Power system dispatching method, device, equipment and medium

Abstract

The present invention discloses a method, device, equipment and medium for dispatching a power system. The method determines the fuzzy set corresponding to the wind power output and constructs a distributed robust joint opportunity constraint dispatching model for the power system. Then, the convex expression condition corresponding to the distributed robust joint opportunity constraint condition in the dispatching model is determined based on the fuzzy set. The optimal solution of the dispatching model is calculated through the convex expression condition and the objective function in the dispatching model to determine the target dispatching scheme of the power system. This realizes optimized dispatching that takes into account the uncertainty of wind power output. There is no need to determine the precise probability distribution of the uncertain variable of wind power output. This method can be applied to solve power system dispatching problems under uncertain environments. At the same time, the dispatching results show good performance in terms of economy and conservatism. Moreover, by converting the convex expression condition, the dispatching model can be efficiently solved, thereby improving the dispatching efficiency of the power system.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a power system scheduling method, device, equipment and medium. Background Art

[0002] The randomness and intermittency of wind power output pose significant challenges to optimal power system scheduling. Modeling and solving power system scheduling problems under uncertainty is a difficult problem. Existing methods for addressing the uncertainty of wind power output in power system scheduling fall into three main categories: stochastic programming methods based on simulation scenarios, chance-constrained methods, and robust optimization methods.

[0003] Stochastic programming methods based on simulation scenarios rely on precise distribution functions of uncertain variables. However, in practice, these distribution functions are often difficult to accurately determine. Furthermore, their computational scale increases significantly due to the "combinatorial explosion" in the number of simulation scenarios, making them unsuitable for online decision-making in large systems. Optimization models based on chance constraints also typically require precise probability distributions of uncertain variables. Robust optimization methods seek to determine the worst-case scenario, maintain a moderate decision-making scale, and do not rely on distribution functions of uncertain variables. However, robust optimization strategies are often conservative, resulting in higher system operating costs and poor economic efficiency. Summary of the Invention

[0004] The present invention provides a power system scheduling method, device, equipment and medium to achieve optimized scheduling of the power system taking into account the uncertainty of wind power output, without the need to determine the precise probability distribution of the uncertain variable of wind power output. At the same time, the scheduling results show good performance in terms of economy and conservatism.

[0005] According to one aspect of the present invention, a method for dispatching a power system is provided, the method comprising:

[0006] Determine the fuzzy set corresponding to the wind power output in the power system;

[0007] Constructing a distributed robust joint opportunity constrained dispatch model for the power system, wherein the distributed robust joint opportunity constrained dispatch model includes an objective function and distributed robust joint opportunity constraints;

[0008] Determining a convex expression condition corresponding to the distributed robust joint chance constraint condition based on the fuzzy set;

[0009] Based on the convex expression condition and the objective function, the optimal solution corresponding to the distributed robust joint chance-constrained scheduling model is determined, and the optimal solution is determined as the target scheduling scheme of the power system.

[0010] Optionally, the constructing of the distributed robust joint chance-constrained dispatch model for the power system includes:

[0011] According to the principle of minimizing the operating cost of the power system, constructing the objective function of the distributed robust joint opportunity constrained scheduling model;

[0012] For each wind turbine in the power system, based on the dispatchable output, standby output and wind power output of each wind turbine, a distributed robust joint opportunity constraint condition in the distributed robust joint opportunity constraint scheduling model is established.

[0013] Optionally, establishing the distributed robust joint opportunity constraint conditions in the distributed robust joint opportunity constraint scheduling model based on the dispatchable output, standby output and wind power output of each wind turbine generator set includes:

[0014] Obtain the preset constraint probability corresponding to the distributed robust joint chance constraint condition;

[0015] The probability that the sum of the dispatchable output and the reserve output of each wind turbine generator set does not exceed the wind power output is not less than the preset constraint probability, and is used as the distributed robust joint opportunity constraint condition.

[0016] Optionally, the formula corresponding to the distributed robust joint opportunity constraint condition is as follows:

[0017]

[0018] in, is the dispatchable output of the w-th wind turbine at time t, is the reserve output of the w-th wind turbine at time t, is the wind power output of the w-th wind turbine at time t, represents the set of wind turbines in the wind farm, represents the probability distribution, represents the fuzzy set, ε is the allowed violation probability of the distributed robust joint chance constraint, and 1-ε is the preset constraint probability.

[0019] Optionally, the distributed blue-rod joint opportunity constraint scheduling model also includes generator output constraints, generator standby constraints, generator set climbing constraints, line transmission power constraints, system active power balance constraints, wind power standby output constraints and dispatchable wind power output constraints.

[0020] Optionally, the formula corresponding to the objective function is as follows:

[0021]

[0022] in, represents a collection of generator sets, represents the set of wind turbines in the wind farm, represents the set of time periods, c i is the marginal generation cost of the i-th generator; is the backup cost of the i-th generator set; P i,t and R i,t are the power generation output and standby output of the i-th generator set at time t respectively; is the backup cost provided by the w-th wind turbine; is the reserve output provided by the w-th wind turbine at time t.

[0023] Optionally, determining a convex expression condition corresponding to the distributed robust joint chance constraint condition based on the fuzzy set includes:

[0024] Converting the distributed robust joint chance constraint condition into a joint constraint condition group based on the fuzzy set, wherein the joint constraint condition group includes the joint constraint condition to be converted, the first constraint condition corresponding to the allowed violation probability, and the second constraint condition corresponding to the allowed violation probability;

[0025] The joint constraint to be converted is converted into a deterministic constraint, and the deterministic constraint is converted into a wind power output constraint, a first standard second-order cone constraint corresponding to the allowed violation probability, and a second standard second-order cone constraint corresponding to the allowed violation probability by presetting auxiliary variables.

[0026] According to another aspect of the present invention, there is provided a power system dispatching device, the device comprising:

[0027] A fuzzy set determination module is used to determine the fuzzy set corresponding to the wind power output in the power system;

[0028] A dispatch model construction module, configured to construct a distributed robust joint opportunity constrained dispatch model for the power system, wherein the distributed robust joint opportunity constrained dispatch model includes an objective function and distributed robust joint opportunity constraints;

[0029] A convex expression derivation module, configured to determine a convex expression condition corresponding to the distributed robust joint chance constraint condition based on the fuzzy set;

[0030] A scheduling model solving module is used to determine the optimal solution corresponding to the distributed robust joint opportunity constraint scheduling model based on the convex expression condition and the objective function, and determine the optimal solution as the target scheduling scheme of the power system.

[0031] According to another aspect of the present invention, an electronic device is provided, comprising:

[0032] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the power system dispatching method described in any embodiment of the present invention.

[0033] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the power system scheduling method described in any embodiment of the present invention when executed.

[0034] The technical solution of the embodiment of the present invention determines the fuzzy set corresponding to the wind power output and constructs a distributed robust joint opportunity constraint scheduling model for the power system, and then determines the convex expression conditions corresponding to the distributed robust joint opportunity constraint conditions in the distributed robust joint opportunity constraint scheduling model based on the fuzzy set. Through the convex expression conditions and the objective function in the distributed robust joint opportunity constraint scheduling model, the optimal solution of the distributed robust joint opportunity constraint scheduling model is calculated to determine the target scheduling scheme of the power system, thereby realizing the optimal scheduling of the power system considering the uncertainty of wind power output. There is no need to determine the precise probability distribution of the uncertain variable of wind power output, and it can be applied to solve the power system scheduling problem in an uncertain environment. At the same time, the scheduling results show good performance in terms of economy and conservatism. Moreover, by deriving the convex approximate expression of the distributed robust joint opportunity constraint conditions, the distributed robust joint opportunity constraint scheduling model can be efficiently solved, thereby improving the scheduling efficiency of the power system.

[0035] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0037] Figure 1 This is a flow chart of a power system dispatching method provided in the first embodiment of the present invention;

[0038] Figure 2 This is a flow chart of a power system dispatching method provided in the second embodiment of the present invention;

[0039] Figure 3 This is a flow chart of a power system dispatching method provided in the third embodiment of the present invention;

[0040] Figure 4 This is a schematic structural diagram of a power system dispatching device provided by a fourth embodiment of the present invention;

[0041] Figure 5 This is a structural diagram of an electronic device provided in Example 5 of the present invention. DETAILED DESCRIPTION

[0042] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0043] It should be noted that the terms "first", "second", etc. in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0044] Example 1

[0045] Figure 1 This is a flow chart of a power system dispatching method provided in the first embodiment of the present invention. This embodiment is applicable to the dispatching of power systems under uncertain wind power output. The method can be executed by a power system dispatching device, which can be implemented in the form of hardware and / or software. The power system dispatching device can be configured in electronic devices such as mobile phones, computers, and tablets. Figure 1 As shown, the method includes:

[0046] S110. Determine the fuzzy set corresponding to the wind power output in the power system.

[0047] Wind power output can be the active power output of wind turbines in a power system. Due to the randomness and intermittent nature of wind power output from wind turbines, wind power output is uncertain. Therefore, in this embodiment, the uncertain wind power output can be modeled by constructing a fuzzy set corresponding to the wind power output.

[0048] Specifically, the uncertainty of wind power output can be characterized by a fuzzy set of moment information. The fuzzy set can be a set of probability distributions used to describe the uncertainty of wind power output. The formula for the fuzzy set of wind power output can be as follows:

[0049]

[0050] in, is the fuzzy set corresponding to wind power output, represents the probability distribution of wind power output, Represents the probability distribution Expectations of seeking; is the uncertain wind power output of the w-th wind turbine at time t, represents a collection of time periods, represents the set of wind turbines in the wind farm, μ w,t and σ w,t They represent the mean and standard deviation of the uncertain wind power output of the w-th wind turbine at time t.

[0051] In the formula for the fuzzy set corresponding to wind power output described above, the mean and standard deviation of wind power output can be calculated using historical wind power output data. By setting the mean and standard deviation of wind power output, a fuzzy set based on moment information can be established. In this embodiment, establishing a fuzzy set corresponding to wind power output eliminates the need to determine the precise probability distribution of wind power output, thus resolving the technical issue in existing technologies of difficulty in obtaining a precise probability distribution of uncertain variables.

[0052] S120. Construct a distributed robust joint chance-constrained dispatch model for the power system, wherein the distributed robust joint chance-constrained dispatch model includes an objective function and distributed robust joint chance constraints.

[0053] The distributed robust joint opportunity-constrained dispatch model can be used to calculate the optimal dispatch plan for the power system. Specifically, the optimal dispatch plan can be obtained by solving some variables in the distributed robust joint opportunity-constrained dispatch model based on the objective function and the distributed robust joint opportunity constraints in the model.

[0054] In this embodiment, an objective function and a distributed robust joint opportunity constraint condition can be established separately to realize the construction of a distributed robust joint opportunity constraint scheduling model. Among them, the objective function can be to minimize the operating cost of the power system, and the distributed robust joint opportunity constraint condition can be a constraint condition on the dispatchable output, standby output, and wind power output of each wind turbine in the power system.

[0055] In an optional embodiment, constructing the distributed robust joint chance constrained scheduling model of the power system includes: constructing the objective function in the distributed robust joint chance constrained scheduling model according to the principle of minimizing the operating cost of the power system; for each wind turbine in the power system, establishing the distributed robust joint chance constraint conditions in the distributed robust joint chance constrained scheduling model based on the dispatchable output, standby output and wind power output of each wind turbine.

[0056] Among them, the operating cost minimization principle can be the cost minimization principle of the power system's generator sets and standby units.

[0057] Specifically, the formula corresponding to the objective function may be as follows:

[0058]

[0059] in, represents a collection of generator sets, represents the set of wind turbines in the wind farm, represents the set of time periods, c i is the marginal generation cost of the i-th generator; is the backup cost of the i-th generator set; P i,t and R i,t are the power generation output and standby output of the i-th generator set at time t respectively; is the backup cost provided by the w-th wind turbine; is the reserve output provided by the w-th wind turbine at time t. The reserve output can be understood as the deviation capacity reserved by the generator set or wind turbine.

[0060] In the above objective function formula, the construction of the objective function can be achieved by minimizing the operating cost of the power system.

[0061] Furthermore, it is necessary to establish the distributed blue-robin joint opportunity constraint conditions in the distributed blue-robin joint opportunity constraint scheduling model based on the dispatchable output, reserve output, and wind power output of each wind turbine in the power system. Dispatched output can be the active power output of the wind turbine that can be dispatched by the power system, reserve output can be the active power output of the wind turbine that is reserved for the power system, and wind power output can be the active power actually output by the wind turbine.

[0062] Specifically, the distributed robust joint opportunity constraint condition can be constructed by limiting the relationship between the dispatchable output, standby output and wind power output of each wind turbine.

[0063] It should be noted that, in this embodiment, the purpose of constructing distributed and robust joint opportunity constraints based on the dispatchable output, standby output and wind power output of each wind turbine is to construct joint opportunity constraints by constraining all wind turbines in the wind power system. Compared with a single opportunity constraint, the combined opportunity constraint provides a stronger reliability guarantee for the distributed and robust joint opportunity constraint scheduling model.

[0064] Of course, the distributed blue-robust joint opportunity constraint scheduling model in this embodiment may also include other constraints. For example, optionally, the distributed blue-robust joint opportunity constraint scheduling model also includes generator output constraints, generator standby constraints, generator set ramp constraints, line transmission power constraints, system active power balance constraints, wind power standby output constraints, and dispatchable wind power output constraints.

[0065] The generator output constraint condition may be a condition for constraining the size of the power output of the generator set. For example, the generator output constraint condition may satisfy the following formula:

[0066]

[0067] in, and They represent the lower and upper output limits of the i-th generator set, P i,t is the power output of the i-th generator set at time t.

[0068] The generator standby constraint condition may be a condition for constraining the size of the standby output of the generator set. For example, the generator standby output constraint condition may satisfy the following formula:

[0069]

[0070] in, is the spare capacity of the i-th generator set, R i,tis the reserve output of the i-th generator set at time t.

[0071] The generator set climbing constraint condition may be a condition for constraining the output change value of the generator set. For example, the generator set climbing constraint condition may satisfy the following formula:

[0072] -RD i ≤P i,t +R i,t -P i,t-1 -R i,t-1 ≤RU i ;

[0073] Among them, RD i and RU i are the downward climbing limit and upward climbing limit of the i-th generator set respectively.

[0074] The line transmission power constraint condition may be a condition for constraining the transmission power of the transmission line. For example, the line transmission power constraint condition may satisfy the following formula:

[0075]

[0076] in, is the transmission capacity of the lth transmission line, ψ l,i is the transfer factor of the i-th generator set to the l-th transmission line; ψ l,w is the transfer factor of the w-th wind turbine to the l-th transmission line, is the load d e The transfer factor for the lth transmission line is, is the load d at time t e level.

[0077] The system active power balance constraint condition may be a condition for constraining the active power of the power system. For example, the system active power balance constraint condition may satisfy the following formula:

[0078]

[0079] in, is the dispatchable power of the w-th wind turbine at time t, i.e., the dispatchable output, A collection of loads.

[0080] The wind power reserve output constraint condition may be a condition for constraining the size of the reserve output of the wind turbine generator set. For example, the wind power reserve output constraint condition may satisfy the following formula:

[0081]

[0082] in, is the maximum reserve output of the w-th wind turbine at time t, is the upper limit of the standby output of the w-th wind turbine.

[0083] The dispatchable wind power output constraint condition may be a condition for constraining the size of the dispatchable output of the wind turbine. For example, the dispatchable wind power output constraint condition may satisfy the following formula:

[0084]

[0085] in, is the dispatchable output of the w-th wind turbine at time t.

[0086] S130. Determine a convex expression condition corresponding to the distributed robust joint chance constraint condition based on the fuzzy set.

[0087] Specifically, after constructing the objective function and the distributed robust joint opportunity constraint conditions in the distributed robust joint opportunity constraint scheduling model, the convex expression conditions corresponding to the distributed robust joint opportunity constraint conditions can be determined based on the constructed fuzzy sets.

[0088] It should be noted that the purpose of determining the convex expression conditions corresponding to the distributed robust joint chance constraints is: by converting the distributed robust joint chance constraints into approximate convex expression conditions, the distributed robust joint chance constraint scheduling model can be efficiently solved, thereby improving the scheduling efficiency.

[0089] Specifically, the expression formula of the fuzzy set can be substituted into the distributed robust joint chance constraint condition, and the distributed robust joint chance constraint condition can be transformed through the generalized Chebyshev inequality, and then the auxiliary variables can be introduced to transform the distributed robust joint chance constraint condition into a convex expression condition.

[0090] S140. Determine the optimal solution corresponding to the distributed robust joint chance-constrained scheduling model based on the convex expression condition and the objective function, and determine the optimal solution as the target scheduling scheme of the power system.

[0091] Specifically, after obtaining the convex expression corresponding to the distributed robust joint opportunity constraint, the optimal solution for the variables in the convex expression and the objective function can be calculated based on the convex expression and the objective function, and the optimal solution can be determined as the optimal dispatch plan for the power system, i.e., the target dispatch plan. The number of optimal solutions can be one or more. If there are multiple optimal solutions, the number of target dispatch plans for the power system can also be multiple.

[0092] For example, by convexly expressing conditions and objective functions, the optimal standby output of each generator set, the optimal power output of each generator set, the optimal number of generator sets, the optimal standby output of each wind turbine set, the optimal power output of each wind turbine set, the optimal number of wind turbine sets, etc. in the distributed robust joint chance-constrained scheduling model are calculated and used as the target scheduling plan for the power system.

[0093] The technical solution of this embodiment is to determine the fuzzy set corresponding to the wind power output and construct a distributed robust joint opportunity constraint scheduling model for the power system, and then determine the convex expression conditions corresponding to the distributed robust joint opportunity constraint conditions in the distributed robust joint opportunity constraint scheduling model based on the fuzzy set. Through the convex expression conditions and the objective function in the distributed robust joint opportunity constraint scheduling model, the optimal solution of the distributed robust joint opportunity constraint scheduling model is calculated to determine the target scheduling scheme of the power system, thereby realizing the optimal scheduling of the power system considering the uncertainty of wind power output. There is no need to determine the precise probability distribution of the uncertain variable of wind power output, and it can be applied to solve the power system scheduling problem in an uncertain environment. At the same time, the scheduling results show good performance in terms of economy and conservatism. Moreover, by deriving the convex approximate expression of the distributed robust joint opportunity constraint conditions, the distributed robust joint opportunity constraint scheduling model can be efficiently solved, thereby improving the scheduling efficiency of the power system.

[0094] Example 2

[0095] Figure 2 A flow chart of a power system dispatching method provided in the second embodiment of the present invention, based on the above embodiments, this embodiment optionally establishes the distributed blue joint opportunity constraint conditions in the distributed blue joint opportunity constraint scheduling model based on the dispatchable output, standby output and wind power output of each wind turbine, including: obtaining the preset constraint probability corresponding to the distributed blue joint opportunity constraint conditions; taking the probability that the sum of the dispatchable output and standby output of each wind turbine does not exceed the wind power output as not less than the preset constraint probability as the distributed blue joint opportunity constraint conditions. Figure 2 As shown, the method includes:

[0096] S210. Determine the fuzzy set corresponding to the wind power output in the power system.

[0097] S220. According to the principle of minimizing the operating cost of the power system, construct the objective function of the distributed robust joint opportunity constrained scheduling model.

[0098] S230. For each wind turbine in the power system, obtain a preset constraint probability corresponding to the distributed robust joint opportunity constraint condition, and use the probability that the sum of the dispatchable output and the standby output of each wind turbine does not exceed the wind power output as the distributed robust joint opportunity constraint condition, which is not less than the preset constraint probability.

[0099] The preset constraint probability can be obtained based on the allowed violation probability of the distributed robust joint opportunity constraint, such as, for example, preset constraint probability = (1-allowed violation probability). Specifically, the distributed robust joint opportunity constraint can be: for each wind turbine in the wind farm, the probability that the sum of the dispatchable output and the reserve output of each wind turbine does not exceed the wind power output is no less than the preset constraint probability. In other words, the wind power output of each wind turbine is as great as possible from the sum of the dispatchable output and the reserve output.

[0100] For example, the distributed robust joint opportunity constraint condition can be expressed by the formula:

[0101]

[0102] in, is the dispatchable output of the w-th wind turbine at time t, is the reserve output of the w-th wind turbine at time t, is the wind power output of the w-th wind turbine at time t, represents the set of wind turbines in the wind farm, represents the probability distribution, represents the fuzzy set, ε is the allowed violation probability of the distributed robust joint chance constraint, and 1-ε is the preset constraint probability.

[0103] It should be noted that in the above formula, It is limited that for each wind turbine in the wind farm, the dispatchable output of the wind turbine must be and standby output The sum does not exceed the wind power output The probability of is not less than 1-ε, which realizes the construction of joint chance constraints. Compared with the modeling of single distributed robust chance constraints, the modeling of joint chance constraints provides stronger reliability guarantee.

[0104] S240. Determine the convex expression condition corresponding to the distributed robust joint chance constraint condition based on the fuzzy set, determine the optimal solution corresponding to the distributed robust joint chance constraint scheduling model based on the convex expression condition and the objective function, and determine the optimal solution as the target scheduling plan of the power system.

[0105] The technical solution of this embodiment obtains the preset constraint probability corresponding to the distributed and robust joint opportunity constraint condition, and then uses the probability that the sum of the dispatchable output and the standby output of each wind turbine in the power system does not exceed the wind power output as a distributed and robust joint opportunity constraint condition, thereby realizing the establishment of the joint opportunity constraint, providing reliability guarantee for the construction of the distributed and robust joint opportunity constraint scheduling model, and thus improving the reliability of the optimal scheduling plan calculated by the distributed and robust joint opportunity constraint scheduling model.

[0106] Example 3

[0107] Figure 3 A flowchart of a power system dispatching method provided in the third embodiment of the present invention, based on the above embodiments, this embodiment optionally determines the convex expression corresponding to the distributed robust joint chance constraint based on the fuzzy set, including: converting the distributed robust joint chance constraint into a joint constraint group based on the fuzzy set, wherein the joint constraint group includes the joint constraint to be converted, the first constraint corresponding to the allowed violation probability, and the second constraint corresponding to the allowed violation probability; converting the joint constraint to be converted into a deterministic constraint, and converting the deterministic constraint into a wind power output constraint, the first standard second-order cone constraint corresponding to the allowed violation probability, and the second standard second-order cone constraint corresponding to the allowed violation probability through preset auxiliary variables. As Figure 3 As shown, the method includes:

[0108] S310. Determine the fuzzy set corresponding to the wind power output in the power system, and construct a distributed robust joint opportunity constraint scheduling model for the power system, wherein the distributed robust joint opportunity constraint scheduling model includes an objective function and distributed robust joint opportunity constraint conditions.

[0109] S320. Convert the distributed robust joint chance constraint condition into a joint constraint condition group based on the fuzzy set, wherein the joint constraint condition group includes the joint constraint condition to be converted, the first constraint condition corresponding to the allowed violation probability, and the second constraint condition corresponding to the allowed violation probability.

[0110] Specifically, the expression of the fuzzy set can be substituted into the distributed robust joint chance constraint condition to obtain the Bonferroni approximation of the distributed robust joint chance constraint condition to obtain the joint constraint condition group.

[0111] For example, the expression formula of the joint constraint condition group is as follows:

[0112] Joint constraints to be converted:

[0113] The first constraint corresponding to the probability of violation is allowed:

[0114] The second constraint corresponding to the probability of violation is allowed: ε w,t ≥0;

[0115] in, for The probability distribution of ε wt is the allowed violation probability of a single chance constraint condition. for Fuzzy set, specific, The formula can be as follows:

[0116]

[0117] S330. Convert the joint constraint to be converted into a deterministic constraint, and convert the deterministic constraint into a wind power output constraint, a first standard second-order cone constraint corresponding to the allowed violation probability, and a second standard second-order cone constraint corresponding to the allowed violation probability through preset auxiliary variables.

[0118] Specifically, after converting the distributed robust joint opportunity constraints into a joint constraint group, the joint constraints to be converted in the joint constraint group can be further converted to convert the joint constraints to be converted into a convex approximate expression, thereby obtaining convex approximate wind power output constraints, first standard second-order cone constraints, and second standard second-order cone constraints.

[0119] Optionally, the joint constraint to be converted can be converted into a deterministic constraint based on the generalized Chebyshev inequality. The deterministic constraint can be expressed as follows:

[0120]

[0121] Due to the distribution of the single chance constraint condition, the allowed violation probability ε w,t For optimizing variables, the above deterministic constraint is a strong non-convex constraint. Furthermore, preset auxiliary variables can be introduced to further transform the deterministic constraint.

[0122] For example, introducing the preset auxiliary variable r w,t , the above deterministic constraints can be transformed into wind power output constraints, and the preset auxiliary variables r w,t The corresponding conditions to be converted are shown in the following formula:

[0123] Wind power output constraints:

[0124] Preset auxiliary variable r w,t Corresponding conditions to be converted:

[0125] Among them, for 0≤ε w,t ≤ε, there is Therefore, the auxiliary variable r is preset w,t The corresponding condition to be converted is a non-convex constraint. In this embodiment, the preset auxiliary variable r w,t The corresponding conditions to be converted are approximately converted to obtain the following formula:

[0126]

[0127] Furthermore, by squaring both sides of the formula, we can obtain:

[0128]

[0129] At this point, we can introduce the preset auxiliary variable s w,t , can be further Translates to:

[0130]

[0131] 1≤s w,t r w,t ;

[0132] Furthermore, the above two formulas can be converted into standard second-order cone constraints, that is, the first standard second-order cone constraint corresponding to the allowed violation probability and the second standard second-order cone constraint corresponding to the allowed violation probability, as shown in the following formula:

[0133] The first standard second-order cone constraints corresponding to the probability of violation are allowed:

[0134] The second standard second-order cone constraint corresponding to the probability of violation is allowed:

[0135] So far, the distributed robust joint chance constraints have been transformed into a set of convex approximations. The distributed robust joint chance constraints can be expressed as the following approximations of convex constraints, namely:

[0136] The first constraint corresponding to the probability of violation is allowed:

[0137] The second constraint corresponding to the probability of violation is allowed: ε w,t ≥0;

[0138] Wind power output constraints:

[0139] The first standard second-order cone constraint:

[0140] The second standard second-order cone constraint:

[0141] Through the above five convex expression conditions, the distributed robust joint chance-constrained scheduling model of the power system is transformed into a second-order cone programming problem, which can be efficiently solved by existing optimization software (such as GUROBI, CPLEX).

[0142] S340. Determine the optimal solution corresponding to the distributed robust joint chance-constrained scheduling model based on the convex expression condition and the objective function, and determine the optimal solution as the target scheduling scheme of the power system.

[0143] The technical solution of this embodiment converts the distributed robust joint chance constraint conditions into joint constraints to be converted, first constraints corresponding to the allowed violation probability, and second constraints corresponding to the allowed violation probability through fuzzy sets, and then converts the joint constraints to be converted into deterministic constraints, and further converts the deterministic constraints into wind power output constraints, first standard second-order cone constraints, and second standard second-order cone constraints through preset auxiliary variables, thereby realizing the convex constraint expression of the distributed robust joint chance constraint conditions, and then realizing the efficient solution of the distributed robust joint chance constraint scheduling model, thereby improving the scheduling efficiency of the power system.

[0144] Example 4

[0145] Figure 4 This is a schematic diagram of the structure of a power system dispatching device provided by the fourth embodiment of the present invention. Figure 4 As shown, the apparatus includes: a fuzzy set determination module 410 , a scheduling model construction module 420 , a convex expression derivation module 430 and a scheduling model solving module 440 .

[0146] A fuzzy set determination module 410 is used to determine the fuzzy set corresponding to the wind power output in the power system;

[0147] A dispatch model construction module 420 is used to construct a distributed robust joint opportunity constraint dispatch model for the power system, wherein the distributed robust joint opportunity constraint dispatch model includes an objective function and distributed robust joint opportunity constraint conditions;

[0148] A convex expression derivation module 430 is configured to determine a convex expression condition corresponding to the distributed robust joint chance constraint condition based on the fuzzy set;

[0149] The scheduling model solving module 440 is used to determine the optimal solution corresponding to the distributed robust joint chance constraint scheduling model based on the convex expression condition and the objective function, and determine the optimal solution as the target scheduling scheme of the power system.

[0150] In this embodiment, by determining the fuzzy set corresponding to the wind power output and constructing a distributed robust joint opportunity constraint scheduling model for the power system, and then determining the convex expression conditions corresponding to the distributed robust joint opportunity constraint conditions in the distributed robust joint opportunity constraint scheduling model based on the fuzzy set, the optimal solution of the distributed robust joint opportunity constraint scheduling model is calculated through the convex expression conditions and the objective function in the distributed robust joint opportunity constraint scheduling model to determine the target scheduling scheme of the power system, thereby realizing the optimal scheduling of the power system considering the uncertainty of wind power output. There is no need to determine the precise probability distribution of the uncertain variable of wind power output, and it can be applied to solve the power system scheduling problem under uncertain environment. At the same time, the scheduling results show good performance in terms of economy and conservatism. Moreover, by deriving the convex approximate expression of the distributed robust joint opportunity constraint conditions, the distributed robust joint opportunity constraint scheduling model can be efficiently solved, thereby improving the scheduling efficiency of the power system.

[0151] Optionally, the scheduling model construction module 420 includes an objective function construction unit and a constraint condition construction unit; wherein the objective function construction unit is used to construct the objective function in the distributed robust joint opportunity constraint scheduling model according to the principle of minimizing the operating cost of the power system;

[0152] The constraint condition construction unit is used to establish the distributed robust joint opportunity constraint conditions in the distributed robust joint opportunity constraint scheduling model for each wind turbine in the power system based on the dispatchable output, standby output and wind power output of each wind turbine.

[0153] Optionally, the constraint condition construction unit is specifically used to:

[0154] Obtain a preset constraint probability corresponding to the distributed robust joint opportunity constraint condition; and use the probability that the sum of the dispatchable output and the standby output of each wind turbine does not exceed the wind power output as the distributed robust joint opportunity constraint condition, which is not less than the preset constraint probability.

[0155] Optionally, the formula corresponding to the distributed robust joint opportunity constraint condition is as follows:

[0156]

[0157] in, is the dispatchable output of the w-th wind turbine at time t, is the reserve output of the w-th wind turbine at time t, is the wind power output of the w-th wind turbine at time t, represents the set of wind turbines in the wind farm, represents the probability distribution, represents the fuzzy set, ε is the allowed violation probability of the distributed robust joint chance constraint, and 1-ε is the preset constraint probability.

[0158] Optionally, the distributed blue-rod joint opportunity constraint scheduling model also includes generator output constraints, generator standby constraints, generator set climbing constraints, line transmission power constraints, system active power balance constraints, wind power standby output constraints and dispatchable wind power output constraints.

[0159] Optionally, the formula corresponding to the objective function is as follows:

[0160]

[0161] in, represents a collection of generator sets, represents the set of wind turbines in the wind farm, represents the set of time periods, c i is the marginal generation cost of the i-th generator; is the backup cost of the i-th generator set; P i,t and R i,t are the power generation output and standby output of the i-th generator set at time t respectively; is the backup cost provided by the w-th wind turbine; is the reserve output provided by the w-th wind turbine at time t.

[0162] Optionally, the convex expression derivation module 430 is specifically configured to:

[0163] Based on the fuzzy set, the distributed robust joint chance constraint condition is converted into a joint constraint condition group, wherein the joint constraint condition group includes the joint constraint condition to be converted, the first constraint condition corresponding to the allowed violation probability, and the second constraint condition corresponding to the allowed violation probability; the joint constraint condition to be converted is converted into a deterministic constraint condition, and the deterministic constraint condition is converted into a wind power output constraint condition, the first standard second-order cone constraint condition corresponding to the allowed violation probability, and the second standard second-order cone constraint condition corresponding to the allowed violation probability through preset auxiliary variables.

[0164] The power system dispatching device provided in the embodiment of the present invention can execute the power system dispatching method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0165] Example 5

[0166] Figure 5 This is a structural diagram of an electronic device provided in Embodiment 5 of the present invention. Figure 5 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0167] like Figure 5 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11. The memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 into the random access memory (RAM) 13. Various programs and data required for the operation of the electronic device 10 can also be stored in the RAM 13. The processor 11, ROM 12, and RAM 13 are connected to each other via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0168] Multiple components in the electronic device 10 are connected to the I / O interface 15, including an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0169] The processor 11 may be any general-purpose and / or specialized processing component with processing and computing capabilities. Examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors for running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as the power system dispatch method.

[0170] In some embodiments, the power system dispatch method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the power system dispatch method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the power system dispatch method in any other appropriate manner (for example, by means of firmware).

[0171] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0172] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0173] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0174] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0175] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0176] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0177] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0178] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A power system dispatching method, characterized in that: include: Determine a fuzzy set corresponding to wind power output in the power system; wherein the fuzzy set is a set used to describe the probability distribution of uncertain wind power output, and the fuzzy set is established based on moment information through the mean and standard deviation of the wind power output; Constructing a distributed robust joint opportunity constrained dispatch model for the power system, wherein the distributed robust joint opportunity constrained dispatch model includes an objective function and distributed robust joint opportunity constraints; Determining a convex expression condition corresponding to the distributed robust joint chance constraint condition based on the fuzzy set; Determining an optimal solution corresponding to the distributed robust joint chance-constrained scheduling model based on the convex expression condition and the objective function, and determining the optimal solution as a target scheduling scheme for the power system; Wherein, the determining of the convex expression condition corresponding to the distributed robust joint chance constraint condition based on the fuzzy set includes: Converting the distributed robust joint chance constraint condition into a joint constraint condition group based on the fuzzy set, wherein the joint constraint condition group includes the joint constraint condition to be converted, a first constraint condition corresponding to an allowed violation probability, and a second constraint condition corresponding to the allowed violation probability; Converting the to-be-converted joint constraint into a deterministic constraint, and converting the deterministic constraint into a wind power output constraint, a first standard second-order cone constraint corresponding to the allowed violation probability, and a second standard second-order cone constraint corresponding to the allowed violation probability by presetting auxiliary variables; The converting of the to-be-converted joint constraint into a deterministic constraint, and converting the deterministic constraint into a wind power output constraint, a first standard second-order cone constraint corresponding to the allowable violation probability, and a second standard second-order cone constraint corresponding to the allowable violation probability by presetting auxiliary variables, includes: Based on the generalized Chebyshev inequality, converting the joint constraint to be converted into the deterministic constraint; By presetting auxiliary variables, the deterministic constraint conditions are converted into the wind power output constraint conditions and the conditions to be converted corresponding to the pre-set auxiliary variables; Approximately transforming the to-be-transformed condition corresponding to the preset auxiliary variable, and transforming the transformed formula again into a standard second-order cone constraint, namely, a first standard second-order cone constraint condition corresponding to the allowed violation probability, and a second standard second-order cone constraint condition corresponding to the allowed violation probability; The step of constructing the distributed robust joint opportunity-constrained dispatch model for the power system includes: According to the principle of minimizing the operating cost of the power system, constructing the objective function of the distributed robust joint opportunity constrained scheduling model; For each wind turbine in the power system, based on the dispatchable output, standby output and wind power output of each wind turbine, a distributed robust joint opportunity constraint condition in the distributed robust joint opportunity constraint scheduling model is established; The formula corresponding to the objective function is as follows: Where G represents the set of generators, W represents the set of wind turbines in the wind farm, T represents the set of time periods, and c i is the marginal generation cost of the i-th generator; is the backup cost of the i-th generator set; P i,t and R i,t are the power generation output and standby output of the i-th generator set at time t respectively; is the backup cost provided by the w-th wind turbine; is the reserve output provided by the w-th wind turbine at time t.

2. The method according to claim 1, characterized in that The step of establishing the distributed robust joint opportunity constraint conditions in the distributed robust joint opportunity constraint scheduling model based on the dispatchable output, standby output, and wind power output of each wind turbine generator set includes: Obtain the preset constraint probability corresponding to the distributed robust joint chance constraint condition; The probability that the sum of the dispatchable output and the reserve output of each wind turbine generator set does not exceed the wind power output is not less than the preset constraint probability, and is used as the distributed robust joint opportunity constraint condition.

3. The method according to claim 2, characterized in that The formula corresponding to the distributed robust joint opportunity constraint is as follows: in, is the dispatchable output of the w-th wind turbine at time t, is the reserve output of the w-th wind turbine at time t, is the wind power output of the w-th wind turbine at time t, W represents the set of wind turbines in the wind farm, P represents the probability distribution, D represents the fuzzy set, ε is the allowed violation probability of the distributed robust joint chance constraint, and 1-ε is the preset constraint probability.

4. The method according to claim 1, wherein The distributed blue-rod joint opportunity constraint scheduling model also includes generator output constraints, generator standby constraints, generator set ramp constraints, line transmission power constraints, system active power balance constraints, wind power standby output constraints and dispatchable wind power output constraints.

5. A power system dispatching device, characterized in that: The device comprises: A fuzzy set determination module is used to determine a fuzzy set corresponding to the wind power output in the power system; wherein the fuzzy set is a set used to describe the probability distribution of the uncertain wind power output, and the fuzzy set is established based on the moment information through the mean and standard deviation of the wind power output; A dispatch model construction module, configured to construct a distributed robust joint opportunity constrained dispatch model for the power system, wherein the distributed robust joint opportunity constrained dispatch model includes an objective function and distributed robust joint opportunity constraints; A convex expression derivation module, configured to determine a convex expression condition corresponding to the distributed robust joint chance constraint condition based on the fuzzy set; a scheduling model solving module, configured to determine an optimal solution corresponding to the distributed robust joint chance-constrained scheduling model based on the convex expression condition and the objective function, and determine the optimal solution as a target scheduling scheme for the power system; wherein the convex expression derivation module is specifically used to convert the distributed robust joint chance constraint condition into a joint constraint condition group based on the fuzzy set, wherein the joint constraint condition group includes the joint constraint condition to be converted, the first constraint condition corresponding to the allowed violation probability, and the second constraint condition corresponding to the allowed violation probability; convert the joint constraint condition to be converted into a deterministic constraint condition, and convert the deterministic constraint condition into a wind power output constraint condition, the first standard second-order cone constraint condition corresponding to the allowed violation probability, and the second standard second-order cone constraint condition corresponding to the allowed violation probability through preset auxiliary variables; The convex expression derivation module is specifically configured to convert the joint constraint to be converted into the deterministic constraint based on the generalized Chebyshev inequality; convert the deterministic constraint into the wind power output constraint and the condition to be converted corresponding to the preset auxiliary variable by presetting the auxiliary variable; perform an approximate conversion on the condition to be converted corresponding to the preset auxiliary variable, and convert the converted formula again into a standard second-order cone constraint, namely, a first standard second-order cone constraint corresponding to the allowed violation probability, and a second standard second-order cone constraint corresponding to the allowed violation probability; Wherein, the scheduling model construction module includes an objective function construction unit and a constraint condition construction unit; The objective function construction unit is used to construct the objective function in the distributed robust joint chance-constrained scheduling model according to the principle of minimizing the operating cost of the power system; The constraint condition construction unit is used to establish, for each wind turbine in the power system, a distributed robust joint opportunity constraint condition in the distributed robust joint opportunity constraint scheduling model based on the dispatchable output, standby output and wind power output of each wind turbine; The formula corresponding to the objective function is as follows: Where G represents the set of generators, W represents the set of wind turbines in the wind farm, T represents the set of time periods, and c i is the marginal generation cost of the i-th generator; is the backup cost of the i-th generator set; P i,t and R i,t are the power generation output and standby output of the i-th generator set at time t respectively; is the backup cost provided by the w-th wind turbine; is the reserve output provided by the w-th wind turbine at time t.

6. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the power system dispatching method according to any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the power system dispatching method according to any one of claims 1 to 4 when executed.

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