (e) 1 ,e 2 ,e 3 ) A variety of scenarios may be generated.
Finally, it is assumed that the prediction error of the photo follows a normal distribution N (μ, σ) 2 ) The discrete probability distribution is used to replace the continuous probability distribution, and the probability (rho) corresponding to each scene can be calculated 1 ,ρ 2 ,ρ 3 ) The calculation formula is as follows:
2. and establishing a water-light complementary power station unit combination mathematical model, and determining a target function, a constraint condition and a decision variable of the model.
The optimization objectives of the model are: the average water consumption of the hydroelectric generating set under multiple scenarios is minimum, and the calculation formula is as follows:
in the formula: f. of rph The relation among the unit excess flow, output and water head in the dynamic characteristic curve of the hydroelectric generating set; f. of vz The relation between water level and reservoir capacity is formed; f. of qz The relationship of the lower discharge flow and the tail water level is adopted;the output of the nth unit in the s scene in the t time period;the water purification head, the dam front water level, the tail water level and the head loss in the t time period in the s type of scenes are respectively;andthe storage capacity of the reservoir is at the beginning of the t-th time period and at the end of the t-th time period in the s-th scene.
The constraints considered by the model are: the system comprises a water quantity balance constraint, a storage capacity constraint, an over-machine flow constraint, a unit output constraint, an electric power balance constraint, a load standby constraint, an output lifting constraint, a minimum start-stop constraint and a vibration area constraint.
In the formula: i is t The reservoir warehousing flow is the t-th time period;the total generating flow of the reservoir in the t-th scene is obtained; WP t s The water discharge rate of the reservoir in the t th time period in the s th scene is obtained;andrespectively the lower limit and the upper limit of the storage capacity;andrespectively is a lower limit value and an upper limit value of the generating flow of the nth unit in the t period;andrespectively the lower limit and the upper limit of the unit output; p is t s Photoelectric force value is generated for the t-th time period of the s-th scene; d t The total load assigned to the system; LR t A load reserve value at the t-th time period of the hydropower station; Δ p d And Δ p u Respectively the upper limit values of the output descending speed and the output ascending speed of the hydropower station; SU n And SD n Minimum holding of starting and stopping states of hydroelectric generating setContinuing for a time; su n,t Indicating the state for the starting process of the unit (1 is starting, and 0 is non-starting); sd n,t Indicating the state for the shutdown process of the unit (1 is closed and 0 is closed);andrespectively the lower limit and the upper limit of the vibration area of the unit.
The model inputs are: total load given by the system, reservoir inflow and different photoelectric prediction scenarios.
The variables to be optimized in the above model are: the on-off state of the unit, the output of the unit, the flow of the unit passing through the unit and the water head.
3. And constructing a nested optimization method, wherein the outer layer adopts an intelligent algorithm to optimize the number of the starting units of the unit, and the inner layer adopts a dynamic planning method to determine the optimal load distribution strategy under the given number of the starting units of the unit.
Considering that most of the existing hydropower stations are composed of the same units, the on-off state of the optimized units in the model can be further converted into the number of the started optimized units.
Due to the existence of the minimum startup and shutdown constraint, the number of the startup units of the unit is required to be kept unchanged in a plurality of continuous adjacent time intervals. As shown in fig. 2, optimizing the number of unit boots during the whole scheduling period may further be converted into optimizing the time when the number of unit boots changes and the number of unit boots. Therefore, the encoding method of the intelligent algorithm solution can be represented by the following formula:
the number of the sets started in the whole scheduling period can be obtained by the following decoding mode:
in the formula:is an individual (solution) of the intelligent algorithm; y is a decision variable set in the whole scheduling period;is the solution of the intelligent algorithm; m is the number of time segments for dividing the whole scheduling period according to the number of the online units; t is the number of time segments in the whole scheduling period; x is a radical of a fluorine atom 1 ,x 2 ,…,x m-1 Respectively changing the number of the units in the whole scheduling period and the number of the previous period;are respectively 1 to x 1 ,x 1 +1～x 2 ,…,x m-1 And starting the units within the time interval of + 1-T.
When the dynamic planning method is adopted in the inner layer to optimize the load distribution strategy, the calculation of the dynamic planning can be completed in advance due to the fact that the calculation time of the dynamic planning is long. Namely, calculating the optimal distribution strategy of all possible water head loads (the load born by each unit and the generated flow) under different starting units. When the nesting optimization is carried out, related calculation results are directly called. When the optimal load distribution is carried out by adopting dynamic planning, a two-stage recursion equation is as follows:
in the formula:as a total load ofOptimal water consumption distributed among the d units; f. of rph (p d,t ,h t ) Is a load of p d,t Head of water h t Water consumption of the d-th unit;as a total load ofOptimal water consumption is allocated among d-1 units.
The above embodiments are merely illustrative of the technical solutions of the present invention. The nested optimization method for solving the problem of the water-light complementary power station unit combination is not limited to the contents described in the above embodiments, but is subject to the scope defined by the claims. Any modification or supplement or equivalent replacement made by a person skilled in the art on the basis of this embodiment is within the scope of the invention as claimed in the claims.
Claims (8)
1. A nesting optimization method for solving a water-light complementary power station unit combination problem is characterized by comprising the following steps:
the method comprises the following steps: predicting a photoelectric output process, generating various photoelectric output scenes by considering the prediction uncertainty, and calculating the probability corresponding to each scene;
step two: establishing a water-light complementary power station unit combination mathematical model, and determining a target function, a constraint condition and a decision variable of the model;
the optimization target of the water-light complementary power station unit combination mathematical model is that the average water consumption of the hydroelectric generating set is minimum under multiple scenarios, and the calculation formula is as follows:
in the formula: f is the average water consumption of the water-light complementary power station in the whole scheduling period; n is the number of hydropower station units; t is the number of scheduling period; s is the number of photoelectric output scenes; n, t and s are respectively the unit number, the scheduling time interval number and the photoelectric scene number; u. u n,t The on-off state (0-1 variable) of the unit is set;the machine unit flow rate is measured; delta t is the scheduling period length;
step three: and constructing a nested optimization method, wherein the outer layer optimizes the number of the started units by adopting an intelligent algorithm, and the inner layer determines the optimal load distribution strategy by adopting a dynamic planning method under the condition of giving the number of the started units.
2. The method for nested optimization for solving the problem of the combination of the water-light complementary power station set according to claim 1, is characterized in that:
wherein, in the step one: firstly, a mathematical statistical method or a physical method is used to predict the photoelectric output process (P) of the next day t T =1, \ 8230;, T); second, the predicted contribution process is subtracted by a different prediction error (e) 1 ,e 2 ,e 3 ) A variety of scenarios may be generated; finally, it is assumed that the prediction error of the photo follows a normal distribution N (μ, σ) 2 ) The discrete probability distribution is used to replace the continuous probability distribution, and the probability (rho) corresponding to each scene can be calculated 1 ,ρ 2 ,ρ 3 ) The calculation formula is as follows:
3. the method for nested optimization for solving the problem of the combination of the water-light complementary power station set according to claim 1, is characterized in that:
wherein, in the step two:
in the formula: f. of rph The relation among the unit excess flow, output and water head in the dynamic characteristic curve of the hydroelectric generating set; f. of vz The relation between water level and reservoir capacity is formed; f. of qz The relationship between the downward discharge flow and the tail water level is adopted;the output of the nth unit in the s scene in the t time period;the water purification head, the dam front water level, the tail water level and the head loss in the t-th time period in the s-th scene are respectively;andthe storage capacity of the reservoir is at the beginning of the t-th time period and at the end of the t-th time period in the s-th scene.
4. The method for nested optimization for solving the problem of the combination of the water-light complementary power station set according to claim 3, wherein the method comprises the following steps:
in the second step, the constraint conditions considered by the model are as follows: water balance constraint, reservoir capacity constraint, excessive flow constraint, unit output constraint, power balance constraint, load standby constraint, output lifting constraint, minimum start-stop constraint and vibration area constraint,
in the formula: i is t The reservoir warehousing flow is the t-th time period;the total generating flow of the reservoir in the t-th scene is obtained; WP of t s For the s th scene of reservoirWater reject flow in the middle t period;andrespectively the lower limit and the upper limit of the storage capacity;andrespectively is a lower limit value and an upper limit value of the generating flow of the nth unit in the t period;andrespectively the lower limit and the upper limit of the unit output; p is t s Photoelectric output value is obtained for the t-th time period in the s-th situation; d t The total load assigned to the system; LR t A load reserve value at the t-th time period of the hydropower station; Δ p d And Δ p u Respectively the upper limit values of the output descending and ascending speeds of the hydropower station; SU n And SD n The minimum duration of the starting and stopping states of the hydroelectric generating set; su n,t Indicating the state for the starting process of the unit (1 is starting, and 0 is non-starting); sd n,t Indicating the state (1 is closed and 0 is not closed) for the shutdown process of the unit;andrespectively the lower limit and the upper limit of the vibration area of the unit.
5. The nested optimization method for solving the problem of the water-light complementary power station unit combination as claimed in claim 1, wherein:
in the third step, the startup and shutdown states of the determined units are converted into the number of the startup units of the determined units, so that the dimension reduction is realized.
6. The method for nested optimization for solving the problem of the combination of the water-light complementary power station set according to claim 1, is characterized in that:
in the third step, when the outer layer adopts the intelligent algorithm to optimize the number of the units during startup, in order to effectively process the minimum startup and shutdown constraints in the unit combination model and simultaneously realize dimension reduction, the coding mode of the intelligent algorithm solution is as follows:
in the formula:is the solution of the intelligent algorithm; m is the number of time segments for dividing the whole scheduling period according to the number of the online units; t is the number of time segments in the whole scheduling period; x is the number of 1 ,x 2 ,…,x m-1 Respectively numbering the time interval before the number of the units started in the whole scheduling period is changed;are respectively 1 to x 1 ,x 1 +1～x 2 ,…,x m-1 And starting the units within the time interval of + 1-T.
7. The nested optimization method for solving the problem of the water-light complementary power station unit combination as claimed in claim 1, wherein:
wherein, in the third step, when the inner layer adopts dynamic planning to carry out the optimal load distribution, the two-stage recurrence equation is as follows:
in the formula:as a total load ofOptimal water consumption distributed among the d units; f. of rph (p d,t ,h t ) Is a load of p d,t Water head of h t Water consumption of the d-th unit;as a total load ofOptimal water consumption for distribution among d-1 units.
8. The nested optimization method for solving the problem of the water-light complementary power station unit combination as claimed in claim 1, wherein:
before executing nested optimization, a dynamic programming method is adopted in advance to calculate a unit optimal load distribution strategy, and the optimal load distribution strategy is stored in a database; the optimization results are directly invoked when performing nested optimizations.
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