Method for optimizing output of wind and light storage power generation system under influence of uncertainty of coal scene

Abstract

The invention discloses a wind and light storage power generation system output optimization method under the influence of coal scene uncertainty. The method comprises the following steps: establishing an electric power clearing model of an electric power system based on coal scene uncertainty, inputting power generation costs of annual and current coal electric fields in the electric power system into the model, and solving by using a solver to obtain clearing electric quantity cost parameters in each coal scene; using a clustering algorithm to obtain an average value and probability distribution of clearing power cost parameters in each coal scene; and establishing a stochastic optimization model of the wind and light storage power generation system, inputting the average value and the probability distribution of the clear electric quantity cost parameters into the model, and outputting the demand quantity and the supply quantity of the wind and light storage power generation system after processing so as to realize output optimization. According to the method, the change of the power clearing electric quantity cost parameter under the coal scene uncertainty is considered, the output optimization strategy of the wind and light storage and power generation system is established through a random optimization method, and the optimal output of the wind and light storage and power generation system under the coal scene change condition can be realized.

Description

Technical Field

[0001] The present invention relates to a method for optimizing the output of a wind, solar, and energy storage power generation system, and relates to the field of power optimization, and in particular to a method for optimizing the output of a wind, solar, and energy storage power generation system under the influence of coal scenario uncertainty. Background Art

[0002] In recent years, affected by various factors, coal cost parameters have experienced dramatic fluctuations. The instability of coal cost parameters as a primary energy source directly leads to irregular changes in the power generation cost of coal-fired power units. In the power structure, coal-fired units account for a large proportion. Therefore, changes in coal cost parameters will directly affect the final clearing electricity cost parameters of the power field. Specifically, the increase in coal cost parameters will lead to an increase in the power generation cost of coal-fired units, and then increase the current set cost parameters of coal-fired units, and ultimately increase the overall clearing electricity cost parameters; conversely, it will reduce the overall clearing electricity cost parameters.

[0003] The change in the current clearing electricity cost parameters will have new impacts on the decision-making of other power generation entities. Among them, the wind, solar and storage combined power generation system, as an emerging participant, is also faced with the decision-making difficulties under the conditions of fluctuations in coal standby parameters and current cost parameters. Although the wind, solar and storage combined power generation system has a strong flexible adjustment capability, it can obtain input costs under different conditions by quickly adjusting power generation plans and strategies. However, there is currently a lack of output optimization methods for wind, solar and storage power generation systems under the conditions of uncertainty in coal electricity cost parameters, which makes the wind, solar and storage power generation system face greater risks and difficult to achieve optimization. Summary of the invention

[0004] In order to solve the problems existing in the background technology, the present invention provides a method for optimizing the output of a wind, solar and energy storage power generation system under the influence of coal scene uncertainty.

[0005] The technical solution adopted by the present invention is:

[0006] The method for optimizing the output of a wind-solar-storage power generation system under the influence of coal scene uncertainty of the present invention comprises:

[0007] Step 1: Establish a power clearing model for the power system based on the uncertainty of coal scenarios, input the annual and current coal power generation costs of the power system into the power clearing model, and use the solver to process to obtain the power clearing electricity cost parameters of the power system under various coal scenarios. The cost can be measured in electricity.

[0008] Step 2: Use a clustering algorithm to obtain the average value and probability distribution of the clearing electricity cost parameters of the power system under various coal scenarios.

[0009] Step 3: Establish a random optimization model for the wind, solar, and storage power generation system in the power system, input the average value and probability distribution of the power clearing cost parameters of the power system under various coal scenarios into the random optimization model, and output the demand and supply of the wind, solar, and storage power generation system after processing by the random optimization model to achieve output optimization of the wind, solar, and storage power generation system.

[0010] In step 1, the power system includes coal-fired power plants, wind power plants, hydropower plants, and wind, solar, and storage power generation systems. The power clearing model of the power system includes an objective function and constraints. The objective function is to minimize the power generation cost, which is the total power generation cost of multiple coal-fired power units in this case. A unified clearing method is used for clearing. The specific objective function is as follows:

[0011]

[0012] Where T represents the total electricity clearing period of the coal-fired power plant; N g 、N w and N r They represent the coal power plant collection, wind power plant collection and hydropower plant collection of the power system respectively; and They represent the preset power generation and preset power cost parameters of the nth coal power plant in the tth period under the ωth coal scenario respectively; and They represent the preset power and preset power generation cost parameters of the w-th large wind farm in the t-th period respectively; and They respectively represent the preset power quantity and preset power generation cost parameters of the rth hydropower plant in the tth period.

[0013] The constraints of the power clearing model of the power system include the power system power balance and the upper and lower limits of the output of coal-fired power units, as follows:

[0014]

[0015] Among them, D t represents the load demand of the power system in the tth period; and They represent the maximum and minimum power generation of the coal power plant in the tth period respectively; and They represent the maximum and minimum power generation of a large wind farm in the tth period, and They represent the maximum and minimum power generation of the hydropower plant in the tth period respectively.

[0016] After solving the power clearing model using the CPLEX solver, the clearing electricity cost parameters of the power system under various coal scenarios are obtained. The power generation cost of the coal-fired power plant under each coal scenario is different.

[0017] In the total electricity clearing period T of the coal-fired power plant, the preset electricity cost parameter of the nth coal-fired power plant in the ωth coal scenario is The details are as follows:

[0018]

[0019] in, and They represent the annual and current power generation costs of the nth coal-fired power plant under the ωth coal scenario; k 1 and k 2 Represent the first and second cost conversion coefficients of the first stage ladder, γ 1 and γ 2 Respectively represent the first and second cost conversion coefficients of the second stage ladder, represents the preset power generation of the nth coal-fired power plant under the ωth coal scenario, h represents the staged step power generation of the coal-fired power plant, and represents the length of the preset cost parameter interval; represents normal distribution sampling; and They represent the mean and standard deviation of the annual power generation cost of the nth coal power plant under the ωth coal scenario; and They respectively represent the mean and standard deviation of the current power generation cost of the nth coal-fired power plant under the ωth coal scenario.

[0020] In step 2, the K-means clustering algorithm is used to perform clustering to obtain the average value and probability distribution of the clearing power cost parameters of the power system under various coal scenarios, as follows:

[0021]

[0022] in, and They represent the average value and probability of the kth type of clearing electricity cost parameter of the power system, C k represents the clearing power cost parameter of the power system under the ωth coal scenario The collection of N k represents the clearing power cost parameter of the power system under the ωth coal scenario The collection C k The number of cluster samples in, N total Represents the total number of cluster samples in the set of clearing electricity cost parameters of the power system under various coal scenarios.

[0023] In step 3, the stochastic optimization model of the wind-solar-storage power generation system is as follows:

[0024]

[0025] Among them, F k,profit and They represent the average value of the k-th type of clearing electricity cost parameter of the power system in the t-th period. The electricity supply cost and demand cost of the downwind photovoltaic storage power generation system; N t represents the total random optimization period of the wind-solar-storage power generation system; represents the probability of the kth type of clearing electricity cost parameter of the power system; P t bid and P t ask They respectively represent the supply and demand of the wind, solar and storage power generation system in the tth period.

[0026] In step 3, the stochastic optimization model of the wind-solar-storage power generation system satisfies the following constraints:

[0027] a) Balance constraints:

[0028]

[0029] in, and They represent the discharge and charge of the energy storage system in the tth period respectively; P t WD and P t PV They respectively represent the power generation of the wind turbine and photovoltaic unit in the wind-solar-storage power generation system in the tth period.

[0030] b) Energy storage charging and discharging constraints:

[0031]

[0032] in, and They represent the maximum and minimum discharge power of the wind-solar-storage power generation system, and Indicates the maximum and minimum charging power of the wind-solar-storage power generation system. and They respectively represent the energy storage discharge and charge of the wind-solar-storage power generation system in the tth period under the kth type of clearing power cost parameter of the power system; and They represent the energy storage discharge state variable and energy storage charging state variable of the wind-solar-storage power generation system in the tth period under the kth type of clearing power cost parameter of the power system, and They respectively represent the energy storage discharging state variable and energy storage charging state variable of the wind-solar-storage power generation system under the k-th type of clearing electricity cost parameter of the power system; and They represent the energy storage capacity of the wind-solar-storage power generation system in the tth and t-1th periods under the kth type of clearing power cost parameter of the power system, and They respectively represent the energy storage capacity of the wind-solar-storage power generation system in the initial and final states under the kth type of clearing electricity cost parameter of the power system; η represents the energy storage charging and discharging efficiency of the wind-solar-storage power generation system.

[0033] c) Wind and solar power output constraints:

[0034] Since the wind and solar power curtailment is taken into account, the wind and solar power output of the wind, solar and energy storage power generation system is smaller than the predicted value.

[0035]

[0036] in, and They represent the power generation of the wind turbine and photovoltaic units in the wind-solar-storage power generation system in the tth period under the kth type of clearing power cost parameter of the power system; P t WD,fore and P t PV,fore They respectively represent the predicted wind turbine power and photovoltaic predicted power of the wind-solar-storage power generation system in the tth period under the kth type of clearing electricity cost parameter of the power system.

[0037] The electronic device of the present invention comprises: a memory and a processor coupled to each other, wherein the memory stores program data, and the processor calls the program data to execute the method as described above.

[0038] The computer-readable storage medium of the present invention stores program data thereon, and when the program data is executed by a processor, the method described above is implemented.

[0039] The beneficial effects of the present invention are:

[0040] The method of the present invention takes into account the changes in power clearing electricity cost parameters under the uncertainty of coal scenarios, and establishes an output optimization strategy for the wind, solar, and storage power generation system through a random optimization method, which can achieve the optimal output of the wind, solar, and storage power generation system under the conditions of changes in coal scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1Flow chart of the method of the present invention. DETAILED DESCRIPTION

[0042] The present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0043] like Figure 1 As shown, the method for optimizing the output of a wind-solar-storage power generation system under the influence of coal scene uncertainty of the present invention is as follows:

[0044] Step 1: The power system includes coal-fired power plants, wind power plants, hydropower plants, and wind, solar, and storage power generation systems. A power clearing model for the power system based on the uncertainty of coal scenarios is established. The power clearing model includes objective functions and constraints. The objective function is to minimize the power generation cost, which is the total power generation cost of multiple coal-fired power units in this case. A unified clearing method is used for clearing. The specific objective function is as follows:

[0045]

[0046] Where T represents the total electricity clearing period of the coal-fired power plant; N g 、N w and N r They represent the coal power plant collection, wind power plant collection and hydropower plant collection of the power system respectively; and They represent the preset power generation and preset power cost parameters of the nth coal power plant in the tth period under the ωth coal scenario respectively; and They represent the preset power and preset power generation cost parameters of the w-th large-scale wind farm in the t-th period respectively; and They respectively represent the preset power quantity and preset power generation cost parameters of the rth hydropower plant in the tth period.

[0047] The constraints of the power clearing model of the power system include the power system power balance and the upper and lower limits of the output of coal-fired power units, as follows:

[0048]

[0049] Among them, D t represents the load demand of the power system in the tth period; and They represent the maximum and minimum power generation of the coal power plant in the tth period respectively; and They represent the maximum and minimum power generation of a large wind farm in the tth period, and They represent the maximum and minimum power generation of the hydropower plant in the tth period respectively.

[0050] The annual and current coal-fired power generation costs of the power system are input into the power clearing model, and processed using the CPLEX solver to obtain the power clearing electricity cost parameters of the power system under various coal scenarios. The power generation cost of the coal-fired power plant under each coal scenario is different; the cost can be specifically measured in terms of electricity.

[0051] In the total electricity clearing period T of the coal-fired power plant, the preset electricity cost parameter of the nth coal-fired power plant in the ωth coal scenario is The details are as follows:

[0052]

[0053] in, and They represent the annual and current power generation costs of the nth coal-fired power plant under the ωth coal scenario; k 1 and k 2 Represent the first and second cost conversion coefficients of the first stage ladder, γ 1 and γ 2 Respectively represent the first and second cost conversion coefficients of the second stage ladder, represents the preset power generation of the nth coal-fired power plant under the ωth coal scenario, h represents the staged step power generation of the coal-fired power plant, and represents the length of the preset cost parameter interval; represents normal distribution sampling; and They represent the mean and standard deviation of the annual power generation cost of the nth coal power plant under the ωth coal scenario; and They respectively represent the mean and standard deviation of the current power generation cost of the nth coal-fired power plant under the ωth coal scenario.

[0054] Step 2: Use the K-means clustering algorithm to obtain the average value and probability distribution of the clearing power cost parameters of the power system under various coal scenarios, as follows:

[0055]

[0056]

[0057] in, and They represent the average value and probability of the kth type of clearing electricity cost parameter of the power system, C k represents the clearing power cost parameter of the power system under the ωth coal scenario The collection of N k represents the clearing power cost parameter of the power system under the ωth coal scenario The collection C kThe number of cluster samples in, N total Represents the total number of cluster samples in the set of clearing electricity cost parameters of the power system under various coal scenarios.

[0058] After obtaining the unified clearing power cost parameters in each scenario, cluster analysis is performed on the scenarios: first, the new distance from each sample to the cluster center is calculated and assigned to c ω , update each cluster center according to the assigned category Repeat the above steps until ε represents the preset threshold, and finally the average clearing power cost parameter and probability of each scenario are calculated as follows:

[0059]

[0060] Among them, c ω Indicates that the sample is assigned to the category of the nearest cluster center. represents the mean value of the power cost parameter of all samples in this category, Represents the set of clearing electricity cost parameters for the kth category at the i-th iteration.

[0061] Step 3: Establish a stochastic optimization model of the wind, solar, and storage power generation system in the power system, as follows:

[0062]

[0063] Among them, F k,profit and They represent the average value of the k-th type of clearing electricity cost parameter of the power system in the t-th period. The electricity supply cost and demand cost of the downwind photovoltaic storage power generation system; N t represents the total random optimization period of the wind-solar-storage power generation system; represents the probability of the kth type of clearing electricity cost parameter of the power system; P t bid and P t ask They respectively represent the supply and demand of the wind, solar and storage power generation system in the tth period.

[0064] The stochastic optimization model of the wind-solar-storage power generation system meets the following constraints:

[0065] a) Balance constraints:

[0066]

[0067] in, and They represent the discharge and charge of the energy storage system in the tth period respectively; P tWD and P t PV They respectively represent the power generation of the wind turbine and photovoltaic unit in the wind-solar-storage power generation system in the tth period.

[0068] b) Energy storage charging and discharging constraints:

[0069]

[0070] in, and They represent the maximum and minimum discharge power of the wind-solar-storage power generation system, and Indicates the maximum and minimum charging power of the wind-solar-storage power generation system. and They respectively represent the energy storage discharge and charge of the wind-solar-storage power generation system in the tth period under the kth type of clearing power cost parameter of the power system; and They represent the energy storage discharge state variable and energy storage charging state variable of the wind-solar-storage power generation system in the tth period under the kth type of clearing power cost parameter of the power system, and They respectively represent the energy storage discharging state variable and energy storage charging state variable of the wind-solar-storage power generation system under the k-th type of clearing electricity cost parameter of the power system; and They represent the energy storage capacity of the wind-solar-storage power generation system in the tth and t-1th periods under the kth type of clearing power cost parameter of the power system, and They respectively represent the energy storage capacity of the wind-solar-storage power generation system in the initial and final states under the kth type of clearing electricity cost parameter of the power system; η represents the energy storage charging and discharging efficiency of the wind-solar-storage power generation system.

[0071] c) Wind and solar power output constraints:

[0072] Since the wind and solar power curtailment is taken into account, the wind and solar power output of the wind, solar and energy storage power generation system is smaller than the predicted value.

[0073]

[0074] in, and They represent the power generation of the wind turbine and photovoltaic units in the wind-solar-storage power generation system in the tth period under the kth type of clearing power cost parameter of the power system; P t WD,fore and P t PV,foreThey respectively represent the predicted wind turbine power and photovoltaic predicted power of the wind-solar-storage power generation system in the tth period under the kth type of clearing electricity cost parameter of the power system.

[0075] The average value and probability distribution of the power system's clearing electricity cost parameters under various coal scenarios are input into the stochastic optimization model. After processing, the stochastic optimization model outputs the demand and supply of the wind, solar, and storage power generation system to achieve output optimization of the wind, solar, and storage power generation system.

[0076] The method of the present invention first analyzes and models the coal cost under multi-time scale coal scenarios, sets normally distributed coal cost parameters in the annual and current coal power fields, and generates the cost scenario of coal required by the current coal-fired power units through sampling; then establishes a power clearing model, sets corresponding preset cost parameters of multiple coal-fired power units according to the different cost scenarios generated in the previous step, and performs unified clearing; then, uses a clustering algorithm to cluster the clearing cost parameter scenarios to generate typical scenarios of the clearing cost parameters; finally, establishes a random optimization model of a wind, solar, and storage power generation system, based on multi-scenario clearing electricity cost parameters and corresponding scenario probabilities, with the goal of maximizing the supply cost of the wind, solar, and storage power generation system, optimizes the decision-making of corresponding strategies, and realizes the maximization of the supply cost of the wind, solar, and storage power generation system.

[0077] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems or computer program products. Therefore, the application can adopt the form of a complete hardware embodiment, a complete software embodiment or an embodiment in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages. The application is described according to the flowchart of the method, system and computer program product of the embodiments of the present application.

[0078] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the present invention is intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0079] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application belong to the scope of equivalent technologies of the present invention, the present application is also intended to include these modifications and variations.

Claims

1. A method for optimizing the output of a wind, solar and energy storage power generation system under the influence of coal scene uncertainty, characterized in that: include: Step 1: Establish a power clearing model for the power system based on the uncertainty of coal scenarios, input the annual and current coal power plant generation costs in the power system into the power clearing model, and use the solver to process and obtain the power clearing cost parameters of the power system under various coal scenarios; Step 2: Use a clustering algorithm to obtain the average value and probability distribution of the power system's clearing electricity cost parameters under various coal scenarios; Step 3: Establish a random optimization model for the wind, solar, and storage power generation system in the power system, input the average value and probability distribution of the power clearing cost parameters of the power system under various coal scenarios into the random optimization model, and output the demand and supply of the wind, solar, and storage power generation system after processing by the random optimization model to achieve output optimization of the wind, solar, and storage power generation system.

2. The method for optimizing the output of a wind-solar-storage power generation system under the influence of coal scenario uncertainty according to claim 1 is characterized in that: In step 1, the power system includes coal power plants, wind power plants, hydropower plants and wind, solar and storage power generation systems. The power clearing model of the power system includes an objective function and constraints. The objective function is as follows: Where T represents the total electricity clearing period of the coal-fired power plant; N g 、N w and N r They represent the coal power plant collection, wind power plant collection and hydropower plant collection of the power system respectively; and They represent the preset power generation and preset power cost parameters of the nth coal power plant in the tth period under the ωth coal scenario respectively; and They represent the preset power and preset power generation cost parameters of the w-th wind farm in the t-th period respectively; and They represent the preset power and the preset power generation cost parameters of the r-th hydropower plant in the t-th period respectively; The constraints of the power clearing model of the power system are as follows: Among them, D t represents the load demand of the power system in the tth period; and They represent the maximum and minimum power generation of the coal power plant in the tth period respectively; and They represent the maximum and minimum power generation of the wind farm in the tth period, and They represent the maximum and minimum power generation of the hydropower plant in the tth period respectively; After solving the power clearing model using the CPLEX solver, the clearing electricity cost parameters of the power system under various coal scenarios are obtained. The power generation cost of the coal-fired power plant under each coal scenario is different.

3. The method for optimizing the output of a wind-solar-storage power generation system under the influence of coal scene uncertainty according to claim 1 is characterized in that: In the total electricity clearing period T of the coal-fired power plant, the preset electricity cost parameter of the nth coal-fired power plant in the ωth coal scenario is The details are as follows: in, and They represent the annual and current power generation costs of the nth coal-fired power plant under the ωth coal scenario, respectively; k1 and k2 represent the first and second cost conversion coefficients of the first stage ladder, respectively; γ1 and γ2 represent the first and second cost conversion coefficients of the second stage ladder, respectively. represents the preset power generation of the nth coal-fired power plant under the ωth coal scenario, and h represents the staged step power generation of the coal-fired power plant; represents normal distribution sampling; and They represent the mean and standard deviation of the annual power generation cost of the nth coal power plant under the ωth coal scenario; and They respectively represent the mean and standard deviation of the current power generation cost of the nth coal-fired power plant under the ωth coal scenario.

4. The method for optimizing the output of a wind-solar-storage power generation system under the influence of coal scene uncertainty according to claim 1 is characterized in that: In step 2, the K-means clustering algorithm is used to cluster to obtain the average value and probability distribution of the clearing power cost parameters of the power system under various coal scenarios, as follows: in, and They represent the average value and probability of the kth type of clearing electricity cost parameter of the power system, C k represents the clearing power cost parameter of the power system under the ωth coal scenario The collection of N k represents the clearing power cost parameter of the power system under the ωth coal scenario The collection C k The number of cluster samples in, N total Represents the total number of cluster samples in the set of clearing electricity cost parameters of the power system under various coal scenarios.

5. The method for optimizing the output of a wind-solar-storage power generation system under the influence of coal scenario uncertainty according to claim 1 is characterized in that: In step 3, the stochastic optimization model of the wind-solar-storage power generation system is as follows: Among them, F k,profit and They represent the average value of the k-th type of clearing electricity cost parameter of the power system in the t-th period. The electricity supply cost and demand cost of the downwind photovoltaic storage power generation system; N t represents the total random optimization period of the wind-solar-storage power generation system; represents the probability of the kth type of clearing electricity cost parameter of the power system; P t bid and P t ask They respectively represent the supply and demand of the wind, solar and storage power generation system in the tth period.

6. The method for optimizing the output of a wind-solar-storage power generation system under the influence of coal scene uncertainty according to claim 5 is characterized in that: In step 3, the stochastic optimization model of the wind-solar-storage power generation system satisfies the following constraints: a) Balance constraints: in, and They represent the discharge and charge of the energy storage system in the tth period respectively; P t WD and P t PV They represent the power generation of the wind turbine and photovoltaic units in the wind-solar-storage power generation system in the tth period respectively; b) Energy storage charging and discharging constraints: in, and They represent the maximum and minimum discharge power of the wind-solar-storage power generation system, and Indicates the maximum and minimum charging power of the wind-solar-storage power generation system. and They respectively represent the energy storage discharge and charge of the wind-solar-storage power generation system in the tth period under the kth type of clearing power cost parameter of the power system; and They represent the energy storage discharge state variable and energy storage charging state variable of the wind-solar-storage power generation system in the tth period under the kth type of clearing power cost parameter of the power system, and They respectively represent the energy storage discharging state variable and energy storage charging state variable of the wind-solar-storage power generation system under the k-th type of clearing electricity cost parameter of the power system; and They represent the energy storage capacity of the wind-solar-storage power generation system in the tth and t-1th periods under the kth type of clearing power cost parameter of the power system, and They represent the energy storage capacity of the wind-solar-storage power generation system in the initial state and the final state under the k-th type of power clearing cost parameter of the power system; η represents the energy storage charging and discharging efficiency of the wind-solar-storage power generation system; c) Wind and solar power output constraints: in, and They represent the power generation of the wind turbine and photovoltaic units in the wind-solar-storage power generation system in the tth period under the kth type of clearing power cost parameter of the power system; P t WD,fore and P t PV,fore They respectively represent the predicted wind turbine power and photovoltaic predicted power of the wind-solar-storage power generation system in the tth period under the kth type of clearing electricity cost parameter of the power system.

7. An electronic device, characterized in that: include: A memory and a processor coupled to each other, wherein the memory stores program data, and the processor calls the program data to execute the method according to any one of claims 1 to 6.

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

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