Reserve capacity calling method for coping with wind power downhill climbing

Abstract

The invention provides a reserve capacity calling method for coping with climbing under wind power, and belongs to the technical field of wind power dispatching, and the method comprises the steps: obtaining wind power output sample data, and employing a climbing power formula to calculate and obtain climbing power data under wind power; calculating an edge distribution function of the wind power output sample data and the climbing power data under the wind power, and constructing a joint distribution model by using a Copula function; calculating a conditional probability expression of wind power climbing under a wind power prediction power condition, and drawing a wind power output-wind power downhill boundary curve; designing a climbing standby service including a time scale; a wind power climbing standby demand prediction value is obtained by using the boundary curve, a climbing standby model is constructed, a climbing standby demand is obtained, and optimal calling of the climbing standby capacity under wind power is obtained; according to the method, standby capacity calling of climbing under wind power under a relatively long time scale is provided, cold standby resources are used to participate in power optimization scheduling, wind curtailment is reduced, and the economical efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of wind power dispatching, and in particular relates to a method for calling spare capacity to cope with down-slope climbing of wind power. Background Art

[0002] Wind power output is highly volatile. Wind power output fluctuates slightly on a time scale of seconds, and the possible fluctuations gradually increase as the time scale increases. Existing technologies generally focus on the relationship between predicted wind power output in conventional scenarios and output fluctuations on a short time scale, and lack consideration of wind power ramp-down on a longer time scale. However, in scenarios with high penetration of new energy, the continuous ramp-down of wind power output on a longer time scale will cause a large power shortage. It is necessary to pay attention to the ramp-down of wind power on a longer time scale to mitigate the risks of ramp-down events to the power balance of the power system.

[0003] In scenarios with high penetration of new energy, most conventional units are in cold standby mode, and flexible adjustment resources with a response speed of less than 1 hour are in short supply. However, current power optimization dispatch models generally only consider flexible adjustment resources with fast response speeds, such as energy storage and hot standby, and rarely consider the participation of cold standby resources. In order to alleviate the shortage of standby resources, a large amount of wind power needs to be abandoned, which affects economic efficiency. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a method for calling reserve capacity to cope with wind power ramping down, which solves the problems that the prior art lacks attention to wind power ramping down over a longer time scale, there are few cold reserve resources involved in the power optimization scheduling model, and a large amount of wind abandonment is arranged to alleviate the shortage of reserve resources.

[0005] In order to achieve the above purpose, the technical solution adopted by the present invention is: a method for calling the reserve capacity to cope with the downhill climbing of wind power, comprising the following steps: S1. Obtain wind power output sample data, and calculate wind power down-slope power data based on the wind power output sample data; S2, respectively calculating the marginal distribution functions of the wind power output sample data and the wind power ramp-down power data, and using the Copula function to construct a joint distribution model of wind power output and wind power ramp-down; S3. According to the joint distribution model of wind power output and wind power ramp-down, the conditional probability equation of wind power ramp-down under the wind power forecast power condition is calculated, and the historical data of wind power output and wind power ramp-down are input into the conditional probability equation of wind power ramp-down to obtain the wind power output-wind power ramp-down boundary curve; S4. Design a ramping standby service with a time scale based on the response time of the cold standby gas group; S5. Use the predicted wind power value to bring into the wind power output-wind power ramp boundary curve to obtain the predicted value of wind power ramp reserve demand, and combine the rotating reserve and ramp reserve to build a ramp reserve model; and use the ramp reserve model to obtain the ramp reserve demand, and use the ramp reserve service to obtain the optimal call of the wind power ramp reserve capacity.

[0006] The beneficial effects of the present invention are as follows: the present invention utilizes the mutual coordination of ramp reserve and rotating reserve to cope with the uncertain ramping of wind power, increases wind power consumption under the scenario of high wind power penetration, reduces the operating cost of the power system, and ensures the power balance of the power system; the present invention uses Copula function to construct a joint distribution model to find out the correlation between wind power output and wind power ramping power, and determines the ramping reserve demand based on the predicted wind power output value, which is combined with the ramping reserve model to improve the economy of the present invention on the basis of ensuring the abundance of electric power.

[0007] Further, the S2 comprises the following steps: S201, respectively calculating the marginal distribution functions of wind power output sample data and wind power down-ramp power data; S202, determining the parameters of the Copula function using maximum likelihood estimation according to the marginal distribution function of the wind power output sample data and the marginal distribution function of the wind power down-ramp power data; S203, evaluating and screening the Copula function using the Akaike Information Criterion according to the goodness of fit and the number of parameters of the Copula function; S204. Using the selected Copula function, a joint distribution model of wind power output and wind power ramp down is constructed.

[0008] The beneficial effect of the above further scheme is: the present invention uses maximum relief estimation to determine the parameters of the Copula function, and combines the Akaike information criterion to construct a joint distribution model of optimal wind power output and wind power ramping, thereby improving the fitting degree and accuracy of the ramping reserve model.

[0009] Furthermore, the S5 comprises the following steps: S501, bringing the wind power prediction value into the wind power output-wind power down-ramp boundary curve, and calculating the wind power ramp reserve demand prediction value; S502, using spinning reserve and ramping reserve to provide reserve capacity; S503, using the wind power ramp reserve demand forecast value, load fluctuation, spinning reserve capacity and ramp reserve capacity to build a ramp reserve model, and using spinning reserve constraints to reserve adjustment capacity; S504. In combination with the predicted value of the wind power ramping reserve demand, the ramping reserve demand is obtained using the ramping reserve model, and the optimal call of the wind power ramping reserve capacity is obtained using the ramping reserve service and auxiliary service related constraints including the time scale.

[0010] Furthermore, the expression of the climbing standby model is as follows: ; ; ; in, express t The time scale of the period is x The climbing reserve demand, express t The time scale of the period is x The wind power ramp reserve demand forecast value, express Load fluctuations during the period, express t Load fluctuations during the period, express t The time scale of the period is x The ramp reserve capacity, express Spinning reserve capacity for the period, express t Spinning reserve capacity for the period, Indicates climbing service RMx The maximum scheduling period.

[0011] The beneficial effects of the above further scheme are as follows: the present invention utilizes ramp reserve and rotating reserve to provide reserve capacity to cope with the ramp power shortage under wind power, uncertainty in wind power output and load fluctuations; rotating reserve copes with smaller new energy sources or load fluctuations, ensuring power balance on a shorter time scale; ramp reserve copes with larger ramp power shortage under wind power, ensuring sufficient capacity of the power system on a longer time scale.

[0012] Further, the auxiliary service related constraints include: ramping reserve constraints, positive and negative spinning reserve constraints, and ramping reserve service duration constraints; The expression of the climbing reserve constraint is as follows: ; ; ; in, represents the set of units that provide ramp reserve service with a time scale of 1, represents the set of units that provide ramp reserve service with a time scale of 3, represents the set of units that provide ramp reserve service with a time scale of 8, represents the collection of pumped storage units, Represents a collection of conventional units, Indicated in t Units with a time scale of 1 i The status of participating in the ramp standby service, Indicated in Units with a time scale of 1 i The status of participating in the ramp standby service, Indicated in Units with a time scale of 1 i The status of participating in the ramp standby service, Indicated in t Units with a time scale of 3 i The status of participating in the ramp standby service, Indicated in t Units with a time scale of 8 i The status of participating in the ramp standby service, Represents a unit with a time scale of 1 i The response capacity of the participating ramp-up reserve service, Represents a unit with a time scale of 3 i The response capacity of the participating ramp-up reserve service, Represents a unit with a time scale of 8 i The response capacity of the participating ramp-up reserve service, Pumped storage unit i The maximum output, express Period pumped storage unit i The power generation capacity, express Period pumped storage unit i The power generation capacity, express Period pumped storage unit i The power generation capacity, express Period pumped storage unit i The pumping power, express Period pumped storage unit i The pumping power, express Period pumped storage unit i The pumping power, express Time unit iThe positive spinning reserve capacity provided, express Time unit i The positive spinning reserve capacity provided, express Time unit i The positive spinning reserve capacity provided, express t The ramp reserve demand with a time scale of 1, express t The ramp reserve demand with a time scale of 3, T Indicates the maximum scheduling period.

[0013] Furthermore, the expression of the positive and negative spinning reserve constraints is as follows: ; ; in, express t Time unit i The positive spinning reserve capacity provided, express t Period pumped storage unit i The power generation capacity, express t Period pumped storage unit i The pumping power, express t Load fluctuations during the period, express t Time unit i Negative spinning reserve capacity provided, express t Period pumped storage unit i The output power, express t Wind turbines i The predicted output of express t Wind turbines i of wind power abandoned; The expression of the ramp standby service duration constraint is as follows: ; in, Indicates the unit i The minimum duration for providing ramp backup service, h Indicates the current period. Indicated in t The time scale of the period is x The crew i The status of participating in the ramp standby service, Indicated in The time scale of the period is x The crew i The status of participating in the ramp standby service, x represents the time scale, T Indicates the maximum scheduling period.

[0014] The beneficial effects of the above further scheme are as follows: the present invention adopts ramping reserve constraints to enable auxiliary services of different scales to cooperate with each other, and sets ramping reserve duration constraints to enable auxiliary services to be provided continuously, thereby increasing the enthusiasm of auxiliary service providers and ensuring the reliability of power system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The figure is a flow chart of the method of the present invention.

[0016] Figure 2 This is a diagram of the climbing standby model in this embodiment.

[0017] Figure 3 This is the IEEE24RTS system topology diagram in this embodiment.

[0018] Figure 4 This is a parameter diagram of a conventional unit in this embodiment.

[0019] Figure 5 This is a graph showing predicted wind power output and actual wind power output in this embodiment.

[0020] Figure 6 This is the Copula fitting result diagram for the 8-hour wind power ramp-up scenario in this embodiment.

[0021] Figure 7 This is a diagram of the power and electricity balance index of Scheme I, Scheme II, Scheme III and Scheme IV in this embodiment.

[0022] Figure 8 This is a diagram of the spare capacity of Scheme I, Scheme II, Scheme III and Scheme IV in this embodiment.

[0023] Fig. 9 This is the power and electricity balance indicator diagram of Scheme IV, Scheme V and Scheme VI in this embodiment.

[0024] Fig.10 This is a diagram showing the forecast of the ramp demand under 8-hour wind power for Scheme IV, Scheme V, Scheme VI and actual wind power ramping in this embodiment. DETAILED DESCRIPTION

[0025] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0026] Before describing this embodiment, the following terms are explained: Copula function: connection function; Gaussian-Copula: Gaussian copula function; t-Copula: t-distribution link function; AIC: Akaike Information Criterion.

[0027] Example 1 like Figure 1 As shown, the present invention provides a method for calling the reserve capacity to cope with the downhill climbing of wind power, and the implementation method thereof is as follows: S1. Obtain wind power output sample data, and calculate wind power down-slope power data based on the wind power output sample data.

[0028] In this embodiment, the risk of wind power down-ramp events in a power system with a high proportion of wind power is far greater than the risk of wind power up-ramp events, so the present invention focuses on how to deal with wind power down-ramp events. In scenarios with large wind speed variability (such as extreme weather), wind power output often continues to decline within a certain period of time and causes a large power shortage. It is possible to consider analyzing the relationship between wind power output and wind power down-ramp and purchasing auxiliary service capacity to avoid the power imbalance that may be caused by wind power output shortage.

[0029] In this embodiment, after obtaining the wind power output sample data, the climbing power formula is used to calculate the wind power downhill power data. The expression of the climbing power formula is as follows: ; in, Indicates the wind power output sample data t Wind power output during the period x Downhill climbing power in hours, Indicates that wind power output in the sample data is t Output during the period, Indicates that wind power output in the sample data is Output during the period.

[0030] S2. Calculate the marginal distribution functions of wind power output sample data and wind power ramp-down power data respectively, and use the Copula function to construct a joint distribution model of wind power output and wind power ramp-down. The specific steps are as follows: S201, respectively calculating the marginal distribution functions of wind power output sample data and wind power down-ramp power data; S202, determining the parameters of the Copula function using maximum likelihood estimation according to the marginal distribution function of the wind power output sample data and the marginal distribution function of the wind power down-ramp power data; S203, evaluating and screening the Copula function using the Akaike Information Criterion according to the goodness of fit and the number of parameters of the Copula function; S204. Using the selected Copula function, a joint distribution model of wind power output and wind power ramp down is constructed.

[0031] In this embodiment, the marginal distribution function is calculated for the wind power output sample data and the wind power downhill climbing data respectively, and the Copula function is used Construct a joint distribution model of wind power output and wind power ramp down: ; in, and They represent the marginal distribution functions of wind power output and wind power ramp down, Indicates that the wind power output in the wind power output sample data is x Downhill climbing power in hours, Indicates the wind power output in the wind power output sample data; The parameters of the Copula function are determined using the maximum likelihood estimation, and the type of the Copula function to be used is determined. The expression of the maximum likelihood estimation is as follows: ; in, represents the total number of samples, represents the parameter vector of the Copula function, Represents the maximum likelihood estimate of the parameter vector of the Copula function; The Akaike Information Criterion is used to evaluate the Copula function from two dimensions: goodness of fit and parameter quantity, and a suitable Copula function is selected. The expression of AIC is as follows: ; in, Indicates the number of parameters in the Copula function, reflecting the complexity of the model fitted by the Copula function. Indicates the maximum likelihood function value of the Copula function, reflecting the degree of fit between the model fitted by the Copula function and the actual data. A represents the Akaike Information Criterion, the smaller A It indicates that the overall goodness of the model fitted by the Copula function is good.

[0032] S3. According to the joint distribution model of wind power output and wind power ramp-down, the conditional probability equation of wind power ramp-down under the wind power forecast power condition is calculated, and the historical data of wind power output and wind power ramp-down are input into the conditional probability equation of wind power ramp-down to obtain the wind power output-wind power ramp-down boundary curve.

[0033] In this embodiment, according to the joint distribution model of wind power output and wind power ramping established by the Copula function, the conditional probability expression of wind power ramping under the wind power forecast power condition is calculated, and the conditional probability expression is as follows: ; ; Among them, the wind power output can be obtained from the conditional probability expression W for The confidence level is Wind power downhill climbing boundary , and use the minimum formula to ensure the final output wind power down-ramp power No more than wind power output, Indicates the possible wind power ramping power; The historical data of wind power output and wind power ramp-down are input into the conditional probability expression of wind power ramp-down to obtain the wind power output-wind power ramp-down boundary curve.

[0034] S4. Design a ramping standby service with a time scale based on the response time of the cold standby gas group.

[0035] In this embodiment, as shown in Table 1, Table 1 is a ramp standby service table. According to the response time of the cold standby gas group, the ramp standby services of different time scales are designed. The design is mainly to refine the start-up time of the gas unit to ensure that the cold standby capacity of the gas unit can be called in time in the prescribed practice class to alleviate the peak load pressure of the power system. The standby services include: spinning standby, RM 1. RM 3 and RM 8. For RM 1. Participation RM Unit 1 needs to be started within 1 hour after receiving the order to call capacity and ensure that it can maintain the specified power for 2 hours. RM 1 and RM 3 can be RM8. The auxiliary service fee is negatively correlated with the unit response time, and the auxiliary service fee is low when the response time is long.

[0036] Table 1

[0037] S5. Use the wind power prediction value to bring into the wind power output-wind power ramp boundary curve to obtain the wind power ramp reserve demand prediction value, and combine the rotating reserve and the ramp reserve to build a ramp reserve model; and use the ramp reserve model to obtain the ramp reserve demand, and use the ramp reserve service to obtain the optimal call of the wind power ramp reserve capacity. The specific steps are as follows: S501, bringing the wind power prediction value into the wind power output-wind power down-ramp boundary curve, and calculating the wind power ramp reserve demand prediction value; S502, using spinning reserve and ramping reserve to provide reserve capacity; S503, using the wind power ramp reserve demand forecast value, load fluctuation, spinning reserve capacity and ramp reserve capacity to build a ramp reserve model, and using spinning reserve constraints to reserve adjustment capacity; S504. In combination with the predicted value of the wind power ramping reserve demand, the ramping reserve demand is obtained using the ramping reserve model, and the optimal call of the wind power ramping reserve capacity is obtained using the ramping reserve service and auxiliary service related constraints including the time scale.

[0038] In this embodiment, the wind power prediction value is brought into the wind power output-wind power down-ramp boundary curve to calculate the wind power ramp reserve demand prediction value , and use spinning reserve and climbing reserve in the optimal dispatch to provide reserve capacity to cope with the uncertainty of wind power output and load fluctuation. Since the climbing reserve service has a slow response speed, in order to ensure that the power system can reserve enough flexible adjustment capacity, the spinning reserve constraint is added, and the predicted value of wind power climbing reserve demand, load fluctuation, spinning reserve capacity and climbing reserve capacity are used, such as Figure 2 As shown, a ramp reserve model is constructed, and the flexible adjustment capacity is reserved by using the rotating reserve constraint; the ramp reserve demand is obtained by using the ramp reserve model in combination with the predicted value of the wind power ramp reserve demand, and the optimal call of the wind power ramp reserve capacity is obtained by using the ramp reserve service and auxiliary service related constraints, so as to complete the call of the wind power ramp reserve capacity; The expression of the ramp-up standby model is as follows: ; ; ; in, expresst The time scale of the period is x The climbing reserve demand, express t The time scale of the period is x The wind power ramp reserve demand forecast value, express Load fluctuations during the period, express t Load fluctuations during the period, express t The time scale of the period is x The ramp reserve capacity, express Spinning reserve capacity for the period, express t The spinning reserve capacity of the time period is to provide the cold reserve capacity (i.e. t Reserve sufficient response time for the units that are offline during this period. t Time period determination The required ramp reserve capacity during the period ensures There is sufficient reserve capacity during the period to cope with changes in wind power output. Since the ramp reserve service responds slowly, adding spinning reserve constraints ensures that the power system can reserve sufficient flexible adjustment capacity. Indicates climbing service RMx The maximum scheduling period.

[0039] In this embodiment, ramp reserve and spinning reserve simultaneously provide reserve capacity to cope with the ramp power shortage under wind power. The spinning reserve can cope with smaller new energy or load fluctuations and ensure power balance on a shorter time scale. The ramp reserve mainly ensures that the power system has sufficient capacity on a longer time scale and can cope with larger ramp power shortages under wind power.

[0040] Example 2 In this embodiment, Figure 3 As shown in Table 2-5 and Figure 4 As shown in Table 2, the participation of each unit in standby service is shown in Table 2. Figure 4Table 3 is the parameters of conventional units, Table 4 is the parameters of hydropower units, Table 5 is the ramp reserve cost ratio, at this time, the down-ramp amplitude of wind power within 6 hours exceeds 30% of the installed capacity. The wind abandonment penalty and load shedding penalty are set to 700 yuan / MW and 2100 yuan / MW respectively, the dispatch cycle is set to 24hr, the dispatch interval is set to 1hr, the confidence level of wind power output fluctuation is 95%, and the load fluctuation is set to 10% of the load in this period. This embodiment is written using MATLAB R2021a, and the optimization dispatch model is solved using GUROBI 9.5.2.

[0041] Table 2

[0042] Table 3

[0043] Table 4

[0044] Table 5

[0045] In this embodiment, Figure 5 As shown, the wind power output data set of the Irish National Grid from 2020 to 2021 was selected to establish a sample set for wind power ramp-down prediction. The obtained wind power ramp-down prediction results were distributed to the wind farm nodes according to the load proportion of the example. The 6-hour ramp-down power of the selected wind farm output scenario exceeded 30% of the wind power installed capacity.

[0046] In this embodiment, the goal is to minimize the operating cost of the power system, including the operating cost of conventional units, pumped storage units, ancillary service costs, wind curtailment costs, and load shedding costs. The units in the power system include conventional units, pumped storage units, and wind turbine units; conventional units include: coal-fired power units, large gas-fired units, peak-shaving units, hydropower units, and nuclear power units; The power system operating costs The expression is as follows: ; ; ; ; ; ; Among them, the cost is: represents the operating cost of conventional units, represents the operating cost of the pumped storage unit, represents the ancillary service cost, represents the cost of wind curtailment, represents the load shedding cost; gather: Represents a collection of conventional units, represents the collection of pumped storage units, represents the set of units providing spinning reserve, represents the set of units that provide ramp backup service, represents the set of wind turbines, represents the node set of hydropower units, represents the set of load nodes, represents the set of response times of ramp-up standby products; variable: express t Regular units during the period i Output power, and Respectively t Regular units during the period i The startup decision variable and the shutdown decision variable are variable, and Respectively t Period pumped storage unit i The output power and pumping power, and Respectively t Time unit i The positive spinning reserve capacity and negative spinning reserve capacity provided, express t Time unit i For hill climbing backup service RMx The capacity provided, express t Wind turbines i The wind power curtailment express t Wind turbines i The wind power abandoned express t The load removed during the period, Indicates climbing service RMx The maximum scheduling period, T Indicates the maximum scheduling time.

[0047] coefficient: , as well as Respectively represent controllable conventional units i The power generation cost coefficient, startup cost coefficient and shutdown cost coefficient are and Represents pumped storage units i The output cost coefficient and pumping cost coefficient, and Respectively represent the unit i The cost coefficients for providing positive spinning reserve and negative spinning reserve, Indicates the unit i Provides hill climbing backup service RMx The power generation cost coefficient, and They represent the wind abandonment cost coefficient and the water abandonment cost coefficient, Represents a cut node i The cost factor for the load.

[0048] In this embodiment, a DC power flow model is used to model the power system, and its expression is as follows: ; ; ; in, express t Time period node i The load at and Respectively t Time Route l The phase angle of the voltage flowing into the bus and the phase angle of the voltage flowing out of the bus, Indicates line l The admittance of Representation Node m To Node n The maximum capacity of the line.

[0049] In this embodiment, the expression of the conventional unit constraint is as follows: The expression of the unit capacity constraint is as follows: ; ; ; ; The expression of the unit climbing constraint is as follows: ; The expression for the maximum start-stop times constraint of conventional units is as follows: ; The expressions of the unit state transfer equation and the start-stop time constraint are as follows: ; ; ; in, express t Regular units during the period i Is it in running state and variable, express Regular units during the period i Is it in operation? and Respectively represent the unit i Minimum output power and maximum output power, and Respectively represent the unit i Maximum up and down climbing rate, and Respectively t Regular units during the period i Startup decision variables and shutdown decision variables, T Indicates the maximum dispatch time. For equipment safety considerations, coal-fired power units and large gas-fired units need to limit the number of starts and stops on the same day. Indicates the unit i Maximum number of starts, and Respectively represent the unit i The minimum startup time and minimum shutdown time are guaranteed, while the nuclear power units remain in the normally open state.

[0050] In this embodiment, for the wind turbine generator set, it is required that the wind power output cannot exceed the wind power prediction value, so the expression of the wind turbine generator set constraint is as follows: ; ; ; in, express t Wind turbines i The wind power abandoned and Respectively t Wind turbines i The dispatch output and predicted output.

[0051] In this embodiment, for the hydropower unit, considering the water quantity limitation, it is necessary to add the hydropower daily distribution power constraint, so the expressions of the unit power generation capacity constraint and the unit pumping state output constraint are as follows: ; ; in, T represents the maximum scheduling time, express t Time period hydroelectric unit i of efforts, express t Wind turbines i The wind power abandoned Indicates hydroelectric unit i Daily power allocation.

[0052] In this embodiment, for the pumped storage unit, considering the reservoir capacity limitation and the pumping-power generation state conversion, it is necessary to add a reservoir capacity constraint and a water balance constraint. The expression of the reservoir capacity constraint is as follows: ; ; The expression of water balance constraint is as follows: ; in, and express t Period pumped storage unit i The power generation state and pumping state of variable, and Pumped storage unit i The minimum and maximum output of Indicates energy storage unit i The initial storage capacity of the reservoir, and Respectively represent energy storage units i The minimum and maximum storage capacity of the reservoir, represents the storage capacity-electricity conversion coefficient, express t Period pumped storage unit i The cumulative power generation Indicates that the pumped storage unit is t Total water consumption during the period, Indicates the unit i Efficiency in pumping state.

[0053] In this embodiment, auxiliary service related constraints are also constructed, and the auxiliary service related constraints include: ramping reserve constraints, positive and negative spinning reserve constraints, and ramping reserve service duration constraints; The expression of the climbing reserve constraint is as follows: ; ; ; in, represents the set of units that provide ramp backup service, represents the collection of pumped storage units, Represents a collection of conventional units, Indicated in t The time scale of the period is x The crew i The status of participating in the ramp standby service, The time scale is x The crew i The response capacity of the participating ramp-up reserve service, Pumped storage unit i The maximum output, express Period pumped storage unit i The power generation capacity, express Period pumped storage unit i The pumping power, express Time unit i The positive spinning reserve capacity provided, express t The ramp reserve demand with a time scale of 1, express t Ramp reserve demand with a time scale of 3.

[0054] The expressions of the positive and negative spinning reserve constraints are as follows: ; ; in, Represents a collection of conventional units, represents the collection of pumped storage units, express t Time unit i The positive spinning reserve capacity provided, Pumped storage unit i The maximum output, express t Period pumped storage unit i The power generation capacity, express t Period pumped storage unit i The pumping power, express t Load fluctuations during the period, express t Time unit i Negative spinning reserve capacity provided, express t Period pumped storage unit i The output power, express t Wind turbines i The predicted output of express t Wind turbines i of wind power abandoned; The expression of the ramp standby service duration constraint is as follows: ; in, Indicates the unit i The minimum duration for providing ramp backup service, h Indicates the current period. Indicated in t The time scale of the period is x The crew i The status of participating in the ramp standby service, Indicated in The time scale of the period is x The crew i The status of participating in the ramp standby service, x represents the time scale, T Indicates the maximum scheduling period.

[0055] Example 3 In this embodiment, the correlation between wind power output and wind power ramping is modeled by the Copula function. For the wind power ramping scenarios with time scales of 1 hr, 3 hr and 8 hr, the AIC calculation results of the Copula function are shown in Table 6. According to the AIC index, it can be seen that Clayton-Copula has the best fitting goodness of fit in the three time scale scenarios. Therefore, Clayton-Copula is used in the joint distribution model of wind power output and wind power ramping to predict the wind power ramping power.

[0056] Table 6

[0057] like Figure 6 As shown, as the wind power output increases, the possible wind power down-climbing power also gradually increases, which is consistent with actual experience. In this embodiment, the down-climbing prediction fitted by the joint distribution model of wind power output and wind power down-climbing can cover most historical wind power down-climbing scenarios, so it can prove the effectiveness of the wind power down-climbing prediction result of the joint distribution model of wind power output and wind power down-climbing, and the reserved spare capacity based on the prediction result can cope with most wind power down-climbing events.

[0058] In this embodiment, four schemes are set as follows: Solution I: Deterministic optimization + spinning reserve; Solution II: Robust optimization + spinning reserve; Scheme III: Considering the output-downhill correlation + cold and hot standby coupling for ramp standby; Solution IV: Considering the output-downhill correlation + only considering the climbing reserve of cold reserve + spinning reserve (method of this embodiment); Among them, as shown in Table 7, Table 7 is the power balance evaluation index. Taking into account the influence of wind power uncertainty, the practical net load calculation is used when calculating the power balance evaluation index; in order to simulate the actual scheduling situation, the adequacy test uses the actual wind power output data; and each power balance evaluation index is processed by standardization to facilitate the comparison of the performance of different power balance evaluation indicators.

[0059] Table 7

[0060] In this embodiment, as shown in Table 8, Table 8 compares the day-ahead planned costs of the four schemes, and it can be obtained that the cost of Scheme IV is higher than that of Scheme II, lower than that of Scheme I, and equal to that of Scheme III. Schemes IV and III have more sources of auxiliary service capacity, so they are more inclined to meet the requirements of power and electricity abundance by purchasing auxiliary services; while Scheme II has limited available reserve capacity. In scenarios with high wind power uncertainty, it is necessary to reduce wind power output and arrange negative reserve to ensure that the reserve capacity can cover the uncertainty of wind power output in order to meet the requirements of safe operation, so its economic efficiency is relatively low; Scheme I chooses to absorb wind power to the maximum extent to avoid wind abandonment penalties. Although it has the highest economic efficiency, it does not consider the impact of uncertainty factors, does not purchase sufficient auxiliary service capacity or conduct certain wind abandonment, and it is difficult to cope with net load fluctuations. In addition, Scheme I has lower spinning reserve costs because hydropower and pumped storage with lower costs bear part of the upper reserve capacity.

[0061] Table 8

[0062] In this embodiment, Figure 7As shown in the figure, the power balance indicators of the four schemes are displayed. In terms of power adequacy, Schemes II, III and IV take into account the adverse effects that wind power output uncertainty may have on the power system, allowing the power system to maintain a relatively high power adequacy, while Scheme I does not take into account the uncertainty of wind power output, and the problem of insufficient adequacy occurs during actual operation. In terms of power supply balance, Schemes I and II use wind abandonment and hot standby capacity to deal with wind power uncertainty, and can flexibly adjust the standby capacity to make the adequacy of each period more balanced. Schemes III and IV introduce cold standby capacity. Since the call of cold standby capacity requires a long response time and duration, the flexibility is limited, so the balance is poor. However, Scheme IV adds constraints related to hot standby capacity, allowing the power system to retain more hot standby capacity, increasing the flexible adjustment capability of the power system, so the power supply balance indicator of Scheme IV is better than that of Scheme III.

[0063] In this embodiment, Figure 8 As shown in the figure, the reserve capacity of the four schemes is shown. It can be seen that Scheme I does not consider the uncertainty of wind power and purchases less upper reserve capacity; Scheme II chooses to increase wind abandonment and purchase a large amount of lower reserve capacity to ensure that the overall reserve capacity can cover the entire wind power uncertainty interval. The overall reserve capacity of Scheme III is close to that of Scheme IV, but Scheme III does not restrict the hot reserve capacity and is more inclined to purchase low-priced cold reserve capacity to increase economic efficiency; while Scheme IV increases the constraints on hot reserve capacity and purchases more hot reserve resources. When there is a small fluctuation in wind power or load during daily operation, there is more sufficient rotating reserve capacity to be directly called, making the power system more flexible.

[0064] In this embodiment, in order to verify the application effect of the uncertain demand forecasting model, scheme V and scheme VI are added: Plan V: interval optimization + climbing reserve + spinning reserve; Scheme VI: No consideration of treatment - downhill correlation + climbing reserve + spinning reserve; Among them, the method for generating uncertain intervals of wind power output in interval optimization is the same as that of robust optimization, and the ramp reserve demand of interval optimization takes the maximum distance between the two time periods of the power generation interval. Not considering the correlation between wind power output and down-climbing means that only the corresponding quantile data is given based on the historical down-climbing situation, without considering the relationship between wind power ramping and wind power output.

[0065] In this embodiment, as shown in Table 9, the day-ahead planning costs of Scheme IV, Scheme V and Scheme VI are shown, where the cost of Scheme V is increased by 5.5% and 7.4% compared to the costs of Scheme IV and Scheme VI, respectively, and the increased costs mainly come from ancillary service costs and operating costs. Fig. 9As shown in the figure, the power and electricity balance indicators of Scheme IV, Scheme V and Scheme VI were evaluated. The other indicators of the three schemes are relatively close. Scheme VI has the highest peak-shaving available capacity, and the peak-shaving available capacities of the other two schemes are close. Further analysis is needed on the causes of the situation.

[0066] Table 9

[0067] like Fig.10 As shown in the figure, the down-ramp demand forecast with a time scale of 8 hours is selected for analysis. In the scenario of wind power changes, Scheme IV and Scheme V can predict the changing trend of the actual down-ramp demand. However, Scheme V only relies on the indefinite interval of the down-ramp demand for calculation, and does not relax the constraints based on the historical output-down-ramp situation. Therefore, the calculated reserve demand is too conservative and has poor economic efficiency. Scheme VI does not consider the relationship between wind power output and wind power down-ramp during the dispatch period, and cannot adjust the reserve capacity according to the possible changing trend of down-ramp demand. Although Fig. 9 From the evaluation index, Scheme VI has a larger available peak load capacity, but when the wind power output is small, Scheme VI still maintains a high reserve demand, which far exceeds the actual reserve demand. Therefore, the economic efficiency of Scheme VI is poor.

[0068] In this embodiment, in order to analyze the feasibility of the method of this embodiment under different new energy penetration, Scheme II, Scheme IV and Scheme VI are selected for comparison, and 3 scenarios are set for experiments; Scenario I: The total installed capacity of wind power is 20% of the total installed capacity; Scenario II: The total installed capacity of wind power is 25% of the total installed capacity; Scenario III: The total installed capacity of wind power is 30% of the total installed capacity; As shown in Table 10, the table shows the operating costs of the power system under different wind power penetration rates. As the wind power penetration rate continues to rise, on the one hand, the power system can save power generation costs by absorbing a large amount of wind power. On the other hand, the cost of auxiliary services required to cope with the uncertainty of wind power output gradually increases. Compared with Scheme IV, Scheme II does not have abundant reserve resources to support a higher level of wind power consumption. Therefore, Scheme II increases planned wind abandonment and purchases a large amount of rotating reserve. While the auxiliary service costs increase, the power generation costs saved are limited. The total operating cost of Scheme II is increased. Therefore, Scheme IV introduces ramp reserve services and uses cold reserve resources to cope with wind power uncertainty, which can alleviate the difficulty of wind power consumption in high-proportion new energy systems and save power system operating costs. While Scheme VI has a slightly lower cost when the wind power penetration rate increases, since Scheme VI does not model wind power output and ramp power more finely, when the wind power output is small, the redundant reserve capacity is more, so the economic efficiency of Scheme VI is poor.

[0069] Table 10

Claims

1. A method for calling reserve capacity to cope with wind power ramp down, applied to a power system, characterized in that: The following steps are involved: S1. Obtain wind power output sample data, and calculate wind power down-slope power data based on the wind power output sample data; S2, respectively calculating the marginal distribution functions of the wind power output sample data and the wind power ramp-down power data, and using the Copula function to construct a joint distribution model of wind power output and wind power ramp-down; S3. According to the joint distribution model of wind power output and wind power ramp-down, the conditional probability equation of wind power ramp-down under the wind power forecast power condition is calculated, and the historical data of wind power output and wind power ramp-down are input into the conditional probability equation of wind power ramp-down to obtain the wind power output-wind power ramp-down boundary curve; S4. Design a ramping standby service with a time scale based on the response time of the cold standby gas group; S5. Use the predicted wind power value to bring into the wind power output-wind power ramp boundary curve to obtain the predicted value of wind power ramp reserve demand, and combine the rotating reserve and ramp reserve to build a ramp reserve model; and use the ramp reserve model to obtain the ramp reserve demand, and use the ramp reserve service to obtain the optimal call of the wind power ramp reserve capacity.

2. The method for calling the reserve capacity for wind power down-slope according to claim 1 is characterized in that: The S2 comprises the following steps: S201, respectively calculating the marginal distribution functions of wind power output sample data and wind power down-ramp power data; S202, determining the parameters of the Copula function using maximum likelihood estimation according to the marginal distribution function of the wind power output sample data and the marginal distribution function of the wind power down-ramp power data; S203, evaluating and screening the Copula function using the Akaike Information Criterion according to the goodness of fit and the number of parameters of the Copula function; S204. Using the selected Copula function, a joint distribution model of wind power output and wind power ramp down is constructed.

3. The method for calling the reserve capacity for wind power down-slope according to claim 1 is characterized in that: The S5 comprises the following steps: S501, bringing the wind power prediction value into the wind power output-wind power down-ramp boundary curve, and calculating the wind power ramp reserve demand prediction value; S502, using spinning reserve and ramping reserve to provide reserve capacity; S503, using the wind power ramp reserve demand forecast value, load fluctuation, spinning reserve capacity and ramp reserve capacity to build a ramp reserve model, and using spinning reserve constraints to reserve adjustment capacity; S504. In combination with the predicted value of the wind power ramping reserve demand, the ramping reserve demand is obtained using the ramping reserve model, and the optimal call of the wind power ramping reserve capacity is obtained using the ramping reserve service and auxiliary service related constraints including the time scale.

4. The method for calling the reserve capacity for wind power down-slope according to claim 3 is characterized in that: The expression of the ramp-up standby model is as follows: ； ； ； in, express t The time scale of the period is x The climbing reserve demand, express t The time scale of the period is x The wind power ramp reserve demand forecast value, express Load fluctuations during the period, express t Load fluctuations during the period, express t The time scale of the period is x The ramp reserve capacity, express Spinning reserve capacity for the period, express t Spinning reserve capacity for the period, Indicates climbing service RMx The maximum scheduling period.

5. The method for calling the reserve capacity for wind power down-slope according to claim 3 is characterized in that: The auxiliary service related constraints include: climbing reserve constraints, positive and negative spinning reserve constraints, and climbing reserve service duration constraints; The expression of the climbing reserve constraint is as follows: ； ； ； in, represents the set of units that provide ramp reserve service with a time scale of 1, represents the set of units that provide ramp reserve service with a time scale of 3, represents the set of units that provide ramp reserve service with a time scale of 8, represents the collection of pumped storage units, Represents a collection of conventional units, Indicated in t Units with a time scale of 1 i The status of participating in the ramp standby service, Indicated in Units with a time scale of 1 i The status of participating in the ramp standby service, Indicated in Units with a time scale of 1 i The status of participating in the ramp standby service, Indicated in t Units with a time scale of 3 i The status of participating in the ramp standby service, Indicated in t Units with a time scale of 8 i The status of participating in the ramp standby service, Represents a unit with a time scale of 1 i The response capacity of the participating ramp-up reserve service, Represents a unit with a time scale of 3 i The response capacity of the participating ramp-up reserve service, Represents a unit with a time scale of 8 i The response capacity of the participating ramp-up reserve service, Pumped storage unit i The maximum output, express Period pumped storage unit i The power generation capacity, express Period pumped storage unit i The power generation capacity, express Period pumped storage unit i The power generation capacity, express Period pumped storage unit i The pumping power, express Period pumped storage unit i The pumping power, express Period pumped storage unit i The pumping power, express Time unit i The positive spinning reserve capacity provided, express Time unit i The positive spinning reserve capacity provided, express Time unit i The positive spinning reserve capacity provided, express t The ramp reserve demand with a time scale of 1, express t The ramp reserve demand with a time scale of 3, T Indicates the maximum scheduling period.

6. The method for calling the reserve capacity for wind power down-slope according to claim 5 is characterized in that: The expressions of the positive and negative spinning reserve constraints are as follows: ； ； in, express t Time unit i The positive spinning reserve capacity provided, express t Period pumped storage unit i The power generation capacity, express t Period pumped storage unit i The pumping power, express t Load fluctuations during the period, express t Time unit i Negative spinning reserve capacity provided, express t Period pumped storage unit i The output power, express t Wind turbines i The predicted output of express t Wind turbines i of wind power abandoned; The expression of the ramp standby service duration constraint is as follows: ； in, Indicates the unit i The minimum duration for providing ramp backup service, h Indicates the current period. Indicated in t The time scale of the period is x The crew i The status of participating in the ramp standby service, Indicated in The time scale of the period is x The crew i The status of participating in the ramp standby service, x Indicates the time scale.

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