A method and system for coordinated random optimization and control of energy of wind, solar and storage stations

Abstract

The present invention discloses a method and system for stochastic optimization and control of wind, solar and energy storage station energy collaboration. The method comprises: constructing a control model for wind, solar and energy storage station to participate in multi-service energy reuse; determining uncertainty basic data according to the control model; based on the uncertainty basic data, using a Monte-Carlo algorithm to generate a random initial scenario, and using a K-means clustering algorithm to cluster the initial scenario to obtain a clustered final scenario; establishing a multi-scenario stochastic optimization model for wind, solar and energy storage station to participate in multi-service energy reuse; determining the wind, solar and energy storage station output under the basic scenario according to the multi-scenario stochastic optimization model; correcting the wind, solar and energy storage station output under the basic scenario to obtain a corrected wind, solar and energy storage station output; and selecting the final output from the wind, solar and energy storage station output under the basic scenario and the corrected wind, solar and energy storage station output with the goal of maximizing benefits. The present invention can make the optimization results of wind, solar and energy storage station to participate in multi-service energy reuse more accurate.

Description

Technical Field

[0001] The present invention relates to the technical field of energy regulation of wind-solar-storage stations, and in particular to a method and system for coordinated random optimization regulation of energy of wind-solar-storage stations. Background Art

[0002] The investment cost of wind, solar, and storage is high, and the current profitability of wind, solar, and storage under the traditional model is insufficient. Wind, solar, and storage stations face serious economic challenges. Therefore, exploring new profit models for wind, solar, and storage stations to increase their revenue and profits has become a core issue in their development. Due to the inherent volatility of wind and solar output, wind and solar power have limited competitiveness in traditional energy arbitrage. Furthermore, their poor regulation capabilities make them a poor resource for participating in ancillary services such as frequency regulation. Energy storage, with its rapid charging and discharging capabilities and the flexibility to time-shift energy, is considered a high-quality participant in frequency regulation ancillary services. Therefore, large-scale wind, solar, and storage stations can improve their economic viability by participating in multiple services. However, the frequency regulation ancillary service and energy arbitrage are coupled, and their incentive signals are highly volatile. This coupling and uncertainty pose significant risks and challenges to wind, solar, and storage stations participating in these services. However, existing technical research on wind, solar, and storage participating in multiple services primarily focuses on integrating wind, solar, and storage within a single process. This approach fails to achieve simultaneous participation of wind, solar, and storage in multiple processes, limiting their applicability. Furthermore, in order to deal with the volatility of the incentive signal, the existing random optimization of wind, solar and storage participating in multiple services, the final output of which is the result under the basic scenario, fails to fully consider the impact of the results of other scenarios, and its accuracy is impaired. The lack of applicability and low accuracy of existing technical research further affect the economic feasibility of wind, solar and storage participating in multiple services. Therefore, how to design the coordination mode of wind, solar and storage stations so that they can reasonably participate in multiple services, establish a reasonable benefit-cost model, and how to reasonably consider the uncertainty of wind, solar and storage stations participating in the energy reuse process of multiple services to maximize their benefits have become important issues. Summary of the Invention

[0003] In response to the above problems, the present invention provides a method and system for stochastic optimization and control of energy coordinated between wind, solar and storage stations.

[0004] To achieve the above object, the present invention provides the following solutions:

[0005] A method for stochastic optimization and control of energy coordination between a wind-solar-storage station and a solar-energy storage station, comprising:

[0006] Based on the incentive signals of multiple service energy reuse received by the wind, solar and storage stations, a control model for the wind, solar and storage stations to participate in multiple service energy reuse is constructed;

[0007] Determine uncertainty basic data according to the control model; the uncertainty basic data includes frequency regulation capacity, mileage incentive signal, wind and solar power output, and incentive signals in the energy arbitrage link;

[0008] Based on the uncertainty basic data, a Monte-Carlo algorithm is used to generate a random initial scene, and a K-means clustering algorithm is used to cluster the initial scene to obtain a clustered final scene;

[0009] Based on the final scenario after clustering, a multi-scenario stochastic optimization model for wind, solar and storage participating in multi-service energy reuse is established with the goal of maximizing total benefits and considering multiple constraints.

[0010] Determine the wind-solar energy storage station output under the basic scenario according to the multi-scenario stochastic optimization model;

[0011] Correcting the wind-solar-energy-storage station output under the basic scenario to obtain a corrected wind-solar-energy-storage station output;

[0012] With the goal of maximizing benefits, the final output is selected from the wind-solar-energy-storage station output under the basic scenario and the revised wind-solar-energy-storage station output.

[0013] Optionally, the control model includes a revenue control model and a cost control model;

[0014] The income regulation model is as follows:

[0015]

[0016]

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[0019]

[0020]

[0021]

[0022]

[0023] in, Indicates the total revenue of wind, solar and storage stations participating in diversified services. It means the income after the wind and solar storage station provides frequency increase. It means the income after the wind and solar storage station provides frequency reduction. It represents the income of wind and solar storage station in energy arbitrage. Indicates the power reported by the wind, solar and storage stations to participate in the frequency increase. Indicates the power reported by the wind, solar and storage stations to participate in frequency reduction; Indicates the power sent by the wind, solar and storage stations when participating in frequency increase. Indicates the power sent by the wind and solar storage station when participating in frequency reduction. Indicates the power of photovoltaics participating in the energy arbitrage link, Indicates the power of wind power participating in the energy arbitrage link, Indicates the frequency regulation auxiliary service capacity incentive signal, Indicates the mileage incentive signal, Indicates the wind and solar incentive signal in the energy arbitrage link, Indicates the time-sharing incentive signal in the energy arbitrage link, Indicates the energy storage charging power, Indicates the energy storage discharge power, Wind power self-limiting power, Indicates the photovoltaic self-limiting power, This indicates the upper limit of the frequency increase power reported by wind, solar and storage stations announced recently. This indicates the upper limit of the power reduction for the frequency reported by the wind, solar and storage stations announced recently. Indicates the total demand for real-time frequency increase, Indicates the total demand for real-time frequency reduction; SI ES Indicates the energy storage historical frequency regulation capacity mileage ratio, SI WT,PV Indicates the historical frequency regulation capacity-mileage ratio of wind and solar power;

[0024] The cost control model is as follows:

[0025]

[0026]

[0027]

[0028]

[0029] in, Indicates the total cost of wind, solar and storage stations participating in multiple service energy reuse, represents the energy storage charging cost, represents the opportunity cost of wind, solar, charging and energy storage, Represents the cost of wind and solar self-limiting electricity.

[0030] Optionally, the multi-scenario stochastic optimization model is as follows:

[0031]

[0032]

[0033]

[0034]

[0035] in, represents the benefit of the wind-solar-storage station after the frequency increase in the jth scenario, represents the revenue after the frequency reduction provided by the wind-solar-storage station in the jth scenario, represents the income of the wind-solar-storage station in the energy arbitrage link under the jth scenario, represents the total cost of wind, solar and storage stations participating in multiple service energy reuse in the jth scenario, represents the operating cost of the wind-solar-storage station under the jth scenario, represents the life loss cost of the wind-solar-storage station under the jth scenario, represents the cost of every 1% life loss in the jth scenario, represents the life loss in the jth scenario, β represents the life loss rate, Cap ES represents the total capacity of the wind, solar and storage station, C0 represents the initial investment cost, represents the energy storage discharge power in the jth scenario, Represents the energy storage charging power in the jth scenario.

[0036] Optionally, the constraints include wind, solar and storage internal energy constraints, energy storage life constraints and SoC constraints.

[0037] Optionally, the formula for correcting the output of the wind-solar-energy storage station in the basic scenario is as follows:

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[0055] in, Indicates the deviation of energy storage discharge power in other scenarios from that in the basic scenario. Indicates the deviation of energy storage charging power in other scenarios from that in the basic scenario. Indicates the deviation of wind power self-limiting power in other scenarios from that in the basic scenario. Indicates the deviation of PV self-limiting power in other scenarios from that in the basic scenario. Indicates the energy storage charging power in the basic scenario, Indicates the energy storage discharge power in the basic scenario, Indicates the charging efficiency of wind and solar self-limited power storage, Indicates the discharge efficiency of wind and solar self-limited power storage, Indicates the corrected charging power reserved for energy storage. Indicates the corrected discharge power reserved for energy storage upwards, Indicates the corrected charging power that can be reserved downward. Indicates the corrected discharge power that can be reserved downward. Indicates the upward reserve for energy storage charging, Indicates the upward reserve for energy storage discharge, Indicates the downward reserve for energy storage charging, Indicates the downward reserve for energy storage discharge, represents the energy storage SoC at time t+1 under the condition of upward reservation, represents the charging efficiency of energy storage, Indicates the discharge efficiency of energy storage, loss self％ Indicates the self-consumption rate, Cap ES Indicates the total capacity of the wind, solar and storage station. represents the energy storage discharge power at time t under the upward reservation condition, represents the energy storage charging power at time t under the upward reservation condition, represents the wind power charging to energy storage at time t under the condition of upward reservation, represents the photovoltaic power charging to the energy storage at time t under the condition of upward reservation, represents the energy storage SoC at time t+1 under the downward reservation condition, represents the energy storage discharge power at time t under downward reservation, represents the energy storage charging power at time t under downward reservation, represents the wind power charging to energy storage power at time t under the condition of downward reservation, Indicates the photovoltaic power charging to the energy storage at time t under the condition of downward reservation, SoC min Indicates the minimum SoC value allowed for energy storage, SoC max Indicates the maximum SoC value allowed by energy storage, Indicates the maximum energy storage discharge power, Indicates the maximum energy storage charging power, Indicates the upward reserve of the energy storage discharge power at time t, Indicates the downward reserve of the energy storage discharge power at time t, Indicates the upward reserved amount of charging power at time t, Indicates the downward reserved amount of charging power at time t.

[0056] The present invention also provides a wind-solar-storage station energy coordinated random optimization control system, comprising:

[0057] A control model construction module is used to construct a control model for wind, solar and storage stations to participate in multiple service energy reuse based on the excitation signal of multiple service energy reuse received by the wind, solar and storage stations;

[0058] An uncertainty basic data determination module is used to determine uncertainty basic data according to the control model; the uncertainty basic data includes frequency regulation capacity, mileage incentive signal, wind and solar power output, and incentive signals in the energy arbitrage link;

[0059] A final scene determination module is used to generate a random initial scene based on the uncertainty basic data using a Monte-Carlo algorithm, and cluster the initial scene using a K-means clustering algorithm to obtain a clustered final scene;

[0060] A multi-scenario stochastic optimization model construction module is used to establish a multi-scenario stochastic optimization model for wind, solar and energy storage participating in multi-service energy reuse based on the final scenario after clustering, with the goal of maximizing total benefits and considering multiple constraints;

[0061] A wind-solar-energy storage station output determination module under a basic scenario, configured to determine the wind-solar-energy storage station output under a basic scenario according to the multi-scenario stochastic optimization model;

[0062] A correction module, configured to correct the wind-solar-energy-storage station output under the basic scenario to obtain a corrected wind-solar-energy-storage station output;

[0063] The final output selection module is used to select the final output from the wind-solar-energy storage station output under the basic scenario and the revised wind-solar-energy storage station output with the goal of maximizing the benefit.

[0064] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0065] (1) The present invention takes into account the coupling between the frequency regulation link and the energy arbitrage link, the possibility of wind and solar power utilizing self-limiting power and energy storage to participate in the frequency regulation link, and the coordination of internal energy between wind and solar power and energy storage, and establishes a revenue control and cost control model for wind, solar and energy storage stations to participate in the reuse of multiple service energy, making the control model for wind, solar and energy storage stations to participate in the reuse of multiple service energy more comprehensive.

[0066] (2) The present invention fully considers the multivariate uncertainties in the process of wind, solar and storage stations participating in the multi-service energy reuse, and uses the Monte-Carlo and K-means clustering methods to quantify this multivariate uncertainty into a random optimization problem under multiple scenarios, making the multi-scenario random optimization model of wind, solar and storage stations participating in the multi-service energy reuse more realistic.

[0067] (3) The present invention corrects the final results of multi-scenario random optimization through the coordination of wind, solar and energy storage, and makes full use of the reserved capacity of energy storage and the coordination of wind and solar, so that the optimization results of wind, solar and energy storage stations participating in the multi-service energy reuse are more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0069] Figure 1 Flowchart of the wind-solar-storage station energy collaborative random optimization control method provided by an embodiment of the present invention;

[0070] Figure 2 Schematic diagram of the wind-solar-storage station energy collaborative random optimization control method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0071] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0072] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0073] like Figure 1-Figure 2 As shown, the wind-solar-storage station energy coordinated random optimization control method provided by the present invention includes the following steps:

[0074] Step 101: Based on the incentive signal of the wind-solar-storage station for multiple service energy reuse received by the wind-solar-storage station, a control model for the wind-solar-storage station to participate in the multiple service energy reuse is constructed. The control model includes a revenue control model and a cost control model.

[0075] (1) In the frequency regulation auxiliary service part of the multi-service, the wind, solar and storage stations, as the recipients of the incentive signal, report the capacity reserved for participating in frequency increase and frequency decrease on the day before, and complete the corresponding frequency regulation auxiliary service in real time according to the distribution mechanism on the next day. In the energy arbitrage link, the wind, solar and storage stations, as the complete recipients of the incentive signal, report their own power for participating in the energy arbitrage link. Therefore, the benefits of the wind, solar and storage stations participating in the energy reuse of the multi-service come from the reserved capacity benefits reported on the day before, as well as the mileage benefits of participating in the frequency regulation auxiliary service distributed in real time and the benefits in the energy arbitrage link. Specifically, it can be expressed as:

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[0084] In formula (1): They represent the total revenue of wind, solar and storage stations participating in multiple services, providing frequency adjustment and frequency reduction services, and the revenue in energy arbitrage. This shows that the total revenue of wind, solar and storage stations participating in multiple services is equal to the sum of the revenue from providing frequency adjustment and frequency reduction services and energy arbitrage. The revenue from providing frequency adjustment and frequency reduction services and energy arbitrage is calculated by formulas (2)-(4) respectively. In formulas (2)-(4), They respectively represent the power reported by wind, solar and storage stations for participating in frequency adjustment and down-regulation; They represent the power sent by wind, solar and storage stations when participating in frequency adjustment and down-regulation respectively; They represent the power of wind and solar power participating in the energy arbitrage link respectively; They represent the frequency regulation auxiliary service capacity incentive signal and mileage incentive signal respectively; Respectively represent the wind and solar incentive signal and time-sharing incentive signal in the energy arbitrage link. Among them, formula (2) indicates that considering the coupling between the energy arbitrage link and the frequency regulation auxiliary service link, the power released by participating in the frequency increase will also receive the benefits of the frequency regulation auxiliary service link and the energy arbitrage link. Formulas (5)-(6) are the power of the wind and solar storage station participating in the frequency increase and decrease, which means that the form of wind and solar storage station participating in the frequency increase is mainly the discharge of energy storage, and the form of participating in the frequency decrease is mainly the charging of energy storage and the self-limiting power of wind and solar. Indicates the energy storage charging and discharging power; Represents the self-limiting power of wind power and photovoltaic power. Equations (7)-(8) are the delivery mechanism of the frequency regulation auxiliary service link, where The upper limit of power adjustment for the frequency reported by wind, solar and storage stations announced recently; The total demand for real-time frequency adjustment is respectively: ES 、SI WT,PV They are the historical frequency regulation capacity-mileage ratios of energy storage and wind and solar power, respectively.

[0085] (2) The cost of wind-solar-storage stations participating in the multi-service energy reuse mainly includes the energy storage charging cost, the wind-solar self-limiting power cost, and the opportunity cost of wind-solar charging and energy storage, which can be specifically expressed as:

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[0090] In formula (9), They represent the total cost of wind-solar-storage station participating in joint frequency regulation and energy storage charging cost, the opportunity cost of wind-solar charging and energy storage, and the cost of wind-solar self-limiting electricity. The energy storage charging cost, the opportunity cost of wind-solar charging and energy storage, and the cost of wind-solar self-limiting electricity are calculated using equations (10)-(12).

[0091] Step 102: Determine uncertainty basic data according to the control model; the uncertainty basic data includes frequency regulation capacity, mileage incentive signal, wind and solar power output, and incentive signals in the energy arbitrage link.

[0092] Based on the regulation model, the uncertainty of wind, solar and storage participating in multiple service energy mainly comes from: the uncertainty of capacity incentive signals and mileage incentive signals in the frequency regulation auxiliary service link; the uncertainty of incentive signals in the energy arbitrage link; and the uncertainty of wind and solar output.

[0093] Step 103: Based on the uncertainty basic data, a Monte-Carlo algorithm is used to generate a random initial scene, and a K-means clustering algorithm is used to cluster the initial scene to obtain a clustered final scene.

[0094] In order to quantify this uncertainty, the various incentive signals and wind and solar power outputs in the basic data are first converted into per-unit values. Then, the Monte-Carlo method is used to generate random initial scenarios, and the K-means clustering method is used to reduce the initial scenarios.

[0095] Generally speaking, for wind and solar power, the uncertainty follows a specific distribution, but the random distribution of each stimulus signal is usually unknown. Therefore, to simplify the model, when generating the initial scene, the uncertainty per unit value of each stimulus signal and output that follows a standard normal distribution is uniformly generated:

[0096]

[0097] Formula (13) is the basis for generating the initial scene, where: Represents each uncertainty Equation (13) shows that the mean and standard deviation of the normal distribution are both the per-unit value of the uncertainty itself.

[0098] After generating the initial scene, in order to reduce the computational scale, the initial scene is reduced using K-means clustering:

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[0100]

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[0102] Formulas (14)-(16) are methods for determining cluster centers, where k is the number of scenes that are expected to be clustered; UNT is the set of all the non-missing quantitative components in the initial scene; ω i is the final cluster center; is the Euclidean square distance; τ represents the number of clustering; unt p Represents an element in a collection.

[0103] Step 104: Based on the final scenario after clustering, with the goal of maximizing total benefits and considering multiple constraints, a multi-scenario stochastic optimization model for wind, solar and storage participating in multi-service energy reuse is established.

[0104] According to the final scenario obtained by clustering in step 103, with the goal of maximizing total benefits, taking into account the internal energy coordination and related energy constraints of wind, solar and storage, as well as the life constraint and SoC constraint of energy storage, a multi-scenario stochastic optimization model for wind, solar and storage participating in multi-service energy reuse is established.

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[0109] In formula (17): They are the frequency increase and decrease services provided in scenario j at time t, the income in the energy arbitrage link, and the total cost of wind, solar and storage stations participating in the multi-service energy reuse, which can be obtained through equations (2)-(12). is the operating cost of the wind-solar-storage station, calculated by equations (18)-(20), where; The life loss cost of the wind, solar and storage station; Cost per 1% of life loss; is the life loss; β is the life loss rate; Cap ES is the total capacity of the wind, solar and storage station; C0 is the initial investment cost.

[0110] The constraints are:

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[0112] SoC t_start,j =SoC t_end,j (twenty two)

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[0124] Equations (21)-(23) are the SoC constraints of energy storage, loss self％ is the self-consumption rate; They are respectively the charging and discharging efficiency of energy storage; SoC t_start,j , SoC t_end,j They are the initial and final SoC values ​​of energy storage; SoC min , SoC max They are the minimum and maximum SoC values ​​allowed for energy storage, respectively. Among them, formula (21) is the SoC recursive relationship equality constraint; formula (22) stipulates that the SoC values ​​at the beginning and end are equal; formula (23) is the upper and lower limits of the SoC value. Formulas (24)-(25) are the life loss constraints of energy storage, D is the maximum daily allowable life loss; Lifeloss is the life loss replacement threshold; N is the expected number of years of use. Formulas (26)-(33) are the energy constraints of wind, solar and storage. A 0-1 variable that limits the energy storage from being charged and discharged simultaneously; are 0-1 variables that limit wind power, solar power charging and self-limiting power from occurring simultaneously; θ WT ,θ PV are the maximum permitted wind and solar self-limiting rates, respectively. Among them, equations (26) and (27) are the upper and lower limits of energy storage charging and discharging power and the limits on declaring participation in frequency regulation; equation (28) is the declared power limit of wind, solar and storage station self-limiting power; equation (29) is the limit that constrains storage from charging and discharging simultaneously; equations (30) and (31) are the upper and lower limits of wind and solar self-limiting power; equations (32) and (33) stipulate that wind and solar charging energy storage and self-limiting power cannot occur simultaneously.

[0125] Step 105: Determine the wind-solar-energy storage station output under the basic scenario according to the multi-scenario stochastic optimization model.

[0126] Step 106: Correct the wind-solar-energy-storage station output under the basic scenario to obtain a corrected wind-solar-energy-storage station output.

[0127] Step 107: With the goal of maximizing the benefit, a final output is selected from the wind-solar-energy-storage station output under the basic scenario and the revised wind-solar-energy-storage station output.

[0128] According to the optimization control model of the wind, solar and storage station and the multi-scenario random optimization model, the charging and discharging power, self-limiting power, wind and solar charging power to energy storage, and power involved in energy arbitrage of the wind, solar and storage station in the basic scenario are obtained. The basic scenario is defined as the scenario with the maximum clustering probability. However, although this result preliminarily takes into account the influence of other scenarios through weighted probability, when the k value, that is, when there are too many final clustering scenarios, the probability of the basic scenario is the largest, but when the convergence is poor, its value will not be too large, which means that the probability of the basic scenario does not have an absolute advantage. Therefore, a method of coordinated correction is proposed to correct the output results under the basic scenario. The method is as follows:

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[0146] Formula (34) is used to calculate the deviation between the energy storage charging and discharging power and the wind and solar self-limiting power in other scenarios and the basic scenario. They are the deviations of energy storage charging and discharging power and wind and solar self-limiting power in other scenarios from those in the basic scenario; are the energy storage charging and discharging power and the charging and discharging efficiency of wind and solar self-limiting power storage under the basic scenario. Equations (35)-(38) are the calculation formulas for the modified upward reserved charging and discharging power of energy storage under the conditions of upward reservation and downward reservation. They are the corrected charging and discharging powers reserved for upward and downward storage respectively; are the upward and downward reserves for energy storage charging and discharging, respectively. Equations (39)-(42) are the energy storage SoC constraints during the reserve correction process. Their meaning is that during the correction process, the SoC calculated according to the corrected energy storage charging and discharging power must be within the constraints, otherwise the energy storage is considered to have no reserve capacity. Equations (43)-(46) are the energy storage charge and discharge power constraints after correction. Their meaning is that during the correction process, the corrected charge and discharge must be within the constraints, otherwise the energy storage is considered to have no reserve capacity. Equations (47)-(50) are the power constraints for energy storage reserve. According to Equation (49), when energy storage reserves charging power upward, it is necessary not only to reserve the deviation between the charging power of other scenarios and the basic scenario, but also to reserve the deviation between the wind and solar self-limiting power of other scenarios and the basic scenario. In addition, when energy storage is in a state of no reserve capacity, the amount of wind and solar charging to energy storage can assist in the correction.

[0147] After the corrections, the wind, solar, and storage stations will report their output to the dispatch center based on the maximum total benefit from the upward reservation, downward reservation, and base scenarios. This final optimized power reported to the dispatch center firstly accounts for the coordination of wind, solar, and storage, and the simultaneous participation of multiple services, making it more widely applicable. Secondly, this result is the result of a random optimization after quantifying uncertainty. Furthermore, this result further modifies the random optimization result, fully considering the impact of other scenarios on the final result, resulting in higher accuracy. Therefore, this final result reported to the dispatch center solves the problems existing in existing technologies.

[0148] The present invention also provides a wind-solar-storage station energy coordinated random optimization control system, comprising:

[0149] A control model construction module is used to construct a control model for wind, solar and storage stations to participate in multiple service energy reuse based on the excitation signal of multiple service energy reuse received by the wind, solar and storage stations;

[0150] An uncertainty basic data determination module is used to determine uncertainty basic data according to the control model; the uncertainty basic data includes frequency regulation capacity, mileage incentive signal, wind and solar power output, and incentive signals in the energy arbitrage link;

[0151] A final scene determination module is used to generate a random initial scene based on the uncertainty basic data using a Monte-Carlo algorithm, and cluster the initial scene using a K-means clustering algorithm to obtain a clustered final scene;

[0152] A multi-scenario stochastic optimization model construction module is used to establish a multi-scenario stochastic optimization model for wind, solar and energy storage participating in multi-service energy reuse based on the final scenario after clustering, with the goal of maximizing total benefits and considering multiple constraints;

[0153] A wind-solar-energy storage station output determination module under a basic scenario, configured to determine the wind-solar-energy storage station output under a basic scenario according to the multi-scenario stochastic optimization model;

[0154] A correction module, configured to correct the wind-solar-energy-storage station output under the basic scenario to obtain a corrected wind-solar-energy-storage station output;

[0155] The final output selection module is used to select the final output from the wind-solar-energy storage station output under the basic scenario and the revised wind-solar-energy storage station output with the goal of maximizing the benefit.

[0156] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Reference can be made to the common and similar parts between the various embodiments. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple, and the relevant parts can be referred to the method description.

[0157] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only intended to help understand the method and core concept of the present invention. At the same time, those skilled in the art will find that the specific implementation methods and application scopes may vary based on the concept of the present invention. In summary, the contents of this specification should not be construed as limiting the present invention.

Claims

1. A method for stochastic optimization and control of wind, solar and storage station energy collaboration, characterized in that: include: Based on the incentive signals of multiple service energy reuse received by the wind, solar and storage stations, a control model for the wind, solar and storage stations to participate in multiple service energy reuse is constructed; Determine uncertainty basic data according to the control model; the uncertainty basic data includes frequency regulation capacity, mileage incentive signal, wind and solar power output, and incentive signals in the energy arbitrage link; Based on the uncertainty basic data, a Monte-Carlo algorithm is used to generate a random initial scene, and a K-means clustering algorithm is used to cluster the initial scene to obtain a clustered final scene; Based on the final scenario after clustering, a multi-scenario stochastic optimization model for wind, solar and storage participating in multi-service energy reuse is established with the goal of maximizing total benefits and considering multiple constraints. Determine the wind-solar energy storage station output under the basic scenario according to the multi-scenario stochastic optimization model; Correcting the wind-solar-energy-storage station output under the basic scenario to obtain a corrected wind-solar-energy-storage station output; With the goal of maximizing benefits, a final output is selected from the wind-solar-energy-storage station output under the basic scenario and the revised wind-solar-energy-storage station output; Wherein, the control model includes a revenue control model and a cost control model; The income regulation model is as follows: in, Indicates the total revenue of wind, solar and storage stations participating in diversified services. It means the income after the wind and solar storage station provides frequency increase. It means the income after the wind and solar storage station provides frequency reduction. It represents the income of wind and solar storage station in energy arbitrage. Indicates the power reported by the wind, solar and storage stations to participate in the frequency increase. Indicates the power reported by the wind, solar and storage stations to participate in frequency reduction; Indicates the power sent by the wind, solar and storage stations when participating in frequency increase. Indicates the power sent by the wind and solar storage station when participating in frequency reduction, P t E,PV Indicates the power of photovoltaics participating in the energy arbitrage link, P t E,WT Indicates the power of wind power participating in the energy arbitrage link, Indicates the frequency regulation auxiliary service capacity incentive signal, Indicates the mileage incentive signal, Indicates the wind and solar incentive signal in the energy arbitrage link, Indicates the time-sharing incentive signal in the energy arbitrage link, Indicates the energy storage charging power, Indicates the energy storage discharge power, Wind power self-limiting power, Indicates the photovoltaic self-limiting power, This indicates the upper limit of the frequency increase power reported by wind, solar and storage stations announced recently. This indicates the upper limit of the power reduction for the frequency reported by the wind, solar and storage stations announced recently. Indicates the total demand for real-time frequency increase, Indicates the total demand for real-time frequency reduction; SI ES Indicates the energy storage historical frequency regulation capacity mileage ratio, SI WT,PV Indicates the historical frequency regulation capacity-mileage ratio of wind and solar power; The cost control model is as follows: in, Indicates the total cost of wind, solar and storage stations participating in multiple service energy reuse, represents the energy storage charging cost, represents the opportunity cost of wind, solar, charging and energy storage, Represents the cost of wind and solar self-limiting electricity.

2. The method for stochastic optimization and control of wind-solar-storage station energy coordination according to claim 1 is characterized in that: The multi-scenario stochastic optimization model is as follows: in, represents the benefit of the wind-solar-storage station after the frequency increase in the jth scenario, represents the revenue after the frequency reduction provided by the wind-solar-storage station in the jth scenario, represents the income of the wind-solar-storage station in the energy arbitrage link under the jth scenario, represents the total cost of wind, solar and storage stations participating in multiple service energy reuse in the jth scenario, represents the operating cost of the wind-solar-storage station under the jth scenario, represents the life loss cost of the wind-solar-storage station under the jth scenario, represents the cost of every 1% life loss in the jth scenario, represents the life loss in the jth scenario, β represents the life loss rate, Cap ES represents the total capacity of the wind, solar and storage station, C0 represents the initial investment cost, represents the energy storage discharge power in the jth scenario, Represents the energy storage charging power in the jth scenario.

3. The method for stochastic optimization and control of wind-solar-storage station energy coordination according to claim 1 is characterized in that: The constraints include internal energy constraints of wind, solar and storage, life constraints of energy storage and SoC constraints.

4. The method for stochastic optimization and control of wind, solar and storage station energy coordination according to claim 2 is characterized in that: The formula for correcting the output of the wind-solar-energy storage station under the basic scenario is as follows: in, Indicates the deviation of energy storage discharge power in other scenarios from that in the basic scenario. Indicates the deviation of energy storage charging power in other scenarios from that in the basic scenario. Indicates the deviation of wind power self-limiting power in other scenarios from that in the basic scenario. Indicates the deviation of PV self-limiting power in other scenarios from that in the basic scenario. Indicates the energy storage charging power in the basic scenario, Indicates the energy storage discharge power in the basic scenario, Indicates the charging efficiency of wind and solar self-limited power storage, Indicates the discharge efficiency of wind and solar self-limited power storage, Indicates the corrected charging power reserved for energy storage. Indicates the corrected discharge power reserved for energy storage upwards, Indicates the corrected charging power reserved for energy storage. Indicates the corrected discharge power reserved for energy storage downwards, Indicates the upward reserve for energy storage charging, Indicates the upward reserve for energy storage discharge, Indicates the downward reserve for energy storage charging, Indicates the downward reserve for energy storage discharge, represents the energy storage SoC at time t+1 under the condition of upward reservation, represents the charging efficiency of energy storage, Indicates the discharge efficiency of energy storage, loss self％ Indicates the self-consumption rate, Cap ES Indicates the total capacity of the wind, solar and storage station. represents the energy storage discharge power at time t under the upward reservation condition, represents the energy storage charging power at time t under the upward reservation condition, represents the wind power charging to energy storage at time t under the condition of upward reservation, represents the photovoltaic power charging to the energy storage at time t under the condition of upward reservation, represents the energy storage SoC at time t+1 under the downward reservation condition, represents the energy storage discharge power at time t under downward reservation, represents the energy storage charging power at time t under downward reservation, represents the wind power charging to energy storage power at time t under the condition of downward reservation, Indicates the photovoltaic power charging to the energy storage at time t under the condition of downward reservation, SoC min Indicates the minimum SoC value allowed for energy storage, SoC max Indicates the maximum SoC value allowed by energy storage, Indicates the maximum energy storage discharge power, Indicates the maximum energy storage charging power.

5. A wind-solar-storage station energy coordinated random optimization control system applied to the wind-solar-storage station energy coordinated random optimization control method according to any one of claims 1 to 4, characterized in that: include: A control model construction module is used to construct a control model for wind, solar and storage stations to participate in multiple service energy reuse based on the excitation signal of multiple service energy reuse received by the wind, solar and storage stations; An uncertainty basic data determination module is used to determine uncertainty basic data according to the control model; the uncertainty basic data includes frequency regulation capacity, mileage incentive signal, wind and solar power output, and incentive signals in the energy arbitrage link; A final scene determination module is used to generate a random initial scene based on the uncertainty basic data using a Monte-Carlo algorithm, and cluster the initial scene using a K-means clustering algorithm to obtain a clustered final scene; A multi-scenario stochastic optimization model construction module is used to establish a multi-scenario stochastic optimization model for wind, solar and energy storage participating in multi-service energy reuse based on the final scenario after clustering, with the goal of maximizing total benefits and considering multiple constraints; A wind-solar-energy storage station output determination module under a basic scenario, configured to determine the wind-solar-energy storage station output under a basic scenario according to the multi-scenario stochastic optimization model; A correction module, configured to correct the wind-solar-energy-storage station output under the basic scenario to obtain a corrected wind-solar-energy-storage station output; The final output selection module is used to select the final output from the wind-solar-energy storage station output under the basic scenario and the revised wind-solar-energy storage station output with the goal of maximizing the benefit.

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