Large-scale energy base energy storage configuration method considering new energy consumption

Abstract

The invention relates to the technical field of power system automatic control, in particular to a large-scale energy base energy storage configuration method considering new energy consumption, which comprises the following steps: S1, data collection: collecting meteorological station data and collecting load data; s2, prediction network establishment: forming a trained output prediction network; s3, load data clustering and load scene construction: constructing K types of load scenes, and generating K types of delivery plan curves; s4, output curve prediction: forming a wind power plant and photovoltaic power station prediction output curve; s5, transmission power constraint and confidence probability analysis: setting different power transmission confidence probabilities in combination with the matching condition of the outgoing active planned power and the load curve of the receiving-end power grid; s6, optimal configuration of the energy storage system: adopting an NSGA-II algorithm for optimization, and finally determining the optimal configuration capacity of the energy storage system; according to the invention, the stability of the power transmission plan power can be ensured, and the power transmission efficiency of the DC channel is maximized.

Description

Technical Field

[0001] The present invention relates to the technical field of power system automatic control, and in particular to a large-scale energy base energy storage configuration method taking into account new energy consumption. Background Art

[0002] Accelerate the construction of large-scale wind power and photovoltaic bases with a focus on deserts, Gobi and wasteland areas. The construction of large-scale new energy bases is the focus of renewable energy development. However, the surrounding areas of large energy bases often have small electricity loads, limited local consumption scale, and a long construction period for dedicated transmission channels. The consumption of new energy faces difficulties. In addition, with the implementation of midday valley electricity prices in various places, the income of photovoltaic projects has been significantly affected. The on-grid electricity price has fallen and the consumption cost has risen. Some projects are no longer economical. In this case, the advantages of new energy storage should be brought into play to form a reasonable ratio of wind, light and storage. Energy storage helps to smooth out fluctuations in new energy output and optimize the energy transmission curve. It plays a key role in improving the new energy consumption capacity and improving the economic situation of large energy bases such as Shagoba and Huang.

[0003] At present, there are few studies on the storage capacity of large energy bases. Most new energy storage projects are fixed values ​​required by regulations. Some studies have high requirements for historical wind and solar power output data, which are not suitable for large base projects that lack historical data. If the energy storage capacity is too large, the system cost will be too high, which will increase the economic burden of large energy base projects. If the energy storage capacity is too small, it will not be conducive to balancing the fluctuations in new energy output and may cause problems such as instability of the power system. In order to solve the problems of poor output stability of the main power supply of large energy bases, high electricity consumption and regulation costs at the sending and receiving ends, it is necessary to consider maximizing the matching degree between new energy consumption and the load curve of the receiving power grid, and study the optimal energy storage capacity configuration method for large energy bases. Summary of the invention

[0004] The present invention provides a large-scale energy base energy storage configuration method taking into account the consumption of new energy.

[0005] A method for configuring energy storage in a large energy base considering the consumption of new energy sources, comprising the following steps:

[0006] S1, data collection: collect weather station data in large energy base areas, including wind speed and direction, temperature, solar radiation intensity and altitude angle, air humidity and atmospheric pressure within ten minutes of forecast and actual measurement, and collect load data of 96 points at the receiving end of the access node in the area for more than half a year;

[0007] S2, forecast network establishment: Based on the supervised BP neural network, the regional meteorological data is input to form a trained output forecast network;

[0008] S3, load data clustering and load scenario construction: use K-means algorithm to cluster load data, use silhouette coefficient method to determine the number of clusters, construct K-type load scenarios, and generate K-type delivery plan curves;

[0009] S4, output curve prediction: Based on the output prediction network, meteorological data is selected to form the predicted output curves of wind farms and photovoltaic power stations;

[0010] S5, Transmission power constraint and confidence probability analysis: Considering the K-type load scenario, combined with the matching of the planned active power transmission and the load curve of the receiving power grid, different transmission confidence probabilities are set;

[0011] S6, energy storage system optimization configuration: By analyzing energy storage reliability, defining charging and discharging constraints, constructing a dual objective function, and using the NSGA-II algorithm for optimization, the optimal configuration capacity of the energy storage system is finally determined.

[0012] Optionally, the prediction network establishment in S2 includes:

[0013] S21, normalization of meteorological data: normalization of forecast and measured meteorological data, non-wind direction and illumination angle normalization is expressed as:

[0014]

[0015] Among them, λ t is the value of weather forecast, |λ max | is the absolute value of the maximum value monitored in the region;

[0016] S22, BP neural network training: input the normalized meteorological data into the traditional BP neural network for training, record the model parameters that ultimately meet the error requirements, and form a wind power and photovoltaic output prediction network.

[0017] Optionally, the normalized meteorological data and the wind power and photovoltaic output fitness value function are expressed as:

[0018]

[0019] Among them, P w is the wind power output, A is the swept area of ​​the wind rotor blades, ρ is the air density, η is the conversion efficiency of the photovoltaic panel at a fixed angle, P pv is the photovoltaic output power, S is the total area of ​​the photovoltaic panel, Pr g , H g , T g , P b They are atmospheric pressure, humidity, temperature, and saturated water vapor pressure respectively.

[0020] Optionally, the silhouette coefficient method is expressed as:

[0021]

[0022] Among them, a(i) is the intra-cluster dissimilarity, b(i) is the inter-cluster dissimilarity, and n is the number of load curves.

[0023] Optionally, the transmission power constraint and confidence probability analysis in S5 includes:

[0024] S51, Transmission Plan Power Constraints: Define the constraints on the transmission plan power of large energy bases and receiving power grids, expressed as:

[0025]

[0026] Where T is the number of time periods, u t is a 0-1 variable, u t =1 means adjusting the DC channel transmission power during time period t, u t =0 means that the transmission curve power is not adjusted during time period t, P t ETP is the planned power of the transmission curve for time period t, M is the coefficient of the big M method, I is the number of load curves corresponding to the current scenario category, is the value of the i-th load curve in time period t, and Δt is the time interval of the unit time period;

[0027] S52, maximize DC channel efficiency: optimize the matching degree between the transmission curve and the load curve to maximize the DC channel efficiency, expressed as:

[0028]

[0029] S53, confidence probability constraint: For typical load scenarios of type K, set the confidence probability constraint of the power transmission curve.

[0030] Optionally, the power transmission curve confidence probability constraint is expressed as:

[0031]

[0032] Where ρ=[ρ1,ρ2,...,ρ K ] T is the K-dimensional probability distribution vector, ρ k The probability of the occurrence of the k-dimensional scene, z = [z1, z2, ..., z k ] T is a K-dimensional 0-1 variable, z k =0 means that scenario K is a fully powered scenario, i.e., the external power transmission plan is met, z k=1 indicates that scenario K is an incomplete power transmission scenario. There are time periods when the planned power transmission is not met. The probability of the incomplete power transmission scenario does not exceed 1-a, where a represents the probability that the actual power transmission of the large base meets the power transmission plan in all time periods.

[0033] Optionally, the energy storage system optimization configuration in S6 includes:

[0034] S61, Reliability analysis of energy storage system grid connection: Considering the reliability of energy storage system grid connection, the energy storage power state equation is established;

[0035] S62, charge and discharge constraints: define the constraints of the charge and discharge process of the energy storage system;

[0036] S63, construction of objective function: considering the matching degree between new energy consumption and transmission curve load, construct a dual objective function to minimize the difference between the transmission curve and the receiving end load curve and maximize new energy consumption, while considering the economic feasibility of energy storage investment;

[0037] S64, optimization of dual objective function: The NSGA-II algorithm is used to optimize the dual objective function. Through comparative analysis, the optimal capacity of the energy storage system configuration under the best optimization goal is finally obtained.

[0038] Optionally, the energy storage capacity state equation is expressed as:

[0039]

[0040] Among them, E k,t+1 ,E k,t are the energy stored in the energy storage in time period t under scenario k, is the power transmitted from wind, solar and thermal power to energy storage in period t of scenario k, is the energy storage transmission power in scenario k and period t, and η is the energy storage charging and discharging efficiency.

[0041] Optionally, the charge and discharge constraints in S62 include:

[0042]

[0043] Among them, E d 、E u , P u They are the lower limit of energy storage, the upper limit of energy storage and the upper limit of energy storage power respectively.

[0044] Optionally, the dual objective function in S63 is expressed as:

[0045]

[0046] Among them, P t netis the fluctuating power of the receiving grid, P t ws is the comprehensive output power of wind and solar power generation, P t ref is the energy storage output power, P t g is the thermal power output power, C inv is the comprehensive investment cost, c p 、c e are the unit power and unit capacity investment costs of energy storage, P e 、E e are the unit power investment costs of photovoltaic and wind power, c l is the average renewable energy curtailment rate, C wsl The cost of building new energy.

[0047] Beneficial effects of the present invention:

[0048] The present invention uses BP neural network to make accurate output predictions, and combines K-means algorithm to cluster load data and construct scenarios, ensuring that the energy storage system is reasonably scheduled according to different load demands, thereby improving the matching degree between the transmission curve and the load curve. The set transmission curve confidence probability constraint can ensure the stability of the transmission plan power and maximize the transmission efficiency of the DC channel.

[0049] The present invention optimizes the configuration of the energy storage system and optimizes the dual objective function using the NSGA-II algorithm, which can minimize the difference between the transmission curve and the load curve, maximize the efficiency of new energy consumption, and effectively control the investment cost of the energy storage system. The grid-connected reliability analysis and charge and discharge constraints of the energy storage system ensure the stable operation of the energy storage system in different scenarios, avoid the economy and stability problems caused by unreasonable energy storage capacity, and provide an economical and efficient energy storage configuration solution for large energy bases. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 This is a schematic diagram of meteorological normalized data according to an embodiment of the present invention;

[0052] Figure 2 A schematic diagram of a typical load clustering scenario in an embodiment of the present invention;

[0053] Figure 3The figure is a flow chart of a configuration method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies; and the accompanying drawings are only for more specific description of the embodiments, and are not intended to specifically limit the present invention.

[0055] It should be noted that the references to "one embodiment", "an embodiment", "an exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0056] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0057] like Figure 1-Figure 3 As shown, a large-scale energy base energy storage configuration method considering new energy consumption includes the following steps:

[0058] (1) Collect the forecast and actual measurement data of wind speed and direction, temperature, solar radiation intensity and its altitude angle, and air density within ten minutes in the area where the large base is established for more than half a year, and the time-sharing load data of the receiving end power grid at the access node in the area for more than half a year.

[0059] (2) Normalize the forecast and measured meteorological data, train them using the traditional BP neural network, record the parameters that ultimately meet the error, and form a wind power and photovoltaic output prediction network. The method for normalizing non-wind direction and light angle is:

[0060]

[0061] In the formula, λ t is the value of weather forecast, |λ max | is the absolute value of the maximum value monitored in the region. In addition, the wind direction and light angle are normalized by sine and cosine value mapping.

[0062] For normalized meteorological data, its adaptability function with wind power and photovoltaic output is:

[0063]

[0064] Where P w is the wind power output (MW); A is the swept area of ​​the wind rotor blades (m 2 );ρ is air density (kg / m 3 ), η is the conversion efficiency of the photovoltaic panel at a fixed angle (%); P pv is the photovoltaic output power (MW); S is the total area of ​​the photovoltaic panel (m 2 );Pr g , H g , T g , P b They are atmospheric pressure, humidity, temperature, and saturated water vapor pressure respectively.

[0065] For the load curve of the receiving power grid, the K-means algorithm is used to cluster the load data and construct K typical load scenarios. The number of clusters is calculated using the silhouette coefficient method:

[0066]

[0067] Among them, a(i) is the intra-cluster dissimilarity, b(i) is the inter-cluster dissimilarity, and n is the number of load curves. By calculating the average silhouette coefficient for different K values, the K value that maximizes the average silhouette coefficient is selected as the optimal number of clusters.

[0068] (3) Based on the trained new energy output prediction network, typical meteorological data are selected to form typical output curves for wind farms and photovoltaic power stations.

[0069] (4) The planned power transmission between the large base and the receiving power grid must meet the following constraints:

[0070]

[0071] In the formula, T is the number of time periods, u t is a 0-1 variable, u t =1 means adjusting the DC channel transmission power during time period t, u t =0 means that the transmission curve power is not adjusted during time period t; P t ETP is the planned power of the transmission curve for time period t; M is the coefficient of the big M method, and I is the number of load curves corresponding to the current scenario category; is the value of the i-th load curve in time period t, and Δt is the time interval of the unit time period.

[0072] At the same time, the transmission curve should be kept as close to the load curve as possible to maximize the efficiency of the DC channel:

[0073]

[0074] For the typical load scenario of Class K, the following power transmission curve confidence probability constraints are met:

[0075]

[0076] Where ρ=[ρ1,ρ2,...,ρ K ] T is the K-dimensional probability distribution vector, ρ k The probability of the occurrence of the k-dimensional scene, z = [z1, z2, ..., z k ] T is a K-dimensional 0-1 variable; z k =0 means that scenario K is a fully powered scenario, i.e., the external power transmission plan is met, z k =1 indicates that scenario K is an incomplete power transmission scenario, and there are periods when the planned power transmission is not met; the probability of the incomplete power transmission scenario does not exceed 1-a, where a represents the probability that the actual power transmission of the large base meets the transmission plan in all periods.

[0077] (5) Considering the reliability of the energy storage system connected to the grid, the energy storage state equation can be expressed as:

[0078]

[0079] In the formula, E k,t+1 ,E k,t are the energy stored in the energy storage in time period t under scenario k, is the power transmitted from wind, solar and thermal power to energy storage in period t of scenario k, is the energy storage delivery power in scenario k and time period t, and η is the energy storage charging and discharging efficiency. The energy storage charging and discharging constraints are as follows:

[0080]

[0081] In the formula, E d 、E u , P u They are the lower limit of energy storage, the upper limit of energy storage and the upper limit of energy storage power respectively.

[0082] Considering the matching degree between the new energy consumption and the transmission curve load, based on the above scheme, the objective function is to minimize the difference between the transmission curve and the receiving end load curve, and maximize the new energy consumption, and consider the economic efficiency of energy storage investment:

[0083]

[0084] Where Pt net is the fluctuating power of the receiving grid; P t ws P is the comprehensive output power of wind and solar power generation; t ref is the energy storage output power; P t g is the thermal power output power; C inv is the comprehensive investment cost, c p 、c e are the unit power and unit capacity investment costs of energy storage, P e 、E e are the unit power investment costs of photovoltaic and wind power, c l is the average renewable energy curtailment rate, C wsl The cost of building new energy.

[0085] NSGA-II is used to optimize the dual objective function, and the optimal capacity of the energy storage system configuration under the best optimization objective is obtained through comparative analysis.

[0086] The following are some examples:

[0087] Taking the relevant data of a large base in a certain region as an example, the simulation obtains the appropriate energy storage configuration capacity of a large energy base considering the consumption of new energy. The scale of the wind farm is selected as 100MW and the scale of the photovoltaic single station is 40MW. The day with the highest prediction accuracy is selected as the typical meteorological day, and the normalized meteorological data is obtained as follows Figure 1 By clustering the load data, the silhouette coefficient is the largest when K=5. At this time, the typical load clustering data is as follows: Figure 2 .

[0088] This produces a typical wind and solar output curve and the corresponding transmission plan power curve under different load scenarios (typical load 2 is taken as an example):

[0089] Table 1 Predicted wind and solar power output and power transmission in typical scenarios

[0090]

[0091]

[0092] The proportion of load curves in the cluster is used as the probability of scene occurrence, ρ = [0.26, 0.21, 0.14, 0.35, 0.04] T , take η = 0.8, c p = 600,000 yuan / MW, c e =1.5 million yuan / MWh, C wsl= 460 million yuan, set the NSGA-II algorithm population to 100, the number of iterations to 200, set a = 0.95 and a = 1.0 respectively to solve the objective function, and the results are as follows:

[0093] Table 2 Iteration results

[0094]

[0095] Increasing the power transmission confidence probability from 0.95 to 1 will significantly increase the investment cost of energy storage capacity, but it can effectively reduce the maximum power fluctuation. Large fluctuations in power transmission are usually caused by very few extreme scenarios. Therefore, the energy storage configuration result of the large base in the case is 17.21MW / 60.1MWh. At this time, the energy storage power capacity accounts for 12.29% of the installed capacity of new energy, which is in line with reality and can provide a reference for the energy storage capacity configuration of large energy bases.

[0096] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0097] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for configuring energy storage in a large energy base considering the consumption of new energy, characterized in that: The following steps are involved: S1, data collection: collect weather station data in large energy base areas, including wind speed and direction, temperature, solar radiation intensity and altitude angle, air humidity and atmospheric pressure within ten minutes of forecast and actual measurement, and collect load data of 96 points at the receiving end of the access node in the area for more than half a year; S2, forecast network establishment: Based on the supervised BP neural network, the regional meteorological data is input to form a trained output forecast network; S3, load data clustering and load scenario construction: use K-means algorithm to cluster load data, use silhouette coefficient method to determine the number of clusters, construct K-type load scenarios, and generate K-type delivery plan curves; S4, output curve prediction: Based on the output prediction network, meteorological data is selected to form the predicted output curves of wind farms and photovoltaic power stations; S5, Transmission power constraint and confidence probability analysis: Considering the K-type load scenario, combined with the matching of the planned active power transmission and the load curve of the receiving power grid, different transmission confidence probabilities are set; S6, energy storage system optimization configuration: By analyzing energy storage reliability, defining charging and discharging constraints, constructing a dual objective function, and using the NSGA-II algorithm for optimization, the optimal configuration capacity of the energy storage system is finally determined.

2. A method for configuring energy storage in a large energy base considering new energy consumption according to claim 1, characterized in that: The prediction network establishment in S2 includes: S21, normalization of meteorological data: normalization of forecast and measured meteorological data, non-wind direction and illumination angle normalization is expressed as: Among them, λ t is the value of weather forecast, |λ max | is the absolute value of the maximum value monitored in the region; S22, BP neural network training: input the normalized meteorological data into the traditional BP neural network for training, record the model parameters that ultimately meet the error requirements, and form a wind power and photovoltaic output prediction network.

3. A method for configuring energy storage in a large energy base considering new energy consumption according to claim 2, characterized in that: The normalized meteorological data and wind power and photovoltaic output adaptability value function are expressed as: Among them, P w is the wind power output, A is the swept area of ​​the wind rotor blades, ρ is the air density, η is the conversion efficiency of the photovoltaic panel at a fixed angle, P pv is the photovoltaic output power, S is the total area of ​​the photovoltaic panel, Pr g , H g , T g , P b They are atmospheric pressure, humidity, temperature, and saturated water vapor pressure respectively.

4. A method for configuring energy storage in a large energy base considering new energy consumption according to claim 3, characterized in that: The silhouette coefficient method is expressed as: Among them, a(i) is the intra-cluster dissimilarity, b(i) is the inter-cluster dissimilarity, and n is the number of load curves.

5. A method for configuring energy storage in a large energy base considering new energy consumption according to claim 4, characterized in that: The transmission power constraint and confidence probability analysis in S5 includes: S51, Transmission Plan Power Constraints: Define the constraints on the transmission plan power of large energy bases and receiving power grids, expressed as: Where T is the number of time periods, u t is a 0-1 variable, u t =1 means adjusting the DC channel transmission power during time period t, u t =0 means that the transmission curve power is not adjusted during time period t, P t ETP is the planned power of the transmission curve for time period t, M is the coefficient of the big M method, I is the number of load curves corresponding to the current scenario category, is the value of the i-th load curve in time period t, and Δt is the time interval of the unit time period; S52, maximize DC channel efficiency: optimize the matching degree between the transmission curve and the load curve to maximize the DC channel efficiency, expressed as: S53, confidence probability constraint: For typical load scenarios of type K, set the confidence probability constraint of the power transmission curve.

6. A method for configuring energy storage in a large energy base considering new energy consumption according to claim 5, characterized in that: The power transmission curve confidence probability constraint is expressed as: Where ρ=[ρ1,ρ2,...,ρ K ] T is the K-dimensional probability distribution vector, ρ k The probability of the occurrence of the k-dimensional scene, z = [z1, z2, ..., z k ] T is a K-dimensional 0-1 variable, z k =0 means that scenario K is a fully powered scenario, i.e., the external power transmission plan is met, z k =1 indicates that scenario K is an incomplete power transmission scenario. There are time periods when the planned power transmission is not met. The probability of the incomplete power transmission scenario does not exceed 1-a, where a represents the probability that the actual power transmission of the large base meets the power transmission plan in all time periods.

7. A method for configuring energy storage in a large energy base considering new energy consumption according to claim 6, characterized in that: The energy storage system optimization configuration in S6 includes: S61, Reliability analysis of energy storage system grid connection: Considering the reliability of energy storage system grid connection, the energy storage power state equation is established; S62, charge and discharge constraints: define the constraints of the charge and discharge process of the energy storage system; S63, construction of objective function: considering the matching degree between new energy consumption and transmission curve load, construct a dual objective function to minimize the difference between the transmission curve and the receiving end load curve and maximize new energy consumption, while considering the economic feasibility of energy storage investment; S64, optimization of dual objective function: The NSGA-II algorithm is used to optimize the dual objective function. Through comparative analysis, the optimal capacity of the energy storage system configuration under the best optimization goal is finally obtained.

8. A method for configuring energy storage in a large energy base considering new energy consumption according to claim 7, characterized in that: The energy storage state equation is expressed as: Among them, E k,t+1 ,E k,t are the energy stored in the energy storage in time period t under scenario k, is the power transmitted from wind, solar and thermal power to energy storage in period t of scenario k, is the energy storage transmission power in scenario k and period t, and η is the energy storage charging and discharging efficiency.

9. A method for configuring energy storage in a large energy base considering new energy consumption according to claim 8, characterized in that: The charge and discharge constraints in S62 include: Among them, E d 、E u , P u They are the lower limit of energy storage, the upper limit of energy storage and the upper limit of energy storage power.

10. A method for configuring energy storage in a large energy base considering new energy consumption according to claim 9, characterized in that: The dual objective function in S63 is expressed as: Among them, P t net is the fluctuating power of the receiving grid, P t ws is the comprehensive output power of wind and solar power generation, P t ref is the energy storage output power, P t g is the thermal power output power, C inv is the comprehensive investment cost, c p 、c e are the unit power and unit capacity investment costs of energy storage, P e 、E e are the unit power investment costs of photovoltaic and wind power, c l is the average renewable energy curtailment rate, C wsl The cost of building new energy.

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