Scheduling strategy acquisition method and device for quantitative consumption of power system, and control system

Abstract

The invention discloses a scheduling strategy acquisition method and device for quantitative consumption of a power system and a control system, and belongs to the technical field of power system scheduling, and the method comprises the steps: constructing a random consumption rate index based on a wind power day-ahead prediction output curve and random output probability distribution information, and quantifying the renewable energy consumption level; the calculation of a random consumption rate index is realized in a day-ahead scheduling process, and an implicit affine strategy is adopted to guarantee the unexpectability of a scheduling strategy, so that the reliability and safety of the scheduling strategy are improved; the maximum random consumption rate and the minimum random consumption rate of the power system are evaluated through the random consumption rate evaluation model, and the random consumption rate constraint is constructed on the basis, so that the quantitative constraint on the consumption level of the system is realized, the problems of too high operation cost of the power system and too low consumption rate of renewable energy sources caused by unreasonable setting of the power abandoning punishment cost are avoided, and the power abandoning punishment efficiency is improved. And finally, the reliability and the economical efficiency of the scheduling strategy are improved on the basis of quantitatively ensuring that the consumption level reaches the given requirement.

Description

Technical Field

[0001] The present invention belongs to the technical field of power system dispatching, and more specifically, relates to a method, device and control system for obtaining a dispatching strategy for quantitative consumption of a power system. Background Art

[0002] In recent years, in response to energy crises, environmental pollution and other issues, renewable energy represented by wind power has developed rapidly. With the increasing penetration rate in the power system, the randomness and volatility of its output have brought huge challenges to the operation of the system and put forward higher requirements for day-ahead dispatch decisions.

[0003] Stochastic optimization and robust optimization are two main methods for dealing with the randomness of renewable energy output in existing day-ahead dispatching methods. Stochastic optimization builds models based on scenarios and cannot take into account all possible scenarios. The more scenarios considered, the more reliable the dispatching strategy and the lower the computational efficiency. Robust optimization can ensure reliable operation in any scenario in a pre-given uncertainty set, but does not take into account the operational risks corresponding to those low-probability scenarios outside the set. At the same time, existing methods often promote the consumption of renewable energy by setting penalties for renewable energy curtailment. However, excessive curtailment penalty costs may increase operating costs while improving the consumption level, while excessive curtailment penalty costs may lead to the failure to meet the requirements for the consumption level. In addition, in existing methods, under the dispatching strategy decided, the consumption level that the power system can achieve can only be known through scenario simulation or actual system operation. In other words, there is an urgent need for a quantitative method for the consumption level of renewable energy to achieve quantitative evaluation and characterization of the consumption level of renewable energy in the day-ahead dispatching process, so as to more effectively ensure the consumption. In addition, when dealing with randomness, existing methods often decide on scheduling strategies based on the complete renewable energy output curve, that is, the ideal situation. This violates the timing logic of random output realization and does not meet the unpredictability of the scheduling strategy. This will cause the decided scheduling strategy to be infeasible during the daily operation and unable to achieve the absorption level in the day-ahead scheduling process. In other words, the day-ahead scheduling results are too optimistic, which is not conducive to ensuring the safety of system operation and the effectiveness of renewable energy absorption.

[0004] In summary, safety, economy and greenness are three very important goals for the operation of power systems. How to quantitatively guarantee absorption and improve the economic efficiency of power system operation while ensuring operational safety is an urgent problem to be solved. Summary of the invention

[0005] In response to the above defects or improvement needs of the prior art, the present invention provides a method, device and control system for obtaining a dispatching strategy for quantitative absorption of an electric power system, which aims to solve the technical problem of how to quantitatively ensure absorption and improve the economic efficiency of electric power system operation while ensuring operational safety.

[0006] To achieve the above object, according to one aspect of the present invention, a method for obtaining a dispatching strategy for quantitative consumption of a power system is provided, comprising:

[0007] S1: A day-ahead robust dispatch model for a power system is constructed based on a dynamic uncertainty set and an implicit affine strategy; the objective function of the day-ahead robust dispatch model for a power system is to minimize the sum of the expected unit operating costs and load shedding penalty costs under a system prediction scenario;

[0008] S2: Calculate a random consumption rate index based on the wind power day-ahead predicted output curve and random output probability distribution information, and linearize the random consumption rate index; the random consumption rate index is a function with the upper bound of the dynamic uncertainty set as an independent variable;

[0009] S3: constructing a random absorption rate evaluation model based on the constructed power system day-ahead robust dispatch model and the linearized random absorption rate index; the constraints of the random absorption rate evaluation model are the same as the constraints of the power system day-ahead robust dispatch model, and the objective function is to maximize the system random absorption rate index;

[0010] S4: using the random absorption rate evaluation model to evaluate the maximum random absorption rate of the power system, using the linearized random absorption rate index and the wind power day-ahead forecast output curve to calculate the minimum random absorption rate of the power system, and using the maximum and minimum random absorption rates to construct a random absorption rate constraint;

[0011] S5: embedding the random absorption rate constraint into the day-ahead robust dispatch model of the power system to obtain the day-ahead robust dispatch model of the power system for quantitative absorption, and solving the day-ahead robust dispatch model of the power system for quantitative absorption to obtain the dispatch strategy.

[0012] In one embodiment, in S1: using represents the objective function of the day-ahead robust dispatch model of the power system;

[0013] Wherein, the subscripts i and t are the node number, unit number and time period number respectively, N TU is the number of thermal power units, Δt is the time period length, and T is the number of scheduling time periods; c sd is the cost of a single start-up and shutdown of the unit; U is a 0-1 variable for the start-up and shutdown action state of the thermal power unit; is the output of the thermal power unit in the forecast scenario, C f (·) is the fuel cost function of thermal power units, ELS is the expected load shedding, c l is the corresponding penalty cost coefficient, a ls 、b ls and c ls is the coefficient of the ELS quadratic calculation formula,P w is the lower bound of wind power output uncertainty set.

[0014] In one embodiment, in S1: the constraints of constructing a day-ahead robust dispatch model of a power system using a dynamic uncertainty set and an implicit affine strategy include:

[0015]

[0016]

[0017] Among them, U D is a dynamic uncertainty set, where the subscripts i and t are the node and unit numbers and time period numbers respectively. For wind power output prediction, P w,max The maximum output of wind power, P w and are the lower and upper bound variables of the dynamic uncertainty set to be decided, σ is the standard deviation; u is the start-stop state 0-1 variable of the thermal power unit, 1 means the unit is in the start state, and 0 means the unit is in the shutdown state, T i on and T i off are the minimum startup and shutdown time of thermal power units respectively; P TU,min and P TU,max are the minimum and maximum output of thermal power units respectively; R U and R D are the maximum up and down ramp rates of thermal power units respectively; P cha，max and P dis,max are the maximum charging and discharging power of energy storage, respectively, cha and η dis are the charge and discharge efficiency, S min and S max are the minimum and maximum charge states, E r is the rated capacity of the energy storage, E is the energy stored in the energy storage; and For the dispatchable output range variables of the introduced thermal power units and energy storage, is the energy storage power under the prediction scenario; N w , N BES and N d are the number of wind farms, energy storage and loads respectively, P d is the load power; s is the line power transmission distribution factor, subscript l is the line number, P l max is the upper limit of line transmission capacity; the robustness within the dynamic uncertainty set is verified using a column and constraint generation algorithm.

[0018] In one embodiment, the step S2 calculates the random consumption rate index based on the wind power day-ahead predicted output curve and random output probability distribution information, including:

[0019] Defining the random absorption rate indicator Using polynomials Approximate the random output probability density function, and then calculate the random consumption rate index

[0020] Among them, E{·} represents expectation, is the wind power abandoned, N p is the polynomial order, p n are the polynomial coefficients, and a, b, and c are the coefficients of the SAR quadratic calculation formula.

[0021] In one embodiment, the linearization of the random absorption rate indicator in S2 includes: Where n is the segment number, N pw is the number of linearization segments, q 1 and q 0 Calculate the expression coefficients for the linearized SAR, is the introduced linear slack variable.

[0022] In one embodiment, the S4 includes:

[0023] Using the formula SAR ≥ SAR req represents the determination of the random absorption rate constraint, where Among them, SAR req is the required value of the system random absorption rate, SAR is the minimum random absorption rate, is the maximum random absorption rate.

[0024] In one embodiment, using the formula The minimum random absorption rate is determined.

[0025] In one embodiment, As the objective function, a random consumption rate evaluation model is constructed to evaluate the maximum random consumption rate of the power system. Evaluation is performed, and SAR is the random absorption rate indicator.

[0026] According to another aspect of the present invention, a dispatching strategy acquisition device for quantitative consumption of a power system is provided, comprising:

[0027] A modeling module is used to construct a day-ahead robust dispatching model for the power system based on a dynamic uncertainty set and an implicit affine strategy; the objective function of the day-ahead robust dispatching model for the power system is to minimize the sum of the expected unit operating costs and load shedding penalty costs under the system prediction scenario;

[0028] A calculation module, used for calculating a random absorption rate index based on the wind power day-ahead predicted output curve and random output probability distribution information, and linearizing the random absorption rate index; the random absorption rate index is a function with the upper bound of the dynamic uncertainty set as an independent variable;

[0029] A construction module is used to construct a random absorption rate evaluation model based on the constructed power system day-ahead robust dispatch model and the linearized random absorption rate index; the constraints of the random absorption rate evaluation model are the same as the constraints of the power system day-ahead robust dispatch model, and the objective function is to maximize the system random absorption rate index;

[0030] An evaluation module is used to evaluate the maximum random absorption rate of the power system using the random absorption rate evaluation model, calculate the minimum random absorption rate of the power system using the linearized random absorption rate index and the wind power day-ahead predicted output curve, and construct a random absorption rate constraint using the maximum and minimum random absorption rates;

[0031] A solution module is used to embed the random absorption rate constraint into the day-ahead robust dispatch model of the power system to obtain the day-ahead robust dispatch model of the power system for quantitative absorption, and solve the day-ahead robust dispatch model of the power system for quantitative absorption to obtain a dispatch strategy.

[0032] According to another aspect of the present invention, a control system of an electric power system is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method described when executing the computer program.

[0033] According to another aspect of the present invention, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method described above are implemented.

[0034] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0035] (1) The present invention provides a method for obtaining a dispatching strategy for quantitative absorption of a power system. Based on the wind power day-ahead predicted output curve and random output probability distribution information, a random absorption rate index is constructed to achieve the quantification of the absorption level of renewable energy. The random absorption rate index is calculated in the day-ahead dispatching process, and an implicit affine strategy is used to ensure the unpredictability of the dispatching strategy, thereby improving the reliability and safety of the dispatching strategy. The maximum and minimum random absorption rates of the power system are evaluated through a random absorption rate evaluation model, and a random absorption rate constraint is constructed based on this model to achieve quantitative constraints on the absorption level of the system, thereby avoiding the problems of excessively high operating costs of the power system and too low absorption rate of renewable energy caused by unreasonable setting of the penalty cost for power abandonment. Finally, the reliability and economy of the dispatching strategy are improved on the basis of quantitatively ensuring that the absorption level meets the given requirements.

[0036] (2) This scheme uses It represents the objective function of the day-ahead robust dispatch model of the power system; the objective function no longer needs to include the power abandonment penalty fee, which can avoid the problems of excessively high power system operating costs and too low renewable energy consumption rate caused by unreasonable setting of power abandonment penalty costs.

[0037] (3) This scheme adopts a dynamic uncertainty set and takes the uncertain output boundary of wind power as the decision variable, which can improve the flexibility of the scheduling scheme while realizing the calculation of the random absorption rate. The implicit affine strategy is adopted to ensure the unexpectedness of the scheduling strategy, and avoid overestimation of the random absorption rate indicator while improving the reliability and safety of the scheduling strategy.

[0038] (4) This scheme uses Define the random absorption rate indicator; by constructing and deriving the calculation method of the random absorption rate indicator, the random absorption rate indicator can be calculated in the day-ahead scheduling process, thereby quantifying the level of renewable energy absorption.

[0039] (5) The formula used in this scheme is Linearizing the random absorption rate index can avoid nonlinear terms in the model and improve the solution efficiency of the model.

[0040] (6) In this scheme, the formula SAR ≥ SAR is used req It means to determine the random absorption rate constraint. By constructing the random absorption rate constraint, the quantitative guarantee of the system absorption level can be achieved from the constraint level.

[0041] (7) In this scheme, the formula The minimum random absorption rate is determined. By calculating the minimum random absorption rate of the power system, the lowest absorption level of the power system can be known, and a lower limit of the range is provided for setting the random absorption rate constraint.

[0042] (8) In this scheme, As the objective function, a random consumption rate evaluation model is constructed to evaluate the maximum random consumption rate of the power system. By evaluating the maximum random absorption rate of the power system, the highest absorption level of the power system can be known, and an upper limit of the range can be provided for the setting of the random absorption rate constraint. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 A flow chart of a method for obtaining a dispatching strategy for quantitative consumption of a power system provided in Embodiment 1 of the present invention;

[0044] Figure 2 A topology diagram of the example verification system provided in Example 1 of the present invention;

[0045] Figure 3 The electric load curve and wind power forecast output curve provided in Example 1 of the present invention;

[0046] Figure 4 The dynamic boundary graph obtained by the method of the present invention and the comparative method 2 provided in Example 1 of the present invention;

[0047] Figure 5 An actual wind power output curve within the dynamic boundary provided in Embodiment 1 of the present invention;

[0048] Figure 6 The scheduling results of the comparison method 2 provided in Example 1 of the present invention under ideal and actual conditions;

[0049] Figure 7 The dispatchable output range of the unit determined by the method proposed in the present invention is provided in Example 1 of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0051] Example 1

[0052] like Figure 1 As shown, this embodiment provides a method for obtaining a dispatching strategy for quantitative consumption of an electric power system, including: S1-S5.

[0053] S1: A day-ahead robust dispatch model for power systems is constructed based on a dynamic uncertainty set and an implicit affine strategy. The objective function of the day-ahead robust dispatch model for power systems is to minimize the expected sum of unit operating costs and load shedding penalty costs under system prediction scenarios.

[0054] S2: Calculate the random absorption rate index based on the wind power day-ahead forecast output curve and random output probability distribution information, and linearize the random absorption rate index; the random absorption rate index is a function with the upper bound of the dynamic uncertainty set as the independent variable.

[0055] S3: A random absorption rate evaluation model is constructed based on the constructed power system day-ahead robust dispatch model and the linearized random absorption rate index; the constraints of the random absorption rate evaluation model are the same as those of the power system day-ahead robust dispatch model, and the objective function is to maximize the system random absorption rate index.

[0056] S4: The maximum random absorption rate of the power system is evaluated using the random absorption rate evaluation model. The minimum random absorption rate of the power system is calculated using the linearized random absorption rate index and the day-ahead wind power output curve. The random absorption rate constraint is constructed using the maximum and minimum random absorption rates.

[0057] S5: Embed the random absorption rate constraint into the day-ahead robust dispatch model of the power system to obtain the day-ahead robust dispatch model of the power system for quantitative absorption, and solve the day-ahead robust dispatch model of the power system for quantitative absorption to obtain the dispatch strategy; specifically, it can include the day-ahead start and stop mode of thermal power units, the dispatchable output range of thermal power and energy storage units, the unit output under the prediction scenario, and the dynamic boundary of the wind power uncertainty set. The obtained dispatch strategy can improve the reliability and economy of the system operation under the premise of ensuring that the wind power absorption level meets the given requirements.

[0058] In order to construct the day-ahead robust dispatch model of the power system designed in this application, firstly, the power system parameters and day-ahead forecast curves are collected, including:

[0059] (1) Topological structure, line parameters and equipment technical parameters of the power system;

[0060] (2) Wind power output prediction curve, output probability density function and load curve.

[0061] The objective function of the day-ahead robust dispatch model of the power system designed in this application is constructed as follows:

[0062]

[0063] Wherein, formula (1) minimizes the expected sum of the unit operating cost and load shedding penalty cost under the prediction scenario, the subscripts i and t are the node / equipment number and time period number respectively, N TUis the number of thermal power units, Δt is the time period length, and T is the number of scheduling time periods; c sd is the cost of a single start-up and shutdown of the unit; U is a 0-1 variable for the start-up and shutdown action state of the thermal power unit, where 1 indicates the unit is started, and 0 indicates the unit is shut down; Formula (2) is the calculation formula for the fuel cost of the thermal power unit, is the output of the thermal power unit in the forecast scenario, C f (·) is the fuel cost function of thermal power units, c 2 、c 1 、c 0 is the cost coefficient; ELS is the expected load shedding, c l is the corresponding penalty cost coefficient.

[0064] The uncertainty set of wind power in the existing power system day-ahead robust dispatch model is mostly described by the following formula:

[0065]

[0066] Among them, U is the uncertainty set of wind power output, P w and They are wind power uncertainty output, output lower bound and output upper bound respectively.

[0067] For formula (3), P w and All of them are given, and the operating risks outside the uncertainty boundary cannot be taken into account. Although the probability of the wind power random output scenario appearing outside the set is small, it may bring serious consequences; on the other hand, different unit start-stop strategies correspond to different regulation capabilities, and correspondingly, the system's absorption level and operating economy are also different. In this regard, the method proposed in the present invention constructs a dynamic uncertainty set and proposes an implicit affine strategy that takes into account the dynamic boundary of the random output of renewable energy. While considering the above two factors, the unpredictability of the scheduling strategy can be guaranteed, and safe and reliable operation can be guaranteed, as follows:

[0068]

[0069]

[0070] Among them, U D is the constructed dynamic uncertainty set, where the subscripts i and t are the node and unit numbers and time period numbers respectively. For wind power output prediction, P w,max The maximum output of wind power, P w and are the lower and upper bound variables of the dynamic uncertainty set to be decided, σ is the standard deviation; u is the start-stop state 0-1 variable of the thermal power unit, 1 means the unit is in the start state, 0 means the unit is in the shutdown state, T i on and T i off are the minimum startup and shutdown time of thermal power units respectively; P TU,min and P TU,max are the minimum and maximum output of thermal power units respectively; R U and R D are the maximum up and down ramp rates of thermal power units respectively; P cha，max and P dis,max are the maximum charging and discharging power of energy storage, η cha and η dis are the charge and discharge efficiency, S min and S max are the minimum and maximum charge states, E r is the rated capacity of the energy storage, E is the energy stored in the energy storage; and For the dispatchable output range variables of the introduced thermal power units and energy storage, is the energy storage power under the prediction scenario; N w , N BES and N d are the number of wind farms, energy storage and loads, respectively, d is the load power; s is the line power transmission distribution factor, subscript l is the line number, P l max is the upper limit of line transmission capacity; the robustness within the dynamic uncertainty set is verified using a column and constraint generation algorithm.

[0071] As a preferred embodiment, in S1: using represents the objective function of the day-ahead robust dispatch model of the power system; where the subscripts i and t are the node and unit numbers and time period numbers, respectively, and N TU is the number of thermal power units, Δt is the time period length, and T is the number of scheduling time periods; c sd is the cost of a single start-up and shutdown of the unit; U is a 0-1 variable for the start-up and shutdown action state of the thermal power unit; is the output of the thermal power unit in the forecast scenario, C f (·) is the fuel cost function of thermal power units; ELS is the expected load shedding, c l is the corresponding penalty cost coefficient, Using polynomials Approximate random output probability density function f i,t , and then calculate the expected load shedding amount to obtain the secondary calculation formula a ls 、bls and c ls is the coefficient of the ELS quadratic calculation formula, P w is the lower bound of wind power output uncertainty set.

[0072] As a preferred implementation, in S2, the random absorption rate index is calculated based on the wind power day-ahead predicted output curve and the random output probability distribution information, including: defining the random absorption rate index Using polynomials Approximate random output probability density function f i,t , and then calculate the random absorption rate index Among them, E{·} represents expectation, is the wind power abandoned, N p is the polynomial order, p n are the polynomial coefficients, and a, b, and c are the coefficients of the SAR quadratic calculation formula.

[0073] As a preferred implementation, the random absorption rate index is linearized in S2, including: Where n is the segment number, N pw is the number of linearization segments, q 1 and q 0 Calculate the expression coefficients for the linearized SAR, is the introduced linear relaxation variable; the load shedding expectation ELS can be linearized in the same way.

[0074] As a preferred implementation, S4 includes: using the formula SAR≥SAR req represents the determination of the random absorption rate constraint, where Among them, SAR req is the required value of the system random absorption rate, SAR is the minimum random absorption rate, is the maximum random absorption rate.

[0075] As a preferred embodiment, using the formula Determine the minimum random absorption rate.

[0076] As a preferred embodiment, As the objective function, a random consumption rate evaluation model is constructed to evaluate the maximum random consumption rate of the power system. For evaluation, SAR is the random absorption rate indicator.

[0077] The simulation data described herein is only used to explain the present invention and is not used to limit the present invention.

[0078] consider Figure 2The verification system in the example has three thermal power units (TU1, TU2, and TU3) with parameters as shown in Table 1. The energy storage (BES) capacity is 25MW / 100MWh, the charging and discharging efficiency is 95%, the initial, minimum, and maximum SOC are 60%, 20%, and 100%, respectively. The wind power installed capacity is 150MW. The electric load curve and wind power predicted output curve are shown in Figure 3 As shown, the wind power output follows a Gaussian distribution, the standard deviation is 20% of the predicted output, and the load shedding penalty cost is 1000$ / MWh. To illustrate the effectiveness of the method proposed in the present invention, it is compared with the existing method. The method proposed in the present invention and the comparison method are introduced as follows.

[0079] The method proposed in the present invention: a dispatching method based on an implicit affine strategy and taking into account the constraints of random absorption rate. Comparative method 1: a dispatching method based on an implicit affine strategy and ensuring absorption through wind abandonment penalty costs. Comparative method 2: a dispatching method based on traditional two-stage robust optimization and taking into account the constraints of random absorption rate.

[0080] Table 1 Parameters of thermal power units

[0081]

[0082] The results of the proposed method and method 1 are compared to verify the effectiveness of the proposed absorption rate index and random absorption rate constraint. The minimum and maximum random absorption rates of the system evaluated by the proposed method are 92.02% and 99.88% respectively. Five different SARs are set within this range. req The system day-ahead dispatching cost and the operating cost under random scenarios within 1000 days are obtained as shown in Table 2. It can be seen that the average consumption rate under each scenario in the daily operation simulation is higher than the SAR index value corresponding to the day-ahead dispatching strategy. Therefore, the method proposed in the present invention can effectively quantify the system consumption level in the day-ahead dispatching process. In addition, as the required consumption level increases, the system start-up and shutdown costs and the total operating costs also increase. When SAR req An increase of 1% from 97% and 98% respectively increases the start-up and shutdown costs of the units by $1,500 and $6,000. That is, the same degree of improvement in the absorption level brings about different degrees of increase in operating costs. Excessive pursuit of an increase in the absorption rate will lead to a loss in operating economy.

[0083] For method 1, the wind abandonment penalty cost is set to 100, 200, 300 and 400 $ / MWh respectively. The system day-ahead dispatch cost and the operating cost results under random scenarios within 1000 days are shown in Table 3. It can be seen that the wind abandonment penalty cost has a great impact on the system absorption level and operating cost. With the increase of the wind abandonment penalty cost, the system absorption level will increase while the total operating cost will also increase. Under this method, the system absorption level cannot be quantified and effectively guaranteed during the day-ahead dispatch process. Setting too large / too small wind abandonment penalty cost will result in too high operating cost / too low absorption level.

[0084] Table 2 Solution results of the proposed method

[0085]

[0086] Table 3 Solution results of method 1

[0087]

[0088]

[0089] The results of the proposed method and method 2 are compared to verify the effectiveness of the implicit affine strategy for the dynamic boundary of the random output of renewable energy. The maximum random absorption rate of the system evaluated by method 2 is 99.94%, which is higher than the result of the proposed method. req The dynamic boundary is set to 98%, and the method proposed by the present invention and method 2 are used respectively. Figure 4 As shown in Figure 2, a wind power output curve is taken between the dynamic boundaries solved by method 2 as follows: Figure 5 As shown in Figure 2, the scheduling solution obtained by making scheduling decisions based on the complete output curve (ideal situation) is as follows: Figure 6 As shown in the left half of the figure, there is no wind abandonment and load shedding at this time. In the actual dispatching process, decisions can only be made based on the current known wind power output and future forecast values. The corresponding dispatching scheme is as follows Figure 6 As shown in the right half of the figure, it can be seen that wind curtailment occurs at 4-5h and 23-24h. Therefore, the result obtained by method 2 is too optimistic and is not conducive to ensuring the safe and reliable operation of the power system. However, by using the method proposed in the present invention, the dispatchable output range of the unit at each time can be obtained at the same time, as shown in Figure 7 As shown in the figure, for any wind power output within the dynamic boundary, a feasible scheduling scheme can be found within the dispatchable output range without affecting the feasibility of subsequent time periods, thereby ensuring the reliability of the scheduling scheme.

[0090] Example 2

[0091] This embodiment provides a dispatch strategy acquisition device for quantitative consumption of an electric power system, including: a modeling module, a calculation module, a construction module, an evaluation module and a solution module.

[0092] The modeling module is used to construct a day-ahead robust dispatching model for the power system based on a dynamic uncertainty set and an implicit affine strategy; the objective function of the day-ahead robust dispatching model for the power system is to minimize the sum of the expected unit operating costs and load shedding penalty costs under the system prediction scenario.

[0093] The calculation module is used to calculate the random absorption rate index based on the wind power day-ahead predicted output curve and the random output probability distribution information, and linearize the random absorption rate index; the random absorption rate index is a function with the upper bound of the dynamic uncertainty set as the independent variable.

[0094] A construction module is used to construct a random absorption rate evaluation model based on the constructed power system day-ahead robust dispatch model and the linearized random absorption rate index; the constraints of the random absorption rate evaluation model are the same as those of the power system day-ahead robust dispatch model, and the objective function is to maximize the system random absorption rate index.

[0095] The evaluation module is used to evaluate the maximum random absorption rate of the power system using the random absorption rate evaluation model, calculate the minimum random absorption rate of the power system using the linearized random absorption rate index and the wind power day-ahead forecast output curve, and construct the random absorption rate constraint using the maximum and minimum random absorption rates.

[0096] The solution module is used to embed the random absorption rate constraint into the day-ahead robust dispatch model of the power system to obtain the day-ahead robust dispatch model of the power system for quantitative absorption, and solve the day-ahead robust dispatch model of the power system for quantitative absorption to obtain the dispatch strategy.

[0097] Example 3

[0098] This embodiment provides a control system for an electric power system, including a memory and a processor, wherein the memory stores a computer program, and the steps of the method are implemented when the processor executes the computer program.

[0099] Example 4

[0100] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method are implemented.

[0101] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for obtaining a dispatching strategy for quantitative consumption of a power system, characterized in that: include: S1: Constructing a day-ahead robust dispatch model for power systems based on dynamic uncertainty sets and implicit affine strategy; The objective function of the power system day-ahead robust dispatch model is to minimize the expected sum of unit operating costs and load shedding penalty costs under the system prediction scenario; S2: Calculate a random consumption rate index based on the wind power day-ahead predicted output curve and random output probability distribution information, and linearize the random consumption rate index; the random consumption rate index is a function with the upper bound of the dynamic uncertainty set as an independent variable; S3: constructing a random absorption rate evaluation model based on the constructed power system day-ahead robust dispatch model and the linearized random absorption rate index; the constraints of the random absorption rate evaluation model are the same as the constraints of the power system day-ahead robust dispatch model, and the objective function is to maximize the system random absorption rate index; S4: using the random absorption rate evaluation model to evaluate the maximum random absorption rate of the power system, using the linearized random absorption rate index and the wind power day-ahead forecast output curve to calculate the minimum random absorption rate of the power system, and using the maximum and minimum random absorption rates to construct a random absorption rate constraint; S5: embedding the random absorption rate constraint into the day-ahead robust dispatch model of the power system to obtain the day-ahead robust dispatch model of the power system for quantitative absorption, and solving the day-ahead robust dispatch model of the power system for quantitative absorption to obtain the dispatch strategy.

2. The method for obtaining a dispatching strategy for quantitative consumption of a power system according to claim 1, characterized in that: In S1: using represents the objective function of the day-ahead robust dispatch model of the power system; Wherein, the subscripts i and t are the node number, unit number and time period number respectively, N TU is the number of thermal power units, Δt is the time period length, and T is the number of scheduling time periods; c sd is the cost of a single start-up and shutdown of the unit; U is a 0-1 variable for the start-up and shutdown action state of the thermal power unit; is the output of the thermal power unit in the forecast scenario, C f (·) is the fuel cost function of thermal power units, ELS is the expected load shedding, c l is the corresponding penalty cost coefficient, a ls , b ls and c ls is the coefficient of the ELS quadratic calculation formula, P w is the lower bound of wind power output uncertainty set.

3. The method for obtaining a dispatching strategy for quantitative consumption of a power system according to claim 1, characterized in that: In S1, the constraints of constructing the day-ahead robust dispatch model of the power system using the dynamic uncertainty set and the implicit affine strategy include: Among them, U D is a dynamic uncertainty set, where the subscripts i and t are the node and unit numbers and time period numbers respectively. For wind power output prediction, P w,max The maximum output of wind power, P w and are the lower and upper bound variables of the dynamic uncertainty set to be decided, σ is the standard deviation; u is the start-stop state 0-1 variable of the thermal power unit, 1 means the unit is in the start state, and 0 means the unit is in the shutdown state, T i on and T i off are the minimum startup and shutdown time of thermal power units respectively; P TU,min and P TU,max are the minimum and maximum output of thermal power units respectively; R U and R D are the maximum up and down ramp rates of thermal power units respectively; P cha，max and P dis,max are the maximum charging and discharging power of energy storage, η cha and η dis are the charge and discharge efficiency, S min and S max are the minimum and maximum charge states, E r is the rated capacity of the energy storage, E is the energy stored in the energy storage; and For the dispatchable output range variables of the introduced thermal power units and energy storage, is the energy storage power under the prediction scenario; N w , N BES and N d are the number of wind farms, energy storage and loads, respectively, d is the load power; s is the line power transmission distribution factor, subscript l is the line number, P l max is the upper limit of line transmission capacity; the robustness within the dynamic uncertainty set is verified using a column and constraint generation algorithm.

4. The method for obtaining a dispatching strategy for quantitative consumption of a power system according to claim 1, characterized in that: In S2, the random consumption rate index is calculated based on the wind power day-ahead predicted output curve and random output probability distribution information, including: Defining the random absorption rate indicator Using polynomials Approximate the random output probability density function, and then calculate the random consumption rate index Among them, E{·} represents expectation, is the wind power abandoned, N p is the polynomial order, p n are the polynomial coefficients, and a, b, and c are the coefficients of the SAR quadratic calculation formula.

5. The method for obtaining a dispatching strategy for quantitative consumption of a power system according to claim 1, characterized in that: The random absorption rate indicator is linearized in S2, including: Where n is the segment number, N pw is the number of linearization segments, q 1 and q 0 Calculate the expression coefficients for the linearized SAR, is the introduced linear slack variable.

6. The method for obtaining a dispatching strategy for quantitative consumption of a power system according to claim 1, characterized in that: The S4 includes: Using the formula SAR ≥ SAR req represents the determination of the random absorption rate constraint, where Among them, SAR req is the required value of the system random absorption rate, SAR is the minimum random absorption rate, is the maximum random absorption rate.

7. The method for obtaining a dispatching strategy for quantitative consumption of a power system according to claim 6, characterized in that: Using the formula The minimum random absorption rate is determined.

8. The method for obtaining a dispatching strategy for quantitative consumption of a power system according to claim 6, characterized in that: by As the objective function, a random consumption rate evaluation model is constructed to evaluate the maximum random consumption rate of the power system. Evaluation is performed, and SAR is the random absorption rate indicator.

9. A dispatching strategy acquisition device for quantitative consumption of power system, characterized in that: include: Modeling module, used to construct a day-ahead robust dispatch model for power systems based on dynamic uncertainty sets and implicit affine strategies; The objective function of the power system day-ahead robust dispatch model is to minimize the expected sum of unit operating costs and load shedding penalty costs under the system prediction scenario; A calculation module, used for calculating a random absorption rate index based on the wind power day-ahead predicted output curve and random output probability distribution information, and linearizing the random absorption rate index; the random absorption rate index is a function with the upper bound of the dynamic uncertainty set as an independent variable; A construction module is used to construct a random absorption rate evaluation model based on the constructed power system day-ahead robust dispatch model and the linearized random absorption rate index; the constraints of the random absorption rate evaluation model are the same as the constraints of the power system day-ahead robust dispatch model, and the objective function is to maximize the system random absorption rate index; An evaluation module is used to evaluate the maximum random absorption rate of the power system using the random absorption rate evaluation model, calculate the minimum random absorption rate of the power system using the linearized random absorption rate index and the wind power day-ahead predicted output curve, and construct a random absorption rate constraint using the maximum and minimum random absorption rates; A solution module is used to embed the random absorption rate constraint into the day-ahead robust dispatch model of the power system to obtain the day-ahead robust dispatch model of the power system for quantitative absorption, and solve the day-ahead robust dispatch model of the power system for quantitative absorption to obtain a dispatch strategy.

10. A control system for an electric power system, comprising a memory and a processor, wherein the memory stores a computer program, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

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