CN108090632B - New energy grid-connected power system multi-time scale scheduling method based on robust optimization - Google Patents

New energy grid-connected power system multi-time scale scheduling method based on robust optimization Download PDF

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CN108090632B
CN108090632B CN201810063960.0A CN201810063960A CN108090632B CN 108090632 B CN108090632 B CN 108090632B CN 201810063960 A CN201810063960 A CN 201810063960A CN 108090632 B CN108090632 B CN 108090632B
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朱继忠
熊小伏
禤培正
欧阳金鑫
刘乔波
谢平平
邹金
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Research Institute of Southern Power Grid Co Ltd
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Abstract

The invention discloses a new energy grid-connected power system multi-time scale scheduling method based on robust optimization, wherein the new energy grid-connected power system comprises new energy and schedulable energy, and a target function of each time scale scheduling plan is respectively established by taking schedulable energy output as a decision variable; respectively establishing constraint conditions of each objective function, including robust constraint and traditional physical constraint; establishing a robust optimization scheduling model of each time scale according to each objective function and corresponding constraint conditions; selecting robust level values under each time scale according to a robust level value adjustment rule, and respectively inputting the robust level values into robust optimization scheduling models of each time scale so as to obtain decision variables meeting the objective function under constraint conditions; and adjusting the dispatching plan according to the decision variable so as to dispatch the dispatchable energy. The invention can effectively reduce the influence of uncertain factors on the scheduling plan, has good robustness and realizes the balance of safety and economy.

Description

New energy grid-connected power system multi-time scale scheduling method based on robust optimization
Technical Field
The invention relates to the technical field of scheduling of a new energy grid-connected power system, in particular to a multi-time scale scheduling method based on robust optimization.
Background
With the increasing aggravation of environmental problems and energy exhaustion problems, renewable energy sources such as wind energy, solar energy and the like are greatly developed, and the proportion of the renewable energy sources occupied by the renewable energy sources is greatly increased. However, due to uncertainty of wind power and photovoltaic, large-scale new energy grid connection brings huge challenges to economic dispatching of a power system.
Some existing researches improve the capability of a power grid to deal with uncertain factors through coordination and coordination of a multi-time scale dispatching plan. However, the current method directly utilizes the predicted values of new energy and load when a multi-time scale scheduling plan is prepared, does not deeply consider the uncertainty of the predicted values of different time scales, is a deterministic model essentially, is easy to cause frequent adjustment of the scheduling plan in actual operation, increases the scheduling pressure and has certain load shedding risk. How to carry out uncertainty modeling on wind, light and load predicted values under different time scales needs to be further researched.
The current methods applied to uncertainty modeling are mainly of two types: stochastic programming and robust optimization. The stochastic programming method is an uncertainty analysis method based on probability theory, and mainly comprises a scene analysis method, an opportunity constraint programming method and the like. However, the stochastic programming method depends on a new energy probabilistic model, and the computational complexity is large, and the computational accuracy and safety cannot be guaranteed, so that the application of the stochastic programming method is limited.
The uncertainty is described by a set in the robust optimization, the probability distribution of uncertain parameters is not depended on, the description is easy, only the worst case of the uncertainty needs to be considered, and the method is suitable for large-scale calculation. Robust optimization has been widely used in power system optimization operation research in recent years. However, most of the current documents aim at adjusting the conservative degree of the robust model of the scheduling plan in the day ahead, and the control and coordination problem of the conservative degree of the robust model in the multi-time scale scheduling plan is not considered.
Based on this, it is desirable to have a hybrid system multi-timescale, variable confidence level robust scheduling model that overcomes or at least mitigates the above-mentioned deficiencies of the prior art.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a new energy grid-connected power system multi-time scale scheduling method based on robust optimization, solves the technical problem that the scheduling plan is frequently adjusted in actual operation because the uncertainty of a new energy predicted value is not considered in multi-time scale scheduling in the prior art, can effectively reduce the influence of uncertainty factors on the scheduling plan of the new energy grid-connected power system, has good robustness, and realizes the balance of safety and economy.
In order to solve the technical problems, the technical scheme of the invention is as follows: a new energy grid-connected power system multi-time scale scheduling method based on robust optimization is disclosed, wherein the new energy grid-connected power system comprises new energy and schedulable energy, and the method comprises the following steps:
step 1: respectively establishing a target function of each time scale dispatching plan by taking the dispatchable energy output in the new energy grid-connected power system as a decision variable;
step 2: respectively establishing constraint conditions including robust constraint and traditional physical constraint of each time scale scheduling plan; the robust constraint is established according to uncertainty factors and a robust level value, wherein the uncertainty factors comprise new energy output and load;
and step 3: establishing a robust optimization scheduling model of each time scale according to each objective function and corresponding constraint conditions;
and 4, step 4: selecting robust level values under each time scale according to a robust level value adjustment rule, and respectively inputting the robust level values into robust optimization scheduling models of each time scale so as to obtain decision variables meeting the objective function under constraint conditions;
and 5: and adjusting the dispatching plan according to the decision variable so as to dispatch the dispatchable energy.
Preferably, before the robust constraint is established, a robust constraint condition set J is established according to uncertainty factors, and the method comprises the following steps:
step 201: using uncertainty constraint as element for building robust constraint set J, Aj∈J，AjJ belongs to { 1.,..,. N }, and N is the number of uncertainty constraint conditions in the jth uncertainty constraint condition in the robust constraint condition set J;
step 202: establishing a set of uncertainty factors for each uncertainty constraint, wherein the jth uncertainty constraint AjThe uncertainty factor set on is Ij，Bl∈Ij，BlIs a set of uncertainty factors IjThe l-th uncertainty factor in (1), l ∈ { 1.
Preferably, the robust optimized scheduling model for each time scale has the following general formula:
Figure BDA0001556107210000021
wherein x is a decision variable; minf (x) is an objective function of the current time scale scheduling plan;
Figure BDA0001556107210000022
is the jth uncertainty constraint AjA coefficient matrix of an upper decision variable x; u. ofljIs the jth uncertainty constraint AjThe parameter of the l uncertainty factor of (1) above is
Figure BDA0001556107210000023
A nominal value of (d);
Figure BDA0001556107210000024
is the jth uncertainty constraint AjThe parameter of the l uncertainty factor of (1) above is
Figure BDA0001556107210000025
The amount of disturbance of; z is a radical ofjFor the jth uncertainty constraint A in the current dispatch planjMaximum value of disturbance quantity of upper uncertainty factor; pmax、PminRespectively is the upper limit and the lower limit of the output of the schedulable energy; and gamma is the robust level value of the current time scale scheduling plan selected according to the robust level value adjustment rule.
Preferably, the new energy comprises wind power output and photovoltaic output, and the schedulable energy comprises hydroelectric output and thermal power output; n is 2, and the robust constraint condition set J comprises two uncertain constraint conditions; the number of uncertainty factors in the uncertainty factor set of each uncertainty constraint condition is M to 3, and the three uncertainty factors are wind power output, photovoltaic output and load respectively;
uncertainty constraint A when j is 11To power balance constraints:
Figure BDA0001556107210000031
uncertainty constraint A when j is 22Rotating the standby constraint for the system:
Figure BDA0001556107210000032
wherein z is1Is the maximum value of disturbance quantity of uncertainty factor of power balance constraint in current scheduling plan, z2For in the current scheduling planMaximum value of disturbance quantity of uncertainty factor of system rotation standby constraint; z is a radical of1＝z2(ii) a Gamma is the robust level value of the current time scale scheduling plan selected according to the robust level value adjustment rule, and gamma belongs to [0, M ∈]；
PG.i.tDetermining the output condition of the thermal power generating unit i at the moment t for the current scheduling plan; ph.tDetermining the output condition of the hydroelectric generating set at the moment t for the current scheduling plan; pw.tDetermining the output condition of the wind turbine generator at the moment t for the current scheduling plan; pL.tLoad at time t determined for the current dispatch plan; pmax.iThe output upper limit is the thermal power generating unit i; ph.maxThe output upper limit of the hydroelectric generating set; and R is the spare capacity of the new energy grid-connected power system.
Preferably, the robust level value adjustment rule is: and adjusting the robust level value by adjusting the confidence level, wherein the robust level value and the confidence level are adjusted in the following relation:
Figure BDA0001556107210000033
wherein epsilon is the confidence level selected under the current time scale plan.
Preferably, the confidence level is adjusted according to the following principle:
first, the basic rule is determined: as the time scale is gradually reduced, the robust level value should be gradually increased;
secondly, on the basis of meeting the basic rule, the following adjustments are carried out according to the disturbance quantity of each uncertain factor and the new energy access proportion:
if the disturbance quantity values of all uncertain factors are closer, the robust level value of the maximum time scale plan is increased, and if the disturbance quantity values of all uncertain factors are larger in difference, the robust water value of the maximum time scale plan is reduced;
and if the new energy access proportion is increased, the robust level value of each time scale scheduling plan is increased.
Compared with the prior art, the invention has the following beneficial effects:
1. the method carries out robust modeling on the wind, light and load predicted values of each time scale, converts deterministic constraints under each time scale into robust constraints for considering uncertainty, and establishes a multi-time scale robust economic dispatching model, so that the uncertainty of the predicted values of different time scales of the wind, light and load is fully considered when a dispatching plan is made, and the influence of the wind, light and load predictive uncertainty is effectively reduced.
2. The invention sets the robustness level which is increased step by step along with the reduction of the time scale so as to reflect the reduction of the tolerance of the system to the worst case of uncertainty factors and the improvement of the attention degree of safety along with the approach of the actual scheduling time, thereby replacing the scheduling plan with larger safety guarantee at smaller economic cost and realizing the balance of safety and economy.
3. The whole scheduling model can effectively reduce the influence of wind, light and load prediction uncertainty, effectively reduce frequent start and stop of a unit, relieve scheduling pressure, and reduce the level of abandoned wind and load shedding, and meanwhile, compared with the traditional robust scheduling model in the day, the whole scheduling model has better economy and realizes coordination of economy and safety.
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Fig. 1 is a flowchart of a new energy grid-connected power system multi-time scale scheduling method based on robust optimization in the present embodiment.
Detailed Description
A new energy grid-connected power system multi-time scale scheduling method based on robust optimization is disclosed, wherein the new energy grid-connected power system comprises new energy and schedulable energy, and the method comprises the following steps:
step 1: respectively establishing a target function of each time scale dispatching plan by taking the dispatchable energy output in the new energy grid-connected power system as a decision variable;
step 2: respectively establishing constraint conditions including robust constraint and traditional physical constraint of each time scale scheduling plan; the robust constraint is established according to uncertainty factors and a robust level value, wherein the uncertainty factors comprise new energy output and load;
and step 3: establishing a robust optimization scheduling model of each time scale according to each objective function and corresponding constraint conditions;
and 4, step 4: selecting robust level values under each time scale according to a robust level value adjustment rule, and respectively inputting the robust level values into robust optimization scheduling models of each time scale so as to obtain decision variables meeting the objective function under constraint conditions;
and 5: and adjusting the dispatching plan according to the decision variable so as to dispatch the dispatchable energy.
In this embodiment, the following three time scale scheduling plans are included: a 24-hour day-ahead scheduling plan, a 4-hour day-in scheduling plan and a real-time 15-min scheduling plan;
24h schedule plan day by day 24: 00, once formulating, and configuring wind power output, photovoltaic output and hydropower output into a virtual power supply by using short-term predicted values of wind power output, photovoltaic output and load for 24h in the future, and participating in scheduling with thermal power output;
rolling and making a 4h scheduling plan every 15min in the day, and preferentially calling hydropower to adjust power deviation according to the latest future 4h wind power output, photovoltaic output and ultra-short-term predicted value of load on the basis of a 24h scheduling plan in the day, so as to correct the schedulable energy output and the thermal power unit combination state;
and the real-time 15-min scheduling plan is also made in a rolling mode once every 15min, and on the basis of the 4-hour scheduling plan in the day, the schedulable energy unit output in the future 15min, namely the next scheduling moment, is corrected according to the latest real-time predicted values of the wind power output, the photovoltaic output and the load in the future 15 min.
The 24h day-ahead scheduling plan provides a virtual power supply and a thermal power output power operation point for a 4h day scheduling plan, and the 4h day scheduling plan provides a thermal power output, a thermal power output and a thermal power unit combination state for a real-time 15min scheduling plan; the real-time 15min scheduling plan arrangement can schedule the real-time output adjustment of the energy unit, and all the rings are buckled and orderly and effectively connected.
The 4h scheduling plan and the real-time 15min scheduling plan in the day are rolling scheduling plans, and the respective previous time scale scheduling plans are rolled and corrected, so that the effective connection and stable transition of the scheduling plans in different time scales are effectively ensured, and the uncertain influence caused by the output of new energy can be better overcome.
In the specific embodiment, before the robust constraint is established, a robust constraint condition set J needs to be established according to uncertainty factors, and the following steps are performed:
step 201: using uncertainty constraint as element for building robust constraint set J, Aj∈J，AjJ belongs to { 1.,..,. N }, and N is the number of uncertainty constraint conditions in the jth uncertainty constraint condition in the robust constraint condition set J;
step 202: establishing a set of uncertainty factors for each uncertainty constraint, wherein the jth uncertainty constraint AjThe uncertainty factor set on is Ij，Bl∈Ij，BlIs a set of uncertainty factors IjThe l-th uncertainty factor in (1), l ∈ { 1.
In this embodiment, the general formula of the robust optimized scheduling model for each time scale is as follows:
Figure BDA0001556107210000051
wherein x is a decision variable; minf (x) is an objective function of the current time scale scheduling plan;
Figure BDA0001556107210000052
is the jth uncertainty constraint AjA coefficient matrix of an upper decision variable x; u. ofljIs the jth uncertainty constraint AjThe parameter of the l uncertainty factor of (1) above is
Figure BDA0001556107210000053
A nominal value of (d);
Figure BDA0001556107210000054
is the jth uncertainty constraint AjThe parameter of the l uncertainty factor of (1) above is
Figure BDA0001556107210000055
The amount of disturbance of; z is a radical ofjFor the jth uncertainty constraint A in the current dispatch planjMaximum value of disturbance quantity of upper uncertainty factor; pmax、PminRespectively is the upper limit and the lower limit of the output of the schedulable energy; and gamma is the robust level value of the current time scale scheduling plan selected according to the robust level value adjustment rule.
In the specific embodiment, the new energy comprises wind power output and photovoltaic output, and the schedulable energy comprises hydroelectric output and thermal power output; n is 2, and the robust constraint condition set J comprises two uncertain constraint conditions; the number of uncertainty factors in the uncertainty factor set of each uncertainty constraint condition is M to 3, and the three uncertainty factors are wind power output, photovoltaic output and load respectively;
uncertainty constraint A when j is 11To power balance constraints:
Figure BDA0001556107210000061
uncertainty constraint A when j is 22Rotating the standby constraint for the system:
Figure BDA0001556107210000062
wherein z is1Is the maximum value of disturbance quantity of uncertainty factor of power balance constraint in current scheduling plan, z2The maximum value of the disturbance quantity of the uncertainty factor of the system rotation standby constraint in the current scheduling plan is obtained; z is a radical of1＝z2(ii) a Gamma is the robust level value of the current time scale scheduling plan selected according to the robust level value adjustment rule, and gamma belongs to [0, M ∈]；
PG.i.tOutput condition of thermal power generating unit i at time t determined for current scheduling plan；Ph.tDetermining the output condition of the hydroelectric generating set at the moment t for the current scheduling plan; pw.tDetermining the output condition of the wind turbine generator at the moment t for the current scheduling plan; pL.tLoad at time t determined for the current dispatch plan; pmax.iThe output upper limit is the thermal power generating unit i; ph.maxThe output upper limit of the hydroelectric generating set; and R is the spare capacity of the new energy grid-connected power system.
In this specific embodiment, the robust constraints of the 24h day-ahead scheduling plan include a power balance constraint and a system rotation standby constraint, which are respectively as follows:
and power balance constraint:
Figure BDA0001556107210000063
and (3) system rotation standby constraint:
Figure BDA0001556107210000064
wherein the content of the first and second substances,
Figure BDA0001556107210000065
for the maximum value of the disturbance quantity of uncertainty factors of the power balance constraint in the 24h scheduling plan,
Figure BDA0001556107210000066
the maximum value of the disturbance quantity of the uncertainty factor of the system rotation standby constraint in the 24h dispatching plan,
Figure BDA0001556107210000067
Γ24hthe robust level value of the scheduling plan 24h before the day is selected according to the robust level value adjustment rule;
Figure BDA0001556107210000068
planning the determined output condition of the thermal power generating unit i at the moment t for 24h before the day;
Figure BDA0001556107210000069
the output condition of the hydroelectric generating set at the moment t is planned and determined 24h before the day;
Figure BDA0001556107210000071
the output condition of the wind turbine generator at the time t is determined for 24h before the day;
Figure BDA0001556107210000072
the determined load at time t is planned for 24h before the day.
In this specific embodiment, the robust constraints of the intra-day 4h scheduling plan include a power balance constraint and a system rotation standby constraint, which are respectively as follows:
and power balance constraint:
Figure BDA0001556107210000073
and (3) system rotation standby constraint:
Figure BDA0001556107210000074
wherein the content of the first and second substances,
Figure BDA0001556107210000075
the maximum value of the disturbance quantity of the uncertainty factor of the power balance constraint in the 4h scheduling plan in the day,
Figure BDA0001556107210000076
the maximum value of the disturbance quantity of the uncertainty factor of the system rotation standby constraint in the 4h scheduling plan in the day,
Figure BDA0001556107210000077
Γ4hthe robust level value of the scheduling plan of 4h in a day selected according to the robust level value adjustment rule;
Figure BDA0001556107210000078
fire determined for 4h dispatch plan within dayThe output condition of the motor set i at the moment t;
Figure BDA0001556107210000079
the output condition of the hydroelectric generating set at the time t determined by the scheduling plan for 4 hours in the day;
Figure BDA00015561072100000710
the output condition of the wind turbine generator at the time t is determined for a 4h scheduling plan in the day;
Figure BDA00015561072100000711
the load at time t is determined for the 4h scheduling plan in the day.
In this embodiment, the robust constraints of the real-time 15min scheduling plan include a power balance constraint and a system rotation standby constraint, which are respectively as follows:
and power balance constraint:
Figure BDA00015561072100000712
and (3) system rotation standby constraint:
Figure BDA00015561072100000713
wherein the content of the first and second substances,
Figure BDA00015561072100000714
the maximum value of the disturbance quantity of the uncertainty factor of the power balance constraint in the real-time 15min scheduling plan,
Figure BDA00015561072100000715
the maximum value of the disturbance quantity of the uncertainty factor of the system rotation standby constraint in the day real-time 15min scheduling plan,
Figure BDA00015561072100000716
Γ15minthe robust level value of the real-time 15min scheduling plan selected according to the robust level value adjustment rule;
Figure BDA00015561072100000717
the output condition of the thermal power generating unit i at the moment t is determined for a real-time 15min scheduling plan;
Figure BDA00015561072100000718
the output condition of the hydroelectric generating set at the time t determined by the real-time 15min scheduling plan;
Figure BDA00015561072100000719
the output condition of the wind turbine generator at the time t is determined for a real-time 15min scheduling plan;
Figure BDA00015561072100000720
the load at time t is determined for the real-time 15min dispatch plan.
In the specific embodiment, the objective function of the 24-hour day-ahead scheduling plan is established according to the following steps:
step 501: the wind power output, the photovoltaic output and the hydroelectric output are configured into a virtual power supply, and the index N is tracked by the load of the virtual power supplyrThe minimum is used as a first layer objective function, and a virtual power output curve VP and an optimized load curve P are obtained under the condition of meeting the first layer objective functionr(ii) a The first layer objective function is as follows:
minNr＝Dt+Ds+Dc；
wherein D istFor the rate of fluctuation of new energy output with respect to load, DsAs standard deviation of load fluctuation, DcIs the load power rate of change; dtThe smaller the output curve of the virtual power supply VP is, the closer the output curve of the virtual power supply VP is to the load curve, namely the better the tracking capability of the virtual power supply VP on the load is; dsAs standard deviation of load fluctuation, DcFor the load power change rate, the two indexes jointly represent the optimized load curve P after the output curve VP of the virtual power supply is stabilizedrThe smaller the value, the more the optimized load curve P is representedrThe smoother and smaller the fluctuation;
step 502: with thermal power machineThe lowest group total power generation cost is the second layer objective function and is in the optimized load curve PrArranging working positions of thermal power output meeting the second layer of objective function; the second layer objective function is as follows:
Figure BDA0001556107210000081
wherein the content of the first and second substances,
Figure BDA0001556107210000082
the number of time segments divided for the 24h scheduling plan day ahead; n is a radical ofgThe total number of the thermal power generating units; u shapei,tThe starting and stopping state, U, of the thermal power generating unit i at the moment t determined for the current scheduling plani,t∈{0,1}；Ui,t-1The starting and stopping state, U, of the thermal power generating unit i at the moment t-1 determined for the current scheduling plani,t-1∈{0,1}；
Figure BDA0001556107210000083
Planning the determined output condition of the thermal power generating unit i at the moment t for 24h before the day; siThe starting cost of the thermal power generating unit i is obtained; a isi、bi、ciThe first economic characteristic parameter, the first economic characteristic parameter and the third economic characteristic parameter of the thermal power generating unit i are respectively.
In the specific embodiment, hydropower is preferentially called by a 4h scheduling plan in a day to adjust power deviation, the lowest hydropower output adjustment cost and the lowest start-stop cost in the period are taken as target functions, and the fine adjustment of the combined state of the thermal power generating units is mainly used for arranging the quick start-stop of the small thermal power generating units according to the unit start-up priority determined by a priority method;
the objective function of the intra-day 4h dispatch plan is as follows:
Figure BDA0001556107210000084
wherein the content of the first and second substances,
Figure BDA0001556107210000085
hours divided for 4h scheduling plan in dayThe number of stages; epsiloni.tAdjusting the cost for the unit output of the thermal power generating unit; delta PG.i.tThe output adjustment quantity delta P is the output adjustment quantity of the thermal power generating unit i at the moment tG′.i.tThe difference value of the output condition of the medium-voltage generator set i in the 4h scheduling plan in the day and the output condition of the medium-voltage generator set i in the 24h scheduling in the day.
In the specific embodiment, the real-time 15-min scheduling plan preferentially calls hydropower to correct the power deviation, and meanwhile, due to the fact that the ultra-short-term prediction accuracy under the last time scale is high, the hydropower and thermal power output adjustment amount under the time scale is small, and the unit combination state is not adjusted; the minimum real-time adjustment cost of the thermal power generating unit is taken as a target, and no start-stop expense item exists; the objective function of the real-time 15min dispatch plan is as follows:
Figure BDA0001556107210000091
wherein N isgThe total number of the thermal power generating units; u shapei,tThe starting and stopping state, U, of the thermal power generating unit i at the moment t determined for the current scheduling plani,t∈{0,1}；εi.tAdjusting the cost for the unit output of the thermal power generating unit; delta PG.i.tThe output adjustment quantity of the thermal power generating unit i at the moment t, the output adjustment quantity delta P ″)G.i.tThe difference value of the output condition of the I-type live-wire generator set in the real-time 15-min dispatching plan and the output condition of the I-type live-wire generator set in the 4-hour dispatching plan in the day is shown.
In the specific embodiment, the traditional physical constraints of each time scale scheduling plan include unit active output constraints, unit climbing capacity constraints, unit minimum on-off time constraints and wind/light abandoning constraints;
active power output constraint of the current time scale scheduling plan:
Figure BDA0001556107210000092
unit climbing capacity constraint of the current time scale scheduling plan:
Figure BDA0001556107210000093
wind/light curtailment constraints of the current time scale scheduling plan:
Figure BDA0001556107210000094
the minimum on-off time constraint of the unit of the current time scale scheduling plan is divided into the following two cases:
the minimum on-off time constraints of the unit of the 24h hour day-ahead scheduling plan and the real-time 15min scheduling plan are as follows:
Figure BDA0001556107210000095
the minimum start-up and shut-down time constraint of the unit of the 4h scheduling plan in the day is as follows:
Figure BDA0001556107210000096
in the above formula, the parameters have the following meanings: t isstart.i、Tstop.iRespectively scheduling the starting and stopping time of the fire generator set i in the plan for 4h in a day; pG.i.t、PG.i.t-1Respectively determining the output conditions of the thermal power generating unit i at the time t and the time t-1 determined by the current scheduling plan; u shapei,t、Ui,t-1The starting and stopping states, U, of the thermal power generating unit i at the time t and the time t-1 determined by the current scheduling plan respectivelyi,t∈{0,1}，Ui,t-1∈{0,1}；Ph.tDetermining the output condition of the hydroelectric generating set at the moment t for the current scheduling plan; pw.tDetermining the output condition of the wind turbine generator at the moment t for the current scheduling plan; pL.tLoad at time t determined for the current dispatch plan; pmax.i、Pmin.iRespectively representing the upper output limit and the lower output limit of the thermal power generating unit i; ph.max、Ph.minRespectively representing the upper output limit and the lower output limit of the hydroelectric generating set; ru.i、Rd.iClimbing rate and slip of thermal power generating unit iA ramp rate;
Figure BDA0001556107210000101
respectively the continuous startup time and the continuous shutdown time of the thermal power generating unit from the moment i to the moment t-1;
Figure BDA0001556107210000102
respectively determining the minimum continuous starting time and the minimum continuous stopping time of the thermal power generating unit i; delta1、δ2Respectively the allowed maximum wind abandoning rate and the maximum light abandoning rate;
Figure BDA0001556107210000103
and
Figure BDA0001556107210000104
the maximum wind power and the photovoltaic available output at the moment t are respectively.
In this embodiment, the robust level value adjustment rule is as follows: and adjusting the robust level value by adjusting the confidence level, wherein the robust level value and the confidence level are adjusted in the following relation:
Figure BDA0001556107210000105
wherein epsilon is a confidence level selected under a current time scale plan, and a robust level value can be adjusted by adjusting the confidence level, so that robust optimization of multi-time scale scheduling is realized, the higher the robust level value is, the higher the system security is, and the lower the robust level value is, the better the system economy is, therefore, an appropriate robust level value needs to be selected to realize balance of the system economy and the security, and therefore, the following robust level value adjustment rule is established:
in this embodiment, the confidence level is adjusted according to the following principle:
first, the basic rule is determined: as the time scale is gradually reduced, the robust level value should be gradually increased; and the robust level value of the 24h day-ahead dispatch plan does not need to be set too high. The robust level value of the scheduling plan 24h before the day can be set to be smaller properly, and the robust level value is increased step by step in the subsequent time scale scheduling plan, so that the system scheduling safety is increased step by step at relatively lower economic cost, and the balance between the safety and the economy is realized;
secondly, on the basis of meeting the basic rule, the following adjustments are carried out according to the disturbance quantity of each uncertain factor and the new energy access proportion:
if the disturbance quantity values of all uncertain factors are closer, the robust level value of the maximum time scale plan, namely the day-ahead 24h scheduling plan, is increased, and if the disturbance quantity values of all uncertain factors are larger in difference, the robust water value of the maximum time scale plan is reduced to be close to the actual worst condition;
if the new energy access proportion is increased, the robust level value of each time scale scheduling plan is increased; the higher the new energy access proportion is, the larger the value of the predicted deviation value of the new energy output is, the more difficult the system can not cope with the bad situation, and the more difficult the system safety is to be ensured, so a higher robust level is set to improve the conservative degree of a scheduling plan, and the system safety is ensured as much as possible.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, which should be covered by the claims of the present invention.

Claims (6)

1. A new energy grid-connected power system multi-time scale scheduling method based on robust optimization is disclosed, wherein the new energy grid-connected power system comprises new energy and schedulable energy, and is characterized in that: the method comprises the following steps:
step 1: respectively establishing a target function of each time scale dispatching plan by taking the dispatchable energy output in the new energy grid-connected power system as a decision variable;
step 2: respectively establishing constraint conditions including robust constraint and traditional physical constraint of each time scale scheduling plan; the robust constraint is established according to uncertainty factors and a robust level value, wherein the uncertainty factors comprise new energy output and load;
and step 3: establishing a robust optimization scheduling model of each time scale according to each objective function and corresponding constraint conditions;
and 4, step 4: selecting robust level values under each time scale according to a robust level value adjustment rule, and respectively inputting the robust level values into robust optimization scheduling models of each time scale so as to obtain decision variables meeting the objective function under constraint conditions;
and 5: adjusting the scheduling plan according to the decision variable so as to schedule the schedulable energy;
before the robust constraint is established, a robust constraint condition set J is established according to uncertainty factors, and the method comprises the following steps:
step 201: using uncertainty constraint as element for building robust constraint set J, Aj∈J，AjJ belongs to { 1.,..,. N }, and N is the number of uncertainty constraint conditions in the jth uncertainty constraint condition in the robust constraint condition set J;
step 202: establishing a set of uncertainty factors for each uncertainty constraint, wherein the jth uncertainty constraint AjThe uncertainty factor set on is Ij，Bl∈Ij，BlIs a set of uncertainty factors IjThe l is in the form of { 1.,..,. M }, and M is the number of uncertainty factors;
the new energy comprises wind power output and photovoltaic output, and the schedulable energy comprises water power output and thermal power output; n is 2, and the robust constraint condition set J comprises two uncertain constraint conditions; the number of uncertainty factors in the uncertainty factor set of each uncertainty constraint condition is M to 3, and the three uncertainty factors are wind power output, photovoltaic output and load respectively;
uncertainty constraint A when j is 11To power balance constraints:
Figure FDA0002416336860000011
uncertainty constraint A when j is 22Rotating the standby constraint for the system:
Figure FDA0002416336860000012
wherein z is1Is the maximum value of disturbance quantity of uncertainty factor of power balance constraint in current scheduling plan, z2The maximum value of the disturbance quantity of the uncertainty factor of the system rotation standby constraint in the current scheduling plan is obtained; z is a radical of1＝z2(ii) a Gamma is the robust level value of the current time scale scheduling plan selected according to the robust level value adjustment rule, and gamma belongs to [0, M ∈]；
PG.i.tDetermining the output condition of the thermal power generating unit i at the moment t for the current scheduling plan; ph.tDetermining the output condition of the hydroelectric generating set at the moment t for the current scheduling plan; pw.tDetermining the output condition of the wind turbine generator at the moment t for the current scheduling plan; pL.tLoad at time t determined for the current dispatch plan; pmax.iThe output upper limit is the thermal power generating unit i; ph.maxThe output upper limit of the hydroelectric generating set; and R is the spare capacity of the new energy grid-connected power system.
2. The robust optimization-based new energy grid-connected power system multi-time scale scheduling method according to claim 1, characterized in that: the general formula of the robust optimized scheduling model for each time scale is as follows:
Figure FDA0002416336860000021
wherein x is a decision variable; minf (x) is an objective function of the current time scale scheduling plan;
Figure FDA0002416336860000022
is the jth uncertainty constraint AjCoefficient of upper decision variable xA matrix; u. ofljIs the jth uncertainty constraint AjThe parameter of the l uncertainty factor of (1) above is
Figure FDA0002416336860000023
A nominal value of (d);
Figure FDA0002416336860000024
is the jth uncertainty constraint AjThe parameter of the l uncertainty factor of (1) above is
Figure FDA0002416336860000025
The amount of disturbance of; z is a radical ofjFor the jth uncertainty constraint A in the current dispatch planjMaximum value of disturbance quantity of upper uncertainty factor; pmax、PminRespectively is the upper limit and the lower limit of the output of the schedulable energy; and gamma is the robust level value of the current time scale scheduling plan selected according to the robust level value adjustment rule.
3. The robust optimization-based new energy grid-connected power system multi-time scale scheduling method according to claim 1, characterized in that: the method comprises the following three time scale scheduling plans: a 24-hour day-ahead scheduling plan, a 4-hour day-in scheduling plan and a real-time 15-min scheduling plan;
the day-ahead 24h scheduling plan configures wind power output, photovoltaic output and hydropower output into a virtual power supply by using the short-term predicted values of the wind power output, the photovoltaic output and the load of 24h in the future, and participates in scheduling together with the thermal power output;
on the basis of a day-ahead 24h scheduling plan, the in-day 4h scheduling plan preferentially calls hydropower to adjust power deviation according to the latest future 4h wind power output, photovoltaic output and the ultra-short-term predicted value of the load, and corrects the schedulable energy output and the combination state of the thermal power generating unit;
the 15min dispatching plan corrects the output of the dispatchable energy unit in the future 15min, namely the next dispatching moment, according to the latest real-time predicted values of wind power output, photovoltaic output and load in the future 15min on the basis of a 4h dispatching plan in the day.
4. The robust optimization-based new energy grid-connected power system multi-time scale scheduling method according to claim 1, characterized in that: robust level value adjustment rule: and adjusting the robust level value by adjusting the confidence level, wherein the robust level value and the confidence level are adjusted in the following relation:
Figure FDA0002416336860000031
wherein epsilon is the confidence level selected under the current time scale plan.
5. The robust optimization-based new energy grid-connected power system multi-time scale scheduling method according to claim 3, characterized in that: the adjustment of the confidence level follows the following principle:
first, the basic rule is determined: as the time scale is gradually reduced, the robust level value should be gradually increased;
secondly, on the basis of meeting the basic rule, the following adjustments are carried out according to the disturbance quantity of each uncertain factor and the new energy access proportion:
if the disturbance quantity values of all uncertain factors are closer, the robust level value of the maximum time scale plan is increased, and if the disturbance quantity values of all uncertain factors are larger in difference, the robust water value of the maximum time scale plan is reduced;
and if the new energy access proportion is increased, the robust level value of each time scale scheduling plan is increased.
6. The robust optimization-based new energy grid-connected power system multi-time scale scheduling method according to claim 1, characterized in that: the traditional physical constraints comprise unit active output constraints, unit climbing capacity constraints, unit minimum starting and stopping time constraints and wind/light abandoning constraints.
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