Power system low-carbon scheduling method and device, computer equipment, readable storage medium and program product

Abstract

The invention relates to a power system low-carbon scheduling method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: constructing a source load uncertainty set of wind power and loads in a power system; constructing a system operation constraint based on robust optimization according to the source load uncertainty set, an adjustment strategy of each adjustment resource on source load fluctuation, a power balance constraint, a wind power operation constraint, an adjustment resource operation constraint and a line transmission constraint; constructing a reference cost function according to the reference cost parameter, constructing an adjustment expected cost based on random optimization according to the source load uncertainty set, and obtaining a scheduling cost function according to the reference cost function and the adjustment expected cost; constructing a scheduling model according to the system operation constraint and the scheduling cost function; and carrying out optimization solution on the scheduling model by taking the minimum scheduling cost function as an optimization target to obtain a scheduling strategy parameter, and scheduling the power system. By adopting the method, safe and economical operation of the power system can be effectively guaranteed.

Description

Technical Field

[0001] The present application relates to the technical field of electric power, and in particular, to a low-carbon scheduling method, device, computer device, computer-readable storage medium, and computer program product for an electric power system. Background Art

[0002] With the development of electric power technology, wind turbines have gradually become an important part of the electric power system. The large-scale integration of wind power is of great significance for promoting energy conservation and emission reduction in the electric power system. However, the output of wind power is easily affected by natural environmental changes, showing obvious volatility and uncertainty. At the same time, the load demand in the electric power system also has volatility. Under the combined action of wind power and load fluctuations, the stable operation of the electric power system faces severe challenges.

[0003] Among them, the electric power system usually adopts a scheduling strategy to cope with the uncertainty brought by wind power and load fluctuations and ensure the stable operation of the system. However, the electric power scheduling methods in related technologies usually have difficulty in taking into account the safety and economy of system operation while coping with wind power and load fluctuations. Summary of the Invention

[0004] Based on this, it is necessary to provide a low-carbon scheduling method, device, computer device, computer-readable storage medium, and computer program product for an electric power system to solve the above technical problems.

[0005] In a first aspect, the present application provides a low-carbon scheduling method for an electric power system, including:

[0006] Constructing a source-load uncertainty set of wind power and load in the electric power system;

[0007] According to the source-load uncertainty set, the regulation strategies of each regulation resource in the electric power system for the source-load fluctuations in the electric power system, and the power balance constraint, wind power operation constraint, regulation resource operation constraint, and line transmission constraint of the electric power system, constructing a system operation constraint based on robust optimization;

[0008] Constructing a benchmark cost function according to the benchmark cost parameters of the electric power system, constructing a regulation expected cost based on stochastic optimization according to the source-load uncertainty set, and obtaining a scheduling cost function of the electric power system according to the benchmark cost function and the regulation expected cost;

[0009] Constructing a scheduling model of the electric power system according to the system operation constraint and the scheduling cost function;

[0010] Taking the minimum of the scheduling cost function as the optimization objective, optimizing and solving the scheduling model to obtain the scheduling strategy parameters of the electric power system, and scheduling the electric power system according to the scheduling strategy parameters.

[0011] In one embodiment, the benchmark cost function includes a curtailment cost function corresponding to a curtailment strategy; constructing the source-load uncertainty set of wind power and load in the power system includes: constructing the curtailment strategy of the power system according to the wind power prediction error parameter and the upper bound of allowable wind power fluctuation of the power system; constructing the wind power output uncertainty set of the power system according to the wind power prediction error parameter and the curtailment strategy; constructing the load uncertainty set of the power system according to the load prediction error parameter of the power system; and obtaining the source-load uncertainty set according to the wind power output uncertainty set and the load uncertainty set.

[0012] In one embodiment, constructing the benchmark cost function of the power system according to the benchmark cost parameter of the power system includes: constructing the power generation cost function of the power system according to the quadratic power generation cost function of each thermal power unit in the regulation resources; constructing the curtailment cost function of the power system according to the curtailment penalty parameter of the power system; constructing the carbon trading cost function of the power system according to the carbon emission parameter of each thermal power unit; and obtaining the benchmark cost function according to the power generation cost function, the curtailment cost function and the carbon trading cost function.

[0013] In one embodiment, constructing the regulation expected cost based on stochastic optimization according to the source-load uncertainty set includes: constructing the regulation cost function of the power system according to the regulation cost coefficient, the benchmark output parameter and the curtailment penalty parameter of each regulation resource; constructing the regulation output constraint of each regulation resource according to the source-load uncertainty set; and constructing the regulation expected cost by using the multi-point estimation method according to the source-load uncertainty set, the regulation cost function and the regulation output constraint.

[0014] In one embodiment, taking the minimization of the scheduling cost function as the optimization objective and optimizing and solving the scheduling model to obtain the scheduling strategy parameters of the power system includes: performing convexification processing on the non-convex constraints included in the scheduling model to obtain the convex difference function corresponding to the non-convex constraints; obtaining the objective function of the scheduling model according to the scheduling cost function and the convex difference function; and taking the minimization of the objective function as the optimization objective and optimizing and solving the scheduling model to obtain the scheduling strategy parameters of the power system.

[0015] In one embodiment, the objective function includes a penalty function; taking minimizing the objective function as the optimization objective, optimizing and solving the scheduling model to obtain the scheduling policy parameters of the power system, including: using the initial scheduling policy parameters as the current scheduling policy parameters, and using the initial penalty coefficient as the current penalty coefficient of the penalty function; according to the current scheduling policy parameters and the current penalty coefficient, optimizing and solving the objective function to obtain optimized scheduling policy parameters and slack variables; according to the current scheduling policy parameters, the optimized scheduling policy parameters, and the slack variables, checking whether the scheduling cost and the slack variables meet the convergence criterion; if convergent, obtaining the scheduling policy parameters of the power system according to the optimized scheduling policy parameters; if not convergent, using the optimized scheduling policy parameters as the current scheduling policy parameters, and updating the current penalty coefficient, and performing the step of optimizing and solving the objective function according to the current scheduling policy parameters and the current penalty coefficient to obtain optimized scheduling policy parameters.

[0016] In a second aspect, the present application also provides a low-carbon scheduling device for a power system, including:

[0017] An uncertainty set construction module, configured to construct a source-load uncertainty set of wind power and load in the power system;

[0018] An operation constraint construction module, configured to construct system operation constraints based on robust optimization according to the source-load uncertainty set, the adjustment strategies of each adjustment resource in the power system for the source-load fluctuations in the power system, and the power balance constraints, wind power operation constraints, adjustment resource operation constraints, and line transmission constraints of the power system;

[0019] A cost function construction module, configured to construct a benchmark cost function according to the benchmark cost parameters of the power system, construct an adjustment expected cost based on stochastic optimization according to the source-load uncertainty set, and obtain the scheduling cost function of the power system according to the benchmark cost function and the adjustment expected cost;

[0020] A model construction module, configured to construct a scheduling model of the power system according to the system operation constraints and the scheduling cost function;

[0021] A model solving module, configured to take minimizing the scheduling cost function as the optimization objective, optimize and solve the scheduling model to obtain the scheduling policy parameters of the power system, and schedule the power system according to the scheduling policy parameters.

[0022] In a third aspect, the present application also provides a computer device, including a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0023] Construct the source-load uncertainty sets of wind power and load in the power system;

[0024] According to the source-load uncertainty sets, the adjustment strategies of each adjustment resource in the power system for the source-load fluctuations in the power system, and the power balance constraints, wind power operation constraints, adjustment resource operation constraints, and line transmission constraints of the power system, construct the system operation constraints based on robust optimization;

[0025] Construct the benchmark cost function according to the benchmark cost parameters of the power system, construct the adjustment expected cost based on stochastic optimization according to the source-load uncertainty sets, and obtain the scheduling cost function of the power system according to the benchmark cost function and the adjustment expected cost;

[0026] Construct the scheduling model of the power system according to the system operation constraints and the scheduling cost function;

[0027] Taking the minimization of the scheduling cost function as the optimization objective, optimize and solve the scheduling model to obtain the scheduling strategy parameters of the power system, and schedule the power system according to the scheduling strategy parameters.

[0028] In a fourth aspect, the present application also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0029] Construct the source-load uncertainty sets of wind power and load in the power system;

[0030] According to the source-load uncertainty sets, the adjustment strategies of each adjustment resource in the power system for the source-load fluctuations in the power system, and the power balance constraints, wind power operation constraints, adjustment resource operation constraints, and line transmission constraints of the power system, construct the system operation constraints based on robust optimization;

[0031] Construct the benchmark cost function according to the benchmark cost parameters of the power system, construct the adjustment expected cost based on stochastic optimization according to the source-load uncertainty sets, and obtain the scheduling cost function of the power system according to the benchmark cost function and the adjustment expected cost;

[0032] Construct the scheduling model of the power system according to the system operation constraints and the scheduling cost function;

[0033] Taking the minimization of the scheduling cost function as the optimization objective, optimize and solve the scheduling model to obtain the scheduling strategy parameters of the power system, and schedule the power system according to the scheduling strategy parameters.

[0034] In a fifth aspect, the present application further provides a computer program product, including a computer program, which when executed by a processor implements the following steps:

[0035] Construct an uncertainty set of the power sources and loads of wind power and loads in the power system;

[0036] According to the source-load uncertainty set, the adjustment strategies of each adjustment resource in the power system for the source-load fluctuations in the power system, and the power balance constraints, wind power operation constraints, adjustment resource operation constraints, and line transmission constraints of the power system, construct system operation constraints based on robust optimization;

[0037] Construct a benchmark cost function according to the benchmark cost parameters of the power system, construct an adjustment expected cost based on stochastic optimization according to the source-load uncertainty set, and obtain the scheduling cost function of the power system according to the benchmark cost function and the adjustment expected cost;

[0038] Construct a scheduling model of the power system according to the system operation constraints and the scheduling cost function;

[0039] Taking the minimization of the scheduling cost function as the optimization objective, optimize and solve the scheduling model to obtain the scheduling strategy parameters of the power system, and schedule the power system according to the scheduling strategy parameters.

[0040] The above-mentioned low-carbon dispatching method, device, computer equipment, computer-readable storage medium and computer program product for a power system first construct an uncertainty set of the power source and load in the power system, and then, according to the uncertainty set of the power source and load, the regulation strategies of various regulation resources in the power system for the fluctuations of the power source and load, as well as the power balance constraint, the wind power operation constraint, the regulation resource operation constraint and the line transmission constraint of the power system, construct a system operation constraint based on robust optimization. At the same time, construct a benchmark cost function according to the benchmark cost parameters of the power system, construct a regulation expected cost based on stochastic optimization according to the uncertainty set of the power source and load, obtain the dispatching cost function of the power system according to the benchmark cost function and the regulation expected cost, and then construct a dispatching model of the power system according to the system operation constraint and the dispatching cost function. Finally, taking the minimization of the dispatching cost function as the optimization objective, optimize and solve the dispatching model to obtain the dispatching strategy parameters of the power system, and dispatch the power system according to the dispatching strategy parameters. In this solution, the uncertainty range of the fluctuations of wind power and load in the power system is accurately described by constructing an uncertainty set of the power source and load; by combining the uncertainty set of the power source and load to construct a system operation constraint based on robust optimization, it can be ensured that the system operation constraint can be strictly satisfied under the condition of power source and load fluctuations; at the same time, by constructing a regulation expected cost based on stochastic optimization according to the uncertainty set of the power source and load, and obtaining the dispatching cost function according to the benchmark cost function and the regulation expected cost, it is beneficial to ensure the economy of power dispatching in the subsequent optimization and solution; thus, by constructing a dispatching model of the power system according to the system operation constraint and the dispatching cost function, and optimizing and solving the dispatching model with the minimization of the dispatching cost function as the objective, dispatching strategy parameters that take into account both safety and economy can be obtained, and then dispatching the power system according to the dispatching strategy parameters is beneficial to ensuring the economic, safe and low-carbon operation of the power system under the combined uncertainty conditions of wind power and load. Description of the Drawings

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0042] Figure 1 It is a schematic flow chart of the low-carbon dispatching method for a power system in an embodiment;

[0043] [[ID=1(2]] Figure 2 It is a schematic flow chart of constructing an uncertainty set of the power source and load in an embodiment;

[0044] Figure 3 It is a schematic flow chart of constructing a regulation expected cost based on stochastic optimization in an embodiment;

[0045] Figure 4 It is a schematic flowchart of obtaining the scheduling strategy parameters of the power system in one embodiment;

[0046] Figure 5 It is a schematic flowchart of obtaining the scheduling strategy parameters with minimizing the objective function as the optimization goal in one embodiment;

[0047] Figure 6 It is a schematic flowchart of the low-carbon scheduling method of the power system in another embodiment;

[0048] Figure 7 It is a schematic flowchart of optimizing and solving the scheduling model in one embodiment;

[0049] Figure 8 It is a structural block diagram of the low-carbon scheduling device of the power system in one embodiment;

[0050] Figure 9 It is the internal structure diagram of a computer device in one embodiment. Detailed implementation manners

[0051] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0052] In one embodiment, as Figure 1 shown, a low-carbon scheduling method of the power system is provided. In this embodiment, the example of this method applied to a server is given. It can be understood that this method can also be applied to a terminal, and can also be applied to a system including a terminal and a server, and is realized through the interaction between the terminal and the server. In this embodiment, the method includes the following steps:

[0053] Step S101, constructing an uncertain set of power sources and loads of wind power and load in the power system.

[0054] Specifically, the available output of each wind farm and the power load of each load node in the power system can be expressed as the sum of the predicted value and the prediction error:

[0055]

[0056] In the formula, is the predicted value of the available output of wind farm m, is the prediction error of the available output of wind farm m, is the actual available output of wind farm m; is the load prediction value of load node n, is the load prediction error of load node n, is the actual load of load node n, and t is the scheduling period index.

[0057] Based on this, in this step, an uncertain set of wind power output and an uncertain set of load can be constructed respectively for the uncertainty of the wind power output prediction error and the load prediction error of the load node. and the load uncertainty set , and then by combining the two, the source-load uncertainty set of the power system can be obtained . Exemplarily, the source-load uncertainty set can be the union of the wind power output uncertainty set and the load uncertainty set , that is: .

[0058] Among them, the wind power output uncertainty set can be constructed according to the wind power prediction error parameters of the power system, and it can be used to describe the uncertainty of the wind power output prediction error of the wind farm. Exemplarily, the wind power prediction error parameters can include the available output prediction error range of each wind farm in the power system, the total available output prediction error range of all wind farms, etc.

[0059] Among them, the load uncertainty set can be constructed according to the load prediction error parameters of the power system, and it can be used to describe the uncertainty of the load prediction error of the load node. Exemplarily, the load prediction error parameters can include the load prediction error range of each load node in the power system, the total load prediction error range of all load nodes, etc.

[0060] Step S102, according to the source-load uncertainty set, the regulation strategies of each regulation resource in the power system for the source-load fluctuations in the power system, and the power balance constraint, wind power operation constraint, regulation resource operation constraint and line transmission constraint of the power system, construct the system operation constraint based on robust optimization.

[0061] Specifically, in addition to wind turbines and load nodes, the power system can also include regulation resources and transmission lines. Among them, the regulation resources can include thermal power units and energy storage. When the wind power and load in the power system deviate from the reference value, the regulation resources can jointly regulate to restore the system power balance.

[0062] Among them, in order to ensure the safe operation of the power system under scheduling, in this step, the system operation constraint based on robust optimization can be constructed by combining the source-load uncertainty set, the regulation strategies of each regulation resource for the source-load fluctuations in the power system, the power balance constraint of the power system, the wind power operation constraint of the wind farm, the regulation resource operation constraint of the regulation resources and the line transmission constraint of the transmission lines.

[0063] Among them, the regulation strategy of the regulation resources for the source-load fluctuations in the power system can be a piecewise affine scheduling strategy for different source-load fluctuation situations. Exemplarily, this strategy can be expressed as:

[0064]

[0065]

[0066]

[0067] In the formula, is the actual output of thermal power unit i, is the reference output of thermal power unit i, and are the downward regulation coefficient and upward regulation coefficient of thermal power unit i respectively, is the actual charge and discharge power of energy storage j, is the reference charge and discharge power of energy storage j, and are the downward and upward regulation coefficients of energy storage j respectively, is the actual total output fluctuation of all wind farms in the power system, is the total load prediction error of all load nodes in the power system.

[0068] Among them, the power balance constraint of the power system can be expressed as:

[0069]

[0070] In the formula, is the reference output of wind farm m, is the reference output of thermal power unit i, is the reference charge and discharge power of energy storage j, is the load prediction value of load node n.

[0071] Among them, the wind power operation constraints of each wind farm in the power system can be expressed as:

[0072]

[0073] In the formula, is the reference output of wind farm m, is the predicted available output value of wind farm m, is the lower bound of the predicted available output error of wind farm m.

[0074] Among them, the operation constraints of the regulation resources of the power system can include the thermal power operation constraints of each thermal power unit and the energy storage constraints of each energy storage.

[0075] Among them, the thermal power operation constraints of each thermal power unit can be expressed as:

[0076]

[0077]

[0078]

[0079]

[0080] wherein, is the reference output of thermal power unit i, and are the downward reserve output and upward reserve output provided by thermal power unit i for source-load fluctuations, and are the lower bound and upper bound of the output of thermal power unit i, and are the downward ramp rate and upward ramp rate of thermal power unit i, and are the downward regulation coefficient and upward regulation coefficient of thermal power unit i respectively, is the upper bound of allowable wind power fluctuations of all wind farms in the power system, is the lower bound of the total available output prediction error of all wind farms in the power system, and are the lower bound and upper bound of the total load prediction error of all load nodes in the power system. It can be understood that when the power dispatching considers the wind curtailment measure, the upper bound of allowable wind power fluctuations is less than the upper bound of the total available output prediction error (that is, if the total available output prediction error exceeds , the excess wind power will be discarded), and when the power dispatching does not consider the wind curtailment measure, the upper bound of allowable wind power fluctuations can be equal to the upper bound of the total available output prediction error .

[0081] Among them, the energy storage constraints of each energy storage can be expressed as:

[0082]

[0083]

[0084]

[0085]

[0086]

[0087] wherein, is the reference charge-discharge power of energy storage j, and are the downward reserve charge-discharge power and upward reserve charge-discharge power provided by energy storage j for source-load fluctuations, and are the lower bound and upper bound of the charge-discharge power of energy storage j, and are the lower bound and upper bound that the stored electricity of energy storage j can reach in the regulation interval respectively, and are the lower bound and upper bound of the stored electricity of energy storage j, is the upper bound of the allowable wind power fluctuation of all wind farms in the power system, is the lower bound of the total available output prediction error of all wind farms in the power system, and are the lower bound and upper bound of the total load prediction error of all load nodes in the power system.

[0088] Among them, the line transmission constraint of the power system can be expressed as:

[0089]

[0090]

[0091] In the formula, is the reference transmission power of line l, is the generation transfer factor of wind farm m, is the generation transfer factor of load node n, is the generation transfer factor of thermal power unit i, is the generation transfer factor of energy storage j, and ]>are the upper bound and lower bound of the transmission power of line l, is the source-load uncertainty set in the subset not greater than 0 of the source-load uncertainty set in the subset greater than 0.

[0092] Among them, according to the source-load uncertainty set, regulation strategy, power balance constraint, wind power operation constraint, regulation resource operation constraint and line transmission constraint of the power system, the system operation constraint corresponding to the overall operation of the power system can be constructed, which can be expressed in the following compact form:

[0093]

[0094] In the formula, j = 1, 2, and respectively represent subsets of the source-load uncertainty set with power fluctuations less than 0 and greater than 0. A and B are constant matrices, c is a constant vector, d is a constant scalar, x is a decision variable vector, and α is a matrix composed of regulation coefficients. 、 、 、 、 、 are the dual variables corresponding to each uncertainty condition.

[0095] Among them, according to the duality theory, the above system operation constraints containing uncertainty variables can be converted into the following deterministic constraints:

[0096]

[0097] Thus, the system operation constraints based on robust optimization can be constructed.

[0098] Step S103: Construct a benchmark cost function according to the benchmark cost parameters of the power system, construct a regulation expected cost based on stochastic optimization according to the source-load uncertainty set, and obtain the scheduling cost function of the power system according to the benchmark cost function and the regulation expected cost.

[0099] Among them, in order to ensure the economy of the power dispatch process, in this step, the benchmark cost function and the regulation expected cost of the power system can be constructed respectively, and then the scheduling cost function of the power system can be obtained by combining the two. Exemplarily, the scheduling cost function of the power system can be expressed as:

[0100]

[0101] In the formula, is the benchmark cost function of the power system, represents the regulation expected cost of the power system, represents the actual regulation cost under the available wind power prediction error and the load prediction error v.

[0102] Among them, the benchmark cost function of the power system can be used to calculate the benchmark cost generated during the operation of the power system, such as the power generation cost, carbon trading cost, etc. Optionally, when the power dispatch considers the wind curtailment measure, the benchmark cost function can also include a function item for calculating the wind curtailment cost.

[0103] Among them, the regulation expected cost of the power system can be the cost to be paid for the power system to adjust for the source-load fluctuations. It can be based on the regulation cost parameters of each regulation resource in the system and the uncertainty variables (available wind power prediction error estimated from the empirical distributions of the power generation and load forecasting errors v).

[0104] Step S104: Construct a scheduling model for the power system according to the system operation constraints and the scheduling cost function.

[0105] Step S105: Take minimizing the scheduling cost function as the optimization objective, optimize and solve the scheduling model to obtain the scheduling strategy parameters of the power system, and schedule the power system according to the scheduling strategy parameters.

[0106] Among them, in step S104, a scheduling model for the power system can be constructed, and this model can be a model that includes system operation constraints and a scheduling cost function.

[0107] Then, in step S105, the scheduling strategy parameters of the power system can be obtained by optimizing and solving the scheduling model, and then the power system can be scheduled using the scheduling strategy parameters. Among them, when optimizing and solving the scheduling model, it can take minimizing the scheduling cost function as the optimization objective, and the optimization objective can be expressed as:

[0108]

[0109] Among them, by optimizing and solving the scheduling model for this optimization objective, the minimization of the scheduling cost can be achieved by combining stochastic optimization, and the strict satisfaction of the system operation constraints can be achieved based on robust optimization, so as to ensure the safe and economic operation of the power system under the condition of source-load fluctuations.

[0110] In the above-mentioned low-carbon scheduling method for the power system, the uncertainty range of fluctuations in wind power and load in the power system is accurately described by constructing a source-load uncertainty set; by combining the source-load uncertainty set to construct system operation constraints based on robust optimization, it can be ensured that the system operation constraints can be strictly satisfied under the condition of source-load fluctuations; at the same time, by constructing a regulation expected cost based on stochastic optimization according to the source-load uncertainty set, and obtaining a scheduling cost function according to the benchmark cost function and the regulation expected cost, it is beneficial to ensure the economy of power scheduling in subsequent optimization and solution; thus, by constructing a scheduling model for the power system according to the system operation constraints and the scheduling cost function, and taking minimizing the scheduling cost function as the objective to optimize and solve this scheduling model, scheduling strategy parameters that take into account both safety and economy can be obtained, and then the power system can be scheduled according to this scheduling strategy parameter, which is beneficial to ensuring the economic and safe operation of the power system under the condition of combined uncertainty of wind power and load.

[0111] In an exemplary embodiment, the benchmark cost function includes a curtailment cost function corresponding to the curtailment strategy; as Figure 2 shown, constructing a source-load uncertainty set for wind power and load in the power system may include:

[0112] Step S201 : constructing a wind curtailment strategy for the power system based on the wind power prediction error parameter and the upper limit of wind power fluctuation allowed by the power system.

[0113] Specifically, the present application may consider wind curtailment measures to ensure safe system operation during the power system dispatch process, and incorporate a wind curtailment cost function corresponding to the wind curtailment strategy into the baseline cost function. Wind curtailment measures may refer to wind farms that can curtail wind power to ensure safe system operation when the power system's regulation capacity is insufficient. The wind curtailment cost function may be a function term within the baseline cost function, describing the costs incurred due to wind curtailment measures.

[0114] Among them, when considering wind curtailment measures, wind curtailment strategies can be implemented in the process of constructing the source-load uncertainty set and incorporated into the source-load uncertainty set.

[0115] Specifically, the wind curtailment strategy can be constructed based on the wind power forecast error parameter and the upper limit of wind power fluctuation in the power system. The wind power forecast error parameter can include the available output forecast error range of each wind farm in the power system. And the total available output forecast error range of all wind farms Among them, the upper limit of wind power fluctuation is allowed It can be determined according to the regulation capability of the power system. Less than the upper bound of the total available output prediction error If the total available output forecast error of all wind farms in the power system exceeds , then the excess wind power will be abandoned. Based on this, a wind abandonment strategy for the power system can be constructed as shown in the following formula:

[0116]

[0117] Where, is the actual available output of wind farm m, is the benchmark output of wind farm m, is the total available output prediction error of all wind farms, is the available output prediction error of wind farm m, is the upper limit of wind power fluctuation, is the available output prediction error range of wind farm m, is the forecast error range of the total available output of all wind farms.

[0118] Step S202 : constructing a wind power output uncertainty set of the power system according to the wind power prediction error parameters and the wind curtailment strategy.

[0119] When considering wind curtailment measures, the actual output fluctuation of wind farms Less than the available output prediction error , so that the system operation constraints of the power system are determined by the actual output fluctuations . Thus, for the actual output fluctuations , a wind power uncertainty set as shown in the following formula can be established :

[0120]

[0121] In the formula, is the actual total output fluctuation of all wind farms

[0122] Step S203, construct a load uncertainty set of the power system according to the load prediction error parameters of the power system

[0123] Among them, for the load prediction error of each load node in the power system, its corresponding load uncertainty set can be constructed according to the load prediction error parameters of the power system. Among them, the load prediction error parameters may include the load prediction error range of each load node and the total load prediction error range of all load nodes . Exemplarily, for the load prediction error of the load node, a load uncertainty set as shown in the following formula can be established :

[0124]

[0125] In the formula, is the total load prediction error of all load nodes

[0126] Step S204, obtain a source-load uncertainty set according to the wind power output uncertainty set and the load uncertainty set

[0127] Among them, according to the wind power output uncertainty set and the load uncertainty set obtained in the foregoing process, in this step, the source-load uncertainty set of the power system can be obtained by taking the union .

[0128] In this embodiment, by considering the curtailment measures and incorporating the curtailment strategy into the source-load uncertainty set, the curtailment operation can be carried out in combination with the actual regulation ability of the power system, which is beneficial to ensuring the safe operation of the system

[0129] In an exemplary embodiment, constructing a benchmark cost function of a power system according to the benchmark cost parameters of the power system may include: constructing a power generation cost function of the power system according to the quadratic power generation cost functions of the thermal power units in the regulation resources; constructing a wind curtailment cost function of the power system according to the wind curtailment penalty parameters of the power system; constructing a carbon trading cost function of the power system according to the carbon emission parameters of each thermal power unit; and obtaining the benchmark cost function according to the power generation cost function, the wind curtailment cost function, and the carbon trading cost function.

[0130] Specifically, considering the wind curtailment measures and low-carbon factors, the benchmark cost function of the power system may include a function term for calculating the power generation cost of thermal power units, a function term for calculating the wind curtailment cost, and a function term for calculating the carbon trading cost. In this embodiment, the power generation cost function, the wind curtailment cost function, and the carbon trading cost function may be constructed separately first and used as function terms to construct the benchmark cost function.

[0131] Exemplarily, the power generation cost function of the power system may be expressed as:

[0132]

[0133] In the formula, is the benchmark output of thermal power unit i, is the quadratic power generation cost function of thermal power unit i.

[0134] Exemplarily, the wind curtailment cost function of the power system may be constructed according to the wind curtailment penalty parameters, where the wind curtailment penalty parameters may include a wind curtailment penalty cost coefficient, and the wind curtailment cost function may be expressed as:

[0135]

[0136] In the formula, is the wind curtailment penalty cost coefficient, is the predicted available output value of wind farm m, is the benchmark output of wind farm m.

[0137] Exemplarily, the carbon trading cost function of the power system may be constructed according to the carbon emission parameters of each thermal power unit, where the carbon emission parameters may include a carbon trading cost coefficient, a carbon emission coefficient, and a carbon emission quota, and the carbon trading cost function may be expressed as:

[0138]

[0139] In the formula, is the carbon trading cost coefficient, is the carbon emission coefficient of thermal power unit i, is the carbon emission quota of thermal power unit i, is the reference output of thermal power unit i.

[0140] Among them, after constructing the power generation cost function, wind curtailment cost function and carbon trading cost function, the reference cost function of the power system can be constructed as follows:

[0141]

[0142] In this embodiment, by incorporating the power generation cost, wind curtailment cost and carbon trading cost of the power system into the calculation of the reference cost, multiple optimization objectives can be incorporated into the optimization framework. Under the premise of meeting power balance, the coupling relationship between unit energy consumption, wind curtailment loss and carbon emission right trading can be quantified, which is beneficial to obtaining the dispatching strategy parameters that can make the system operate economically and with low carbon emissions.

[0143] In an exemplary embodiment, as Figure 3 shown, constructing the regulation expected cost based on stochastic optimization according to the source-load uncertainty set may include:

[0144] Step S301, construct the regulation cost function of the power system according to the regulation cost coefficients, reference output parameters and reference wind curtailment parameters of each regulation resource.

[0145] Among them, the regulation cost function of the power system can be expressed as:

[0146]

[0147] Among them, is the regulation cost coefficient of thermal power unit i (which may include power generation cost and carbon trading cost), is the regulation cost coefficient of energy storage j, is the reference output of thermal power unit i, the reference charge and discharge power of energy storage j, and are respectively the total available output prediction error of all wind farms and the total load prediction error of all load nodes under which, the actual output of thermal power unit i and the actual charge and discharge power of energy storage j, is the wind curtailment penalty cost coefficient, is the actual total output fluctuation of all wind farms.

[0148] Step S302, construct the regulation output constraints of each regulation resource according to the source-load uncertainty set.

[0149] Among them, according to the source-load uncertainty set of wind power and load, the regulation output constraints that each regulation resource needs to meet when regulating the source-load fluctuation can be constructed. Exemplarily, the regulation output constraints can be expressed as:

[0150]

[0151]

[0152]

[0153]

[0154] wherein, is the true total output fluctuation of all wind farms, is the total available output prediction error of all wind farms, is the upper bound of allowable wind power fluctuation, is the total load prediction error of all load nodes, is a flag constant (the value can be {0, 1}). When occurs, the thermal power units and energy storage adopt the downward regulation coefficients and ; when occurs, the units and energy storage adopt the upward regulation coefficients and . It can be understood that when considering the wind curtailment measures, the upper bound of allowable wind power fluctuation is less than the upper bound of total available output prediction error (that is, if the total available output prediction error exceeds , the excess wind power will be discarded), and when the power dispatching does not consider the wind curtailment measures, the upper bound of allowable wind power fluctuation can be equal to the upper bound of total available output prediction error .

[0155] Step S303: Construct the expected regulation cost by using the multi-point estimation method according to the source-load uncertainty set, the regulation cost function, and the regulation output constraint.

[0156] In this step, the expected regulation cost can be constructed by using the multi-point estimation method. Among them, the multi-point estimation method can take multiple estimation points for the uncertainty variables, assign weights to each estimation point, and then combine the weighted summation results of the regulation cost function values corresponding to all estimation points to obtain the expected regulation cost. Exemplarily, the expected regulation cost can be expressed as:

[0157]

[0158]

[0159] wherein, is the uncertainty variable index. The uncertainty variables include the total available output prediction error of all wind farms and the total load prediction error of all load nodes, is the estimation point index, To estimate the weight of point and is the cumulative distribution function of the standard normal distribution, is the inverse of the cumulative distribution function of the total available wind power prediction error and the total load prediction error, is the estimated vector in the standard normal distribution space. In this estimated vector, the th uncertainty variable is equal to the th estimated point, and the other uncertainty variables are equal to 0, are the total available wind power prediction error and the total load prediction error corresponding to the estimated vector respectively.

[0160] In this embodiment, aiming at the source-load uncertainty of the power system, the multi-point estimation method is used to construct the corresponding regulation expected cost, which can combine the empirical distributions of the total available wind power prediction error and the total load prediction error, construct the regulation expected cost based on stochastic optimization, and thus can transform the stochastic problem into a deterministic equivalent form for solution. While reducing the computational complexity, this method can effectively capture the influence of uncertainty, which is beneficial to balancing the economy and robustness of the optimization solution process, obtaining better scheduling strategy parameters, and further improving the safety and economy of the power system operation.

[0161] In an exemplary embodiment, as Figure 4 shown, taking the minimum of the scheduling cost function as the optimization objective, optimizing and solving the scheduling model, the scheduling strategy parameters of the power system can be obtained, including:

[0162] Step S401, convexify the non-convex constraints included in the regulation output constraint to obtain the convex difference function corresponding to the non-convex constraint.

[0163] Specifically, the scheduling model may include non-convex constraints. Exemplarily, the scheduling model in this embodiment can include system operation constraints and the regulation output constraints of each regulation resource, and these constraints may include non-convex constraints. Among them, the regulation output constraint can be the constraint that the output of each regulation resource needs to meet when adjusting for source-load fluctuations. Exemplarily, the regulation output constraint can be expressed as:

[0164]

[0165]

[0166]

[0167]

[0168] In the formula, is the true total output fluctuation of all wind farms, is the total available output prediction error of all wind farms, is the upper bound of allowable wind power fluctuation, is the total load prediction error of all load nodes, is a flag constant (its value can be {0, 1}). When , the thermal power units and energy storage adopt the downward regulation coefficients and . When , the units and energy storage adopt the upward regulation coefficients and . It can be understood that when considering the wind curtailment measures, the upper bound of allowable wind power fluctuation is less than the upper bound of the total available output prediction error (that is, if the total available output prediction error exceeds , the excess wind power will be discarded). When the power dispatching does not consider the wind curtailment measures, the upper bound of allowable wind power fluctuation can be equal to the upper bound of the total available output prediction error .

[0169] Among them, the following constraints in the above regulation output constraints contain bilinear terms, so they are non-convex constraints:

[0170]

[0171]

[0172] The other constraints in the regulation output constraints and the system operation constraints of the power system are convex constraints.

[0173] In this step, for the non-convex constraints included in the regulation output constraints, convexification is performed on them to obtain the corresponding convex difference function. Exemplarily, taking the two bilinear constraints shown in the above formula as an example, it can be summarized in the following form:

[0174]

[0175] In the formula, is a positive constant, represents the decision variable, is a convex function with respect to the decision variable.

[0176] Based on the above formula, the bilinear constraint can be expressed as the difference between two convex functions:

[0177]

[0178] Thus, the convex difference function corresponding to the non-convex constraint can be obtained.

[0179] Step S402: Obtain the objective function of the power system according to the scheduling cost function and the convex difference function.

[0180] Among them, according to the scheduling cost function of the power system and the convex difference function obtained above, the objective function of the scheduling model of the power system can be expressed in the following compact form:

[0181]

[0182] In the formula, is the scheduling cost function of the power system, is the convex difference function, i represents the i-th constraint, and are convex functions with respect to the decision variable .

[0183] Step S403: Take minimizing the objective function as the optimization goal, optimize and solve the scheduling model to obtain the scheduling strategy parameters of the power system.

[0184] Among them, since the objective function is constructed according to the scheduling cost function and the convex difference function, when the objective function is minimized, the scheduling cost function is minimized. Based on this, in this step, when optimizing and solving the scheduling model, it can take minimizing the objective function as the optimization goal, and the optimization goal can be expressed as:

[0185]

[0186] Subsequently, the scheduling model can be optimized and solved for this optimization goal.

[0187] In this embodiment, by convexifying the non-convex constraints included in the scheduling model to convert them into a convex difference function structure, and then combining the convex difference function with the scheduling cost function to obtain the objective function, the complex non-convex problems in the optimization and solution process can be decomposed into a series of convex sub-problems. While ensuring convergence, it takes into account the computational efficiency and the quality of the solution, and finally can obtain the scheduling strategy parameters approaching the optimal solution, further improving the safety and economy of the power system operation under power dispatch.

[0188] In an exemplary embodiment, the objective function includes a penalty function; as Figure 5 shown, taking minimizing the objective function as the optimization goal, optimizing and solving the scheduling model to obtain the scheduling strategy parameters of the power system includes:

[0189] Step S501: Take the initial scheduling strategy parameters as the current scheduling strategy parameters, and take the initial penalty coefficient as the current penalty coefficient of the penalty function.

[0190] Specifically, for the objective function obtained in the foregoing process: , the optimization objective can be transformed by linearizing the minuend function in the convex difference function as follows:

[0191]

[0192] In the formula, is the derivative function, is the penalty function corresponding to the convex difference function, is the penalty coefficient, is the slack variable corresponding to the i-th constraint, is the scheduling cost function, is the decision variable, is the iteration index.

[0193] Among them, the optimization solution process can be initialized first to obtain the initial iteration index , the initial scheduling policy parameter , and the initial penalty coefficient . Then, the initial iteration index can be used as the current iteration index, the initial scheduling policy parameter can be used as the current scheduling policy parameter, and the initial penalty coefficient can be used as the current penalty coefficient of the penalty function.

[0194] Step S502: Optimize and solve the objective function according to the current scheduling policy parameter and the current penalty coefficient to obtain the optimized scheduling policy parameter and the slack variable.

[0195] Among them, according to the current scheduling policy parameter and the current penalty coefficient, the objective function can be optimized and solved to obtain the optimized scheduling policy parameter and the slack variable. Exemplarily, assume that the current scheduling policy parameter is , and the current penalty coefficient is . Then, by optimizing and solving the objective function based on the current scheduling policy parameter and the current penalty coefficient, the optimized scheduling policy parameter and its corresponding slack variable can be obtained.

[0196] Step S503: Check whether the scheduling cost and the slack variable meet the convergence criterion according to the current scheduling policy parameter, the optimized scheduling policy parameter, and the slack variable.

[0197] Among them, the convergence criterion corresponding to the scheduling cost and the slack variable can be preset. In this step, according to the current scheduling policy parameter and the optimized scheduling policy parameter, the corresponding scheduling costs can be calculated respectively using the scheduling cost function, and then it is checked whether the scheduling cost and the slack variable meet the convergence criterion.

[0198] Exemplarily, the convergence criterion can be shown as the following formula:

[0199]

[0200]

[0201] In the formula, is the scheduling cost corresponding to the optimized scheduling strategy parameter ; is the scheduling cost corresponding to the current scheduling strategy parameter ; is the convergence tolerance corresponding to the scheduling cost, is the slack variable corresponding to the i-th constraint, is the convergence tolerance corresponding to the slack variable.

[0202] Step S504: If convergence is achieved, obtain the scheduling strategy parameter of the power system according to the optimized scheduling strategy parameter; if not, use the optimized scheduling strategy parameter as the current scheduling strategy parameter, and update the current penalty coefficient, and perform the step of optimizing and solving the objective function according to the current scheduling strategy parameter and the current penalty coefficient to obtain the optimized scheduling strategy parameter.

[0203] Among them, if the scheduling cost and the slack variable meet the convergence criterion, it can be determined that the optimized scheduling strategy parameter is the optimal solution obtained by the optimization solution, so that it can be used as the scheduling strategy parameter of the power system.

[0204] Among them, if the scheduling cost and the slack variable do not meet the convergence criterion, the optimized scheduling strategy parameter can be used as the current scheduling strategy parameter, and the current penalty coefficient ( is a constant greater than 1) is updated, and then return to step S502 to perform the optimization solution again.

[0205] In this embodiment, by introducing the penalty coefficient and the slack constraint, and using the penalty convex-concave process to optimize and solve the scheduling model, the penalty on the slack variable can be gradually increased during the iteration process to make the optimization process gradually converge. At the same time, by setting the convergence criteria corresponding to the scheduling cost and the slack variable, multi-dimensional verification of the solution result can be realized, ensuring that the obtained scheduling strategy parameter can simultaneously meet the economic requirements of the scheduling cost and the safety requirements of the operation of the power system. Therefore, using this scheduling strategy parameter to schedule the power system can ensure the safe and economic operation of the power system.

[0206] In one embodiment, as Figure 6 shown, a low-carbon scheduling method for a power system in a specific embodiment is provided, which specifically includes the following steps:

[0207] Step S1: Construct a source-load uncertainty set considering the curtailment decision.

[0208] Step S2: Establish a scheduling model based on stochastic robust optimization.

[0209] Step S3, iteratively solve the scheduling strategy parameters using the penalty convex-concave process to perform real-time scheduling on the power system.

[0210] Among them, in step S1, first, according to the wind power prediction error parameter and the upper bound of allowable wind power fluctuation of the power system, a wind curtailment strategy of the power system can be constructed, and then according to the wind power prediction error parameter and the wind curtailment strategy, an uncertain set of wind power output of the power system can be constructed; at the same time, according to the load prediction error parameter of the power system, an uncertain set of load of the power system can be constructed; finally, according to the union of the uncertain set of wind power output and the uncertain set of load, the source-load uncertainty set of the power system can be obtained.

[0211] Among them, in step S2, first, a regulation strategy of each regulation resource in the power system for source-load fluctuations can be established, and this strategy is a piecewise affine scheduling strategy for different source-load fluctuation situations. Then, combining the source-load uncertainty set, the regulation strategy, and the power balance constraint, wind power operation constraint, regulation resource operation constraint, and line transmission constraint of the power system, a system operation constraint based on robust optimization can be constructed. At the same time, a benchmark cost function including the quadratic generation cost of thermal power units, the wind curtailment cost of wind farms, and the carbon trading cost of thermal power units can be constructed according to the benchmark cost parameter of the power system, and an adjustment expected cost based on stochastic optimization can be constructed using the multi-point estimation method according to the source-load uncertainty set. Then, according to the benchmark cost function and the adjustment expected cost, the scheduling cost function of the power system can be obtained. Thus, a scheduling model based on stochastic robust optimization can be constructed, and this model can be used to perform real-time low-carbon scheduling on the power system.

[0212] Among them, in step S3, first, the non-convex constraints included in the scheduling model can be convexified to obtain their corresponding convex difference functions, and then according to the scheduling cost function and the convex difference functions, the objective function of the scheduling model can be obtained. Then, taking the minimization of the objective function as the optimization objective, the scheduling model is optimized and solved to obtain the scheduling strategy parameters of the power system, and then the obtained scheduling strategy parameters are used to schedule the power system.

[0213] Among them, in step S3, the scheduling model can be optimized and solved iteratively to obtain the optimized scheduling strategy parameters. Exemplarily, this iterative process can be as Figure 7 shown and includes the following steps:

[0214] Step S31, initialization: Initialize the iteration index, set the initial scheduling strategy parameters (linearization base points), and the initial penalty coefficient.

[0215] Step S32, relaxation: Take the initial scheduling strategy parameters as the current scheduling strategy parameters, take the initial penalty coefficient as the current penalty coefficient of the penalty function, solve the relaxation problem based on the objective function to obtain the optimized scheduling strategy parameters and the relaxation variables.

[0216] Step S33, convergence criterion verification: According to the current scheduling policy parameters, the optimized scheduling policy parameters, and the slack variables, verify whether the scheduling cost and the slack variables meet the convergence criterion; if convergent, obtain the scheduling policy parameters of the power system according to the optimized scheduling policy parameters; if not convergent, use the optimized scheduling policy parameters as the current scheduling policy parameters (update the linearization base point), and increase the current penalty coefficient, and then execute step S32 again.

[0217] In this embodiment, by constructing a combined wind power and load uncertainty set considering curtailment decisions (i.e., the source-load uncertainty set), the uncertainty of the fluctuation ranges of wind power and load under the influence of curtailment decisions can be effectively described. At the same time, by using the method of stochastic robust optimization to establish a real-time scheduling model for the power system, a piecewise affine scheduling strategy for each regulation resource for different source-load fluctuation conditions in the power system can be established. The robust optimization based on the duality theory can also be used to ensure that the system operation constraints are strictly satisfied under the source-load uncertainty set, and the expected cost of the empirical distribution is minimized through stochastic optimization, thereby improving the economy of scheduling. In addition, this embodiment uses the multi-point estimation method to construct the regulation expected cost, which can accurately fit the regulation expected cost that conforms to the empirical distribution. At the same time, using the penalty convex-concave process to iteratively optimize and solve the scheduling model can effectively reduce the complexity of the optimization solution process and provide support for the efficient and accurate solution of the real-time low-carbon scheduling problem of the power system. Thus, the solution of this embodiment can provide a strong guarantee for the economic and safe operation of the power system under the condition of combined wind power and load uncertainty.

[0218] It should be understood that although the steps in the flowcharts involved in the above embodiments are shown in sequence according to the arrows, these steps do not necessarily have to be executed in the order indicated by the arrows. Unless there is a clear indication in this article, there is no strict order limit for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages. These steps or stages do not necessarily have to be executed at the same time, but can be executed at different times. The execution order of these steps or stages does not necessarily have to be sequential, but can be executed alternately or alternately with at least a part of other steps or steps or stages in other steps.

[0219] Based on the same inventive concept, an embodiment of the present application further provides a low-carbon scheduling device for a power system for implementing the above-mentioned low-carbon scheduling method for a power system. The solution provided by this device for solving problems is similar to the solution described in the above method. Therefore, the specific limitations in one or more embodiments of the low-carbon scheduling device for a power system provided below can refer to the limitations on the low-carbon scheduling method for a power system in the above text, and will not be repeated here.

[0220] In an exemplary embodiment, as Figure 8 shown, a low-carbon scheduling device 800 for a power system is provided, including:

[0221] An uncertainty set construction module 801, configured to construct an uncertainty set of the power source and load of the wind power in the power system.

[0222] An operation constraint construction module 802, configured to construct system operation constraints based on robust optimization according to the uncertainty set of the power source and load, the adjustment strategies of each adjustment resource in the power system for the power source and load fluctuations in the power system, and the power balance constraints, wind power operation constraints, adjustment resource operation constraints, and line transmission constraints in the power system.

[0223] A cost function construction module 803, configured to construct a benchmark cost function according to the benchmark cost parameters of the power system, construct an adjustment expected cost based on stochastic optimization according to the uncertainty set of the power source and load, and obtain a scheduling cost function of the power system according to the benchmark cost function and the adjustment expected cost.

[0224] A model construction module 804, configured to construct a scheduling model of the power system according to the system operation constraints and the scheduling cost function.

[0225] A model solving module 805, configured to perform optimization and solution on the scheduling model with the goal of minimizing the scheduling cost function, obtain scheduling strategy parameters of the power system, and schedule the power system according to the scheduling strategy parameters.

[0226] In an exemplary embodiment, the benchmark cost function includes a curtailment cost function corresponding to the curtailment strategy; the uncertainty set construction module 801 is configured to: construct the curtailment strategy of the power system according to the wind power prediction error parameter and the allowable upper bound of wind power fluctuation of the power system; construct the wind power output uncertainty set of the power system according to the wind power prediction error parameter and the curtailment strategy; construct the load uncertainty set of the power system according to the load prediction error parameter of the power system; and obtain the uncertainty set of the power source and load according to the wind power output uncertainty set and the load uncertainty set.

[0227] In an exemplary embodiment, the cost function construction module 803 is configured to: construct a power generation cost function of the power system according to the secondary power generation cost functions of the thermal power units in the regulating resources; construct a wind curtailment cost function of the power system according to the wind curtailment penalty parameter of the power system; construct a carbon trading cost function of the power system according to the carbon emission parameters of the thermal power units; and obtain the benchmark cost function according to the power generation cost function, the wind curtailment cost function, and the carbon trading cost function.

[0228] In an exemplary embodiment, the cost function construction module 803 is configured to: construct a regulation cost function of the power system according to the regulation cost coefficients, the benchmark output parameters, and the wind curtailment penalty parameters of the regulating resources; construct regulation output constraints for the regulating resources according to the source-load uncertainty set; and construct the regulation expected cost by using the multi-point estimation method according to the source-load uncertainty set, the regulation cost function, and the regulation output constraints.

[0229] In an exemplary embodiment, the model solving module 805 is configured to: perform convexification processing on the non-convex constraints included in the scheduling model to obtain a convex difference function corresponding to the non-convex constraints; obtain an objective function of the scheduling model according to the scheduling cost function and the convex difference function; and perform optimization solving on the scheduling model with the minimization of the objective function as the optimization objective to obtain the scheduling strategy parameters of the power system.

[0230] In an exemplary embodiment, the objective function includes a penalty function; the model solving module 805 is configured to: use the initial scheduling strategy parameters as the current scheduling strategy parameters and use the initial penalty coefficient as the current penalty coefficient of the penalty function; perform optimization solving on the objective function according to the current scheduling strategy parameters and the current penalty coefficient to obtain optimized scheduling strategy parameters and slack variables; check whether the scheduling cost and the slack variables conform to the convergence criterion according to the current scheduling strategy parameters, the optimized scheduling strategy parameters, and the slack variables; if convergent, obtain the scheduling strategy parameters of the power system according to the optimized scheduling strategy parameters; if not convergent, use the optimized scheduling strategy parameters as the current scheduling strategy parameters, update the current penalty coefficient, and execute the step of performing optimization solving on the objective function according to the current scheduling strategy parameters and the current penalty coefficient to obtain optimized scheduling strategy parameters.

[0231] Each module in the above-mentioned low-carbon scheduling device of the power system can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in the processor of the computer device in the form of hardware or be independent of it, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.

[0232] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in Figure 9 . The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as adjustment strategies of the power system, power balance constraints, operation constraints, line transmission constraints, and benchmark cost parameters. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements a low-carbon scheduling method for a power system.

[0233] Those skilled in the art can understand that Figure 9 the structure shown in

[0234] is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0235] In an embodiment, a computer-readable storage medium is also provided, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps in the above method embodiments.

[0236] In an embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, it implements the steps in the above method embodiments.

[0237] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0238] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0239] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered to be within the scope recorded in the present application.

[0240] The above embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A low-carbon dispatching method for a power system, characterized in that, The method includes: Constructing the source-load uncertainty sets of wind power and load in the power system; According to the source-load uncertainty sets, the regulation strategies of each regulation resource in the power system for the source-load fluctuations in the power system, and the power balance constraints, wind power operation constraints, regulation resource operation constraints, and line transmission constraints of the power system, constructing the system operation constraints based on robust optimization; Constructing a benchmark cost function according to the benchmark cost parameters of the power system, constructing a regulation expected cost based on stochastic optimization according to the source-load uncertainty sets, and obtaining the scheduling cost function of the power system according to the benchmark cost function and the regulation expected cost; Constructing a scheduling model of the power system according to the system operation constraints and the scheduling cost function; Taking the minimization of the scheduling cost function as the optimization objective, optimizing and solving the scheduling model to obtain the scheduling strategy parameters of the power system, and scheduling the power system according to the scheduling strategy parameters.

2. The method according to claim 1, characterized in that The benchmark cost function includes a curtailment cost function corresponding to the curtailment strategy; The constructing of the source-load uncertainty sets of wind power and load in the power system includes: According to the wind power prediction error parameters and the allowable upper bound of wind power fluctuations in the power system, constructing the curtailment strategy of the power system; According to the wind power prediction error parameters and the curtailment strategy, constructing the wind power output uncertainty set of the power system; According to the load prediction error parameters of the power system, constructing the load uncertainty set of the power system; Obtaining the source-load uncertainty sets according to the wind power output uncertainty set and the load uncertainty set.

3. The method according to claim 1, wherein The constructing of the benchmark cost function of the power system according to the benchmark cost parameters of the power system includes: According to the quadratic generation cost functions of each thermal power unit in the regulation resources, constructing the generation cost function of the power system; According to the curtailment penalty parameters of the power system, constructing the curtailment cost function of the power system; According to the carbon emission parameters of each thermal power unit, constructing the carbon trading cost function of the power system; Obtaining the benchmark cost function according to the generation cost function, the curtailment cost function, and the carbon trading cost function.

4. The method according to claim 1, characterized in that, The constructing of the regulation expected cost based on stochastic optimization according to the source-load uncertainty sets includes: According to the regulation cost coefficients, benchmark output parameters, and curtailment penalty parameters of each regulation resource, constructing the regulation cost function of the power system; According to the source-load uncertainty sets, constructing the regulation output constraints of each regulation resource; According to the source-load uncertainty sets, the regulation cost function, and the regulation output constraints, using the multi-point estimation method to construct the regulation expected cost.

5. The method according to claim 1, wherein The taking the minimization of the scheduling cost function as the optimization objective, optimizing and solving the scheduling model to obtain the scheduling strategy parameters of the power system includes: Performing convexification processing on the non-convex constraints included in the scheduling model to obtain the convex difference function corresponding to the non-convex constraints; According to the scheduling cost function and the convex difference function, obtaining the objective function of the scheduling model; Taking the minimization of the objective function as the optimization goal, the scheduling model is optimized and solved to obtain the scheduling strategy parameters of the power system.

6. The method according to claim 5, characterized in that, The objective function includes a penalty function; The taking the minimization of the objective function as the optimization goal, the scheduling model is optimized and solved to obtain the scheduling strategy parameters of the power system includes: Taking the initial scheduling strategy parameters as the current scheduling strategy parameters, and taking the initial penalty coefficient as the current penalty coefficient of the penalty function; According to the current scheduling strategy parameters and the current penalty coefficient, the objective function is optimized and solved to obtain optimized scheduling strategy parameters and slack variables; According to the current scheduling strategy parameters, the optimized scheduling strategy parameters, and the slack variables, check whether the scheduling cost and the slack variables meet the convergence criterion; If convergent, the scheduling strategy parameters of the power system are obtained according to the optimized scheduling strategy parameters; If not convergent, taking the optimized scheduling strategy parameters as the current scheduling strategy parameters, and updating the current penalty coefficient, and execute the step of optimizing and solving the objective function according to the current scheduling strategy parameters and the current penalty coefficient to obtain the optimized scheduling strategy parameters.

7. A low-carbon scheduling device for a power system, characterized in that The device includes: An uncertainty set construction module, configured to construct a source-load uncertainty set of wind power and load in the power system; An operation constraint construction module, configured to construct system operation constraints based on robust optimization according to the source-load uncertainty set, the regulation strategies of each regulation resource in the power system for the source-load fluctuations in the power system, and the power balance constraint, wind power operation constraint, regulation resource operation constraint, and line transmission constraint of the power system; A cost function construction module, configured to construct a benchmark cost function according to the benchmark cost parameters of the power system, construct a regulation expected cost based on stochastic optimization according to the source-load uncertainty set, and obtain the scheduling cost function of the power system according to the benchmark cost function and the regulation expected cost; A model construction module, configured to construct a scheduling model of the power system according to the system operation constraints and the scheduling cost function; A model solving module, configured to take the minimization of the scheduling cost function as the optimization goal, optimize and solve the scheduling model to obtain the scheduling strategy parameters of the power system, and schedule the power system according to the scheduling strategy parameters.

8. A computer device, comprising a memory and a processor, the memory storing 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.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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