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 PDF

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CN113852137A
CN113852137A CN202111137349.6A CN202111137349A CN113852137A CN 113852137 A CN113852137 A CN 113852137A CN 202111137349 A CN202111137349 A CN 202111137349A CN 113852137 A CN113852137 A CN 113852137A
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cost
wind
constraint
power system
power
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唐君毅
董雪涛
秦艳辉
李德存
南东亮
刘震
孙冰
朱子民
段青熙
段玉
王小云
祁晓笑
张媛
杨琪
彭寅章
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State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Xinjiang Electric Power Co Ltd
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State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Xinjiang Electric Power Co Ltd
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
    • H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
    • H02J3/144—Demand-response operation of the power transmission or distribution network
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/10—Power transmission or distribution systems management focussing at grid-level, e.g. load flow analysis, node profile computation, meshed network optimisation, active network management or spinning reserve management
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B70/00—Technologies for an efficient end-user side electric power management and consumption
    • Y02B70/30—Systems integrating technologies related to power network operation and communication or information technologies for improving the carbon footprint of the management of residential or tertiary loads, i.e. smart grids as climate change mitigation technology in the buildings sector, including also the last stages of power distribution and the control, monitoring or operating management systems at local level
    • Y02B70/3225—Demand response systems, e.g. load shedding, peak shaving
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
    • Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
    • Y04S20/00—Management or operation of end-user stationary applications or the last stages of power distribution; Controlling, monitoring or operating thereof
    • Y04S20/20—End-user application control systems
    • Y04S20/222—Demand response systems, e.g. load shedding, peak shaving

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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

Two-stage robust optimization power system operation flexibility capacity evaluation method
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),
Figure BDA0003282554890000011
in the formula (1), the reaction mixture is,
Figure BDA0003282554890000012
in order to minimize the cost of the operation,
Figure BDA0003282554890000013
in order to avoid the cost of the wind,
Figure BDA0003282554890000021
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,
Figure BDA0003282554890000022
in order to obtain the coefficient of the running cost of the unit,
Figure BDA0003282554890000023
for the output of the unit i in the evaluation period t,
Figure BDA0003282554890000024
respectively the start-up cost coefficient and the stop cost coefficient of the conventional unit i,
Figure BDA0003282554890000025
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,
Figure BDA0003282554890000026
respectively the upper and lower boundaries of the predicted output interval of the wind turbine generator k,
Figure BDA0003282554890000027
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 bound
Figure BDA0003282554890000028
Exceeds the upper bound of the actual output of wind power
Figure BDA0003282554890000029
And 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:
Figure BDA00032825548900000210
Figure BDA00032825548900000211
Figure BDA00032825548900000212
Figure BDA00032825548900000213
Figure BDA00032825548900000214
Figure BDA00032825548900000215
Figure BDA00032825548900000216
Figure BDA00032825548900000217
Figure BDA00032825548900000218
Figure BDA00032825548900000219
Figure BDA00032825548900000220
in the above-mentioned constraint condition, the first and second,
Figure BDA00032825548900000221
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),
Figure BDA00032825548900000222
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),
Figure BDA0003282554890000031
respectively, are the transmission factors of the signal,
Figure BDA0003282554890000032
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、
Figure BDA0003282554890000033
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);
Figure BDA0003282554890000034
Figure BDA0003282554890000035
Figure BDA0003282554890000036
Figure BDA0003282554890000037
Figure BDA0003282554890000038
Figure BDA0003282554890000039
Figure BDA00032825548900000310
in the constraint conditions, the formula (15) is the power generation capacity constraint, wherein
Figure BDA00032825548900000311
Respectively a lower limit and an upper limit of the unit output; constraints (16) and (17) represent the climbing capacity of the unit, wherein
Figure BDA00032825548900000312
The climbing capacity of the unit is shown,
Figure BDA00032825548900000313
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、
Figure BDA00032825548900000314
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:
Figure BDA00032825548900000315
compared with the original model, the robustness constraint on the uncertain factors is emphasized in the improved model, and in the formula (20),
Figure BDA0003282554890000041
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:
Figure BDA0003282554890000042
Figure BDA0003282554890000043
Figure BDA0003282554890000044
Figure BDA0003282554890000045
0≤qkt≤1 (25)
in the constraint condition of the improved scheduling model of the adjustable robust interval,
Figure BDA0003282554890000046
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:
Figure BDA0003282554890000047
Figure BDA0003282554890000048
Figure BDA0003282554890000051
Figure BDA0003282554890000052
Figure BDA0003282554890000053
Figure BDA0003282554890000054
in the formulae (26) to (31),
Figure BDA0003282554890000055
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;
Figure BDA0003282554890000056
has a value range of [0,1 ]]So as to meet the value of the optimal solution,
Figure BDA0003282554890000057
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;
Figure BDA0003282554890000058
representing the optimal solution of the output of the unit in the t period;
Figure BDA0003282554890000059
represents the total load of the system in the period t;
Figure BDA00032825548900000510
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;
Figure BDA00032825548900000511
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,
Figure BDA00032825548900000512
the optimal value of the upper bound of the k output interval of the accessible wind generating set,
Figure BDA00032825548900000513
the optimal value of the lower bound of the k output interval of the wind turbine generator is accessed,
Figure BDA00032825548900000514
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),
Figure BDA0003282554890000061
in the formula (1), the reaction mixture is,
Figure BDA0003282554890000062
in order to minimize the cost of the operation,
Figure BDA0003282554890000063
in order to avoid the cost of the wind,
Figure BDA0003282554890000064
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,
Figure BDA0003282554890000065
in order to obtain the coefficient of the running cost of the unit,
Figure BDA0003282554890000066
for the output of the unit i in the evaluation period t,
Figure BDA0003282554890000067
respectively the start-up cost coefficient and the stop cost coefficient of the conventional unit i,
Figure BDA0003282554890000068
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,
Figure BDA0003282554890000069
respectively the upper and lower boundaries of the predicted output interval of the wind turbine generator k,
Figure BDA00032825548900000610
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 bound
Figure BDA00032825548900000611
Exceeds the upper bound of the actual output of wind power
Figure BDA00032825548900000612
And 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:
Figure BDA00032825548900000613
Figure BDA00032825548900000614
Figure BDA0003282554890000071
Figure BDA0003282554890000072
Figure BDA0003282554890000073
Figure BDA0003282554890000074
Figure BDA0003282554890000075
Figure BDA0003282554890000076
Figure BDA0003282554890000077
Figure BDA0003282554890000078
Figure BDA0003282554890000079
in the above-mentioned constraint condition, the first and second,
Figure BDA00032825548900000710
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),
Figure BDA00032825548900000711
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),
Figure BDA00032825548900000712
respectively, are the transmission factors of the signal,
Figure BDA00032825548900000713
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、
Figure BDA00032825548900000714
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);
Figure BDA00032825548900000715
Figure BDA00032825548900000716
Figure BDA00032825548900000717
Figure BDA00032825548900000718
Figure BDA00032825548900000719
Figure BDA00032825548900000720
Figure BDA00032825548900000721
in the constraint conditions, the formula (15) is the power generation capacity constraint, wherein
Figure BDA0003282554890000081
Respectively a lower limit and an upper limit of the unit output; constraints (16) and (17) represent the climbing capacity of the unit, wherein
Figure BDA0003282554890000082
The climbing capacity of the unit is shown,
Figure BDA0003282554890000083
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、
Figure BDA00032825548900000812
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:
Figure BDA0003282554890000085
compared with the original model, the robustness constraint on the uncertain factors is emphasized in the improved model, and in the formula (20),
Figure BDA0003282554890000086
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:
Figure BDA0003282554890000087
Figure BDA0003282554890000088
Figure BDA0003282554890000089
Figure BDA00032825548900000810
0≤qkt≤1 (25)
in the constraint condition of the improved scheduling model of the adjustable robust interval,
Figure BDA00032825548900000811
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:
Figure BDA0003282554890000091
Figure BDA0003282554890000092
Figure BDA0003282554890000093
Figure BDA0003282554890000094
Figure BDA0003282554890000095
Figure BDA0003282554890000096
in the formulae (26) to (31),
Figure BDA0003282554890000097
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;
Figure BDA0003282554890000098
has a value range of [0,1 ]]So as to meet the value of the optimal solution,
Figure BDA0003282554890000099
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;
Figure BDA00032825548900000910
representing the optimal solution of the output of the unit in the t period;
Figure BDA00032825548900000911
represents the total load of the system in the period t;
Figure BDA00032825548900000912
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;
Figure BDA00032825548900000913
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
Figure BDA0003282554890000111

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),
Figure FDA0003282554880000011
in the formula (1), the reaction mixture is,
Figure FDA0003282554880000012
in order to minimize the cost of the operation,
Figure FDA0003282554880000013
in order to avoid the cost of the wind,
Figure FDA0003282554880000014
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,
Figure FDA0003282554880000015
in order to obtain the coefficient of the running cost of the unit,
Figure FDA0003282554880000016
for the output of the unit i in the evaluation period t,
Figure FDA0003282554880000017
respectively the start-up cost coefficient and the stop cost coefficient of the conventional unit i,
Figure FDA0003282554880000018
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,
Figure FDA0003282554880000019
respectively the upper and lower boundaries of the predicted output interval of the wind turbine generator k,
Figure FDA00032825548800000110
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:
Figure FDA00032825548800000111
Figure FDA00032825548800000112
Figure FDA00032825548800000113
Figure FDA00032825548800000114
Figure FDA00032825548800000115
Figure FDA00032825548800000116
Figure FDA00032825548800000117
Figure FDA00032825548800000118
Figure FDA00032825548800000119
Figure FDA0003282554880000021
Figure FDA0003282554880000022
in the above-mentioned constraint condition, the first and second,
Figure FDA0003282554880000023
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),
Figure FDA0003282554880000024
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、
Figure FDA0003282554880000025
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);
Figure FDA0003282554880000026
Figure FDA0003282554880000027
Figure FDA0003282554880000028
Figure FDA0003282554880000029
Figure FDA00032825548800000210
Figure FDA00032825548800000211
Figure FDA00032825548800000212
in the constraint conditions, the formula (15) is the power generation capacity constraint, wherein
Figure FDA00032825548800000213
Figure FDA00032825548800000214
An upper limit; constraints (16) and (17) represent the climbing capacity of the unit, wherein
Figure FDA00032825548800000215
The climbing capacity of the unit is shown,
Figure FDA00032825548800000216
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、
Figure FDA00032825548800000217
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:
Figure FDA0003282554880000031
in the formula (20), the reaction mixture is,
Figure FDA0003282554880000032
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:
Figure FDA0003282554880000033
Figure FDA0003282554880000034
Figure FDA0003282554880000035
Figure FDA0003282554880000036
0≤qkt≤1 (25)
in the constraint condition of the improved scheduling model of the adjustable robust interval,
Figure FDA0003282554880000037
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:
Figure FDA0003282554880000038
Figure FDA0003282554880000039
Figure FDA00032825548800000310
Figure FDA00032825548800000311
Figure FDA0003282554880000041
Figure FDA0003282554880000042
in the formulae (26) to (31),
Figure FDA0003282554880000043
when the main problem is linearized, parameter variables introduced by a big-M algorithm are applied and used for approximating nonlinear variables;
Figure FDA0003282554880000044
has a value range of [0,1 ]]So as to meet the value of the optimal solution,
Figure FDA0003282554880000045
Figure FDA0003282554880000046
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;
Figure FDA0003282554880000047
representing the optimal solution of the output of the unit in the t period;
Figure FDA0003282554880000048
represents the total load of the system in the period t;
Figure FDA0003282554880000049
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;
Figure FDA00032825548800000410
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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