CN113852137A - Two-stage robust optimization power system operation flexibility capacity evaluation method - Google Patents
Two-stage robust optimization power system operation flexibility capacity evaluation method Download PDFInfo
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Abstract
The invention relates to the technical field of power system operation control methods, in particular to a two-stage robust optimization power system operation flexibility capacity evaluation method, which comprises the steps of constructing a scheduling model of an adjustable robust interval and setting constraint conditions of the scheduling model of the adjustable robust interval; and then, improving the scheduling model of the adjustable robust interval, setting corresponding constraint conditions, and finally performing linear transformation on the two models, and solving by adopting a CCG algorithm to obtain a feasible region and an optimal solution of the power system. The invention can effectively evaluate the operation flexibility capacity of the system and has the following advantages: (1) good economical efficiency: the invention can effectively reduce the system operation cost, reduce the conservatism of the scheduling decision, and further improve the flexibility and economy of the scheduling decision of the system; (2) the scheduling performance is high: the invention evaluates the flexibility of the pre-scheduling of the system reserve capacity by abandoning wind and cutting load, and effectively improves the flexibility of the system operation scheduling.
Description
Technical Field
The invention relates to the technical field of power system operation control methods, in particular to a two-stage robust optimization power system operation flexibility capacity evaluation method.
Background
In order to reduce greenhouse gas emission and sustainable development, the use of renewable energy sources, such as wind, light and the like, as pollution-free clean energy sources is greatly popularized all over the world, and the double-carbon target proposed in China can be realized only by the participation of large-scale renewable energy sources. The highly intermittent renewable energy source grid connection causes the transition of an electric power system from a deterministic system to a highly uncertain system, the transition causes the electric power system to face a new challenge in operation, how to flexibly schedule frequent start-stop, climbing and system standby requirements of a generator set, and the improvement of the system operation flexibility becomes a research hotspot at present. The method comprises the steps of firstly modeling the output uncertainty of the renewable energy source in the flexibility research, wherein the main modeling method is a stochastic optimization method and a robust optimization method, the stochastic optimization method describes the output characteristic of the renewable energy source by deterministic probability distribution, and has better economy in application, but has large difficulty in solving and complex scene, and restricts the wide application of the stochastic optimization method. The robust optimization method models uncertain factors by using the situation of a robust set, has simplicity and convenience in solving compared with a random optimization method, can simultaneously meet constraint conditions, and is currently popularized and applied in a series.
Disclosure of Invention
The invention provides a two-stage robust optimization power system operation flexibility capacity evaluation method, overcomes the defects of the prior art, can effectively reduce the system operation cost and can effectively improve the flexibility of system operation scheduling.
The technical scheme of the invention is realized by the following measures: a two-stage robust optimization power system operation flexibility capacity evaluation method comprises the following steps:
step one, constructing a scheduling model of an adjustable robust interval, and setting a constraint condition of the scheduling model of the adjustable robust interval; in the step, the uncertainty set of the wind power output is used as a decision variable, the optimal uncertainty set of the wind power output uncertainty is considered while the system cost is minimized, the maximum uncertainty region which can be accepted by the power system under the condition of safe and stable operation is determined through the scheduling model, namely, the operation economy of the power system is maximized on the premise of ensuring the safety and stability of the power system, the objective function under the condition of maximizing the operation economy of the power system consists of three parts of minimized operation cost, wind abandoning cost and load shedding cost, and the objective function (namely, the scheduling model of the adjustable robust interval) is represented by a formula (1),
in the formula (1), the reaction mixture is,in order to minimize the cost of the operation,in order to avoid the cost of the wind,for load shedding cost, i is a conventional set, k is a wind turbine set, m is a load set, l is a transmission line set, t is an estimation period,in order to obtain the coefficient of the running cost of the unit,for the output of the unit i in the evaluation period t,respectively the start-up cost coefficient and the stop cost coefficient of the conventional unit i,respectively the start-up and stop states, sc, of the conventional unit i in the evaluation period tt、lctRespectively are the cost coefficients of the waste wind and the load shedding,respectively the upper and lower boundaries of the predicted output interval of the wind turbine generator k,the upper boundary and the lower boundary of a k output interval which can be actually accessed to the wind turbine generator are respectively;
when the predicted output of the wind turbine generator is in the upper boundExceeds the upper bound of the actual output of wind powerAnd similarly, when the predicted lower output bound of the wind turbine generator is smaller than the predicted lower output bound of the wind power, a load shedding occurs, and the load shedding cost coefficient is multiplied by the part exceeding the predicted lower output bound of the wind power to obtain the load shedding cost of the system.
The constraint of the objective function described by equation (1) is as follows:
in the above-mentioned constraint condition, the first and second,the method comprises the steps of starting and stopping a conventional unit based on a worst scene; the formulas (2) and (3) are minimum starting and stopping time constraints generated by the unit; the starting and closing states of the constraint unit are shown as formulas (4) to (6); the capacity constraint of the conventional thermal power generating unit is shown as the formula (7),the output of the conventional thermal power generating unit is in upper and lower bounds; the climbing constraint of the thermal power generating unit is shown as the formulas (8) and (9), wherein RUi、RDiThe climbing speeds of the unit are up and down; the line transmission capacity constraint is as shown in equation (10),respectively, are the transmission factors of the signal,maximum transmission capacity for the transmission line; the power system power balance constraint is shown as equation (11); the output limit of the wind farm is shown as formula (12), where ω iskt、Respectively obtaining an actual wind power output value and a predicted wind power output value;
in the first stage of the scheduling model of the adjustable robust interval, the upper and lower bounds omega of the wind power outputUBAnd ωLBIn order to determine a decision variable, a constraint boundary of the decision variable is shown as a formula (13) and a formula (14), and in a second stage of a scheduling model of an adjustable robust interval, uncertain factors in a power system are mainly depicted, as shown as a formula (15) to a formula (19);
in the constraint conditions, the formula (15) is the power generation capacity constraint, whereinRespectively a lower limit and an upper limit of the unit output; constraints (16) and (17) represent the climbing capacity of the unit, whereinThe climbing capacity of the unit is shown,the climbing capacity of the unit is shown; constraints (18) and (19) are power balance and transmission line power flow under uncertain conditions, where dmtFor the load demand of the power system, wkt、Wind power and conventional generator output under uncertainty are considered; it can be seen in this two-stage model that wind curtailment and load shedding occur in the first-stage scheduling plan due to the reduction of the adjustable uncertainty set, so that all uncertainties can satisfy the power balance and transmission line limit constraints.
Constructing an improved scheduling model of the adjustable robust interval, and setting a constraint condition of the improved scheduling model of the adjustable robust interval;
the scheduling model of the adjustable robust interval is based on the adjustable uncertainty set, the optimal uncertainty set boundary considering the uncertainty of the wind power output is used as a scheduling signal of the wind power output, and for uncertainty factors outside the characteristic uncertainty set, extra wind abandoning cost or load shedding cost can be caused in the second stage optimization, so in the improved model, the output of a unit is optimized and adjusted in the first stage, the cost minimization of rescheduling, wind abandoning and load shedding is considered in the second stage, and the following improved scheduling model of the adjustable robust interval is constructed:
compared with the original model, the robustness constraint on the uncertain factors is emphasized in the improved model, and in the formula (20),for the spare cost factor, sckTo reject the wind cost coefficient, qktFor the representation coefficient of wind power prediction output uncertainty, the improved constraint conditions of the scheduling model of the adjustable robust interval are as follows:
0≤qkt≤1 (25)
in the constraint condition of the improved scheduling model of the adjustable robust interval,the method is used for standby of a unit under the consideration of wind power uncertainty; the improved scheduling model is based on the original adjustable robust interval scheduling model, and power balance constraint and transmission constraint under the condition of considering the adjustable capacity of the wind power plant are represented by an equation (21) and an equation (22); in the constraint conditions of the original schedulable model, except for the constraint conditions (11) to (14)) Besides adjusting the dispatching power generation capacity of the wind power plant, the uncertainty interval of the system is further adjusted; constraints (23) and (24) assume a linear relationship between the uncertainties imposed by the wind power generation on the power system;
and step three, because the model has more constraint conditions, converting the model in the step one and the step two into a solving form conforming to a CCG algorithm, solving by adopting the CCG algorithm, and obtaining a feasible region and an optimal solution of the power system after solving, namely the minimized operation cost, the wind abandoning cost and the load shedding cost of the power system on the premise of ensuring the safe and stable operation of the power system.
The following is further optimization or/and improvement of the technical scheme of the invention:
specifically, in the third step, because the model has more constraint conditions, the model in the first step and the model in the second step are converted to be in a solving form conforming to the CCG algorithm, the CCG algorithm is adopted for solving, a dual theory is introduced during solving, and w is usedktAs variables, linearization by the large M method yields the following mixed integer problem:
in the formulae (26) to (31),in order to linearize the main problem (the objective function shown in the formula (1)), the parameter variables introduced by the big-M algorithm are applied and used for approximating the nonlinear variables;has a value range of [0,1 ]]So as to meet the value of the optimal solution,in order to introduce variables into the linear transformation, the linear transformation is carried out on the coefficients of the up-down climbing speed and the load shedding speed of the fire-electric generator set in the main problem;representing the optimal solution of the output of the unit in the t period;represents the total load of the system in the period t;when the method is a main linearization problem, parameter variables introduced by a big-M algorithm are applied for linearization of line transmission constraint; mbigManually introducing variables into the big-block-M algorithm; bktHas a value range of [0,1 ]]Its role is to constrain the value of M;respectively the cost coefficients of wind abandoning and load shedding m of the wind farm k on the line l.
The feasible region is a set where an optimal result is located after the CCG algorithm is solved, and the optimal solution is a decision variable in a system objective function (equation (26)) which meets constraint conditions (equations (21) to (25)).
The method for evaluating the operation flexibility capacity of the two-stage robust optimized power system can effectively evaluate the operation flexibility capacity of the system, and has the following advantages: (1) good economical efficiency: by introducing the scheduling model of the adjustable robust interval, the invention can effectively reduce the system operation cost, reduce the conservatism of the scheduling decision and further improve the flexibility and economy of the scheduling decision of the system; (2) the scheduling performance is high: the invention evaluates the flexibility of the pre-scheduling of the system reserve capacity by abandoning wind and cutting load, and effectively improves the flexibility of the system operation scheduling.
Drawings
FIG. 1 is a flow chart of a method for estimating the operational flexibility capacity of a two-stage robust optimized power system according to the present invention.
Fig. 2 is a flow chart of the CCG algorithm of the present invention.
FIG. 3 is an upper bound of the optimal set of wind power uncertainties and the prediction set in an embodiment of the present invention.
Fig. 4 is a diagram of a system standby requirement scenario in an embodiment of the present invention.
Fig. 5 is a system backup and predictive backup scenario in an embodiment of the present invention.
In fig. 2, the objective function of the scheduling model of the adjustable robust interval represents the main problem, the objective function of the scheduling model of the improved adjustable robust interval represents the sub-problem,the optimal value of the upper bound of the k output interval of the accessible wind generating set,the optimal value of the lower bound of the k output interval of the wind turbine generator is accessed,is [0,1 ]]The optimal value of the value range.
Detailed Description
The present invention is not limited by the following examples, and specific embodiments may be determined according to the technical solutions and practical situations of the present invention.
In the present invention, the described algorithm is a conventionally known and commonly used algorithm unless otherwise specified.
The invention is further described below with reference to the following examples:
example (b): as shown in fig. 1, the two-stage robust optimization power system operation flexibility capacity evaluation method includes the following steps:
step one, constructing a scheduling model of an adjustable robust interval, and setting a constraint condition of the scheduling model of the adjustable robust interval; in the step, the uncertainty set of the wind power output is used as a decision variable, the optimal uncertainty set of the wind power output uncertainty is considered while the system cost is minimized, the maximum uncertainty region which can be accepted by the power system under the condition of safe and stable operation is determined through the scheduling model, namely, the operation economy of the power system is maximized on the premise of ensuring the safety and stability of the power system, the objective function under the condition of maximizing the operation economy of the power system consists of three parts of minimized operation cost, wind abandoning cost and load shedding cost, and the objective function (namely, the scheduling model of the adjustable robust interval) is represented by a formula (1),
in the formula (1), the reaction mixture is,in order to minimize the cost of the operation,in order to avoid the cost of the wind,for load shedding cost, i is a conventional set, k is a wind turbine set, m is a load set, l is a transmission line set, t is an estimation period,in order to obtain the coefficient of the running cost of the unit,for the output of the unit i in the evaluation period t,respectively the start-up cost coefficient and the stop cost coefficient of the conventional unit i,respectively the start-up and stop states, sc, of the conventional unit i in the evaluation period tt、lctRespectively are the cost coefficients of the waste wind and the load shedding,respectively the upper and lower boundaries of the predicted output interval of the wind turbine generator k,the upper boundary and the lower boundary of a k output interval which can be actually accessed to the wind turbine generator are respectively;
when the predicted output of the wind turbine generator is in the upper boundExceeds the upper bound of the actual output of wind powerAnd similarly, when the predicted lower output bound of the wind turbine generator is smaller than the lower wind power actual output bound, a load shedding occurs, and the load shedding cost coefficient is multiplied by the part exceeding the predicted lower wind power output bound to obtain the load shedding cost of the system.
The constraint of the objective function described by equation (1) is as follows:
in the above-mentioned constraint condition, the first and second,the method comprises the steps of starting and stopping a conventional unit based on a worst scene; the formulas (2) and (3) are minimum starting and stopping time constraints generated by the unit; the starting and closing states of the constraint unit are shown as formulas (4) to (6); the capacity constraint of the conventional thermal power generating unit is shown as the formula (7),the output of the conventional thermal power generating unit is in upper and lower bounds; the climbing constraint of the thermal power generating unit is shown as the formulas (8) and (9), wherein RUi、RDiThe climbing speeds of the unit are up and down; the line transmission capacity constraint is as shown in equation (10),respectively, are the transmission factors of the signal,maximum transmission capacity for the transmission line; the power system power balance constraint is shown as equation (11); the output limit of the wind farm is shown as formula (12), where ω iskt、Respectively obtaining an actual wind power output value and a predicted wind power output value;
in the first stage of the scheduling model of the adjustable robust interval, the upper and lower bounds omega of the wind power outputUBAnd ωLBIn order to determine a decision variable, a constraint boundary of the decision variable is shown as a formula (13) and a formula (14), and in a second stage of a scheduling model of an adjustable robust interval, uncertain factors in a power system are mainly depicted, as shown as a formula (15) to a formula (19);
in the constraint conditions, the formula (15) is the power generation capacity constraint, whereinRespectively a lower limit and an upper limit of the unit output; constraints (16) and (17) represent the climbing capacity of the unit, whereinThe climbing capacity of the unit is shown,the climbing capacity of the unit is shown; constraints (18) and (19) are power balance and transmission line power flow under uncertain conditions, where dmtFor the load demand of the power system, wkt、Wind power and conventional generator output under uncertainty are considered; it can be seen in this two-phase model that windfall and shedding load occur in the first-phase dispatch plan due to the reduction of the tunable uncertainty set, such thatUncertainty can satisfy power balance and transmission line limit constraints.
Constructing an improved scheduling model of the adjustable robust interval, and setting a constraint condition of the improved scheduling model of the adjustable robust interval;
the scheduling model of the adjustable robust interval is based on the adjustable uncertainty set, the optimal uncertainty set boundary considering the uncertainty of the wind power output is used as a scheduling signal of the wind power output, and for uncertainty factors outside the characteristic uncertainty set, extra wind abandoning cost or load shedding cost can be caused in the second stage optimization, so in the improved model, the output of a unit is optimized and adjusted in the first stage, the cost minimization of rescheduling, wind abandoning and load shedding is considered in the second stage, and the following improved scheduling model of the adjustable robust interval is constructed:
compared with the original model, the robustness constraint on the uncertain factors is emphasized in the improved model, and in the formula (20),for the spare cost factor, sckTo reject the wind cost coefficient, qktFor the representation coefficient of wind power prediction output uncertainty, the improved constraint conditions of the scheduling model of the adjustable robust interval are as follows:
0≤qkt≤1 (25)
in the constraint condition of the improved scheduling model of the adjustable robust interval,the method is used for standby of a unit under the consideration of wind power uncertainty; the improved scheduling model is based on the original adjustable robust interval scheduling model, and power balance constraint and transmission constraint under the condition of considering the adjustable capacity of the wind power plant are represented by an equation (21) and an equation (22); in the constraint conditions of the original schedulable model, except for adjusting the scheduling power generation capacity of the wind power plant by the constraint conditions (11) to (14), the uncertainty interval of the system is further adjusted; constraints (23) and (24) assume a linear relationship between the uncertainties imposed by the wind power generation on the power system;
step three, because the constraint conditions in the model are more, the model is converted to be a solving form conforming to the CCG algorithm, the CCG algorithm (as shown in figure 2) is adopted for solving, the dual theory is introduced during solving, and w is usedktAs variables, linearization by the large M method yields the following mixed integer problem:
in the formulae (26) to (31),in order to linearize the main problem (the objective function shown in the formula (1)), the parameter variables introduced by the big-M algorithm are applied and used for approximating the nonlinear variables;has a value range of [0,1 ]]So as to meet the value of the optimal solution,in order to introduce variables into the linear transformation, the linear transformation is carried out on the coefficients of the up-down climbing speed and the load shedding speed of the fire-electric generator set in the main problem;representing the optimal solution of the output of the unit in the t period;represents the total load of the system in the period t;when the method is a main linearization problem, parameter variables introduced by a big-M algorithm are applied for linearization of line transmission constraint; mbigManually introducing variables into the big-block-M algorithm; bktHas a value range of [0,1 ]]Its role is to constrain the value of M;CCG is adopted for cost coefficients of abandoned wind and load shedding m of the wind farm k on the line l respectivelyAnd solving the algorithm to obtain a feasible region and an optimal solution of the power system, namely the minimum operation cost, the wind abandoning cost and the load shedding cost of the power system on the premise of ensuring the safe and stable operation of the power system.
The feasible region is a set where an optimal result is located after the CCG algorithm is solved, and the optimal solution is a decision variable in a system objective function (equation (26)) which meets constraint conditions (equations (21) to (25)).
In the first step of the embodiment, the minimum system operation cost is taken as an objective function, and meanwhile, an optimal uncertainty set is realized, so that a maximum uncertain region acceptable for the system under the condition of safe and stable operation is found, if the wind power output exceeds the upper limit of the system load, wind abandoning is performed, otherwise, load shedding is performed, the size of the uncertain set is determined by the operation cost and the wind abandoning load shedding cost, the uncertain set is narrowed when the minimum system operation cost is considered, and the wind abandoning cost and the load shedding cost are considered to increase the uncertainty set of the wind power.
In the second step, the output of the unit is optimized and adjusted on the basis of the adjustable robust interval scheduling model, and meanwhile, the cost minimization of rescheduling, wind curtailment and load shedding is considered.
In the above embodiment, the wind power uncertainty set is used as an input, the wind power uncertainty set is applied to the model to describe the maximum uncertainty interval that the system can adapt to, the load shedding cost coefficient and the wind curtailment cost coefficient are set to be 1000$/MWh and 100$/MWh, respectively, and the upper bounds of the optimal and predicted uncertainty sets are shown in fig. 3.
In fig. 3, the optimal lower bound and the predicted lower bound of the uncertainty set of the system are equal due to the higher load shedding cost factor, whereas the optimal upper bound is lower than the upper bound for the predictions at 17, 22 and 24, and the net load of the system is negative during these three periods, resulting in a lower capacity of the genset to provide run-down backup, and therefore the system lacks sufficient flexibility to respond to the predicted uncertainty during this period.
Fig. 4 shows the system backup requirement, and the capability of the system to provide backup can predict that the uncertainty set satisfies the required backup capacity in consideration of the determination of the output power of the system generator set under the condition that there is no uncertainty in the generator set capacity, the unit climbing capacity, and the transmission limit, and when the provided backup capacity is greater than the required backup capacity of the system, the power grid will keep safe and stable operation.
FIG. 5 shows the downstream backup provided by the system genset and the system backup required based on the difference between the predicted upper limit of the uncertainty set and the wind expected output, as can be seen in FIG. 5, the system required backup capacity is higher than the system backup capacity at 22 and 23.
In order to compare the functions of the adjustable uncertain set in the two-stage robust model, the adjustable uncertain set is compared with the non-adjustable uncertain set, the comparison result is shown in the attached table 1, in the adjustable uncertain set, the wind power output is assumed to be fixed output in the first stage, and the CCG algorithm has resolvability for both the two uncertain sets.
By utilizing the CCG algorithm, a cut set without a relaxation variable is embedded in each iteration, so that the output power of a system generator set is corrected, and even under the worst wind power condition, the power balance and the transmission line constraint can be met. Under the condition of insufficient flexibility, the CCG method has a feasible solution only by taking an uncertainty set in a main problem as a variable consideration and reducing an uncertainty interval.
Safe operation can be achieved by wind curtailment or load shedding in the event that the generator set lacks sufficient flexible capacity. The load shedding cost under the adjustable robust uncertain set model is smaller than that under the non-adjustable robust uncertain set model, and the relationship of the wind curtailment cost is opposite. On the premise of ensuring the system safety, compared with a curtailed wind cost coefficient, the cost coefficient of the load shedding is higher, and the adjustable robust set has a flexible adjusting function, so that the model used in the method can obtain smaller running cost and total cost.
In summary, the method for evaluating the operation flexibility capacity of the two-stage robust optimized power system can effectively evaluate the operation flexibility capacity of the system, and provide corresponding flexibility improvement measures according to the evaluation result, and the embodiment verifies that the method plays a certain role in improving the flexibility of the system.
The technical characteristics form an embodiment of the invention, which has strong adaptability and implementation effect, and unnecessary technical characteristics can be increased or decreased according to actual needs to meet the requirements of different situations.
TABLE 1 cost comparison of different uncertainty sets
Claims (3)
1. A two-stage robust optimization power system operation flexibility capacity evaluation method is characterized by comprising the following steps:
step one, constructing a scheduling model of an adjustable robust interval, and setting a constraint condition of the scheduling model of the adjustable robust interval; in the step, the uncertainty set of the wind power output is used as a decision variable, the optimal uncertainty set of the wind power output uncertainty is considered while the system cost is minimized, the maximum uncertainty region which can be accepted by the power system under the condition of safe and stable operation is determined through the scheduling model, namely, the operation economy of the power system is maximized on the premise of ensuring the safety and stability of the power system, the objective function under the condition of maximizing the operation economy of the power system consists of three parts of minimized operation cost, wind abandoning cost and load shedding cost, and the objective function is as shown in the formula (1),
in the formula (1), the reaction mixture is,in order to minimize the cost of the operation,in order to avoid the cost of the wind,for load shedding cost, i is a conventional set, k is a wind turbine set, m is a load set, l is a transmission line set, t is an estimation period,in order to obtain the coefficient of the running cost of the unit,for the output of the unit i in the evaluation period t,respectively the start-up cost coefficient and the stop cost coefficient of the conventional unit i,respectively the start-up and stop states, sc, of the conventional unit i in the evaluation period tt、lctRespectively are the cost coefficients of the waste wind and the load shedding,respectively the upper and lower boundaries of the predicted output interval of the wind turbine generator k,the upper boundary and the lower boundary of a k output interval which can be actually accessed to the wind turbine generator are respectively;
the constraint of the objective function described by equation (1) is as follows:
in the above-mentioned constraint condition, the first and second,the method comprises the steps of starting and stopping a conventional unit based on a worst scene; the formulas (2) and (3) are minimum starting and stopping time constraints generated by the unit; the starting and closing states of the constraint unit are shown as formulas (4) to (6); the capacity constraint of the conventional thermal power generating unit is shown as the formula (7), Pi min、Pi maxThe output of the conventional thermal power generating unit is in upper and lower bounds; the climbing constraint of the thermal power generating unit is shown as the formulas (8) and (9), wherein RUi、RDiThe climbing speeds of the unit are up and down; the line transmission capacity constraint is as shown in equation (10),are transmission factors, Fl maxMaximum transmission capacity for the transmission line; the power system power balance constraint is shown as equation (11); the output limit of the wind farm is shown as formula (12), where ω iskt、Respectively obtaining an actual wind power output value and a predicted wind power output value;
in the first stage of the scheduling model of the adjustable robust interval, the upper and lower bounds omega of the wind power outputUBAnd ωLBIn order to determine a decision variable, a constraint boundary of the decision variable is shown as a formula (13) and a formula (14), in a second stage of a scheduling model of an adjustable robust interval, uncertain factors in a power system are depicted, and the formulas are shown as a formula (15) to a formula (19);
in the constraint conditions, the formula (15) is the power generation capacity constraint, wherein An upper limit; constraints (16) and (17) represent the climbing capacity of the unit, whereinThe climbing capacity of the unit is shown,the climbing capacity of the unit is shown; constraints (18) and (19) are power balance and transmission line power flow under uncertain conditions, where dmtFor the load demand of the power system, wkt、Wind power and conventional generator output under uncertainty are considered;
constructing an improved scheduling model of the adjustable robust interval, and setting a constraint condition of the improved scheduling model of the adjustable robust interval;
improved scheduling model of adjustable robust interval:
in the formula (20), the reaction mixture is,for the spare cost factor, sckTo reject the wind cost coefficient, qktFor the representation coefficient of wind power prediction output uncertainty, the improved constraint conditions of the scheduling model of the adjustable robust interval are as follows:
0≤qkt≤1 (25)
in the constraint condition of the improved scheduling model of the adjustable robust interval,the method is used for standby of a unit under the consideration of wind power uncertainty; the power balance constraint and transmission under the consideration of the adjustable capacity of the wind farm are represented by equations (21) and (22)Inputting and constraining; constraints (23) and (24) assume a linear relationship between the uncertainties imposed by the wind power generation on the power system;
and step three, converting the models in the step one and the step two, solving by adopting a CCG algorithm, and obtaining a feasible region and an optimal solution of the power system after the solution, namely the minimum operation cost, the wind abandoning cost and the load shedding cost of the power system on the premise of ensuring the safe and stable operation of the power system.
2. The method for estimating operational flexibility and capacity of a two-stage robust optimized power system according to claim 1, wherein in the third step, the model in the first step and the model in the second step are transformed into a solution form conforming to a CCG algorithm, the CCG algorithm is adopted for solving, a dual theory is introduced during solving, and w is usedktAs variables, linearization by the large M method yields the following mixed integer problem:
in the formulae (26) to (31),when the main problem is linearized, parameter variables introduced by a big-M algorithm are applied and used for approximating nonlinear variables;has a value range of [0,1 ]]So as to meet the value of the optimal solution, in order to introduce variables into the linear transformation, the linear transformation is carried out on the coefficients of the up-down climbing speed and the load shedding speed of the fire-electric generator set in the main problem;representing the optimal solution of the output of the unit in the t period;represents the total load of the system in the period t;when the method is a main linearization problem, parameter variables introduced by a big-M algorithm are applied for linearization of line transmission constraint; mbigManually introducing variables into the big-block-M algorithm; bktHas a value range of [0,1 ]]Its role is to constrain the value of M;respectively the cost coefficients of wind abandoning and load shedding m of the wind farm k on the line l.
3. The method for evaluating the operation flexibility and capacity of the two-stage robust optimization power system according to claim 2, wherein the feasible domain is a set of optimal results after the CCG algorithm is solved, and the optimal solution is a decision variable in a system objective function formula (26) satisfying constraint conditions from a formula (21) to a formula (25).
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