CN111476475B - Short-term optimization scheduling method for cascade hydropower station under multi-constraint condition - Google Patents
Short-term optimization scheduling method for cascade hydropower station under multi-constraint condition Download PDFInfo
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Abstract
The application discloses a short-term optimization scheduling method of a cascade hydropower station under a multi-constraint condition, which comprises the following steps: s1, establishing an objective function with maximum power generation benefit as a target; s2, establishing constraint conditions of the objective function; and S3, solving an objective function to obtain a short-term dispatching process of each power station reservoir in the cascade hydropower station. The method determines the short-term dispatching process of each hydropower station reservoir, so that the power generation benefit of the system is maximum.
Description
Technical Field
The application belongs to the technical field of cascade hydropower station optimal scheduling, and particularly relates to a cascade hydropower station short-term optimal scheduling method under a multi-constraint condition.
Background
Along with the continuous increase of the power market reform, more clean, low-cost, high-efficiency and sustainable hydropower participates in the power market competition, and the optimal allocation of resources is realized. However, due to the complex characteristics of water supply uncertainty, large difference of regulation performance of reservoirs, close cascade hydraulic connection and the like, the water and electricity power generation enterprises can deal with the scheduling of the amount of power delivered according to the electric power market, so that the water and electricity participate in the electric power market with great difficulty. Meanwhile, the cascade scheduling in the market environment does not pursue the maximum power generation amount any more, but realizes the maximization of the hydropower benefit on the premise of conforming to the national clean energy policy. Therefore, based on a multi-element power market and a north-dish river cascade hydropower price evolution mechanism under multiple uncertain influence factors, a practical north-dish river basin cascade hydropower station group scheduling model under the power market condition is provided based on the electricity price factors, and the method is a key problem to be solved.
Disclosure of Invention
The application aims to overcome the defects of the prior art, provides a cascade hydropower station short-term optimal scheduling method under a multi-constraint condition, and determines the short-term scheduling process of reservoirs of each hydropower station so as to maximize the power generation benefit of the system.
In order to solve the technical problems, the application provides a short-term optimization scheduling method for a cascade hydropower station under a multi-constraint condition, which is characterized by comprising the following steps:
s1, establishing an objective function with maximum power generation benefit as a target;
s2, establishing constraint conditions of the objective function;
and S3, solving an objective function to obtain a short-term dispatching process of each power station reservoir in the cascade hydropower station.
Further, the expression of the objective function is:
wherein: f is a power generation benefit function; t, M is the number of schedule periods and the number of hydropower stations;the power output and electricity price of the hydropower station m in the period t are as follows; delta t For a period of time t hours, el m For the electric quantity of m-number power station in the control period with lag time, < >>The average electricity price after the control period of the m-number power station.
Further, the constraint includes:
(1) Water balance
wherein ：for the reservoir m period t+1 primary water storage, < >>The water storage capacity is reserved for the reservoir in the period of m; /> The total storage flow, the point-starting flow and the water discharge flow of the reservoir m in the period t are respectively;the storage flow of the period interval of the m-number hydropower station t is represented, and the total storage flow of the most upstream hydropower station is represented; k (K) m The number of the directly upstream power stations of the m-number hydropower station; u (U) m An upstream power station label array is directly arranged for the m-number hydropower station; as a function f (m, U m [k]T) calculating the sum of the delivery flows of the kth direct upstream power station of the m-number hydropower station in each period and the delivery flows of the kth direct upstream power station in each period to the power station m in the period t,
l k,m representing the maximum and minimum water lag time period number between the kth direct upstream power station of the m-number hydropower station and the m-number power station; />Representing the direct upstream U of a hydropower station of number m m [k]Delivery of number power station in n periodThe flow of the power station with the number m is flowed in the period t; />Is U (U) m [k]Delivery flow of the number hydropower station->Corresponding time delay period numbers;
(2) Last water level control
wherein ：schedule end of period water level, zend for reservoir m m For its control target value;
(3) Power generation flow constraints
wherein ：the maximum power generation reference flow of the reservoir m in the period t is set;
(4) Power station output constraint
wherein ：minimum and maximum output limits of the hydropower station m in a t period;
(5) Cascade total force limit
wherein ：h t ，representing the lower and upper limits of the total output of the hydroelectric system;
(6) Grid partition output limit
The system comprises a plurality of first-level sub-partitions and units, each first-level sub-partition can also comprise a plurality of second-level sub-partitions and units, and so on, then:
wherein ,indicating the lower limit of the output of the ith section in the ith period; />The total effective output is the i-number subarea; i represents the total number of partitions; />Representing a recursive function for calculating the effective output of the i-number partition t period;
(7) Reservoir level constraint
wherein ：representing the primary water level and the upper and lower limits of the period t of the m-number hydropower station;
(8) Delivery flow constraints
wherein ：minimum comprehensive water consumption constraint and maximum delivery flow limit of the reservoir m in the period t;
(9) Hydropower station vibration zone restraint
wherein ：the upper limit and the lower limit of a kth output vibration area of a t period of an m-number hydropower station are expressed, and +.>Related to (I)>The average tail water level of the m-number hydropower station in the t period;
(10) Minimum power-on output constraint
Wherein: pmin m Representing the minimum starting-up output of the number m hydropower station, namelyGreater than pmin m Or 0;
(11) Hydropower station output climbing limit
wherein ：Δpm Representing the maximum output lifting limit of the adjacent time period of the m-number hydropower station;
(12) Hydropower station output fluctuation limit
wherein ：tνm The minimum interval time period number of the output lifting of the m-number hydropower station is that the minimum time period is required to last at the highest and lowest points in the output lifting process of one round m A time period;
(13) Minimum out-force rise and fall time period number limit
The time interval from the rising start to the falling start of the output of the m-number hydropower station or from the falling start to the rising start is not less than tv m A time period.
Further, an objective function is solved by adopting a correlation search method.
Furthermore, in the process of solving the objective function, the power generation flow is used as a decision variable.
Further, the single-step association searching process consists of four basic operations of initial searching, influence range expansion, influence range edge correction and warehouse-in and warehouse-out water quantity difference correction.
Compared with the prior art, the application has the following beneficial effects: according to the cascade hydropower station short-term optimal scheduling method under the multi-constraint condition, the short-term scheduling process of each hydropower station reservoir is determined, and the power generation benefit of the system is maximum.
Drawings
FIG. 1 is a schematic diagram of a three-variable associative search method: (a) A search pattern satisfying the constraint of the output fluctuation (b) a search pattern satisfying the constraint of the output fluctuation and the output climbing;
FIG. 2 is a diagram of a three-variable associative search output process;
FIG. 3 is a schematic diagram of an associative search pattern;
FIG. 4 is a partial adjustment that meets the set output ripple limit;
FIG. 5 is a partial correction to meet the number of flow ramp periods;
FIG. 6 is a partial adjustment that satisfies the edge hill climbing constraint of the flow adjustment period;
FIG. 7 is a partial adjustment of edge power flow smoothness to meet the flow adjustment period;
FIG. 8 is a flow period association constraint in local adjustment that satisfies a constant in-out reservoir water volume difference;
FIG. 9 is a schematic diagram of water balance;
FIG. 10 is a single hydropower station feasible solution adjacent domain construction process;
FIG. 11 is a schematic diagram of a downstream water reservoir cluster;
FIG. 12 is a topological diagram of a North-disk river basin step power station;
FIG. 13 is a graph showing electricity prices for different time periods for different power stations during a day;
FIG. 14 is a graph showing the calculation result of typical days of the maximum dead water period of the power generation efficiency-the output of each power station;
FIG. 15 is a graph showing the calculation result of typical days in the period of maximum dead water of the power generation efficiency, namely the total step output;
FIG. 16 is a graph showing the result of typical daily calculations of the maximum power generation efficiency and the water level of each power station;
FIG. 17 is a graph showing the result of typical day calculation of maximum power generation efficiency-the output of each power station;
FIG. 18 is a graph showing the total step output as a result of a typical day calculation of the maximum power generation benefit;
FIG. 19 shows the result of typical day calculation of maximum power generation efficiency-water level of each power station.
Detailed Description
The application is further described below with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present application, and are not intended to limit the scope of the present application.
And determining a short-term dispatching process of each hydropower station reservoir with long-term dispatching capability under the consideration of various constraint conditions by giving a warehousing flow process and reservoir start and end water levels in a dispatching period, so that the power generation benefit of the system is maximum. The model is suitable for the situation that power generation enterprises optimally schedule step hydropower stations under jurisdiction according to the differences of peak-valley and full-scale electricity prices under market conditions.
The application discloses a short-term optimization scheduling method for a cascade hydropower station under a multi-constraint condition, which comprises the following steps of:
and step 1, establishing an objective function with the maximum power generation benefit as a target.
The expression of the objective function is:
wherein: f is a power generation benefit function; t, M is the number of schedule periods and the number of hydropower stations;the power output and electricity price of the hydropower station m in the period t are as follows; delta t For a period of time t hours, el m For the electric quantity of m-number power station in the control period with lag time, < >>The average electricity price after the control period of the m-number power station.
And 2, establishing constraint conditions of the objective function.
The constraint conditions include:
(1) Water balance
wherein ：for the reservoir m period t+1 primary water storage, < >>The water storage capacity is reserved for the reservoir in the period of m; /> The total storage flow, the point-starting flow and the water discharge flow of the reservoir m in the period t are respectively;the storage flow of the period interval of the m-number hydropower station t is represented, and the total storage flow of the most upstream hydropower station is represented; k (K) m The number of the directly upstream power stations of the m-number hydropower station; u (U) m An upstream power station label array is directly arranged for the m-number hydropower station; as a function f (m, U m [k]T) calculating the sum of the delivery flows of the kth direct upstream power station of the m-number hydropower station in each period and the delivery flows of the kth direct upstream power station in each period to the power station m in the period t,
l k,m representing the maximum and minimum water lag time period number between the kth direct upstream power station of the m-number hydropower station and the m-number power station; />Representing the direct upstream U of a hydropower station of number m m [k]Delivery of number power station in n periodThe flow of the power station with the number m is flowed in the period t; />Is U (U) m [k]Delivery flow of the number hydropower station->Corresponding time lag period number.
(2) Last water level control
wherein ：schedule end of period water level, zend for reservoir m m For which a target value is controlled.
(3) Power generation flow constraints
wherein ：and (5) introducing flow for maximum power generation of the reservoir m in the period t.
(4) Power station output constraint
wherein ：the minimum and maximum output limit of the hydropower station m in the period t is defined.
(5) Cascade total force limit
wherein ：h t ，representing a water-electricity systemAnd the lower limit and the upper limit of the total output are unified.
(6) Grid partition output limit
The system comprises a plurality of first-level sub-partitions and units, each first-level sub-partition can also comprise a plurality of second-level sub-partitions and units, and so on, then:
wherein ,indicating the lower limit of the output of the ith section in the ith period; />The total effective output is the i-number subarea; i represents the total number of partitions; />And representing a recursive function for calculating the effective output of the i-number partition t period.
(7) Reservoir level constraint
wherein ：and the primary water level and the upper limit and the lower limit of the period t of the m-number hydropower station are represented.
(8) Delivery flow constraints
wherein ：and (5) limiting the minimum comprehensive water consumption constraint and the maximum outlet flow of the reservoir m in the period t.
(9) Hydropower station vibration zone restraint
wherein ：the upper limit and the lower limit of a kth output vibration area of a t period of an m-number hydropower station are expressed, and +.>Related to (I)>And the average tail water level of the m-number hydropower station in the t period.
(10) Minimum power-on output constraint
Wherein: pmin m Representing the minimum starting-up output of the number m hydropower station, namelyGreater than pmin m Or 0.
(11) Hydropower station output climbing limit
wherein ：Δpm And the maximum output lifting limit of the adjacent time period of the m-number hydropower station is represented.
(12) Hydropower station output fluctuation limit
wherein ：tνm The minimum interval time period number of the output lifting of the m-number hydropower station is that the minimum time period is required to last at the highest and lowest points in the output lifting process of one round m A time period.
(13) Minimum out-force rise and fall time period number limit
The time interval from the rising start to the falling start of the output of the m-number hydropower station or from the falling start to the rising start is not less than tv m A time period.
Analyzing the constraint characteristics, the constraint characteristics can be classified into four types: firstly, the single-period attribute constraint of the power station, such as water balance, upper and lower limits of power generation flow, upper and lower limits of reservoir water level and output, vibration area constraint and the like; secondly, the control constraint of the power station such as total electric quantity, final water level, average flow control and the like; thirdly, time coupling type constraint such as maximum climbing speed, output stability, output lifting duration time period and the like; and fourthly, space coupling type constraint, such as upper and lower limits of total output of hydropower and the like. The constraint characteristics are different, the constraint characteristics are closely related to each other, the constraint characteristics are difficult to process by adopting a method, and different solving strategies are needed to be adopted aiming at different problems so as to meet complex application requirements.
In short-term optimization scheduling, the power generation flow is preferably used as a decision variable, and the search step length is easy to set, so that time-related constraint condition processing is facilitated, and the calculated amount is small. The climbing output, the minimum starting output, the minimum output and the like in the constraint conditions are related to the output, and at the moment, the average water consumption rate of each hydropower station in the control period is estimated, and the constraint is converted into the constraint related to the flow. Such as limit conditions for climbing forceCan be converted into->Δq m To generate power flow luffing limit Δq m ＝Δp m η m /3600，η m (m 3 MWh) is the average power generation of the m-number hydropower stationWater consumption rate. The conversion has errors, and feasibility judgment and correction are required to be carried out on the conversion constraints after the optimization calculation is finished.
The constraint of the upper limit of the power generation flow, the upper limit of the hydropower station output and the like in the constraint condition is forcedly met in single-period adjustment calculation. And constraint conditions such as water balance, hydropower station output lower limit, total output limit and the like and the requirement of not increasing water discarding are treated by penalty functions. The damage of one of the water level constraint and the flow constraint can be replaced by the satisfaction of the other condition, so that the priority is forced to be satisfied in single-period calculation, and the priority is processed by adopting a punishment function method. The limitations of the output climbing speed, the minimum starting-up output, the vibration area of the hydropower station, the output fluctuation frequency, the number of lifting time periods and the like can be met by adopting the following associated search mode.
And step 3, solving an objective function to obtain a short-term scheduling process of each power station in the cascade hydropower station.
For large-scale problems, multidimensional searching using global optimization algorithms is practically difficult to achieve. And when optimizing only specific variables each time, the time-associated constraint has a larger limiting effect on the solving process: if the method is strictly limited, the number and the adjustment amplitude of the adjustable variables are limited, the algorithm is quickly converged to a local optimal solution, and the quality of the solution is greatly affected by an initial solution; if these constraints are relaxed first, the optimization computation efficiency is reduced. A solving algorithm referencing the idea of a neighborhood searching method is provided, a feasible solution generator is used from an initial solution, and a feasible solution which is better than the current solution is continuously searched in the neighborhood of the current solution, and replaces the current solution until no better solution is found in the neighborhood of the current solution. Because there are constraints such as the climbing of the output, the fluctuation frequency of the output, the number of the output lifting time periods and the like, when one variable is assigned, the feasible value range of the other variable is also changed, and an associated search mode capable of constructing a feasible solution in the current solution domain needs to be designed, namely, the feasibility of the search mode is ensured by correcting the local power generation flow process associated with the search starting point. The association search includes modifying the power generation flow of the search initiating power station to meet time association constraints and end water level control constraints, and also includes adjusting the power generation flow of each downstream power station to meet end water level control thereof. The optimization search in each step can ensure the optimization in a feasible domain by adjusting the values of a plurality of variables, and the adjustment range and the adjustment direction of the variables are determined by the specific form of constraint conditions. And taking the change of a single decision variable along the search direction as a starting point, if the change falls into an infeasible domain, adjusting the variables related to the violation of the constraint according to the sequence from the near to the far of the space-time distance between the single decision variable and the initiating variable, and returning the changed solution to the feasible domain.
To illustrate such problems, the associative search method will be described below taking the constraint of fluctuation in output as an example. Output p in t-1, t, t+1 time period t-1 (MW)、p t (MW)、p t+1 (MW) needs to meet (p t-1 -p t )(p t -p t+1 ) The conditions of 0 or more and the correlation of the output forces of 3 adjacent time periods are shown in the figure 1 (a) which is a search schematic diagram of the specific fluctuation constraint conditions, the cone CDAB and CDEF are the feasible domains, and the original feasible solution is set as the point a, if p t Falling into the infeasible point b after the reduction can be realized by reducing p t+1 Returning to point c in the feasible region, or reducing p t-1 Returning to point d in the feasible region, the search out force process is shown in FIG. 2 (a). At this time, the period t is the starting point of the search, p t+1 and pt-1 To be matched with p due to the constraint condition of output fluctuation t The associated variables. Similarly, if p t E-point falling into infeasible domain when increasing, can pass p t-1 Or p t+1 The increase of (2) is back to the h or f point in the feasible region. FIG. 1 (b) is a constraint (p t-1 -p t )(p t -p t+1 ) Equal to or greater than 0 superimposed force climbing constraint |p t-1 -p t Delta p and p are less than or equal to t -p t+1 After I is less than or equal to Deltap, p is t-1 Initiating search to show that deltap is the force climbing constraint, polyhedron AEHBDFCG and CGKNMJIL are viable domains, and the point p is at the point a t-1 When decreasing, the search output process is shown in fig. 2 (b) by two-step correction through point c back to point d in the feasible region. .
When the number of the associated variables is more, the similar method is adopted to sequentially correct the constraints such as the number of the output lifting time periods, the output fluctuation, the output climbing and the like, and the associated time period output is corrected according to the sequence from the starting point to the far side until the constraint condition is met on the premise that the corrected constraint condition is met. Fig. 3 shows several pattern examples of single-station multivariate association search.
The single step associative search process may be divided into the following operations:
initial search: the starting point searches for specific variables, and the output increases or decreases corresponding to the feasible solution point a in fig. 2 (a) and (b).
Expansion of the impact range: the variables near the starting point are adjusted to meet the force fluctuation and rise and fall period number constraints, corresponding to the searches from b to c, d in fig. 2 (a).
Influence range edge correction: if the margin of the range expansion operation variation range falls into the infeasible point like b, e of fig. 2 (a), or point b at the time of fig. 2 (b), the neighboring variables outside the variation range are adjusted to satisfy the time-period-related constraint.
And (3) adjusting the water quantity difference in warehouse-in and warehouse-out: there are two modes of application for the associative search mode: firstly, in optimizing calculation, the function of the related search mode is to construct a new feasible solution nearby the current solution, and the feasibility of the power generation flow process can be met through initial search, influence range expansion and influence range edge correction, but the final water level control condition can be possibly destroyed. Therefore, after the operation is completed, the output or power generation flow of a plurality of continuous time periods still needs to be adjusted to meet the requirement that the total in-out warehouse water quantity difference is unchanged compared with that before initial searching so as to keep the final water level unchanged, and the output processes of all the downstream power stations are sequentially judged and adjusted so as to ensure that the final water level of each power station is unchanged. Secondly, as the basis of heuristic load distribution, the output adjustment is carried out on the control modes such as final water level and the like in the initial feasible solution generation and result parallelization stage so as to gradually approach to the feasible solution, and at the moment, only the feasible control mode targets of the output process and approaching are required to be met after single-step association search, and the adjustment of the in-out water quantity difference is not required.
The single-step association searching process consists of four basic operations of initial searching, influence range expanding, influence range edge correction and warehouse-in and warehouse-out water quantity difference correction. The search process initiated by the period t for a certain hydropower station is as follows:
(1) Expansion of the impact range
The influence range expansion operation is used for changing the variable quantity of two adjacent variables forwards or backwards so as to meet the output fluctuation constraint and the climbing constraint of the hydropower station unit. If it isThe rising and falling trend of the output in the period t is changed, so that the period association constraint condition is damaged, and the power generation flow of the adjacent period needs to be changed, so that a new dispatching operation mode is feasible.
In order to prevent the output fluctuation limit from being violated after the power generation flow rate is changed in the t period, the expression (14) needs to be satisfied.
If the model has no solution, y1=t-tv is set m +1, adjusting the power generation flow rate between y1 and t time periods to beIf the model has a solution, a feasible flow lifting process between the time periods y1 and t is obtained, as shown in fig. 4. If no feasible solution exists after adjustment, the t+1 period is the end of the lifting process, and the period of the beginning of the lifting process is obtained by using the formula (15).
The optimization model and the ex-warehouse flow correction process are shown in fig. 5. In formula (15), t0 is the maximum value of tt, satisfyingAnd t0 < y2. If y2 is different from the up-down trend of the tt power generation flow, it means that tt is the period of the last unit power generation flow up (or down), y2 is the new starting point of the unit power generation flow down (or up) process, and t+1 is the ending point of the flow up-down process. If t-y2+1 < tp m According toThe flow from y1 to the end of the period is changed a second time:t0＝y1-1,y1-2,…,t-tp m +1, up to->Or (b)t2 is the minimum change period.
(2) Local optimization model meeting edge climbing constraint of flow adjustment period
After the model and flow correction that the constraints of the first two steps meet, climbing constraint damage may occur at the flow adjustment period edge. If corrected at the flow change interval, t2 must satisfy the formula:the correction process of the outlet flow is described by equation (16).
This model can be solved by sequentially changing the flow rates of each period from t2, and the optimal model and flow correction are shown in fig. 6.
(3) Local optimization model meeting flow adjustment period edge power generation flow stability
If, after the step of correcting the generated flow rate from the t period to the dispatch range direction, the minimum output fluctuation constraint cannot be satisfied, it is necessary to search for a z (formula (17)) that connects the two lift processes and satisfies the flow stability constraint.
The model may use a search algorithm to find the z value. Setting the flow rate between x and y time periods to be z ifThe power generation flow is increased in the left time period of y, so that the limit of the output fluctuation of the hydroelectric generating set can not be met, and further correction is needed by adopting an edge correction method.
(4) Correction of power generation flow from t+1 period backward
And (3) using the same adjustment method from the period t to the initial control period in the previous step, and performing influence range expansion and edge correction from the period t+1.
(5) Water balance
a. Water balance of current reservoir m
Reservoir volume calculations may change the reservoir flow rate and the reservoir outlet flow rate. To keep the reservoir end of the last affected period (labeled t 6) constant, a local water balance pattern (equation (19)) needs to be satisfied in which the period coupling constraints are maintained, as shown in fig. 8.
In formula (19), y andall are variables, the constraint (c) is a period of the fluctuation of the water turbine unit output less than or equal to t6, and the constraint (d) is used for ensuring that the minimum power generation flow lifting period in the output lifting process is less than or equal to t6.
Because the model is difficult to solve, a trial and error method is adopted for solving. In order to maintain the difference between the outlet flow and the inlet flow of the reservoir, i.e. the net inlet flow, the time interval [ t', t6 ] is required to be set]The flow in the air is uniformly changed. However, over time, t't may not be satisfied6]The output rising and falling process can be damaged due to the increase of the flow in different periods. Therefore, the time coupling constraint should be avoided from being broken by extending the correction time interval. In order to solve the water balance problem in the whole dispatching period, the initial value of the maximum flow change dt0 is set as tv m The solving steps are as follows:
(1) Using the formulaTo calculate the difference between the reservoir outlet flow and the reservoir inlet flow. If W is more than 0, the steps (2) to (8) are needed to be executed to increase the reservoir outlet flow and reduce the water storage capacity at the end of the reservoir period. If W is less than 0, the steps (2) to (8) are required to be executed to reduce the delivery flow so as to increase the water storage capacity at the end of the reservoir period;
(2) Setting dt 0 ＝tv m ；
(3) Setting dt=1;
(4) Find [ t6-dt+1, t6]A period in which the flow may change within the time interval of (a). N=0 is set. At t=t6, ifAnd->Then n=n+1 is set. At t=t6-dt+1, t6-dt+2, …, t6-1, ifSetting n=n+1;
(5) Setting the flow rate change as
(6) Setting upt=t6-dt+1, t6-dt+2, …, t6, the flow rate is corrected from the dt period to the t6 direction. If->Setting->
(7) By means of(t=t6-dt+1, t6-dt+2, …, t 6) substitution +.>Value determination step (6) at time interval [ t6-dt+1, t6]Whether or not the correction is feasible. If the time-dependent constraint is not satisfied, step (6) modifies +.>The value of (c) will not be saved. However, there may be cases where the feasible correction result in the dt period cannot meet the water balance requirement in the full scheduling period, and the value of W needs to be recalculated. If W is not equal to 0, setting dt=dt+1, and if t6-dt+1 is not less than 1 and dt is not more than dt0, re-executing the step (4);
(8) The value of W is recalculated. If w+.0, dt0 = dt0+1 is set. If t 6-d0+1 is more than or equal to 1, re-executing the step (3), otherwise stopping.
If the net warehouse-in flow cannot be kept unchanged, the method is needed to further correct the power generation flow from the t6+1 period to the end of the regulation period. Because if the net traffic cannot be kept unchanged during the scheduling period, this means that there is no feasible solution in the neighborhood. With a hydropower station tv m ＝4，tp m For example, =8, when dt < 8, the model has no feasible correction solution, as shown in fig. 9.
If the difference between the warehouse-in flow and the warehouse-out flow cannot be kept unchanged, the power generation flow is corrected by a similar correction method to meet the water balance requirement after the scheduling control period is ended.
Clearly, the construction of a feasible solution is related to the short-term scheduling process around a specific period of time in the reservoir. Taking hydropower station m as an example, there aretp m =8 and tv m The construction process of the feasible solution neighborhood is shown in fig. 10, starting from the period t, with three constraints of =4.
b. Downstream reservoir group water balance
After the short-term dispatching process of the upstream reservoir group is changed, a time interval in which the warehousing flow rate of the downstream reservoir group is possibly changed is found, and the end of the last period of the dispatching period is used as a control point of the water storage capacity of the reservoir. Under the condition of not changing the water storage capacity at the end of the reservoir period, the outlet flow of the reservoir must be changed in order to keep the net flow of the reservoir unchanged. The control model for the water storage capacity of the upstream reservoir may also be applied to the downstream reservoir group. However, if the net flow of the reservoir is not guaranteed to be unchanged, the feasible solution structure fails.
For reservoirs with the number m, the outlet flow change interval of the directly upstream reservoir is [ t ]' m-1,0 ,t' m-1,1 ]The time interval of the change of the warehouse-in flow is [ t ] m,0 ,t m,1 ]， T due to the influence of flow lag time m,1 -t m,0 The value of (2) may be equal to t' m-1,1 -t' m-1,0 Is not equal. The outlet flow of the reservoir m is kept unchanged, and the two reasons change min (T, T m,1 ) Reservoir water storage at the end of the time period: the change of the reservoir storage flow rate enables the reservoir water storage capacity to reach an upper limit (or a lower limit); time interval t m,0 ,t m,1 ]Partially exceeding the schedule control period (e.g., m+2 reservoirs and m+3 reservoirs in fig. 11). Can ensure min (T, T) by using an upstream reservoir outlet flow correction model m,1 ) Reservoir water storage at the end of the time period. The outlet flow change interval of the reservoir m is [ t ]' m,0 ,t' m,1 ]The change interval of the warehouse-in flow corresponding to the reservoir m+1 is [ t ] m+1,0 ,t m+1,1 ]. The same reservoir water storage capacity control model is adopted for the reservoir m+1 and the downstream reservoirs thereof, and the operation is stopped until one of the following four conditions is met:
(1) For the downstream reservoir, the delivery flow is kept unchanged, and the requirement of the reservoir water storage capacity at the end of the dispatching period cannot be met in the change interval of the delivery flow;
(2) The warehouse-in flow rate change interval exceeds the control range (such as m+4 reservoirs in FIG. 11);
(3) All downstream reservoirs have been inspected and modified;
(4) And the feasible solution structure fails, namely, no feasible solution exists in the warehouse-in flow change interval, and the water storage capacity requirement of the reservoir at the end of the dispatching period can be met.
Examples
The north-dish river basin step power station (as shown in fig. 12) managed by the Qian source company comprises a sludge-slope power station (with daily regulation performance, installed 185.5 MW), an illumination power station (with incomplete years of regulation performance, installed 1040 MW), a Ma Maya power station (with daily regulation performance, installed 558 MW) and a Dong power station (with daily regulation performance, installed 880 MW), wherein the installed total amount is 2663.5MW. The four-seat hydropower station with the steps of the north-China river has two regulation performances for a day and an incomplete year, and belongs to the main regulation pipe of the middle and south networks in Guizhou in two-by-two mode, wherein the illumination power station is a tap power station at the downstream of the north-China river, and plays roles in controlling, compensating and regulating the downstream power station. As can be seen from the north-dish river grid-connection relationship of fig. 12, the light, horses Ma Ya, dong are incorporated into the same grid through the converter station.
And adopting a scheduling scheme for solving the full-scale, flat and dry model years by adopting the maximum power generation benefit model. The virtual electricity price curve adopted for the test model is shown in figure 13 because the north-disk river basin does not adopt the full-scale time-of-use electricity price.
FIG. 14 is a graph showing the calculation result of typical days of the maximum dead water period of the power generation efficiency-the output of each power station; FIG. 15 is a graph showing the calculation result of typical days in the period of maximum dead water of the power generation efficiency, namely the total step output; FIG. 16 is a graph showing the result of typical daily calculations of the maximum power generation efficiency and the water level of each power station; FIG. 17 is a graph showing the result of typical day calculation of maximum power generation efficiency-the output of each power station; FIG. 18 is a graph showing the total step output as a result of a typical day calculation of the maximum power generation benefit; FIG. 19 shows the result of typical day calculation of maximum power generation efficiency-water level of each power station.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing is merely a preferred embodiment of the present application, and it should be noted that it will be apparent to those skilled in the art that modifications and variations can be made without departing from the technical principles of the present application, and these modifications and variations should also be regarded as the scope of the application.
Claims (1)
1. The short-term optimization scheduling method for the cascade hydropower station under the multi-constraint condition is characterized by comprising the following steps of:
s1, establishing an objective function with maximum power generation benefit as a target; wherein, the expression of the objective function is:
wherein: f is a power generation benefit function; t, M is the number of schedule periods and the number of hydropower stations;the power output and electricity price of the hydropower station m in the period t are as follows; delta t For a period of time t hours, el m For the electricity quantity of hydropower station m control period after the time delay, < + >>The average electricity price after the m control period of the hydropower station;
s2, establishing constraint conditions of the objective function; the constraint conditions include:
(1) Water balance
wherein ：for the initial water storage capacity of hydropower station in m period t+1, < > for the hydropower station in m period t+1>The method comprises the steps of (1) storing water for the beginning of a hydropower station m time period t; />The total warehouse-in flow, the point-sending flow and the water discarding flow of the hydropower station m in the period t are respectively; delta t Time period t hours;
(2) Last water level control
wherein ：schedule end-of-life water level, zend, for hydropower station m m For its control target value;
(3) Power generation flow constraints
wherein ：the maximum power generation reference flow of the hydropower station in the period t is set;
(4) Power station output constraint
wherein ：minimum and maximum output limits of the hydropower station m in a t period;
(5) Cascade total force limit
wherein ：h t ，representing the lower and upper limits of the total output of the hydroelectric system;
(6) Grid partition output limit
The system comprises a plurality of first-level sub-partitions and units, each first-level sub-partition can also comprise a plurality of second-level sub-partitions and units, and so on, then:
wherein ,indicating the lower limit of the output of the ith section in the ith period; />The total effective output is the i-number subarea; i represents the total number of partitions;
(7) Reservoir level constraint
wherein ：representing the primary water level and the upper limit and the lower limit of the primary water level of the hydropower station m in the period t;
(8) Delivery flow constraints
wherein ：minimum comprehensive water consumption constraint and maximum warehouse outlet flow limit of the hydropower station m in the period t;
(9) Hydropower station vibration zone restraint
wherein ：represents the upper limit and the lower limit of the kth output vibration area of the hydropower station m in the t period, and +.>Related to (I)>The average tail water level of the hydropower station m in the period t;
(10) Minimum power-on output constraint
Wherein: pmin m Representing the m minimum startup output of the hydropower station;
(11) Hydropower station output climbing limit
wherein ：Δpm Representing the maximum output lifting limit of the hydropower station m in adjacent time periods;
(12) Hydropower station output fluctuation limit
wherein ：tνm The minimum interval time of the m output lifting of the hydropower station is that the minimum time period is the minimum time period of the t v needed to be sustained at the highest and lowest points in the one-round output lifting process m A time period;
(13) Minimum out-force rise and fall time period number limit
The time interval from the rising start to the falling start of the m output of the hydropower station or from the falling start to the rising start is not less than tv m A time period;
s3, solving an objective function, and obtaining a short-term dispatching process of each power station reservoir in the cascade hydropower station; solving an objective function by adopting a correlation search method, wherein in the process of solving the objective function, the power generation flow is used as a decision variable, and the single-step correlation search process consists of four basic operations of initial search, influence range expansion, influence range edge correction and warehouse-in and warehouse-out water quantity difference correction; the search process initiated by the period t for a certain hydropower station is as follows: comprising the following steps:
(1) Expansion of the impact range:
the influence range expansion operation is used for changing the variable quantity of two adjacent variables forwards or backwards so as to meet the output fluctuation constraint and the climbing constraint of the hydropower station unit; if it isThe rising and falling trend of the output in the period t is changed, so that the period association constraint condition is damaged, and the power generation flow of the adjacent period needs to be changed, so that a new dispatching operation mode is feasible; in order to prevent the occurrence of violation of the output fluctuation limit after the change of the power generation flow rate in the t period, it is necessary to satisfy the expression (14):
if the model has no solution, y1=t-tv is set m +1, adjusting the power generation flow rate between y1 and t time periods to beIf the model has a solution, obtaining a feasible flow lifting process between the time periods y1 and t; if no feasible solution exists after adjustment, the t+1 period is the end of the lifting process, and the period of the beginning of the lifting process is obtained by using the formula (15):
in formula (15), t0 is the maximum value of tt, satisfyingAnd t0 < y2; if y2 and tt power generation flow have different ascending and descending trends, meaning that tt is the period of ascending/descending of the power generation flow of the last unit, y2 is the new starting point of the descending/ascending process of the power generation flow of the unit, and t+1 is the ending point of the ascending/descending process of the flow; if t-y2+1 < tp m The flow rate from y1 to the end of the period is sequentially changed: />t0＝y1-1,y1-2,…,t-tp m +1 up toOr->t2 is the minimum change period;
(2) A local optimization model meeting the edge climbing constraint of the flow adjustment period:
after the model and the flow which are satisfied by the constraint in the first two steps are corrected, climbing constraint damage can occur at the edge of the flow adjustment period; if corrected at the flow change interval, t2 must satisfy the formula:the correction process of the delivery flow is described by equation (16):
the model is solved by sequentially changing the flow of each period from t2 to the back;
(3) The local optimization model meeting the flow adjustment period edge power generation flow stability is as follows:
if the minimum output fluctuation constraint cannot be satisfied after the step of correcting the generated flow from the t period to the scheduling range direction, it is necessary to search for z (formula (17)) that connects two lifting processes and satisfies the flow stability constraint:
the model utilizes a search algorithm to obtain a z value;
(4) The power generation flow rate is corrected backward from the t+1 period:
performing influence range expansion and edge correction from the t+1 period by using the same adjustment method from the period t to the initial control period in the previous step;
(5) Balance of water:
a. water balance of current hydropower station m
Reservoir water volume calculation can change reservoir storage flow and reservoir delivery flow; to keep the reservoir end of the last affected period constant, the local water balance pattern needs to be satisfied, equation (19):
in formula (19), y andall are variables, the constraint (c) is a period that the fluctuation of the output of the water turbine unit is less than or equal to t6, and the constraint (d) is used for ensuring that the minimum power generation flow lifting period in the output lifting process is less than or equal to the lifting periodt6；
b. Downstream water reservoir group water balance:
for hydropower station m, the outlet flow change interval of the directly upstream reservoir is [ t ]' m-1,0 ,t' m-1,1 ]The time interval of the change of the warehouse-in flow is [ t ] m,0 ,t m,1 ]， T due to the influence of flow lag time m,1 -t m,0 The value of (2) may be equal to t' m-1,1 -t' m-1,0 Is not equal in value; keeping the delivery flow of hydropower station m unchanged, two reasons can change min (T, T m,1 ) Reservoir water storage at the end of the time period: the change of the reservoir storage flow rate enables the reservoir water storage capacity to reach the upper limit/lower limit; time interval t m,0 ,t m,1 ]Part of the time exceeds the scheduling control period; ensuring min (T, T) using upstream reservoir outlet flow correction model m,1 ) Reservoir water storage at the end of the time period; the m ex-warehouse flow change interval of the hydropower station is [ t ]' m,0 ,t' m,1 ]The warehouse-in flow change interval corresponding to the hydropower station m+1 is [ t ] m+1,0 ,t m+1,1 ]The method comprises the steps of carrying out a first treatment on the surface of the The same reservoir water storage capacity control model is adopted for the hydropower station m+1 and the downstream water reservoirs thereof, and the operation is stopped until one of the following four conditions is met:
(1) For the downstream reservoir, the delivery flow is kept unchanged, and the requirement of the reservoir water storage capacity at the end of the dispatching period cannot be met in the change interval of the delivery flow;
(2) The warehouse-in flow change interval exceeds the control range;
(3) All downstream reservoirs have been inspected and modified;
and the feasible solution structure fails, namely, no feasible solution exists in the warehouse-in flow change interval, and the water storage capacity requirement of the reservoir at the end of the dispatching period can be met.
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Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2011370924A1 (en) * | 2011-06-17 | 2013-12-12 | Liaoning Electric Power Company Ltd | Coordination and control system for regulated charging and discharging of pure electric vehicle in combination with wind power generation |
| CN104123589A (en) * | 2014-06-24 | 2014-10-29 | 华中科技大学 | Short-term optimized dispatching method for cascade hydropower station |
| CN104504455A (en) * | 2014-12-02 | 2015-04-08 | 大连理工大学 | Method for long-term optimal scheduling of hydropower station group under cascade energy storage control |
| CN106127348A (en) * | 2016-07-04 | 2016-11-16 | 大连理工大学 | A kind of multi-reservoir joint optimal operation feasible decision space discrimination method |
| WO2017071230A1 (en) * | 2015-10-30 | 2017-05-04 | 南京南瑞集团公司 | Method for short-term optimal scheduling of multi-agent hydropower station group |
| CN106920001A (en) * | 2017-01-19 | 2017-07-04 | 贵州黔源电力股份有限公司 | A kind of Cascade Reservoirs Real time optimal dispatch and Benefit Evaluation Method |
| CN107563538A (en) * | 2017-07-13 | 2018-01-09 | 大连理工大学 | Multiple-use reservoir group's method for optimizing scheduling towards crucial water level control under bulk power grid platform |
| CN110472825A (en) * | 2019-07-09 | 2019-11-19 | 贵州黔源电力股份有限公司 | A kind of step power station Real-Time Scheduling abandoning water cutting method that multi-stage scheduling mechanism is coordinated |
| CN110472824A (en) * | 2019-07-09 | 2019-11-19 | 贵州黔源电力股份有限公司 | A kind of short-term Multiobjective Optimal Operation method of step power station considering peak regulation demand |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP5602878B2 (en) * | 2009-12-31 | 2014-10-08 | アーベーベー・リサーチ・リミテッド | Method and constraint analysis module for optimizing load scheduling for power plants |
| US20200051183A1 (en) * | 2018-06-24 | 2020-02-13 | Cube Hydro Partners, LLC | Power generation scheduling optimization |
-
2020
- 2020-04-01 CN CN202010250152.2A patent/CN111476475B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2011370924A1 (en) * | 2011-06-17 | 2013-12-12 | Liaoning Electric Power Company Ltd | Coordination and control system for regulated charging and discharging of pure electric vehicle in combination with wind power generation |
| CN104123589A (en) * | 2014-06-24 | 2014-10-29 | 华中科技大学 | Short-term optimized dispatching method for cascade hydropower station |
| CN104504455A (en) * | 2014-12-02 | 2015-04-08 | 大连理工大学 | Method for long-term optimal scheduling of hydropower station group under cascade energy storage control |
| WO2017071230A1 (en) * | 2015-10-30 | 2017-05-04 | 南京南瑞集团公司 | Method for short-term optimal scheduling of multi-agent hydropower station group |
| CN106127348A (en) * | 2016-07-04 | 2016-11-16 | 大连理工大学 | A kind of multi-reservoir joint optimal operation feasible decision space discrimination method |
| CN106920001A (en) * | 2017-01-19 | 2017-07-04 | 贵州黔源电力股份有限公司 | A kind of Cascade Reservoirs Real time optimal dispatch and Benefit Evaluation Method |
| CN107563538A (en) * | 2017-07-13 | 2018-01-09 | 大连理工大学 | Multiple-use reservoir group's method for optimizing scheduling towards crucial water level control under bulk power grid platform |
| CN110472825A (en) * | 2019-07-09 | 2019-11-19 | 贵州黔源电力股份有限公司 | A kind of step power station Real-Time Scheduling abandoning water cutting method that multi-stage scheduling mechanism is coordinated |
| CN110472824A (en) * | 2019-07-09 | 2019-11-19 | 贵州黔源电力股份有限公司 | A kind of short-term Multiobjective Optimal Operation method of step power station considering peak regulation demand |
Non-Patent Citations (6)
| Title |
|---|
| 基于可行搜索空间优化的电力市场下梯级水电站短期调峰运行研究;白涛;畅建霞;黄强;吴成国;;水力发电学报(第05期);全文 * |
| 基于模拟逐次逼近算法的梯级水电站群优化调度图研究;程春田;杨凤英;武新宇;苏华英;;水力发电学报(第06期);全文 * |
| 大小水电可消纳电量期望值最大短期协调优化调度模型;刘本希;武新宇;程春田;李秀峰;蔡华祥;;水利学报(12);全文 * |
| 大规模水电站群短期优化调度方法Ⅰ:总体概述;程春田;武新宇;申建建;廖胜利;;水利学报(第09期);全文 * |
| 改进量子粒子群算法在水电站群优化调度中的应用;冯仲恺;廖胜利;牛文静;申建建;程春田;李泽宏;;水科学进展(03);全文 * |
| 梯级水电站短期优化调度算法研究;古家平;马光文;黄炜斌;尤渺;;水力发电(12);全文 * |
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