CN104753086A - Minimum spinning reserve scheduling method meeting requirements of robust running of various scenes - Google Patents

Abstract

The invention discloses a minimum spinning standby scheduling method satisfying robust operation in various scenarios, comprising the following steps: (1) establishing a minimum spinning standby scheduling plan model satisfying robust operation in various scenarios; (2) adopting an optimization approximation The scenario subtraction method generates S scenarios and the corresponding probabilities of the scenarios; (3) Substituting the S scenarios and the corresponding probabilities of the scenarios into the objective function and the constraint conditions to solve, and obtain a robust unit output plan and the system at each time period The required minimum positive and negative spinning reserves; (4) When the wind power changes, on the basis of the output plan, adjust the output of the unit according to the output plan, so as to adapt to the change of wind power and achieve power balance. The models established by the invention are all deterministic constraints, and the solution is simple and convenient; the obtained dispatching plan satisfies the possible scenarios of wind power power with a small adjustment amount, avoids large-scale adjustment of the dispatching plan, and reduces the burden of operation dispatchers.

Description

A Minimum Spinning Standby Scheduling Method for Robust Operation in Multiple Scenarios

technical field

The invention belongs to the technical field of wind power generation, and more specifically relates to a minimum spinning reserve scheduling method that satisfies the robust operation of various scenarios.

Background technique

The strong random fluctuation of wind power brings great challenges to the operation and scheduling of power systems. Reasonable formulation of spinning reserve plans is of great significance to the safe, reliable and economical operation of power systems containing large-scale wind power. If the spinning reserve configuration is too high, the economy of the system operation will decrease; if the configuration is too low, the safety and reliability of the system operation will be difficult to guarantee. In the existing disclosed technology, the contradiction between safety and economy of the spinning standby configuration cannot be well coordinated, and the calculation process is complicated, which is not convenient for use in actual operation.

In the Chinese patent specification CN 103151803 A, an optimization method including a wind power system unit and a backup configuration is disclosed. This method only optimizes the allocation of reserves for the given wind power output curves of several scenarios, but this method does not consider how to calculate the minimum rotating reserve that meets the wind power scenarios. .

In the Chinese patent specification CN 103956773 A, a method for optimizing the backup configuration of a wind power system unit is disclosed. This method needs to assume that the load and wind power obey the normal distribution, resulting in a large deviation between the obtained results and the actual situation. However, in most cases, wind power does not obey the normal distribution. And this method does not take into account the minimum spinning reserve.

In the Chinese patent specification CN 102832614 A, a robust optimization method for power generation planning in an uncertain environment is disclosed. However, the range of wind power fluctuation required by this method is not easy to determine, and it involves the conversion of uncertain problems to deterministic problems. The conversion process is relatively complicated, which is not convenient for practical engineering applications.

Contents of the invention

Aiming at the defects of the prior art, the purpose of the present invention is to provide a minimum spinning reserve scheduling method that satisfies the robust operation of various scenarios, aiming to solve the problem that the existing scheduling methods have poor adaptability to wind power scenarios and the adjustment range is large or even impossible Adjustment, complex calculation, and poor practicality.

The present invention provides a minimum spinning standby scheduling method that satisfies the robust operation of various scenarios, including the following steps:

(1) Establish a minimum spinning standby scheduling model that satisfies the robust operation of various scenarios;

Among them, the objective function of the minimum spinning reserve dispatching plan model is obtained by making the mean value of the positive and negative adjustments that the system needs to configure in order to deal with all wind power scenarios to be the smallest; the constraints of the minimum spinning reserve dispatching plan model include the system Power balance constraints, positive and negative regulation limit constraints, unit operation constraints, unit output ramp constraints and unit minimum start-stop time constraints;

(2) Generate S scenes using the optimal reduction scene method The probability p ^s corresponding to the scene;

(3) the S scenes The probability p ^s corresponding to the scene is substituted into the objective function and the constraint conditions to solve, and obtain a robust unit output plan and the minimum positive and negative rotation reserves required by the system for each time period;

(4) When the wind power changes, on the basis of the output plan, the output of the unit is adjusted according to the output plan, so as to adapt to the change of wind power and realize power balance.

Furthermore, the objective function is: in Indicates the positive adjustment amount required by the system to cope with the wind power scenario s in the tth period; Indicates the negative adjustment required by the system to deal with the wind power scenario s in the tth period; p ^s represents the probability of the wind power scenario s; s=1,2,...,S, S represents the total number of scenarios; t=1,2 ,...,T, T represents the total number of time periods;

The system power balance constraint is P _i,t represents the output of the dispatchable unit i in the tth period; u _i,t represents the state of the dispatchable unit i in the t period, u _i,t = 1 means starting up, u _i,t = 0 means shutting down; Indicates the value of the s-th wind power scenario in the t-period; Indicates the load _{of the system at the tth period; i=1,2,...,NG, where N G is} the number of schedulable units;

The positive and negative regulation limit constraints are respectively: in, Indicates the upper limit of dispatchable unit i output; P _i represents the lower limit of dispatchable unit i output;

The operating constraints of the unit are:

The climbing constraint of the unit output is: in Indicates the output limit of unit i climbing uphill in adjacent periods; Indicates the output limit of unit i climbing downhill in adjacent periods;

The minimum start-stop time constraint of the unit is: $\left\{\begin{array}{c}{\underset{\‾}{T}}_i^{\mathrm{on}}\≤T_i^{\mathrm{on}}\\ {\underset{\‾}{T}}_i^{\mathrm{off}}\≤T_i^{\mathrm{off}}\end{array}\right.;$ in, Indicates the continuous running time of unit i; Indicates the duration of continuous shutdown of unit i; Indicates the minimum time limit for continuous operation of unit i; Indicates the minimum time limit for continuous shutdown of unit i.

Further, the step (2) is specifically:

(2.1) Establish a model for optimizing the reduction scene, the model includes $\underset{\tilde{o}}{\min }\frac{1}{T}\underset{k1\∈\{o-\tilde{o}\}}{\mathrm{\Σ}}{p}^{k1}\underset{k2\∈\tilde{o}}{\min }|{\tilde{W}}_{1,...,T}^{k1}-W_{1,...,T}^{k2}|;\underset{\widetilde{\mathrm{the\; s}}=1}{\overset{\tilde{S}}{\mathrm{\Σ}}}{\tilde{p}}^{\widetilde{\mathrm{the\; s}}}=1;\underset{\mathrm{the\; s}=1}{\overset{S}{\mathrm{\Σ}}}{p}^{\mathrm{the\; s}}=1$

Among them, O represents the original scene set; Represents a reduced set of scenes; Indicates the set of deleted scenes; k1 indicates the serial number of the deleted scene; k2 indicates the serial number of the reduced scene; and represent the original scene and its probability respectively; W and p ^s represent the reduced scene and its probability respectively; represents the total number of original scenes, S represents the total number of reduced scenes, s=1,2,...,S.

(2.2) Solve the above model to obtain the optimal reduction scenario and its probability p ^s ;

in, Indicates the value of the sth reduced wind power scenario in the t period; p ^s represents the probability of the sth reduced wind power scenario.

The invention considers the power balance of the system, the constraints of the positive/negative adjustment amount, the output constraint of the unit, the constraint of climbing and the minimum start-stop time constraint, and meets the basic requirements of the system operation. And from the form of objective function and constraint function, the established models are all deterministic constraints, and the solution is simple and convenient. The resulting dispatch plan satisfies the possible scenarios of wind power with a small amount of adjustment, which avoids a large adjustment of the dispatch plan and reduces the burden of operation dispatchers. At the same time, the present invention introduces the positive adjustment amount (positive spinning reserve) and the negative adjustment amount (negative spinning reserve) required by the system to satisfy each scenario based on the wind power power scenarios that may actually occur, and obtains the optimal variable that can satisfy all scenarios at the same time. A robust crew effort plan and spinning reserve plan. Provide a theoretical basis and an effective method for the establishment of spinning reserve for large-scale wind power systems.

Description of drawings

Fig. 1 is an implementation flow chart of a minimum spinning standby scheduling method that satisfies robust operation in various scenarios provided by an embodiment of the present invention.

Figure 2 is a schematic diagram of the positive and negative spinning reserves that need to be configured in response to scenario s.

Figure 3 is a scene of wind power sequence.

Figure 4 is the calculation result of spinning reserve.

Detailed ways

In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

The present invention overcomes the defects of poor adaptability to wind power scenarios, large adjustment range or even inability to adjust, complex calculation and poor practicability of the existing dispatching model, and provides a new spinning standby dispatching plan model, so as to meet the requirements with a small adjustment amount. The possible scenarios of wind power avoid large-scale adjustments in dispatching plans, reduce the burden on dispatchers, and provide theoretical basis and effective methods for the formulation of spinning reserves for large-scale wind power systems.

Figure 1 shows the implementation process of the minimum spinning standby scheduling method that satisfies the robust operation of various scenarios provided by the embodiment of the present invention. For the convenience of description, only the parts related to the embodiment of the present invention are shown, and the details are as follows:

The minimum spinning standby scheduling method that satisfies the robust operation of various scenarios provided by the embodiment of the present invention specifically includes the following steps:

(1) Establish a minimum spinning standby scheduling model that satisfies the robust operation of various scenarios.

In order to build the model, the concepts of positive regulation and negative regulation satisfying the wind power scenario are introduced. Positive adjustment refers to the active output that needs to be increased on the basis of the unit output plan in response to changes in the actual load; negative adjustment refers to the active output that needs to be reduced on the basis of the unit output plan in response to changes in the actual load.

As shown in Figure 2, the two curves respectively represent the sum of the dispatching plan output of the unit and the actual net load scenario s, and the net load is equal to the system load minus the actual wind power. It can be seen from Figure 2 that at some point, it is necessary to increase the active output on the basis of the unit output plan to achieve power balance, assuming that the increased output is recorded as At some point, it is necessary to reduce the active output on the basis of the conventional unit output plan to achieve power balance, assuming that the reduced output is recorded as The following are respectively called and are the positive adjustment amount and negative adjustment amount to meet the wind power scenario s. essentially, and In order to cope with the wind power scenario s, the system needs to be configured with up-spin reserve and down-spin reserve. Taking the minimum mean value of the positive and negative adjustments that the system needs to configure in order to deal with all wind power scenarios as the objective function of optimization, an optimization model of dispatching plan and spinning reserve is established.

(1.1) Objective function: to minimize the mean value of the positive and negative adjustments that the system needs to configure in order to cope with all wind power scenarios; In formula (1), Indicates the positive adjustment amount required by the system to cope with the wind power scenario s in the tth period; Indicates the negative adjustment required by the system to deal with the wind power scenario s in the tth period; p ^s represents the probability of the wind power scenario s; s=1,2,...,S, S represents the total number of scenarios; t=1,2 ,...,T, where T represents the total number of time periods.

(1.2) Constraints:

(1.2.1) System Power Balance Constraints s=1,2,...,S t=1,2,...,T, in formula (2), P _i,t represents the output of dispatchable unit i in the tth period; u _i,t represents the output of dispatchable unit i In the state of the tth time period, u _i,t = 1 means starting up, u _{i, t} = 0 means stopping; Indicates the value of the s-th wind power scenario in the t-period; Indicates the load of the system at period t; ... N _G is the number of dispatchable units, i=1, 2, ..., N _G .

(1.2.2) Positive and negative adjustment volume limit constraints t=1,2,...,T; in formula (3)(4), Indicates the upper limit of dispatchable unit i output; P _i represents the lower limit of dispatchable unit i output.

(1.2.3) Operating constraints of the unit

(1.2.4) Climbing constraint of unit output

${\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; up</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; ></span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 6</span>}<span\; class="notranslate">\; )</span>$

${\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; \&Delta;P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; down</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}}<span\; class="notranslate">\; ></span>{\mathrm{<span\; class="notranslate">\; P</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}<span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; t</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 7</span>}<span\; class="notranslate">\; )</span>$

In formula (6) (7), Indicates the output limit of unit i climbing uphill in adjacent periods; Indicates the output limit of unit i going downhill in the adjacent period.

(1.2.5) The minimum start-stop time constraint of the unit

$\left\{\begin{array}{c}{\underset{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; T</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; on</span>}}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; on</span>}}\\ {\underset{<span\; class="notranslate">\; \&OverBar;</span>}{\mathrm{<span\; class="notranslate">\; T</span>}}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; off</span>}}<span\; class="notranslate">\; \&le;</span>{\mathrm{<span\; class="notranslate">\; T</span>}}_{\mathrm{<span\; class="notranslate">\; i</span>}}^{\mathrm{<span\; class="notranslate">\; off</span>}}\end{array}\right.<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 8</span>}<span\; class="notranslate">\; )</span>$

In formula (8), Indicates the continuous running time of unit i; Indicates the duration of continuous shutdown of unit i; Indicates the minimum time limit for continuous operation of unit i; Indicates the minimum time limit for continuous shutdown of unit i.

(2) Generate wind power scenarios

The wind power power scenario is generated using the optimal reduction scenario method. For specific steps, refer to the document "Scenario reduction and scenario tree construction for power management problems" (Proceedings of IEEE Conference on Power Tech, Bologna, Italy, 2003). The model formula for the optimal reduction scenario is as follows :

$\underset{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; o</span>}}}{\mathrm{<span\; class="notranslate">\; min</span>}}\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; T</span>}}\underset{\mathrm{<span\; class="notranslate">\; k</span>}\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; \&Element;</span><span\; class="notranslate">\; \{</span>\mathrm{<span\; class="notranslate">\; o</span>}<span\; class="notranslate">\; -</span>\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; o</span>}}<span\; class="notranslate">\; \}</span>}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}\underset{\mathrm{<span\; class="notranslate">\; k</span>}\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; \&Element;</span>\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; o</span>}}}{\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; |</span>{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; W</span>}}}_{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; T</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; -</span>{\mathrm{<span\; class="notranslate">\; W</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; .</span><span\; class="notranslate">\; ,</span>\mathrm{<span\; class="notranslate">\; T</span>}}^{\mathrm{<span\; class="notranslate">\; k</span>}\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; |</span>$

s.t.

$\underset{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; S</span>}}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; p</span>}}}^{\stackrel{<span\; class="notranslate">\; ~</span>}{\mathrm{<span\; class="notranslate">\; the\; s</span>}}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}$

$\underset{\mathrm{<span\; class="notranslate">\; the\; s</span>}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; S</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; p</span>}}^{\mathrm{<span\; class="notranslate">\; the\; s</span>}}<span\; class="notranslate">\; =</span>\mathrm{<span\; class="notranslate">\; 1</span>}$

Among them, O represents the original scene set; Represents a reduced set of scenes; Indicates the set of deleted scenes; k1 indicates the serial number of the deleted scene; k2 indicates the serial number of the reduced scene; and represent the original scene and its probability respectively; W and p ^s represent the reduced scene and its probability respectively; represents the total number of original scenes, S represents the total number of reduced scenes, s=1,2,...,S.

Solve the model to obtain the optimal reduction scenario and its probability p ^s , where, Indicates the value of the sth reduced wind power scenario in the t period; p ^s represents the probability of the sth reduced wind power scenario.

(3) Solve the model

The S scenes generated in (2) The probability p ^s corresponding to the scene is respectively substituted into formula (2) and formula (1), so far, the known parameters required to solve the scheduling model described in the technical method (1) of the present invention have been obtained. The model was solved using commercial software GAMS or other software that can solve mixed nonlinear models. The following results are obtained by calculation:

(3.1) Obtain a robust unit output plan u _i,t , P _i,t . In actual operation, the plan can cope with all possible scenarios S through an appropriate amount of adjustment, and is robust.

(3.2) Obtain the positive adjustment required to meet each scenario and negative adjustment Then according to formulas (10) and (11), the minimum positive spinning reserve required for each period t of the system can be obtained and negative spinning reserve $r_t^{\mathrm{up}}=\max \{{\mathrm{\&Delta;r}}_t^1,...,{\mathrm{\&Delta;r}}_t^{\mathrm{the\; s}},...,{\mathrm{\&Delta;r}}_t^S\}---\left(10\right);$ $r_t^{\mathrm{down}}=\max \{{\mathrm{\&Delta;v}}_t^1,...,{\mathrm{\&Delta;v}}_t^{\mathrm{the\; s}},...,{\mathrm{\&Delta;v}}_t^S\}---\left(11\right),$ t=1,2,...,T

(4) Formulate a scheduling plan based on the solution results

According to the solution results, the output plan formulated is u _i,t , P _i,t , where u _i,t represents the state of dispatching unit i in the tth period, and P _i,t represents the output of dispatching unit i in the tth period . The positive spinning reserve that needs to be equipped in each period is Negative spinning reserve is When the wind power changes, the unit output is adjusted on the basis of the output plan, so as to adapt to the change of wind power and achieve power balance.

Advantage and positive effect of the present invention are:

The model considers the power balance of the system, the constraints of positive/negative adjustments, the output constraints of the unit, the constraints of ramping and the minimum start-stop time constraints, and meets the basic requirements of the system operation. And from the form of objective function and constraint function, the established models are all conventional deterministic constraints, and the solution is simple and convenient. The resulting dispatch plan satisfies the possible scenarios of wind power with a small amount of adjustment, which avoids a large adjustment of the dispatch plan and reduces the burden of operation dispatchers. At the same time, based on the actual possible wind power scenarios, the model introduces the positive adjustment amount (positive spinning reserve) and negative adjustment amount (negative spinning reserve) required by the system to meet the needs of each scenario as optimization variables, and obtains the optimal variable that can satisfy all scenarios at the same time. A robust crew effort plan and spinning reserve plan. Provide a theoretical basis and an effective method for the establishment of spinning reserve for large-scale wind power systems.

The present invention proposes a minimum spinning standby scheduling plan model that satisfies the robust operation of various scenarios. This model achieves a series of conventional and easy-to-solve deterministic constraints to characterize the random characteristics of wind power, and meets the possible scenarios of wind power with a small adjustment. The calculation of the model is simple, the result is practical, and it has strong robustness.

The implementation examples of the present invention will be further described below in conjunction with the accompanying drawings.

The total installed capacity of the system in the case is 39.65 million kilowatts, of which, the installed capacity of thermal power is 30.2753 million kilowatts, the installed capacity of nuclear power is 1 million kilowatts, the installed capacity of wind power is 5.63366 million kilowatts, and the total installed capacity of hydropower units is 2.725 million kilowatts. In the calculation process, nuclear power units do not participate in dispatching, and there are 95 units participating in dispatching, that is, N _G =95. Period T=96.

Step 1: Select 161 typical scenarios of historical wind power sequences, and use the method of optimizing and reducing scenarios to compress them to obtain 22 wind power scenarios, that is, S=22. The resulting scenarios are shown in Figure 3, with 22 items in the figure The curve represents the daily 96-point power value of 22 wind power scenarios obtained by compression.

Step 2: The 22 wind power scenarios generated in step 1 The probability p ^s corresponding to the scene is substituted into formula (2) and formula (1) respectively, and CAMS software is used for solution.

The positive and negative spinning reserves required by the system to cope with wind power changes are obtained by solving them, as shown in Figure 4. The positive spinning reserve and the negative spinning reserve given in Fig. 4 are the positive spinning reserve and the negative spinning reserve required by the system satisfying these 22 scenarios at the same time.

It is easy for those skilled in the art to understand that the above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present invention, All should be included within the protection scope of the present invention.

Claims (3)

1. A minimum rotation standby scheduling method for satisfying robust operation of various scenes is characterized by comprising the following steps:

(1) establishing a minimum rotation standby scheduling plan model meeting robust operation of various scenes;

the method comprises the steps that a system obtains a target function of a minimum rotating standby dispatching plan model by taking the minimum mean value of positive and negative regulating variables required to be configured by the system for coping with all wind power scenes as a target; the constraint conditions of the minimum rotating standby scheduling plan model comprise system power balance constraint, positive and negative regulating quantity limit constraint, unit operation constraint, unit output climbing constraint and unit minimum start-stop time constraint;

(2) s scenes W are generated by adopting an optimized reduction scene method_t ^sProbability p corresponding to scene^s；

(3) The S scenes W_t ^sProbability p corresponding to the scene^sSubstituting the target function and the constraint condition for solving to obtain a robust unit output plan and the minimum positive and negative rotation standby required by each time interval of the system;

(4) when the wind power changes, on the basis of an output plan, the output of the unit is adjusted according to the output plan, so that the wind power changes are adapted to realize power balance.

2. The method of minimum rotation reserve scheduling of claim 1, wherein the objective function is:wherein Δ r_t ^sRepresenting the positive regulating quantity required by the system to deal with the wind power scene s in the t-th time period;the negative regulation quantity required by the system to deal with the wind power scene s in the t-th time period is represented; p is a radical of^sRepresenting the probability of a wind power scenario s; s-1, 2, …, S represents the total number of scenes; t is 1,2, …, T denotes the total number of periods;

the system power balance constraint isP_i,tRepresenting the output of the schedulable unit i in the t-th time period; u. of_i,tIndicating the state of the schedulable unit i at the t-th time period u_i,t1 denotes start-up, u_i,t0 represents shutdown; w_t ^sA value representing the s-th wind power scenario during the t-th time period; p_t ^DRepresenting the load of the system during the t-th period; 1,2, …, N_G，N_GThe number of the schedulable units is;

the positive and negative regulation limiting constraints are respectively as follows: wherein,representing the upper limit of the schedulable unit i output;P _irepresenting the lower limit of the schedulable unit i output;

the operation constraint of the unit is as follows:

the climbing restraint of the unit output is as follows:P_i,t>P_i,t-1，P_i,t<P_i,t-1whereinRepresenting the limit of the unit i climbing upward in the adjacent time period;representing the downward climbing output limit of the unit i in the adjacent time period;

the minimum start-stop time constraint of the unit is as follows: <math> <mrow> <mfenced open='{' close=''> <mtable> <mtr> <mtd> <msubsup> <munder> <mi>T</mi> <mo>&OverBar;</mo> </munder> <mi>i</mi> <mi>on</mi> </msubsup> <mo>&le;</mo> <msubsup> <mi>T</mi> <mi>i</mi> <mi>on</mi> </msubsup> </mtd> </mtr> <mtr> <mtd> <msubsup> <munder> <mi>T</mi> <mo>&OverBar;</mo> </munder> <mi>i</mi> <mi>off</mi> </msubsup> <mo>&le;</mo> <msubsup> <mi>T</mi> <mi>i</mi> <mi>off</mi> </msubsup> </mtd> </mtr> </mtable> </mfenced> <mo>;</mo> </mrow> </math> wherein,representing the continuous operation time of the unit i;representing the time of continuous shutdown of the unit i;representing the minimum time limit for the unit i to continuously operate;representing the minimum time limit for the unit i to stay out of service.

3. The minimum rotation standby scheduling method of claim 1, wherein the step (2) is specifically:

(2.1) building a model of an optimized reduction scenario, the model comprising

<math> <mrow> <munder> <mi>min</mi> <mover> <mi>o</mi> <mo>~</mo> </mover> </munder> <mfrac> <mn>1</mn> <mi>T</mi> </mfrac> <munder> <mi>&Sigma;</mi> <mrow> <mi>k</mi> <mn>1</mn> <mo>&Element;</mo> <mo>{</mo> <mi>o</mi> <mo>-</mo> <mover> <mi>o</mi> <mo>~</mo> </mover> <mo>}</mo> </mrow> </munder> <msup> <mi>p</mi> <mrow> <mi>k</mi> <mn>1</mn> </mrow> </msup> <munder> <mi>min</mi> <mrow> <mi>k</mi> <mn>2</mn> <mo>&Element;</mo> <mover> <mi>o</mi> <mo>~</mo> </mover> </mrow> </munder> <mo>|</mo> <msubsup> <mover> <mi>W</mi> <mo>~</mo> </mover> <mrow> <mn>1</mn> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <mi>T</mi> </mrow> <mrow> <mi>k</mi> <mn>1</mn> </mrow> </msubsup> <mo>-</mo> <msubsup> <mi>W</mi> <mrow> <mn>1</mn> <mo>,</mo> <mo>.</mo> <mo>.</mo> <mo>.</mo> <mo>,</mo> <mi>T</mi> </mrow> <mrow> <mi>k</mi> <mn>2</mn> </mrow> </msubsup> <mo>|</mo> <mo>;</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mover> <mi>s</mi> <mo>~</mo> </mover> <mo>=</mo> <mn>1</mn> </mrow> <mover> <mi>S</mi> <mo>~</mo> </mover> </munderover> <msup> <mover> <mi>p</mi> <mo>~</mo> </mover> <mover> <mi>s</mi> <mo>~</mo> </mover> </msup> <mo>=</mo> <mn>1</mn> <mo>;</mo> <munderover> <mi>&Sigma;</mi> <mrow> <mi>s</mi> <mo>=</mo> <mn>1</mn> </mrow> <mi>S</mi> </munderover> <msup> <mi>p</mi> <mi>s</mi> </msup> <mo>=</mo> <mn>1</mn> </mrow> </math>

Wherein O represents the original set of scenes;a set of scenes representing reductions;representing a pruned set of scenes; k1 denotes the truncated scene number; k2 denotes a reduced scene number;andrespectively representing the original scene and the probability thereof; w and p^sRespectively representing the reduced scenes and the probabilities thereof;representing the total number of scenes originally present,s denotes the total number of reduced scenes, S is 1,2, …, S;

(2.2) solving the model to obtain an optimized reduction scene W_t ^sAnd its probability p^s；

Wherein, W_t ^sA value representing the s-th reduced wind power scenario during the t-th time period; p is a radical of^sRepresenting the probability of the s-th reduced wind power scenario.