CN110717694B - Energy storage configuration random decision method and device based on new energy consumption expected value - Google Patents

Abstract

This application relates to a random decision-making method and device for energy storage allocation based on the expected value of new energy consumption. Aiming at the uncertainty of flexible adjustment requirements in new energy consumption, a typical scenario of new energy consumption is established based on historical sample data of output scenarios. and its distribution probability. After that, the required energy storage configuration is obtained for each typical scenario, and the final energy storage configuration scheme is obtained by comprehensively considering the respective probability and the energy storage configuration of each typical scenario. The method and device of the present application can well quantify the uncertainty of demand scenarios brought about by power fluctuations of new energy sources, realize the optimization of target expectations, and play a positive role in improving the quantitative decision-making level of energy storage allocation and promoting the consumption of new energy sources.

Description

Method and device for stochastic decision-making of energy storage allocation based on expected value of new energy consumption

technical field

The application belongs to the field of power system planning and design, and in particular relates to a random decision-making method and device for energy storage configuration based on new energy consumption expectations.

Background technique

In recent years, my country's new energy power generation has developed rapidly, and the installed capacity has increased year by year. As of the end of December 2018, the installed capacity of wind power in my country reached 146 million kilowatts, and the installed capacity of solar power reached 153 million kilowatts. With the large-scale development of new energy, the problem of new energy consumption has gradually become prominent. In some areas, affected by the transmission capacity of the power grid and the flexible adjustment capability of the system, the phenomenon of abandoning wind/solar power and limiting electricity occurs frequently. For this reason, the power sector has promoted and applied wind power, photovoltaic power generation and other new energy power generation technologies on a large scale from various aspects such as power grid construction, dispatching operation, and market transactions, and the proportion of new energy installed capacity in the total installed capacity of the power system has also continued to increase. By the end of 2019, the installed capacity of wind power in my country reached 180 million kilowatts, and the installed capacity of solar power reached 170 million kilowatts. Since new energy power generation is affected by natural resource conditions such as wind speed and sunlight, its output is intermittent, random and volatile. While the installed capacity of new energy power generation has grown on a large scale, the demand for flexible regulation of the power system has also continued to grow. In order to improve the flexible adjustment capability of the system and promote the consumption of new energy, in addition to building large-scale pumped storage power stations, new electric energy storage technologies represented by batteries have also been widely used on the user side and the station side. On the grid side, the construction and demonstration operation of grid-side energy storage power stations have been successively carried out in Henan, Jiangsu and other places, aiming to fully tap and utilize the aggregation effect of multi-point distributed energy storage power stations through rational layout and optimized configuration. In order to meet the flexible adjustment requirements of the power grid in different scenarios. As for the optimal allocation of energy storage, currently it mainly focuses on the energy storage allocation in independent application scenarios such as the user side and the station side, and there is no related technology on how to configure the energy storage on the grid side.

Contents of the invention

The technical problem to be solved by the present invention is to provide a random decision-making method and device for energy storage configuration based on new energy consumption expectation value in order to solve the deficiencies in the prior art.

The technical solution adopted by the present invention to solve the technical problem is: a random decision-making method for energy storage configuration based on the expected value of new energy consumption,

S1: Collect historical sample data of the output scenarios of the new energy power generation system, perform cluster analysis on the historical sample data to establish several typical scenarios of new energy consumption, and obtain the distribution probability corresponding to each typical scenario;

S2: For each typical scenario, configure the energy storage system in a typical scenario of new energy consumption to obtain a new energy grid-connected consumption production simulation model, and solve the energy storage configuration requirements under the model to obtain a storage system that includes all scenarios. Ability to configure scheme sets;

S3: Based on the energy storage configuration plan set, according to the distribution probability of each typical scenario, calculate the expected value of energy storage configuration, and determine the final energy storage configuration plan.

Preferably, in the stochastic decision-making method for energy storage configuration based on new energy consumption expectations of the present invention, in the S1 step, the clustering analysis method is k-means clustering algorithm or central point clustering algorithm.

Preferably, in the stochastic decision-making method for energy storage allocation based on new energy consumption expectations of the present invention, in the S1 step, the typical scenario is wind power output data and load data within a period of time.

Preferably, in the stochastic decision-making method for energy storage configuration based on new energy consumption expectations of the present invention, in the S2 step, when solving the energy storage configuration requirements, the required energy storage system capacity and energy storage system power are: in line with power balance Under the conditions of constraints, unit output constraints, climbing constraints, spinning reserve constraints, and energy storage battery charge and discharge constraints, the capacity and power of the energy storage system required to maximize the actual power generation of new energy in the new energy power generation system.

Preferably, in the stochastic decision-making method for energy storage allocation based on the expected value of new energy consumption in the present invention, in the step S3, the final energy storage allocation scheme is obtained by multiplying the expected value of energy storage allocation and a correction coefficient greater than or equal to 1.

The present invention also provides a random decision-making device for energy storage configuration based on new energy consumption expectations, including:

Typical scenario acquisition module: collect historical sample data of output scenarios of new energy power generation systems, perform cluster analysis on historical sample data to establish several typical scenarios of new energy consumption, and obtain the distribution probability corresponding to each typical scenario;

Energy storage configuration solution set acquisition module: for each typical scenario, configure the energy storage system in a typical scenario of new energy consumption to obtain a new energy grid-connected consumption production simulation model, and solve the energy storage configuration requirements under the model, Obtain a set of energy storage configuration schemes including all scenarios;

Energy storage configuration plan determination module: based on the energy storage configuration plan set, according to the distribution probability of each typical scenario, calculate the expected value of energy storage configuration, and determine the final energy storage configuration plan.

Preferably, in the energy storage configuration stochastic decision-making device based on new energy consumption expectations of the present invention, in the typical scene acquisition module, the clustering analysis method is k-means clustering algorithm or central point clustering algorithm.

Preferably, in the energy storage configuration stochastic decision-making device based on new energy consumption expectations of the present invention, in the typical scene acquisition module, the typical scene is wind power output data and load data within a period of time.

Preferably, in the stochastic decision-making device for energy storage configuration based on the expected value of new energy consumption in the present invention, in the acquisition module of the energy storage configuration scheme set, when solving the energy storage configuration requirements, the required energy storage system capacity and energy storage system power are: Under the conditions of power balance constraints, unit output constraints, climbing constraints, spinning reserve constraints, and energy storage battery charge and discharge constraints, the energy storage system capacity and energy storage required to maximize the actual power generation of new energy in the new energy power generation system system power.

Preferably, in the stochastic decision-making device for energy storage configuration based on the new energy consumption expectation value of the present invention, in the energy storage configuration scheme determination module, the final energy storage configuration scheme is obtained by multiplying the energy storage configuration expectation value and a correction coefficient greater than or equal to 1 .

The beneficial effects of the present invention are:

The random decision-making method and device for energy storage allocation based on the expected value of new energy consumption in the present invention aims at the uncertainty of flexible adjustment requirements in new energy consumption, and establishes a typical scenario of new energy consumption based on historical sample data of output scenarios and its distribution probability. After that, the required energy storage configuration is obtained for each typical scenario, and the final energy storage configuration scheme is obtained by comprehensively considering the respective probability and the energy storage configuration of each typical scenario. The method and device of the present application can well quantify the uncertainty of the demand scene brought about by the power fluctuation of new energy, realize the optimization of the target expectation value, and play a positive role in improving the quantitative decision-making level of energy storage allocation and promoting the consumption of new energy.

Description of drawings

The technical solution of the present application will be further described below in conjunction with the accompanying drawings and embodiments.

Fig. 1 is the structural diagram of the IEEE 30-node system in this effect experiment example (provide picture);

Figure 2 is a graph of the relationship between wind power processing and time under four typical output scenarios in this effect experiment example;

Figure 3 is the load characteristic curve of the IEEE 30-node system in this effect experiment example;

Fig. 4 is a flowchart of a random decision-making method for energy storage configuration based on new energy consumption expectations in an embodiment.

Detailed ways

It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

The technical solution of the present application will be described in detail below with reference to the accompanying drawings and embodiments.

Example

This embodiment provides a random decision-making method for energy storage allocation based on new energy consumption expectations, as shown in Figure 1, including:

S1: Collect historical sample data of the output scenarios of the new energy power generation system, perform cluster analysis on the historical sample data to establish several typical scenarios of new energy consumption, and obtain the distribution probability corresponding to each typical scenario;

Typical cluster analysis methods include k-means clustering algorithm (K-MEANS), medoid clustering algorithm (K-MEDOIDS) and so on. The typical scenario is wind power output and load data within a period of time, which is usually one day, but can also be multiple days.

S2: For each typical scenario, configure the energy storage system in a typical scenario of new energy consumption to obtain a new energy grid-connected consumption production simulation model, and solve the energy storage configuration requirements under the model to obtain a storage system that includes all scenarios. Ability to configure scheme sets;

When solving the energy storage configuration requirements, the required energy storage system capacity and energy storage system power are: under the conditions of power balance constraints, unit output constraints, climbing constraints, spinning reserve constraints, and energy storage battery charge and discharge constraints, the new energy The energy storage system capacity and energy storage system power required to maximize the actual power generation of new energy in the power generation system.

The specific method steps for solving the energy storage configuration requirements are as follows:

S21: Establish objective function

With the goal of maximizing the actual power generation of new energy, a new energy grid-connected consumption production simulation model is established. The mathematical expression is:

In the formula: N is the number of unit periods included in the time period; M is the number of new energy stations; P _ij is the actual output of the j-th new energy station in the i-th period; Duration (1 hour in Figure 2 and Figure 3).

S22: Constraints

The constraints considered in the simulation of new energy grid-connected consumption and production mainly include: power balance constraints, unit output constraints, ramp constraints, spinning reserve constraints, energy storage battery charge and discharge constraints, etc.

Power Balance Constraints

In the formula: G is the number of conventional units; P _ik is the actual output of the kth conventional unit in the i period; P _iD is the system load in the i period; P _iL is the system network loss in the i period.

Conventional Unit Output Constraints

P _k,min ≤P _ik ≤P _k,max (3)

In the formula: P _k,max is the upper limit of the output of the k-th conventional unit; P _k,min is the lower limit of the output of the k-th conventional unit.

(formerly regulated power supply) energy storage system climbing constraints

-R _k,- t _max ≤P _ik -P _(i-1), k≤R _k,+ t _max (4)

In the formula: t _max is the maximum allowable ramp time; R _k,- is the downward adjustment rate of the k-th energy storage system; R _k,+ is the upward adjustment rate of the k-th energy storage system.

Spinning Reserve Constraints

In the formula: P _iR,+ is the requirement of the system to rotate upward in the i-th period; P _iR,- is the requirement of the system to rotate downward in the i-th period.

Energy storage system charging and discharging constraints

P _e,min ≤|P _ie |≤P _e,max (7)

In the formula: P _e,min is the lower limit of the charging and discharging power of the e-th energy storage system; P _e,max is the upper limit of the charging and discharging power of the energy storage system; P _ie is the charging and discharging power of the energy storage facility in the i-th period; is negative.

SOC _min ≤ SOC ≤ SOC _max (9)

In the formula: E _i is the current energy state of the energy storage system; E _rate is the rated energy state of the energy storage system; SOC _min and SOC _max are the upper and lower limits of the charge and discharge depth of the energy storage system.

S3: Based on the energy storage configuration plan set, according to the distribution probability of each typical scenario, calculate the expected value of energy storage configuration, and determine the final energy storage configuration plan.

According to the random probability of various typical scenarios and the reference value of the configuration scheme obtained through production simulation in each scenario, the expected value of the energy storage configuration scheme is calculated, namely:

P _s,ref is the capacity of the energy storage system required to meet the target of new energy consumption in the Sth typical scenario; E _s,ref is the power of the energy storage system required to meet the target of new energy consumption in the Sth typical scenario; q _s is the distribution probability of the Sth typical scene.

In practical engineering applications, in addition to meeting the scene requirements, the energy storage configuration also considers certain backup and reliability requirements. Therefore, the selection of the final energy storage configuration scheme should satisfy:

P _ref =P _ref ·c _p , c _p >1 (12)

E _r ' _ef =E _ref ·c _e , c _e >1 (13)

In the formula, c _{p is c p} , and c _e is the correction coefficient, and the specific value depends on the actual engineering application requirements.

This embodiment also provides a random decision-making device for energy storage configuration based on new energy consumption expectations, corresponding to the method of this embodiment, including:

Typical scenario acquisition module: collect historical sample data of output scenarios of new energy power generation systems, perform cluster analysis on historical sample data to establish several typical scenarios of new energy consumption, and obtain the distribution probability corresponding to each typical scenario; typical cluster analysis Methods include k-means clustering algorithm (K-MEANS), center point clustering algorithm (K-MEDOIDS) and so on. The typical scenario is wind power output and load data within a period of time, which is usually one day, but can also be multiple days.

Energy storage configuration solution set acquisition module: for each typical scenario, configure the energy storage system in a typical scenario of new energy consumption to obtain a new energy grid-connected consumption production simulation model, and solve the energy storage configuration requirements under the model, Obtain the energy storage configuration plan set including all scenarios; when solving the energy storage configuration requirements, the required energy storage system capacity and energy storage system power are: compliance with power balance constraints, unit output constraints, ramp constraints, spinning reserve constraints, storage Under the condition of energy battery charging and discharging constraints, the energy storage system capacity and energy storage system power required to maximize the actual power generation of new energy in the new energy power generation system.

Energy storage configuration plan determination module: based on the energy storage configuration plan set, according to the distribution probability of each typical scenario, calculate the expected value of energy storage configuration, and determine the final energy storage configuration plan.

In the determination module of the energy storage configuration scheme, the final energy storage configuration scheme is obtained by multiplying the expected value of the energy storage configuration and a correction coefficient greater than or equal to 1. The specific value of the correction coefficient depends on the actual engineering application requirements.

Effect experiment example

This effect experiment uses IEEE 30-node system to verify the effectiveness of the random decision-making method for energy storage allocation based on the expected value of new energy consumption. The standard capacity of the IEEE 30-node system is 100MVA; unless otherwise specified, the parameters of each device are in per unit value (pu). Let No. 1 node be the balance node; No. 20 node is the grid-connected point of the wind farm, with an installed capacity of 1pu; both the spinning reserve coefficient and the network loss coefficient are taken as 5% of the system load. According to the historical data of wind power generation in a certain place, through scene clustering and merging, a typical daily output sample set of wind farms is established. In this embodiment, four typical daily wind power output scenarios are finally clustered, and the corresponding distribution probabilities are: 0.3, 0.2, 0.2, and 0.3 (the random probability is the ratio of the number of clusters for each typical scenario to the total number of sample sets) ,as shown in picture 2. The system load characteristic is shown in Fig. 3.

The characteristics of wind power output and load demand at each time period in a typical scenario are shown in Table 1:

Table 1 Output data (pu) and load data (pu) at each time period in typical scenarios

The parameters of each conventional unit are shown in Table 1. Among them, "H" indicates a hydroelectric unit; "G" indicates a coal-fired thermal power unit; "M" indicates a gas-fired unit; "E" indicates a battery energy storage facility, and its capacity is to be determined.

Table 2 Conventional unit parameters

Assuming that the goal of wind power consumption is to control the rate of curtailment of wind power below 5%, based on the parameters of the above-mentioned typical scenarios, and according to the algorithm flow in Figure 1, the production simulation and energy storage allocation requirements in typical scenarios are simulated, and the results are shown in the table 3.

Table 3 Energy storage configuration requirements in different scenarios

In actual engineering, the energy storage configuration also needs to consider certain backup and reliability requirements and the fixed value of unit capacity/energy in the energy storage modular design, and the energy storage configuration scheme should be corrected. In this calculation example, the correction coefficient b _p is set to 1.02, and b _e is set to 1.05, so the final energy storage configuration demand scheme is 24MW/192MWh.

Inspired by the above-mentioned ideal embodiment according to the present application, through the above-mentioned description content, relevant staff can make various changes and modifications within the scope of not departing from the technical idea of this application. The technical scope of this application is not limited to the content in the specification, but must be determined according to the scope of the claims.

Those skilled in the art should understand that the embodiments of the present application may be provided as methods, systems, or computer program products. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The present application is described with reference to flowcharts and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each procedure and/or block in the flowchart and/or block diagram, and a combination of procedures and/or blocks in the flowchart and/or block diagram can be realized by computer program instructions. These computer program instructions may be provided to a general purpose computer, special purpose computer, embedded processor, or processor of other programmable data processing equipment to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing equipment produce a An apparatus for realizing the functions specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to operate in a specific manner, such that the instructions stored in the computer-readable memory produce an article of manufacture comprising instruction means, the instructions The device realizes the function specified in one or more procedures of the flowchart and/or one or more blocks of the block diagram.

These computer program instructions can also be loaded onto a computer or other programmable data processing device, causing a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process, thereby The instructions provide steps for implementing the functions specified in the flow chart or blocks of the flowchart and/or the block or blocks of the block diagrams.

Claims (10)

1. An energy storage configuration random decision method based on new energy consumption expectation value is characterized in that,

s1: collecting historical sample data of an output scene of the new energy power generation system, performing cluster analysis on the historical sample data to establish a plurality of new energy consumption typical scenes, and acquiring the corresponding distribution probability of each typical scene;

s2: configuring an energy storage system to a new energy consumption typical scene to obtain a new energy grid-connected consumption production simulation model aiming at each typical scene, and solving energy storage configuration requirements under the model to obtain an energy storage configuration scheme set comprising all scenes;

s3: and calculating an expected value of the energy storage configuration according to the distribution probability of each typical scene based on the energy storage configuration scheme set, and determining a final energy storage configuration scheme.

2. The energy storage configuration random decision method based on the new energy consumption expected value according to claim 1, wherein in the step S1, the clustering analysis method is a k-means clustering algorithm or a central point clustering algorithm.

3. The energy storage configuration random decision method based on the new energy consumption expectation value according to claim 2, wherein in the step S1, the typical scenes are wind power output data and load data within a period of time.

4. The energy storage configuration random decision method based on the new energy consumption expected value according to any one of claims 1 to 3, wherein in the step S2, when the energy storage configuration requirement is solved, the required energy storage system capacity and energy storage system power are as follows: and under the conditions of power balance constraint, unit output constraint, climbing constraint, rotation standby constraint and energy storage battery charging and discharging constraint, the capacity of the energy storage system and the power of the energy storage system are required when the actual generated energy of the new energy in the new energy power generation system is maximized.

5. The energy storage configuration random decision method based on the new energy consumption expectation value according to any one of claims 1 to 3, wherein in the step S3, the final energy storage configuration scheme is obtained by multiplying the energy storage configuration expectation value by a correction coefficient which is greater than or equal to 1.

6. An energy storage configuration random decision device based on a new energy consumption expectation value, comprising:

a typical scene acquisition module: collecting historical sample data of an output scene of the new energy power generation system, performing cluster analysis on the historical sample data to establish a plurality of new energy consumption typical scenes, and acquiring the corresponding distribution probability of each typical scene;

an energy storage configuration scheme set acquisition module: aiming at each typical scene, configuring an energy storage system into a new energy consumption typical scene to obtain a new energy grid-connected consumption production simulation model, and solving an energy storage configuration requirement under the model to obtain an energy storage configuration scheme set comprising all scenes;

an energy storage configuration scheme determination module: and calculating an expected value of the energy storage configuration according to the distribution probability of each typical scene based on the energy storage configuration scheme set, and determining a final energy storage configuration scheme.

7. The energy storage configuration random decision device based on the new energy consumption expectation value according to claim 6, wherein in the typical scene acquisition module, the clustering analysis method is a k-means clustering algorithm or a central point clustering algorithm.

8. The energy storage configuration random decision device based on the new energy consumption expected value according to claim 7, wherein in the typical scenario acquisition module, the typical scenario is wind power output data and load data within a period of time.

9. The energy storage configuration random decision device based on the new energy consumption expectation value according to any one of claims 6 to 8, wherein in the energy storage configuration scheme set acquisition module, when the energy storage configuration requirement is solved, the required energy storage system capacity and energy storage system power are as follows: and under the conditions of power balance constraint, unit output constraint, climbing constraint, rotation standby constraint and energy storage battery charging and discharging constraint, the capacity of the energy storage system and the power of the energy storage system are required when the actual generated energy of the new energy in the new energy power generation system is maximized.

10. The energy storage configuration random decision device based on the new energy consumption expectation value according to any one of claims 6 to 8, wherein in the energy storage configuration scheme determination module, the final energy storage configuration scheme is obtained by multiplying the energy storage configuration expectation value by a correction coefficient which is greater than or equal to 1.

Images