Multi-scene operation optimization method and device considering new energy and network construction energy storage

Abstract

The invention discloses a multi-scene operation optimization method and device considering new energy and network construction energy storage, and relates to the field of new energy and energy storage optimizing.According to the technical scheme, the method comprises the steps that an output data set of a new energy unit is obtained, the output data set is converted into a data matrix, the data matrix is clustered, and a clustering result is obtained; determining a plurality of demand scenes of new energy consumption and power supply capability of the power system; according to the plurality of demand scenes, constructing an adjustment model in which network construction energy storage participates in power system frequency adjustment and an output model of a new energy unit; according to the adjustment model and an output model of the new energy unit, constructing an objective function with minimum output volatility and output cost of the power system; and calling a multi-scene IGDT method to solve the target function under preset constraint conditions of a hydroelectric generating set, a photovoltaic generating set and network construction energy storage operation, and obtaining an output optimization result considering new energy and network construction energy storage scene operation.

Description

Technical Field

[0001] The present invention relates to the field of new energy and energy storage optimization, and more specifically, to a multi-scenario operation optimization method and device considering new energy and grid-building energy storage. Background Art

[0002] In remote areas with abundant hydropower, hydropower can be integrated with photovoltaic generators and energy storage equipment to build a hydro-photovoltaic-storage power station to achieve multiple energy coupling and complementarity. However, the transmission network in remote areas is often weak, and the photovoltaic system itself does not have inertia and frequency support capabilities, resulting in frequent frequency fluctuations in the power system in remote areas. The frequency support capability of network energy storage provides a solution to this problem.

[0003] At present, the grid connection of new energy equipment such as photovoltaics has brought certain volatility to the operation of the power system. Since there is a certain uncertainty in the predicted output of photovoltaic units, this has brought challenges to the complementary operation of hydro-photovoltaic-storage power stations. Summary of the invention

[0004] The purpose of the present invention is to provide a multi-scenario operation optimization method and device considering new energy and grid-connected energy storage. The present invention adopts information gap decision theory (IGDT method) to deal with the uncertainty of photovoltaic predicted output. However, since the traditional information gap decision theory calculates the fluctuation range of photovoltaic output based on a certain output scenario, the present invention uses random optimization theory to improve the traditional information gap decision theory. First, multiple demand scenarios are determined through the output data of new energy units, and then, according to the multiple demand scenarios, a regulation model for grid-connected energy storage to participate in the frequency regulation of the power system and an output model of the new energy unit are constructed; then, according to the regulation model and the output model of the new energy unit, an objective function of minimizing the output volatility and output cost of the power system is constructed; finally, under the preset constraints of the operation of hydropower units, photovoltaic units and grid-connected energy storage, the multi-scenario IGDT method is called to solve the objective function, and the output optimization result considering the operation of new energy and grid-connected energy storage scenarios is obtained, so as to calculate the average value of the photovoltaic predicted output fluctuation range under multiple demand scenarios, which provides a reference for the complementary operation of hydropower, photovoltaic and energy storage power stations.

[0005] In a first aspect of the present application, a multi-scenario operation optimization method considering new energy and grid-connected energy storage is provided, the method comprising:

[0006] Obtain the output data set of the new energy units, convert the output data set into a data matrix, and cluster the data matrix to determine multiple demand scenarios for the new energy consumption and power supply capacity of the power system;

[0007] According to multiple demand scenarios, a regulation model for grid-connected energy storage to participate in power system frequency regulation and an output model for new energy units are constructed;

[0008] According to the regulation model and the output model of the new energy units, the objective function of minimizing the output volatility and output cost of the power system is constructed;

[0009] Under the preset constraints of hydropower units, photovoltaic units and grid-connected energy storage operation, the multi-scenario IGDT method is called to solve the objective function and obtain the output optimization results considering the operation of new energy and grid-connected energy storage scenarios.

[0010] In one implementation, a K-means clustering algorithm is used to cluster the data matrix to determine multiple demand scenarios for the new energy consumption and power supply capabilities of the power system.

[0011] In one implementation, the objective function is expressed as:

[0012] minF=λ1F1+λ2F2; where F1 represents the output volatility of the power system, F2 represents the grid-connected energy storage operating cost, and λ1 and λ2 represent the weights of the output volatility of the power system and the grid-connected energy storage operating cost, respectively.

[0013] In one implementation, the expression of the output volatility of the power system is: Among them, P t sys Represents the output of the power system in each period, P av It represents the average value of the power system output;

[0014] Among them, the expression of the power system output in each period is: P t sys =P t H +P t PS +P t PV +P t GFM,dis -P t GFM,ch , where P t H Represents the output power of the hydropower unit, P t PS represents the output of the pumped storage unit, P t PV represents the electric power of the photovoltaic unit in period t, P t GFM,ch , P t GFM,dis They represent the charging and discharging power of the grid energy storage in period t respectively.

[0015] In one implementation, the expression of the grid-connected energy storage operation cost is:

[0016] Among them, c cur represents the unit abandonment penalty cost, P t f It indicates the frequency support power purchased from the upper power grid when the system cannot smooth the frequency fluctuation. t grid represents the power purchased by the hydro-solar-storage power station from the upper grid, c f represents the unit cost of grid energy storage to smooth fluctuations, P t f represents the grid-connected energy storage to smooth the fluctuating output, T represents the optimization period, c grid represents the price of electricity purchased from the power grid, Represents the predicted value of photovoltaic output, P t PV Indicates the actual photovoltaic output.

[0017] In one implementation, the multi-scenario IGDT method is called to solve the objective function and obtain the output optimization results considering the operation of new energy and grid-connected energy storage scenarios, including:

[0018] Calculate the objective function value without considering the output uncertainty of the new energy unit according to the objective function;

[0019] Construct an uncertain set of outputs of new energy units;

[0020] According to the objective function value and the uncertainty set, the first decision model under the risk aversion strategy and the second decision model under the opportunity seeking strategy are constructed;

[0021] The first decision model and the second decision model are solved to obtain the output results of the hydropower unit and the photovoltaic output fluctuation range of the photovoltaic unit.

[0022] In one implementation, the first decision model and the second decision model are solved to obtain the output result of the hydropower unit and the photovoltaic output fluctuation range of the photovoltaic unit, specifically:

[0023] The optimal value of the objective function is determined using the predicted value of photovoltaic output;

[0024] The actual available power of photovoltaic power is substituted for the predicted value, and the deviation factor of the first decision model and the second decision model is formulated;

[0025] The objective function, unit output and the photovoltaic output fluctuation range allowed by the hydro-PV-storage power station are optimized under the two strategies.

[0026] In one implementation, the constraints include photovoltaic operation constraints, grid energy storage operation constraints, hydropower and photovoltaic energy storage power station balance constraints, hydropower and photovoltaic energy storage power station quasi-steady-state constraints and hydropower output constraints.

[0027] In a second aspect of the present application, a multi-scenario operation optimization device considering new energy and grid-connected energy storage is provided, the device comprising:

[0028] The scenario determination module is used to obtain the output data set of the new energy unit, convert the output data set into a data matrix, and cluster the data matrix to determine multiple demand scenarios for the new energy consumption and power supply capacity of the power system;

[0029] Model building module, used to build a regulation model for grid-connected energy storage to participate in power system frequency regulation and an output model for new energy units based on multiple demand scenarios;

[0030] The target construction module is used to construct the target function of minimizing the output volatility and output cost of the power system according to the regulation model and the output model of the new energy unit;

[0031] The solution optimization module is used to call the multi-scenario IGDT method to solve the objective function under the preset constraints of the operation of hydropower units, photovoltaic units and grid-connected energy storage, and obtain the output optimization results considering the operation of new energy and grid-connected energy storage scenarios.

[0032] In one implementation, a K-means clustering algorithm is used to cluster the data matrix to determine the demand scenarios for the new energy consumption and power supply capacity of the power system.

[0033] Compared with the prior art, the present invention has the following beneficial effects:

[0034] The present invention adopts the information gap decision theory (IGDT method) to deal with the uncertainty of photovoltaic output prediction. However, since the traditional information gap decision theory calculates the fluctuation range of photovoltaic output based on a certain output scenario, the present invention uses the random optimization theory to improve the traditional information gap decision theory. First, multiple demand scenarios are determined through the output data of new energy units, and then, according to the multiple demand scenarios, a regulation model for grid-connected energy storage to participate in the frequency regulation of the power system and an output model of the new energy unit are constructed; then, according to the regulation model and the output model of the new energy unit, an objective function of minimizing the output volatility and output cost of the power system is constructed; finally, under the preset constraints of the operation of hydropower units, photovoltaic units and grid-connected energy storage, the multi-scenario IGDT method is called to solve the objective function, and the output optimization result considering the operation of new energy and grid-connected energy storage scenarios is obtained, so as to calculate the average value of the fluctuation range of photovoltaic predicted output under multiple demand scenarios, which provides a reference for the complementary operation of hydropower, photovoltaic and energy storage power stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0036] Figure 1 A flowchart of a multi-scenario operation optimization method considering new energy and grid-connected energy storage provided in an embodiment of the present application;

[0037] Figure 2 A schematic diagram of different photovoltaic scenarios for predicting output provided in an embodiment of the present application;

[0038] Figure 3 Operation diagrams of energy storage in different photovoltaic scenarios provided by the embodiments of the present application;

[0039] Figure 4 A graph showing the variation of uncertainty under different IGDT models provided in the embodiments of the present application;

[0040] Figure 5 A flow chart of a multi-scenario operation optimization device considering new energy and grid-connected energy storage is provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0042] It should be noted that the terms "include" or "may include" used in various embodiments of the present application indicate the presence of the function, operation or element applied for, and do not limit the addition of one or more functions, operations or elements. In addition, as used in various embodiments of the present application, the terms "include", "have" and their cognates are only intended to indicate specific features, numbers, steps, operations, elements, components or a combination of the foregoing items, and should not be understood as first excluding the presence of one or more other features, numbers, steps, operations, elements, components or a combination of the foregoing items or the possibility of adding one or more features, numbers, steps, operations, elements, components or a combination of the foregoing items.

[0043] It should be understood that terms such as "first" and "second" are only used for descriptive purposes and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the present invention, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0044] Please refer to Figure 1 , Figure 1 A flowchart of a multi-scenario operation optimization method considering new energy and grid-connected energy storage is provided in an embodiment of the present application, such as Figure 1 As shown, the method includes:

[0045] S101, obtaining an output data set of a new energy unit, converting the output data set into a data matrix, and clustering the data matrix to determine multiple demand scenarios for the new energy consumption and power supply capacity of the power system.

[0046] In this embodiment, the current energy supply status of new energy units in remote areas is analyzed to construct corresponding operation scenarios. First, constructing typical scenarios for new energy consumption can greatly reduce the scale of analysis and calculation. In order to make the scenarios for new energy consumption typical, on the one hand, the sufficiency and correctness of new energy consumption sample data must be guaranteed; on the other hand, the calculation effect of the scenario-based optimization model depends on the sufficiency of the number of scenarios. Even if the decomposition technology based on a specific structure is used, this dependence is still obvious. The construction of new energy scenarios such as wind power and photovoltaics should conform to the meteorological and climatic characteristics of remote areas and be consistent with the characteristics of historical new energy resources. The consumption scenario of new energy should be based on the existing installed capacity, keep the power generation output characteristics of existing photovoltaic power stations and wind farms unchanged, and should be based on the actual situation in remote areas and the power transmission limit of the interconnection lines between regions to analyze the grid consumption demand in the off-grid / grid-connected mode.

[0047] In some embodiments, a K-means clustering algorithm is used to cluster the data matrix to determine multiple demand scenarios for the new energy consumption and power supply capabilities of the power system.

[0048] Specifically, the scenario clustering method is used to aggregate the time series scenarios of renewable energy output and consumption to generate one or more representative typical time series scenarios of renewable energy output. The clustering process can be defined as follows: given n data points in a d-dimensional space, these n data points are divided into k clusters according to the similarity between the data points, that is, similar samples are in the same cluster, and different samples are in different clusters, so that objects in the same cluster have as great similarity as possible, and objects in different clusters have as great dissimilarity as possible. If a cluster containing n samples x1, x2…x n The dataset X is clustered into c subclasses X1, X2…X c , then X1, X2…X c Satisfies: X1∪X2…X c =X;X i ∩X j =f(1≤i≠j≤c).

[0049] Secondly, analyze the demand scenarios for improving power supply capacity in remote areas due to problems such as limited transmission capacity and insufficient voltage support capacity. Build a power supply capacity evaluation model for remote areas, and build corresponding power supply capacity improvement demand scenarios based on the evaluation results.

[0050] 1) Maximum power supply lower limit rate

[0051] The concept of the minimum power supply power refers to the probability that the maximum power supply power is less than the minimum power supply margin in the process of optimizing the power supply capacity. Under the premise of ensuring the reliability of the power grid, it is necessary to ensure that the final optimization result is much greater than the power supply margin target value β N The confidence level χ of (i) is expressed as follows based on the above analysis: k (0,β N )≤χ.

[0052] 2) Maximum power supply over-limit rate

[0053] This indicator refers to the probability that the maximum power supply power in the optimization process far exceeds the maximum value of the power supply margin. The method to ensure efficient and balanced strengthening of the power supply capacity of the distribution network is to define the confidence level χ that the over-limit power supply power is greater than the maximum value of the normal range. The expression of the over-limit power supply power is: N (β M ,∞)≤χ.

[0054] 3) Power supply fluctuation

[0055] The power supply volatility is essentially the actual change of the power supply power of the distribution network when optimizing the power supply capacity of the distribution network. The optimization of power supply volatility can effectively reduce the start and stop times of the distribution network units, and then realize the optimization of the distribution network for dispatching. The expression of the distribution network dispatch optimization objective function is obtained as follows: Where: R D (i) is the rate of change of power supply from the power grid; R P (i) is the real-time power of each load node in the power grid; m is the total duration of power supply capacity optimization; D F (i) is the output power summary of each branch at time i.

[0056] According to the specific power supply capacity evaluation mechanism, the actual power supply capacity improvement needs of the remote areas are analyzed, and the corresponding demand scenarios are constructed. Using the demand-driven multi-scale scenario modeling method, a comprehensive variety of demand scenarios for the clean energy consumption and power supply capacity improvement of the remote areas power grid in the grid-connected / off-grid mode are constructed.

[0057] S102, based on multiple demand scenarios, construct a regulation model for grid-connected energy storage to participate in power system frequency regulation and an output model for new energy units.

[0058] In this embodiment, the role of grid-building energy storage in the power system is clarified, and the relevant models and constraints of grid-building energy storage participating in grid frequency regulation are constructed. Most new energy units do not have rotating components themselves and cannot directly provide inertia or frequency support. This article equips grid-building energy storage equipment to achieve inertia support and frequency regulation energy supply.

[0059] The virtual inertia of network energy storage is expressed as follows:

[0060]

[0061] The required inertia of the power station should meet the following requirements: In the formula, represents the inertia time constant of GFM energy storage in period t; represents the electric power of photovoltaic in period t; Respectively represent the maximum and minimum historical output of photovoltaic power; Indicates the maximum and minimum values ​​of the inertia time constant of GFM energy storage. Indicates the virtual inertia provided by the grid energy storage. max Indicates the maximum frequency change rate allowed by the system. ΔP is the rated power of the grid energy storage. t tr represents the initial disturbance power.

[0062] The frequency modulation power provided by the grid-connected energy storage should meet the following constraints:

[0063] Where: P represents the primary frequency modulation power of GFM energy storage in the period t+nΔt; t GFM,ch , P t GFM,dis They represent the charging and discharging power of GFM energy storage during period t respectively; Indicates the overload capacity of GFM energy storage.

[0064] The grid energy storage operation constraints are constructed as follows: Where P t GFM ,dis , P t GFM,ch Indicates the power generation and charging power of the grid energy storage. Represents a 0-1 variable to constrain the grid energy storage to be in only one state, charging or discharging. Indicates the rated capacity of the grid energy storage.

[0065] In the formula, Represents the inertial support power of the grid energy storage, RoCoF t+nΔt represents the frequency fluctuation rate, Indicates the power shortage of the hydro-solar-storage power station. Represents the moment of inertia provided by the network energy storage.

[0066]

[0067] In the formula, Indicates the primary frequency modulation power of the grid energy storage. Indicates the charge of the grid energy storage, E ESS,min 、E ESS,max Indicates the minimum and maximum charge of the grid energy storage equipment.

[0068] S103, constructing an objective function for minimizing output volatility and output cost of the power system according to the regulation model and the output model of the new energy unit.

[0069] In this embodiment, the purpose of considering the complementary operation of hydropower, photovoltaic and storage power stations is to improve the system's ability to smooth fluctuations, while using grid energy storage to further improve the system's economy and reduce the amount of abandoned light. Therefore, the objective function simultaneously considers the minimum system output volatility and the optimal output cost.

[0070] The expression of the objective function is: minF=λ1F1+λ2F2; wherein F1 represents the output volatility of the power system, F2 represents the grid-connected energy storage operating cost, and λ1 and λ2 represent the weights of the output volatility of the power system and the grid-connected energy storage operating cost, respectively.

[0071] Among them, the expression of power system output volatility is: Among them, P t sys Represents the output of the power system in each period, P av Represents the average value of the power system output.

[0072] Among them, the expression of the power system output in each period is: P t sys =P t H +P t PS +P t PV +P t GFM,dis -P t GFM,ch , where P t H Represents the output power of the hydropower unit, P t PS represents the output of the pumped storage unit, P t PV represents the electric power of the photovoltaic unit in period t, P t GFM,ch , P t GFM,dis They represent the charging and discharging power of the grid energy storage in period t respectively.

[0073] The expression of grid energy storage operation cost is: Among them, c cur represents the unit abandonment penalty cost, P t f It indicates the frequency support power purchased from the upper power grid when the system cannot smooth the frequency fluctuation. t grid represents the power purchased by the hydro-solar-storage power station from the upper grid, c f represents the unit cost of grid energy storage to smooth fluctuations, P t f represents the grid-connected energy storage to smooth the fluctuating output, T represents the optimization period, c grid represents the price of electricity purchased from the power grid, Represents the predicted value of photovoltaic output, P t PV Indicates the actual photovoltaic output.

[0074] S104, under the preset constraints of the operation of hydropower units, photovoltaic units and grid-connected energy storage, the multi-scenario IGDT method is called to solve the objective function to obtain the output optimization result considering the operation of new energy and grid-connected energy storage scenarios.

[0075] Specifically, the preset constraints for the operation of hydropower units, photovoltaic units and grid energy storage are as follows:

[0076] 1. Photovoltaic operation constraints: In the formula, Represents the predicted value of photovoltaic output.

[0077] 2. Balance constraints of hydro-solar-storage power station: P t sys =P t H +P t PS +P t PV +P t grid +P t GFM,dis -P t GFM,ch , where P t sys Indicates the reported power of the hydro-solar-storage power station.

[0078] 3. Steady-state constraint: To ensure that the inertia support provided by the network energy storage equipment is sufficient, it is necessary to calculate the maximum frequency change rate of the system and construct the frequency deviation constraint, that is, In the formula, represents unbalanced power, and f0 represents the rated frequency of the system. t+nΔt , They represent the frequency change rate per unit time and the initial expected power fluctuation in the period t+nΔt respectively; ω max Indicates the maximum frequency change rate that the hydro-solar-storage power station can withstand; Δf st Indicates the system steady-state frequency deviation; Δf st,max It indicates the maximum steady-state frequency deviation that the hydro-solar-storage power station can withstand.

[0079] 4. Hydropower output constraints: Where P t H,min and P t H，max Respectively represent the minimum and maximum output of small hydropower stations. t Reservoir capacity, V t min 、V t max Respectively represent the upper and lower limits of the reservoir capacity. t Represents the water inflow to the reservoir, Q t Indicates the water consumption of the turbine unit, Indicates the minimum and maximum water consumption of the turbine.

[0080] Consider using information gap decision theory to deal with the uncertainty of photovoltaic forecasting, and further improve the traditional information gap decision theory by combining stochastic optimization theory. Based on multiple demand scenarios, the mean of the photovoltaic fluctuation range allowed by the hydro-photovoltaic storage power station is obtained to further enhance the ability of the hydro-photovoltaic storage power station to resist the fluctuation of new energy power. The solution process includes:

[0081] Calculate the objective function value without considering the output uncertainty of the new energy unit according to the objective function;

[0082] Construct an uncertain set of outputs of new energy units;

[0083] According to the objective function value and the uncertainty set, the first decision model under the risk aversion strategy and the second decision model under the opportunity seeking strategy are constructed;

[0084] The first decision model and the second decision model are solved to obtain the output results of the hydropower unit and the photovoltaic output fluctuation range of the photovoltaic unit.

[0085] In this embodiment, the output of the new energy unit is uncertain.

[0086] In the formula, Indicates the actual available output of new energy in the sth scenario, It indicates the output fluctuation range of renewable energy units allowed by the power station under the sth scenario.

[0087] The calculated objective function value is used as a constraint condition to construct the first decision model under the risk avoidance strategy and the second decision model under the opportunity seeking strategy.

[0088] Among them, the expression of the first decision model is In the formula, S represents the total number of demand scenarios, s represents the sth scenario, β RM represents the deviation coefficient under the risk-averse strategy.

[0089] Among them, the expression of the second decision model is In the formula, β OM represents the deviation coefficient under the opportunity seeking strategy.

[0090] It can be seen from the above two models that in order to facilitate the solution, the double-layer model is simplified to a single-layer model, and the average value of the allowable fluctuation range of the hydro-photovoltaic power station is solved in multiple scenarios. Under different strategies, the system's ability to smooth power fluctuations is further improved.

[0091] The specific steps are as follows: using the predicted value of photovoltaic output to determine the optimal value of the objective function;

[0092] The actual available power of photovoltaic power is substituted for the predicted value, and the deviation factor of the first decision model and the second decision model is formulated;

[0093] The objective function, unit output and the photovoltaic output fluctuation range allowed by the hydro-PV-storage power station are optimized under the two strategies.

[0094] This embodiment also provides a calculation example analysis: Figure 2 As shown in the figure, the calculation data set for this embodiment, the installed capacity of the photovoltaic generator is 100MW, and the output of the photovoltaic forecast scene in four different typical days is analyzed based on different maximum frequency change rates to balance the output of the grid energy storage for frequency fluctuations. Figure 3 As shown, it can be seen that when the photovoltaic output is abundant, the power shortage is mainly met by purchasing electricity from the upper-level power grid. The main function of grid-connected energy storage is to smooth out frequency fluctuations. When the photovoltaic output is relatively poor, grid-connected energy storage needs to shift electricity to maintain stable output of the power station.

[0095] If the uncertainty factor only takes into account the uncertainty of the photovoltaic unit, on this basis, the impact of different deviation factors on the operation of the hydro-photovoltaic storage power station under the IGDT robust model and the opportunity model is analyzed. Taking scenario 1 as an example, Figure 4As shown in the figure, as the deviation factor changes between 0.05 and 0.1, under the risk aversion model, the uncertainty increases, and the decision makers believe that uncertainty will have a negative impact on the reduction of scheduling costs. Therefore, as the deviation factor increases, the uncertainty increases, and the scheduling cost increases; under the opportunity seeking model, decision makers believe that uncertainty will have a positive impact on the reduction of scheduling costs. Therefore, as the deviation factor increases, the uncertainty increases, but the scheduling cost decreases.

[0096] Based on the embodiments described above, it can be seen that the present embodiment adopts the information gap decision theory (IGDT method) to deal with the uncertainty of photovoltaic predicted output. However, since the traditional information gap decision theory calculates the fluctuation range of photovoltaic output based on a certain output scenario, the present invention uses the random optimization theory to improve the traditional information gap decision theory. First, multiple demand scenarios are determined through the output data of the new energy unit, and then, according to the multiple demand scenarios, a regulation model for the grid-connected energy storage to participate in the frequency regulation of the power system and an output model of the new energy unit are constructed; then, according to the regulation model and the output model of the new energy unit, an objective function of minimizing the output volatility and output cost of the power system is constructed; finally, under the preset constraints of the operation of the hydropower unit, the photovoltaic unit and the grid-connected energy storage, the multi-scenario IGDT method is called to solve the objective function, and the output optimization result considering the operation of the new energy and grid-connected energy storage scenarios is obtained, thereby calculating the average value of the photovoltaic predicted output fluctuation range under multiple demand scenarios, which provides a reference for the complementary operation of the hydropower, photovoltaic and energy storage power stations.

[0097] Please refer to Figure 5 , Figure 5 A schematic diagram of a process flow of a multi-scenario operation optimization device considering new energy and grid-connected energy storage is provided in an embodiment of the present application, such as Figure 5 As shown, the device comprises:

[0098] A scenario determination module 510 is used to obtain an output data set of a new energy unit, convert the output data set into a data matrix, and cluster the data matrix to determine multiple demand scenarios for the new energy consumption and power supply capacity of the power system;

[0099] Model building module 520, for building a regulation model for grid-connected energy storage to participate in power system frequency regulation and an output model for new energy units according to multiple demand scenarios;

[0100] The target construction module 530 is used to construct the target function of minimizing the output volatility and output cost of the power system according to the regulation model and the output model of the new energy unit;

[0101] The solution optimization module 540 is used to call the multi-scenario IGDT method to solve the objective function under the preset constraints of the operation of the hydropower unit, photovoltaic unit and grid energy storage, and obtain the output optimization result considering the operation of the new energy and grid energy storage scenarios.

[0102] It can be seen that the present embodiment adopts the information gap decision theory (IGDT method) to deal with the uncertainty of photovoltaic predicted output, but because the traditional information gap decision theory calculates the fluctuation range of photovoltaic output based on a certain output scenario, the present invention uses the random optimization theory to improve the traditional information gap decision theory. First, multiple demand scenarios are determined through the output data of the new energy unit, and then, according to the multiple demand scenarios, a regulation model for the grid-connected energy storage to participate in the frequency regulation of the power system and an output model of the new energy unit are constructed; then, according to the regulation model and the output model of the new energy unit, an objective function of minimizing the output volatility and output cost of the power system is constructed; finally, under the preset constraints of the operation of the hydropower unit, the photovoltaic unit and the grid-connected energy storage, the multi-scenario IGDT method is called to solve the objective function, and the output optimization result considering the operation of the new energy and grid-connected energy storage scenarios is obtained, thereby calculating the average value of the photovoltaic predicted output fluctuation range under multiple demand scenarios, which provides a reference for the complementary operation of the hydropower, photovoltaic and energy storage power stations.

[0103] In some embodiments, a K-means clustering algorithm is used to cluster the data matrix to determine the demand scenarios for the new energy consumption and power supply capacity of the power system.

[0104] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A multi-scenario operation optimization method considering new energy and grid-connected energy storage, characterized in that: Methods include: Obtain the output data set of the new energy units, convert the output data set into a data matrix, and cluster the data matrix to determine multiple demand scenarios for the new energy consumption and power supply capacity of the power system; According to multiple demand scenarios, a regulation model for grid-connected energy storage to participate in power system frequency regulation and an output model for new energy units are constructed; According to the regulation model and the output model of the new energy units, the objective function of minimizing the output volatility and output cost of the power system is constructed; Under the preset constraints of hydropower units, photovoltaic units and grid-connected energy storage operation, the multi-scenario IGDT method is called to solve the objective function and obtain the output optimization results considering the operation of new energy and grid-connected energy storage scenarios.

2. The method according to claim 1, characterized in that The K-means clustering algorithm is used to cluster the data matrix to determine multiple demand scenarios for the new energy consumption and power supply capacity of the power system.

3. The method according to claim 1, characterized in that The expression of the objective function is: minF=λ1F1+λ2F2; where F1 represents the output volatility of the power system, F2 represents the grid-connected energy storage operating cost, and λ1 and λ2 represent the weights of the output volatility of the power system and the grid-connected energy storage operating cost, respectively.

4. The method according to claim 3, characterized in that The expression of the output fluctuation of the power system is: Among them, P t sys Represents the output of the power system in each period, P av It represents the average value of the power system output; Among them, the expression of the power system output in each period is: P t sys =P t H +P t PS +P t PV +P t GFM,dis -P t GFM,ch , where P t H Represents the output power of the hydropower unit, P t PS Indicates the output of the pumped storage unit, P t PV represents the electric power of the photovoltaic unit in period t, P t GFM,ch , P t GFM,dis They represent the charging and discharging power of the grid energy storage in period t respectively.

5. The method according to claim 3, characterized in that: The expression of the grid energy storage operation cost is: Among them, c cur represents the unit abandonment penalty cost, P t f It indicates the frequency support power purchased from the upper power grid when the system cannot smooth the frequency fluctuation. t grid represents the power purchased by the hydro-solar-storage power station from the upper grid, c f represents the unit cost of grid energy storage to smooth fluctuations, P t f represents the grid-connected energy storage to smooth the fluctuating output, T represents the optimization period, c grid represents the price of electricity purchased from the power grid, Represents the predicted value of photovoltaic output, P t PV Indicates the actual photovoltaic output.

6. The method according to claim 1, characterized in that The multi-scenario IGDT method is called to solve the objective function and obtain the output optimization results considering the operation of new energy and grid-connected energy storage scenarios, including: Calculate the objective function value without considering the output uncertainty of the new energy unit according to the objective function; Construct an uncertain set of outputs of new energy units; According to the objective function value and the uncertainty set, the first decision model under the risk aversion strategy and the second decision model under the opportunity seeking strategy are constructed; The first decision model and the second decision model are solved to obtain the output results of the hydropower unit and the photovoltaic output fluctuation range of the photovoltaic unit.

7. The method according to claim 6, characterized in that Solve the first decision model and the second decision model to obtain the output results of the hydropower unit and the photovoltaic output fluctuation range of the photovoltaic unit, specifically: The optimal value of the objective function is determined using the predicted value of photovoltaic output; The actual available power of photovoltaic power is substituted for the predicted value, and the deviation factor of the first decision model and the second decision model is formulated; The objective function, unit output and the photovoltaic output fluctuation range allowed by the hydro-PV-storage power station are optimized under the two strategies.

8. The method according to claim 1, characterized in that The constraints include photovoltaic operation constraints, grid-connected energy storage operation constraints, hydropower, photovoltaic and energy storage power station balance constraints, hydropower, photovoltaic and energy storage power station quasi-steady-state constraints and hydropower output constraints.

9. A multi-scenario operation optimization device considering new energy and grid-connected energy storage, characterized in that: The device includes: The scenario determination module is used to obtain the output data set of the new energy unit, convert the output data set into a data matrix, and cluster the data matrix to determine multiple demand scenarios for the new energy consumption and power supply capacity of the power system; Model building module, used to build a regulation model for grid-connected energy storage to participate in power system frequency regulation and an output model for new energy units based on multiple demand scenarios; The target construction module is used to construct the target function of minimizing the output volatility and output cost of the power system according to the regulation model and the output model of the new energy unit; The solution optimization module is used to call the multi-scenario IGDT method to solve the objective function under the preset constraints of the operation of hydropower units, photovoltaic units and grid-connected energy storage, and obtain the output optimization results considering the operation of new energy and grid-connected energy storage scenarios.

10. The device according to claim 9, characterized in that The K-means clustering algorithm is used to cluster the data matrix to determine the demand scenarios for the new energy consumption and power supply capacity of the power system.

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