Energy and standby cooperative scheduling method considering renewable energy output uncertainty

Abstract

The invention discloses an energy and standby cooperative scheduling method considering renewable energy output uncertainty, and the method comprises the steps: building a cooperative scheduling model of renewable energy participating in energy and standby service based on the uncertainty analysis of renewable energy output; and converting an integral item in the collaborative scheduling objective function into an analytic item, and quickly solving through a sequence linear programming method to obtain a collaborative scheduling optimal solution. Through renewable energy output uncertainty analysis, the standby cost and the emergency control cost of the renewable energy are considered, the cooperative scheduling model considering the renewable energy output uncertainty is constructed, and the model fully considers the expected cost of potential renewable energy power abandoning or load shedding, so that the cooperative scheduling efficiency is improved. According to the method, the emergency control capability is improved, insufficient or excessive emergency control capability is avoided, meanwhile, a high-precision approximation technology is adopted to convert an integral item in the target function into an analytic item, and the converted target function is rapidly solved through a sequence linear programming method, so that the cooperative scheduling efficiency is improved.

Description

Technical Field

[0001] The present application relates to the technical field of automatic power generation control of power systems, and in particular to a method for coordinated dispatching of energy and backup taking into account the uncertainty of renewable energy output. Background Art

[0002] With the development of new power systems, the proportion of renewable energy sources such as wind power and solar energy in the power system has gradually increased. However, wind power, photovoltaic and other renewable energy power generation are greatly affected by environmental conditions such as wind speed, light intensity, and temperature, and the output shows random fluctuation characteristics.

[0003] The access of large-scale renewable energy sources such as wind power and photovoltaic power to the power system will inevitably reduce the proportion of thermal power generation in the power system, resulting in a decline in the regulation capacity of the power grid and insufficient resistance to external risks and interference. In order to improve the stability of the system, renewable energy needs to participate more in the power system power reserve and even secondary frequency hopping in the future. Therefore, considering the uncertainty of renewable energy output, it is an urgent problem to achieve the coordinated dispatch of energy and reserve between renewable energy and traditional energy. Summary of the invention

[0004] In order to solve the problems of decreased regulation capacity of the system after large-scale access of renewable energy sources such as wind power and photovoltaics to the power system, insufficient ability to resist external risks and interference, etc., this application proposes a method for coordinated scheduling of energy and reserve that takes into account the uncertainty of renewable energy output, promotes the participation of renewable energy in peak load and reserve control of the power system, and improves the stability of the power system.

[0005] In the first aspect, the present application is implemented through the following technical solutions:

[0006] An energy and reserve coordinated scheduling method considering the uncertainty of renewable energy output, the coordinated scheduling method comprising:

[0007] Based on the uncertainty analysis of renewable energy output, a collaborative scheduling model for renewable energy to participate in energy and backup services is constructed; the collaborative scheduling model includes a collaborative scheduling objective function and system operation constraints;

[0008] The integral term in the collaborative scheduling objective function is converted into an analytical term, and the optimal solution for collaborative scheduling is quickly solved by a sequential linear programming method.

[0009] In some embodiments, the uncertainty analysis based on renewable energy output and the construction of a coordinated dispatch model for renewable energy to participate in energy and backup services specifically include:

[0010] Based on the uncertainty analysis of renewable energy output, the coordinated dispatch objective function of renewable energy participating in energy and reserve services is constructed by considering the power generation cost of thermal power units, the up and down reserve cost of renewable energy, the cost of renewable energy abandonment and the cost of load shedding.

[0011] Considering the power generation capacity of power generation energy, spare capacity, line over-limit tolerance probability and grid safety operation constraints, the system operation constraints are constructed.

[0012] In some implementations, the process of constructing the collaborative scheduling objective function specifically includes:

[0013] Constructing the power generation cost of the thermal power unit according to the power generation capacity of the thermal power unit and the thermal power generation cost coefficient;

[0014] Constructing upper and lower reserve costs of the renewable energy according to the total actual output of the renewable energy, the total planned output of the renewable energy, the upper and lower limits of the total output of the renewable energy, the lower regulation reserve unit cost, the upper regulation reserve unit cost and the probability distribution function of the uncertainty of the renewable energy output;

[0015] Constructing the renewable energy curtailment cost and load shedding cost according to the total actual output of the renewable energy, the total planned output of the renewable energy, the upper and lower limits of the total output of the renewable energy, the unit cost of renewable energy curtailment and the unit cost of load shedding, and the probability distribution function of the uncertainty of renewable energy output;

[0016] The total operating cost of the system is obtained based on the constructed power generation cost of the thermal power units, the upper and lower standby costs of renewable energy, the power abandonment cost of renewable energy and the load shedding cost, and the minimum total operating cost of the system is used as the collaborative scheduling objective function.

[0017] In some embodiments, the probability distribution function of the renewable energy output uncertainty uses a multi-dimensional Gaussian mixture model to describe the probability distribution of the output power uncertainty of all renewable energy sources.

[0018] In some implementations, the process of constructing the system operation constraint conditions specifically includes:

[0019] Construct system operation power balance constraints based on the total number of thermal power units, thermal power unit output, total planned output of renewable energy, total number of loads and load power;

[0020] The system generation capacity constraints are established based on the upper and lower limits of thermal power unit output, the predicted maximum output of renewable energy, the predicted minimum output of renewable energy, and the upper and lower adjustment reserves of thermal power units;

[0021] Establish the thermal power unit climbing constraint according to the thermal power unit climbing rate and the thermal power unit climbing rate;

[0022] The probability constraint of reserve capacity is constructed based on the up and down adjustment of thermal power units, the planned output of renewable energy, the actual output of renewable energy and the acceptable probability of over-limit of power generation reserve;

[0023] The line power flow constraints are constructed based on the output of thermal power units, the number of line nodes, the planned output of renewable energy, the actual output of renewable energy, the node admittance matrix and the acceptable probability of line power flow exceeding the limit.

[0024] In some implementations, converting the integral term in the collaborative scheduling objective function into an analytical term and quickly solving the collaborative scheduling optimal solution through a sequential linear programming method specifically includes:

[0025] By using high-precision approximation technology, the mathematical expectation function in the collaborative scheduling objective function is rewritten into the algebraic sum form of the probability distribution function and the cumulative distribution function of the Gaussian mixture model;

[0026] The Gaussian mixture model cumulative distribution function is approximated by a composite hyperbolic tangent function, and the approximate function of the Gaussian mixture model probability distribution function is the derivative of the cumulative distribution approximate function;

[0027] Substituting the approximate functions of the probability distribution function and the cumulative distribution function of the Gaussian mixture model into the mathematical expectation function to obtain the approximate function of the mathematical expectation function, wherein the approximate function of the mathematical expectation function is an analytical expression and does not contain any integral term;

[0028] Substituting the approximate function of the mathematical expectation function into the collaborative scheduling objective function to obtain an approximate objective function without an integral term;

[0029] The approximate objective function is optimized and solved by a sequential linear programming method to obtain the optimal solution for collaborative scheduling.

[0030] In the second aspect, the present application proposes an energy and backup coordinated dispatching system considering the uncertainty of renewable energy output, and the coordinated dispatching system includes:

[0031] A model building module, wherein the model building module builds a collaborative scheduling model for renewable energy to participate in energy and backup services based on uncertainty analysis of renewable energy output; the collaborative scheduling model includes a collaborative scheduling objective function and system operation constraints;

[0032] And, a model solving module, which converts the integral term in the collaborative scheduling objective function into an analytical term, and quickly solves the collaborative scheduling optimal solution through a sequential linear programming method.

[0033] In some embodiments, the model building module further comprises:

[0034] An objective function construction unit, wherein the objective function construction unit constructs an objective function for the coordinated dispatch of renewable energy participating in energy and reserve services based on uncertainty analysis of renewable energy output, taking into account power generation costs of thermal power units, upper and lower reserve costs of renewable energy, power abandonment costs of renewable energy, and load shedding costs;

[0035] And, a constraint condition construction unit, which considers the power generation capacity of the power generation energy, the spare capacity and the line over-limit tolerance probability and the power grid safe operation constraints to construct the system operation constraint conditions.

[0036] In some embodiments, the model solving module further includes:

[0037] A rewriting unit, wherein the rewriting unit rewrites the mathematical expectation function in the collaborative scheduling objective function into an algebraic sum form of a Gaussian mixture model probability distribution function and a cumulative distribution function through a high-precision approximation technology;

[0038] An approximation unit, wherein the approximation unit approximates the cumulative distribution function of the Gaussian mixture model by a composite hyperbolic tangent function to obtain a cumulative distribution approximate function, wherein the approximate function of the probability distribution function of the Gaussian mixture model is a derivative of the cumulative distribution approximate function; the approximate functions of the probability distribution function of the Gaussian mixture model and the cumulative distribution function are substituted into the mathematical expectation function to obtain an approximate function of the mathematical expectation function, wherein the approximate function of the mathematical expectation function is an analytical expression and does not contain any integral terms; the approximate function of the mathematical expectation function is substituted into the collaborative scheduling objective function to obtain an approximate objective function without integral terms;

[0039] And, a solution unit, wherein the solution unit optimizes and solves the approximate objective function through a sequential linear programming method to obtain an optimal solution for collaborative scheduling.

[0040] In a third aspect, the present application proposes a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0041] The present application proposes a method for coordinated dispatching of energy and reserve considering the uncertainty of renewable energy output. By analyzing the uncertainty of renewable energy output and considering the reserve cost and emergency control cost of renewable energy, a coordinated dispatching model considering the uncertainty of renewable energy output is constructed. The model fully considers the expected cost of potential renewable energy power abandonment or load reduction, avoids insufficient or excessive emergency control capability, and adopts a high-precision approximation technology to convert the integral term in the objective function into an analytical term. The converted objective function can be quickly solved by a sequential linear programming method, thereby improving the efficiency of coordinated dispatching. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The drawings described herein are used to provide a further understanding of the embodiments of the present application, constitute a part of the present application, and do not constitute a limitation on the embodiments of the present application. In the drawings:

[0043] Figure 1 A flowchart of the collaborative scheduling method proposed in the embodiment of the present application;

[0044] Figure 2 This is a principle block diagram of the collaborative scheduling system proposed in an embodiment of the present application. DETAILED DESCRIPTION

[0045] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with examples and drawings. The illustrative implementation scheme of the present application and its description are only used to explain the present application and are not intended to limit the present application.

[0046] Example:

[0047] The access of large-scale renewable energy sources such as wind power and photovoltaic power to the power system will cause the system's conditional capacity to decline, and the lack of ability to resist external risks and interference. In response to this, the present application proposes a method for coordinated dispatching of energy and backup that takes into account the uncertainty of renewable energy output. The coordinated dispatching method proposed in the present application is based on the uncertainty of renewable energy output, and constructs a coordinated dispatching model for renewable energy participating in energy and backup services. The model fully considers the expected costs of potential renewable energy power abandonment and load shedding, avoids insufficient or excessive emergency control capabilities, and thus produces more economical backup decisions than traditional opportunity-constrained dispatching methods; at the same time, in the process of solving the model, the integral term in the objective function is converted into an analytical term, so that the coordinated dispatching model can be quickly solved by a sequential linear programming method.

[0048] like Figure 1 As shown, the collaborative scheduling method proposed in this embodiment includes the following steps:

[0049] Step 100: Based on the uncertainty analysis of renewable energy output, a coordinated scheduling model for renewable energy to participate in energy and backup services is constructed. The coordinated scheduling model includes a coordinated scheduling objective function and system operation constraints.

[0050] Specifically, the specific process of step 100 is as follows:

[0051] Step 101, based on the uncertainty analysis of renewable energy output, considering the power generation cost of thermal power units, the lower reserve cost of renewable energy, the upper reserve cost of renewable energy units, the power abandonment cost of renewable energy and the load shedding cost, construct the coordinated dispatch objective function of renewable energy participating in energy and reserve services, expressed as:

[0052]

[0053] Where W represents the total operating cost of the system; T represents the number of control cycles in the time period; I represents the total number of thermal power units in the system; CT(·) represents the power generation cost of the thermal power units; C dn , C up , C dic , C load They represent the unit cost of downward regulation reserve, unit cost of upward regulation reserve, unit cost of power abandonment and unit cost of load shedding of renewable energy units respectively; represents the total actual output of renewable energy at time t; Φ t represents the total planned output of renewable energy at time t; and Φ t They represent the maximum and minimum predicted total output of renewable energy at time t respectively; and They represent the upper and lower limits of the total output of renewable energy at time t; p i,t represents the power generation of the ith thermal power unit at time t; E(·) represents the mathematical expectation.

[0054] In this step, the calculation models for the total actual output of renewable energy, the total planned output, the total predicted maximum output, the total predicted minimum output, the total output upper limit and the total output lower limit are:

[0055]

[0056] Where J represents the total number of renewable energy units in the system; g j,t , g j,t They represent the actual output, planned output, predicted maximum output and predicted minimum output of the j-th renewable energy unit at time t respectively; They respectively represent the upper regulation reserve and lower regulation reserve of the i-th thermal power unit at time t.

[0057] In this step, the calculation model of the power generation cost of thermal power units, the upward regulation reserve cost of renewable energy, the downward regulation reserve cost, and the emergency control cost (i.e., the power abandonment cost and the load shedding cost) is as follows:

[0058] (1) Power generation cost of thermal power units:

[0059]

[0060] Among them, a i , b i 、c i They represent the cost coefficients of thermal power generation respectively.

[0061] (2) Renewable energy regulation and reserve costs:

[0062]

[0063] in, Represents the probability distribution function of renewable energy output uncertainty.

[0064] (3) Emergency control costs:

[0065]

[0066] In this step, the probability distribution function of renewable energy output uncertainty is as follows:

[0067] Any smooth probability distribution function can be accurately approximated by a Gaussian mixture model, as long as there are enough Gaussian components and specific parameters. Therefore, this embodiment uses a multidimensional Gaussian mixture model to describe the probability distribution of the output power uncertainty of all renewable energy sources at time t, that is:

[0068]

[0069] in, represents the expected value μ m,t and the covariance matrix is ​​Π m,t The probability distribution function of the Gaussian distribution; w m,t is the weight; M represents the dimension of the Gaussian mixture model.

[0070] Step 102 , considering the power generation capacity of the power generation energy, the spare capacity, the line over-limit tolerance probability and the power grid safe operation constraints, construct the system operation constraint conditions.

[0071] In this step, the system operation constraints include system operation power balance constraints, power generation capacity constraints, thermal power ramp constraints, reserve capacity probability constraints and line flow constraints. The specific construction process is as follows:

[0072] (1) System operation power balance constraints

[0073] The power generation and consumption of the power system should be balanced at all times, and the fluctuations of load or renewable energy power should be balanced around the planned power. It can be expressed as:

[0074]

[0075] Among them, l a,t represents the power consumption of the ath load at time t; A represents the total number of power loads in the system; I represents the total number of thermal power units at time t, p i,t Represents the power generation of the i-th thermal power unit at time t.

[0076] (2) System power generation capacity constraints

[0077] The power generation of thermal power units at each moment should be between their maximum and minimum outputs, and the planned output of renewable energy at each moment should be between their predicted maximum and minimum outputs. Specifically, it can be expressed as:

[0078]

[0079] Among them, p i and They represent the minimum output and maximum output of the i-th thermal power unit respectively.

[0080] The capacity of the upper regulation reserve and lower regulation reserve of thermal power units should meet the following requirements:

[0081]

[0082] Among them, V i up and V i dn They represent the ramp-up rate and ramp-down rate of the i-th thermal power unit respectively; Δt represents the control time period interval.

[0083] (3) Climbing constraints of thermal power units

[0084] The difference in power generation of thermal power units at adjacent times satisfies:

[0085] -V i dn Δt≤p i,t -p i,t-1 ≤V i up Δt,i=1,2,…,I

[0086] (4) Probabilistic constraints on spare capacity

[0087] As the main regulating energy source of the power system, thermal power units should ensure that there is sufficient upper and lower reserve capacity during coordinated dispatching to ensure the peak load safety of the system. It can be expressed as:

[0088]

[0089] Among them, α represents the acceptable probability of over-limit of thermal power reserve due to renewable energy or load-side power fluctuation.

[0090] (5) Line flow constraints

[0091] The actual power flow on the transmission line should be limited by the capacity of the transmission line. It can be expressed as:

[0092]

[0093] in, represents the actual power flow on line d at time t; G d,i represents the transmission allocation coefficient of the i-th thermal power unit on line d; G d,j represents the transmission allocation coefficient of the jth renewable energy unit on line d; G d,a represents the transmission distribution coefficient of the ath load on line d; p a,t represents the power consumption of the ath load at time t; γ i represents the power participation coefficient of the i-th thermal power unit; and F d They represent the upper and lower limits of the power flow on line d respectively; β represents the acceptable probability of the power flow exceeding the limit.

[0094] Step 200, convert the integral term in the collaborative scheduling objective function into an analytical term, and quickly solve it through a sequential linear programming method to obtain the optimal solution for the collaborative scheduling.

[0095] Specifically, the specific process of step 200 is as follows:

[0096] Step 201, through high-precision approximation technology, rewrite the mathematical expectation function in the collaborative scheduling objective function into the algebraic sum form of the Gaussian mixture model probability distribution function and the cumulative distribution function.

[0097] In this step, the coordinated dispatch objective function of participating energy and backup services is a nonlinear function, and its nonlinearity mainly comes from the expected calculation of backup cost and emergency control cost. This embodiment analyzes the concavity of the objective function and defines the integral function of the overall distribution probability density function as:

[0098]

[0099] Among them, v is the probability distribution function of the mixed Gaussian model; x is a constant. We obtain the probability density function:

[0100]

[0101] Based on this, we can know that the expected function E(x|x) is a convex function. Substituting the above formula into the objective function, the cost function at time t is:

[0102]

[0103] Among them, the unit cost of abandoned electricity of renewable energy is C dic and unit load shedding cost C load Much larger than the unit cost of renewable energy reserve C up and C dn , and the expected function E(x|x) is a convex function, so It is about Φ t , and Convex function of .

[0104] In this step, the objective function It is the sum of the mathematical expectation E(x|x), but the mathematical expectation function cannot be directly solved by the existing optimization solver. Therefore, in order to improve the efficiency of solving the optimal solution of the cost function, this embodiment rewrites the integral function as follows:

[0105]

[0106] in, and Π m (x) are respectively m th The probability distribution function and cumulative distribution function of the Gaussian component with weight w m , the expectation is μ m and the standard deviation is σ m .

[0107] Step 202, approximate the cumulative distribution function of the Gaussian mixture model by a composite hyperbolic tangent function to obtain a cumulative distribution approximate function, wherein the approximate function of the Gaussian mixture model probability distribution function is a derivative of the cumulative distribution approximate function.

[0108] In this step, in order to simplify the calculation formula of the expectation function, this embodiment approximates the probability distribution function and the cumulative distribution function of the normal distribution as follows:

[0109]

[0110] in, Represents an approximation of the probability distribution function of the normal distribution; It represents the approximation of the normal distribution cumulative distribution function, and its expression can be expressed as:

[0111]

[0112] Step 203, substitute the approximate functions of the Gaussian mixture model probability distribution function and the cumulative distribution function into the mathematical expectation function to obtain the approximate function of the mathematical expectation function, which is an analytical expression and does not contain any integral terms.

[0113] Specifically, the mathematical expectation function E(x|x) can be approximately expressed as:

[0114]

[0115] The above formula is an analytical formula without any integral. Considering The continuity, unity, differentiability and convexity of Π(x|μ,σ) are maintained, so is also convex with respect to the variable x.

[0116] Step 204, substitute the approximate function of the mathematical expectation function into the collaborative scheduling objective function to obtain an approximate objective function without an integral term.

[0117] In this step, the above approximate expected function is substituted into the objective function, and the collaborative scheduling objective function can be expressed as:

[0118]

[0119] Step 205: Under the system safety constraint condition, the above approximate objective function can be optimized and solved by a sequential linear programming method to obtain the optimal solution for collaborative scheduling.

[0120] The collaborative scheduling method proposed in this embodiment analyzes the uncertainty of renewable energy output, considers the backup cost and emergency control cost of renewable energy, and constructs a collaborative scheduling model that considers the uncertainty of renewable energy output. The model fully considers the expected cost of potential renewable energy power abandonment or load reduction, avoids insufficient or excessive emergency control capabilities, and in order to make the collaborative scheduling model easy to solve, this embodiment adopts a high-precision approximation technology to convert the integral term in the objective function into an analytical term. The converted objective function can be quickly solved by a sequential linear programming method, thereby improving the efficiency of collaborative scheduling.

[0121] Based on the same technical concept as above, this embodiment also proposes an energy and backup coordinated dispatching system considering the uncertainty of renewable energy output, specifically as follows: Figure 2 As shown, the collaborative scheduling system proposed in this embodiment includes:

[0122] The model building module builds a coordinated dispatch model for renewable energy participating in energy and backup services based on uncertainty analysis of renewable energy output. The coordinated dispatch model includes a coordinated dispatch objective function and system operation constraints.

[0123] And, a model solving module, which converts the integral term in the collaborative scheduling objective function into an analytical term, and quickly solves the collaborative scheduling optimal solution through a sequential linear programming method.

[0124] Furthermore, the model building module also includes:

[0125] The objective function construction unit, based on the uncertainty analysis of renewable energy processing, considers the power generation cost of thermal power units, the lower standby cost of renewable energy units, the upper standby cost of renewable energy units, the power abandonment cost of renewable energy units and the load shedding cost, and constructs the coordinated dispatch objective function of renewable energy participating in energy and standby services. The objective function construction process is specifically as described in the above step 101, and will not be repeated here.

[0126] And, a constraint condition construction unit, which considers the power generation capacity of the power generation energy, the spare capacity and the line over-limit tolerance probability and the power grid safe operation constraint to construct the system operation constraint. The specific process of constructing each constraint condition is as described in the above step 102, and will not be repeated here.

[0127] Furthermore, the model solving module also includes:

[0128] A rewriting unit, which rewrites the mathematical expectation function in the cooperative scheduling objective function into the algebraic sum form of the Gaussian mixture model probability distribution function and the cumulative distribution function through high-precision approximation technology. The specific process is as described in the above step 201, and will not be repeated here.

[0129] An approximation unit, which approximates the cumulative distribution function of the Gaussian mixture model through a composite hyperbolic tangent function to obtain a cumulative distribution approximation function, and the approximate function of the Gaussian mixture model probability distribution function is the derivative of the cumulative distribution approximation function; the approximate functions of the Gaussian mixture model probability distribution function and the cumulative distribution function are substituted into the mathematical expectation function to obtain an approximate function of the mathematical expectation function, and the approximate function of the mathematical expectation function is an analytical expression and does not contain any integral terms; the approximate function of the mathematical expectation function is substituted into the collaborative scheduling objective function to obtain an approximate objective function without integral terms. The specific process is as described in the above steps 202-204, and will not be repeated here.

[0130] And, a solving unit, which optimizes and solves the above approximate objective function through a sequential linear programming method under system safety constraints to obtain an optimal solution for collaborative scheduling.

[0131] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0132] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0133] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0134] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0135] The specific implementation methods described above further illustrate the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only the specific implementation method of the present application and is not intended to limit the scope of protection of the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A method for coordinated scheduling of energy and reserve considering the uncertainty of renewable energy output, characterized in that: The collaborative scheduling method includes: Based on the uncertainty analysis of renewable energy output, a collaborative scheduling model for renewable energy to participate in energy and backup services is constructed; the collaborative scheduling model includes a collaborative scheduling objective function and system operation constraints; The integral term in the collaborative scheduling objective function is converted into an analytical term, and the optimal solution for collaborative scheduling is quickly solved by a sequential linear programming method.

2. The energy and reserve coordinated scheduling method considering the uncertainty of renewable energy output according to claim 1 is characterized in that: The uncertainty analysis based on renewable energy output and the construction of a coordinated dispatch model for renewable energy to participate in energy and backup services specifically include: Based on the uncertainty analysis of renewable energy output, the coordinated dispatch objective function of renewable energy participating in energy and reserve services is constructed by considering the power generation cost of thermal power units, the up and down reserve cost of renewable energy, the cost of renewable energy abandonment and the cost of load shedding. Considering the power generation capacity of power generation energy, spare capacity, line over-limit tolerance probability and grid safety operation constraints, the system operation constraints are constructed.

3. The energy and reserve coordinated scheduling method considering the uncertainty of renewable energy output according to claim 2 is characterized in that: The construction process of the collaborative scheduling objective function specifically includes: Constructing the power generation cost of the thermal power unit according to the power generation capacity of the thermal power unit and the thermal power generation cost coefficient; Constructing upper and lower reserve costs of the renewable energy according to the total actual output of the renewable energy, the total planned output of the renewable energy, the upper and lower limits of the total output of the renewable energy, the lower regulation reserve unit cost, the upper regulation reserve unit cost and the probability distribution function of the uncertainty of the renewable energy output; Constructing the renewable energy curtailment cost and load shedding cost according to the total actual output of the renewable energy, the total planned output of the renewable energy, the upper and lower limits of the total output of the renewable energy, the unit cost of renewable energy curtailment and the unit cost of load shedding, and the probability distribution function of the uncertainty of renewable energy output; The total operating cost of the system is obtained based on the constructed power generation cost of the thermal power units, the upper and lower standby costs of renewable energy, the power abandonment cost of renewable energy and the load shedding cost, and the minimum total operating cost of the system is used as the collaborative scheduling objective function.

4. The energy and reserve coordinated scheduling method considering the uncertainty of renewable energy output according to claim 3 is characterized in that: The probability distribution function of the uncertainty of renewable energy output uses a multi-dimensional Gaussian mixture model to describe the probability distribution of the uncertainty of output power of all renewable energy sources.

5. The energy and reserve coordinated scheduling method considering the uncertainty of renewable energy output according to claim 2 is characterized in that: The process of constructing the system operation constraint conditions specifically includes: Construct system operation power balance constraints based on the total number of thermal power units, thermal power unit output, total planned output of renewable energy, total number of loads and load power; The system generation capacity constraints are established based on the upper and lower limits of thermal power unit output, the predicted maximum output of renewable energy, the predicted minimum output of renewable energy, and the upper and lower adjustment reserves of thermal power units; Establish the thermal power unit climbing constraint according to the thermal power unit climbing rate and the thermal power unit climbing rate; The probability constraint of reserve capacity is constructed based on the up and down adjustment of thermal power units, the planned output of renewable energy, the actual output of renewable energy and the acceptable probability of over-limit of power generation reserve; The line power flow constraints are constructed based on the output of thermal power units, the number of line nodes, the planned output of renewable energy, the actual output of renewable energy, the node admittance matrix and the acceptable probability of line power flow exceeding the limit.

6. A method for coordinated dispatching of energy and backup considering uncertainty of renewable energy output according to any one of claims 1 to 5, characterized in that: The step of converting the integral term in the cooperative scheduling objective function into an analytical term and quickly solving the cooperative scheduling optimal solution through a sequential linear programming method specifically includes: By using high-precision approximation technology, the mathematical expectation function in the collaborative scheduling objective function is rewritten into the algebraic sum form of the probability distribution function and the cumulative distribution function of the Gaussian mixture model; The cumulative distribution function of the Gaussian mixture model is approximated by a composite hyperbolic tangent function, and the approximate function of the probability distribution function of the Gaussian mixture model is the derivative of the cumulative distribution approximate function; Substituting the approximate functions of the probability distribution function and the cumulative distribution function of the Gaussian mixture model into the mathematical expectation function to obtain the approximate function of the mathematical expectation function, wherein the approximate function of the mathematical expectation function is an analytical expression and does not contain any integral term; Substituting the approximate function of the mathematical expectation function into the collaborative scheduling objective function to obtain an approximate objective function without an integral term; The approximate objective function is optimized and solved by a sequential linear programming method to obtain the optimal solution for collaborative scheduling.

7. An energy and reserve coordinated dispatching system considering the uncertainty of renewable energy output, characterized in that: The collaborative scheduling system comprises: A model building module, wherein the model building module builds a collaborative scheduling model for renewable energy to participate in energy and backup services based on uncertainty analysis of renewable energy output; the collaborative scheduling model includes a collaborative scheduling objective function and system operation constraints; And, a model solving module, which converts the integral term in the collaborative scheduling objective function into an analytical term, and quickly solves the collaborative scheduling optimal solution through a sequential linear programming method.

8. The energy and backup coordinated dispatching system considering the uncertainty of renewable energy output according to claim 7 is characterized in that: The model building module also includes: An objective function construction unit, wherein the objective function construction unit constructs an objective function for the coordinated dispatch of renewable energy participating in energy and reserve services based on uncertainty analysis of renewable energy output, taking into account power generation costs of thermal power units, upper and lower reserve costs of renewable energy, power abandonment costs of renewable energy, and load shedding costs; And, a constraint condition construction unit, which considers the power generation capacity of the power generation energy, the spare capacity and the line over-limit tolerance probability and the power grid safe operation constraints to construct the system operation constraint conditions.

9. The energy and backup coordinated dispatching system considering the uncertainty of renewable energy output according to claim 7 is characterized in that: The model solving module also includes: A rewriting unit, wherein the rewriting unit rewrites the mathematical expectation function in the collaborative scheduling objective function into an algebraic sum form of a Gaussian mixture model probability distribution function and a cumulative distribution function through a high-precision approximation technology; An approximation unit, wherein the approximation unit approximates the cumulative distribution function of the Gaussian mixture model by a composite hyperbolic tangent function to obtain a cumulative distribution approximate function, wherein the approximate function of the probability distribution function of the Gaussian mixture model is a derivative of the cumulative distribution approximate function; the approximate functions of the probability distribution function of the Gaussian mixture model and the cumulative distribution function are substituted into the mathematical expectation function to obtain an approximate function of the mathematical expectation function, wherein the approximate function of the mathematical expectation function is an analytical expression and does not contain any integral terms; the approximate function of the mathematical expectation function is substituted into the collaborative scheduling objective function to obtain an approximate objective function without integral terms; And, a solution unit, wherein the solution unit optimizes and solves the approximate objective function through a sequential linear programming method to obtain an optimal solution for collaborative scheduling.

10. A computer device 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 6 are implemented.

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