CN109256790B - Energy storage system configuration method and device and storage medium - Google Patents
Energy storage system configuration method and device and storage medium Download PDFInfo
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- CN109256790B CN109256790B CN201811230411.4A CN201811230411A CN109256790B CN 109256790 B CN109256790 B CN 109256790B CN 201811230411 A CN201811230411 A CN 201811230411A CN 109256790 B CN109256790 B CN 109256790B
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
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Abstract
The invention provides an energy storage system configuration method, which comprises the following steps: acquiring system contingency event data of each network bus of the current distributed energy system; generating an accidental sensitivity index of each network bus based on the system accidental event data of each network bus; determining the number, the installation positions and the specifications of the energy storage systems to be configured based on the accidental sensitivity index; and executing the energy storage system configuration of the current distributed energy system according to the number, the installation position and the specification of the energy storage systems to be configured. The present invention determines the optimal location for installing ESSs and their optimal specifications based on the optimal allocation strategy of contingency sensitivity index to improve voltage stability and minimize system power consumption in a cost effective manner.
Description
Technical Field
The invention belongs to the technical field of energy storage, and particularly relates to a method and a device for configuring an energy storage system and a storage medium.
Background
Smart grids are a modern infrastructure that controls power systems with advanced Information and Communications Technologies (ICTs). In the last 20 years, the demand for electricity has increased by 2.5% per year, and the grid may soon reach its limits. The popularity of renewable energy sources is increasing continuously to face the limitations of traditional power systems and to achieve sustainable power systems. However, the intermittency of renewable energy sources presents challenges to energy management of smart grids. In recent years, in energy management, Energy Storage Systems (ESSs) play a role in regulating power fluctuations of renewable energy sources and ensuring a stable system state. In recent years, attention has been paid more and more to integration of ESSs in electric power systems. However, the determination of the optimal number, location and specifications of the power systems has a significant impact on the operation of the power systems. Therefore, the integration of ESSs is based essentially on their configuration. Aiming at the problems of site selection and scale of ESSs with different targets, various solutions are provided, such as optimization of economic benefits of investors, improvement of voltage stability of a power system, renewable energy power generation, reduction of transmission blockage, improvement of system reliability and the like.
However, most of these studies do not take into account the risk of potential sporadic events.
Disclosure of Invention
The invention provides a method and a device for configuring an energy storage system and a storage medium, which can realize the configuration of the energy storage system under the condition of considering the risk of accidental events.
The technical scheme provided by the invention is as follows:
according to an exemplary embodiment, a first aspect of the present invention provides an energy storage system configuration method, including:
acquiring system contingency event data of each network bus of the current distributed energy system;
generating an accidental sensitivity index of each network bus based on the system accidental event data of each network bus;
determining the number, the installation positions and the specifications of the energy storage systems to be configured based on the accidental sensitivity index;
and executing the energy storage system configuration of the current distributed energy system according to the number, the installation position and the specification of the energy storage systems to be configured.
In some examples, the generating the contingency sensitivity index for each network bus based on the system contingency event data for each network bus comprises:
generating an accidental sensitivity matrix based on system accidental event data of each network bus of the network buses, wherein rows of the accidental sensitivity matrix represent system accidental events, and columns represent network buses;
and generating an accidental sensitivity index of each network bus based on the accidental sensitivity matrix and the number of the accidental events.
In some examples, the system contingency data comprises:
data on the effects of the system contingency on voltage deviations, line current variations and generator output power variations.
In some examples, the determining the number of energy storage systems to be deployed, the installation location, and the specification based on the contingency sensitivity index includes:
determining the cardinal number of the network buses higher than the set accidental sensitivity index threshold value, and determining the cardinal number as the number of the energy storage systems to be configured;
and preferentially selecting the network bus with higher accidental sensitivity index in all the network buses as the installation position of the energy storage system to be configured.
In some examples, the determining the number of energy storage systems to be deployed, the installation location, and the specification based on the contingency sensitivity index includes:
and determining the specification of the energy storage system to be configured based on the accidental sensitivity index according to a heuristic particle swarm optimization HPSO time-varying acceleration coefficient TVAC.
In a second aspect of the present invention, there is provided an energy storage system configuration apparatus, including:
the acquisition module is used for acquiring system contingency event data of each network bus of the current distributed energy system;
the index generation module is used for generating an accidental sensitivity index of each network bus based on the system accidental event data of each network bus;
the configuration parameter determining module is used for determining the number, the installation position and the specification of the energy storage systems to be configured based on the accidental sensitivity index;
and the configuration module is used for executing the energy storage system configuration of the current distributed energy system according to the number, the installation position and the specification of the energy storage systems to be configured.
In some examples, the index generation module includes:
the matrix generation submodule is used for generating an accidental sensitivity matrix based on system accidental event data of each network bus, rows of the accidental sensitivity matrix represent system accidental events, and columns represent the network buses;
and the index generation submodule is used for generating the accidental sensitivity index of each network bus based on the accidental sensitivity matrix and the number of the accidental events.
In some examples, the system contingency data comprises:
data on the effects of the system contingency on voltage deviations, line current variations and generator output power variations.
In some examples, the configuration parameter determination module includes:
the quantity determination submodule is used for determining the cardinal number of the network bus higher than the set accidental sensitivity index threshold value, and the cardinal number is the quantity of the energy storage systems to be configured;
and the position determining submodule is used for preferentially selecting the network bus with higher accidental sensitivity index in each network bus as the installation position of the energy storage system to be configured.
In some examples, the configuration parameter determining module further includes:
and the specification determining submodule is used for determining the specification of the energy storage system to be configured based on the accidental sensitivity index according to a Time-varying Acceleration Coefficient (TVAC) of a Heuristic Particle Swarm Optimization (HPSO) algorithm.
In a third aspect of the embodiments of the present invention, a storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the method for configuring an energy storage system according to the embodiments of the present invention is implemented.
The energy storage system configuration method provided by the embodiment of the invention provides an optimal allocation strategy based on an accidental sensitivity index, and the optimal allocation strategy is used for determining the optimal position and the optimal specification for installing ESSs (emergency service systems) so as to improve the voltage stability and minimize the system power consumption in a cost-effective mode.
Compared with the traditional method, the method is technically and computationally effective, and allows the reliability of the smart grid in an emergency state to be improved through a heuristic strategy. The heuristic is based on a Contingency Sensitivity Index (CSI) that evaluates the impact of system contingencies on the network bus (i.e., nodes) so that the most vulnerable bus becomes the best place to install the ESSs. Such heuristic strategies allow to circumvent the combinatorial nature of the addressing problem, thereby having advantages in terms of complexity and reduced computational burden. Furthermore, heuristic policies allow that an optimal number of ESS installations can be achieved.
The present invention minimizes voltage deviations and system power consumption in a cost-effective manner. By improving the voltage stability and the system power consumption, the investment cost of the ESSs is reduced, and meanwhile, the optimal integration of the ESSs is ensured.
Drawings
FIG. 1 is a flow chart of a method of configuring an energy storage system according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method of determining a specification for an energy storage system according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an energy storage system configuration device according to an embodiment of the present invention.
Detailed Description
The following detailed description of the embodiments of the present invention will be provided with reference to the drawings and examples, so that how to apply the technical means to solve the technical problems and achieve the technical effects can be fully understood and implemented. It should be noted that, as long as there is no conflict, the embodiments and the features of the embodiments of the present invention may be combined with each other, and the technical solutions formed are within the scope of the present invention.
In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced without some of these specific details or with other methods described herein.
Additionally, the steps illustrated in the flow charts of the figures may be performed in a computer system such as a set of computer-executable instructions and, although a logical order is illustrated in the flow charts, in some cases, the steps illustrated or described may be performed in an order different than here.
The ESSs may operate as a generator when there is a lack of energy, or as a power load when there is an excess of energy. For an efficient integration in the grid, the ESSs should be placed in the most suitable location and set to the most suitable specifications. However, such integration is greatly affected by the operating conditions of the power system and its potential emergencies. The present invention provides an optimal allocation strategy based on contingency sensitivity index for determining the best location, best specification and best number of installed ESSs to improve voltage stability and minimize system power consumption in a cost effective manner.
In the embodiment of the invention, the invention provides a model detection method, a model detection device and a storage medium, which can effectively reduce the complexity of attribute verification in the model detection of a reconfigurable system.
The technical scheme provided by the invention is as follows:
as shown in fig. 1, the present invention provides an energy storage system configuration method, including:
s101, acquiring system contingency event data of each network bus of the current distributed energy system;
s102, generating an accidental sensitivity index of each network bus based on the system accidental event data of each network bus;
s103, determining the number, the installation positions and the specifications of the energy storage systems to be configured based on the accidental sensitivity index;
and S104, according to the number, the installation position and the specification of the energy storage systems to be configured, executing the energy storage system configuration of the current distributed energy system.
In some examples, the system contingency data comprises:
data on the effects of the system contingency on voltage deviations, line current variations and generator output power variations.
The data of the influence of the system contingency event on the voltage deviation can be calculated by the following formula.
Wherein, IVDjThe influence of system contingency events on voltage deviation is shown, and the influence is calculated by evaluating the occurrence of the contingency events and the change of the bus voltage under the base condition. If the variation exceeds a threshold tbThen there is an effect, equal to 1, otherwise there is no, equal to 0.
The data of the influence of the system contingency event on the line current change can be calculated by the following formula.
Wherein the content of the first and second substances,
representing the effect on line current due to i and j line contingencies. This effect is calculated by evaluating the change in line current in the event of an accidental event and in the base case. If the variation exceeds a threshold tlThen there is an effect, equal to 1, otherwise there is no, equal to 0.
The data of the influence of the system contingency event on the output power change of the generator can be calculated by the following formula.
Wherein, the IPGj gRepresents the influence of the contingency event on the output power change of the generator, which is calculated by evaluating the occurrence of the contingency event and the output power change of the generator in the basic case. If the variation exceeds a threshold tgThen there is an effect, equal to 1, otherwise there is no, equal to 0.
In some examples, the generating the contingency sensitivity index for each network bus based on the system contingency event data for each network bus comprises:
generating an accidental sensitivity matrix based on system accidental event data of each network bus of the network buses, wherein rows of the accidental sensitivity matrix represent system accidental events, and columns represent network buses;
and generating an accidental sensitivity index of each network bus based on the accidental sensitivity matrix and the number of the accidental events.
In practice, after calculating the influence of all contingencies, a Contingency Sensitivity Matrix (CSM) may be generated. CSM is defined as follows.
Where the rows represent system contingencies and the columns represent network buses.
The elements in the CSM matrix can be calculated by the following method:
if an contingency event denoted by c affects the voltage deviation on the bus, the value of CSM [ c, j ] will equal 1;
if the line current is affected by an contingency event represented by c, the line current between i and j, CSM [ c, i ] and CSM [ c, j ] will have a value equal to 1;
if the contingency event denoted by c would affect the generation of a generator g located at CSM [ c, j ], then the value of CSM [ c, j ] would be equal to 1.
On the basis of the CSM matrix, calculating the accidental sensitivity index of each network bus by the following method:
In some examples, the determining the number of energy storage systems to be deployed, the installation location, and the specification based on the contingency sensitivity index includes:
determining the cardinal number of the network buses higher than the set accidental sensitivity index threshold value, and determining the cardinal number as the number of the energy storage systems to be configured;
and preferentially selecting the network bus with higher accidental sensitivity index in all the network buses as the installation position of the energy storage system to be configured.
In some examples, the determining the number of energy storage systems to be deployed, the installation location, and the specification based on the contingency sensitivity index includes:
and determining the specification of the energy storage system to be configured based on the accidental sensitivity index according to a heuristic particle swarm optimization HPSO time-varying acceleration coefficient TVAC.
In practical application, after the CSI is calculated, the network buses may be arranged in a descending order, that is, the buses with higher CSI values are preferably considered when the ESSs is installed. The number of ESSs is determined by a given sensitivity threshold tsIs selected so that the base of the bus exceeding the threshold is the number n of energy storage systems to be installedsAs follows.
For the specifications of the ESSs, an optimization problem is solved by an objective function under the condition that the sum of bus voltage deviation, system grid loss and investment cost is minimized. This problem can be solved by means of an HPSO TVAC. The objective function of which may be
Where vector S is the vector of ESS sizes, T is the time plan limit, CLIs the cost of power loss, PL tIs the power loss at time t, CvIs the cost of the voltage deviation, VD tIs the voltage deviation at time t.
CI(ns) is the investment cost that can be assessed in the following manner
Wherein n issNumber of energy storage systems, cfFixed cost of installation for a single ESS, csFor the ith specification to be SiThe price per unit of ESS.
Based on the above objective function, as shown in fig. 2, in practical applications, the specification of the ESS can be calculated by the following method:
the interval between the initialization and the specification input may be set to 15 to 25% of the renewable power generation capacity. Then, the ESS specification is used to initialize the particle swarm to optimize the PSO population. Then, the fitness of each particle is evaluated through the above objective function to find a globally optimal particle. The termination criteria, i.e., maximum number of iterations, is checked and if the maximum number of iterations is not reached, the velocity is updated using the time-varying acceleration coefficients without adding the previous velocities, as follows:
vk+1＝C1*r1*(pbest-xk)+c2*r2*(gbest-xk)
wherein v isk+1To update the speed, c1，c2Is the coefficient of acceleration, r1，r2Is a random variable, xkIs the position of the particle, pbestAnd gbestRespectively the own and global optimum of each particle.
The position of each particle is then updated as follows:
xk+1＝xk+vk+1
wherein xk+1Is the updated position, xkPrevious position, vk+1Is the speed of the update.
When the maximum number of iterations is reached, the optimal specification for the ESS may be set by Sopt ═ argminf(s).
The energy storage system configuration method provided by the embodiment of the invention provides an optimal allocation strategy based on an accidental sensitivity index, and the optimal allocation strategy is used for determining the optimal position and the optimal specification for installing ESSs (emergency service systems) so as to improve the voltage stability and minimize the system power consumption in a cost-effective mode.
Compared with the traditional method, the method is technically and computationally effective, and allows the reliability of the smart grid in an emergency state to be improved through a heuristic strategy. The heuristic is based on a Contingency Sensitivity Index (CSI) that evaluates the impact of system contingencies on the network bus (i.e., nodes) so that the most vulnerable bus becomes the best place to install the ESSs. Such heuristic strategies allow to circumvent the combinatorial nature of the addressing problem, thereby having advantages in terms of complexity and reduced computational burden.
Compared with the weak bus configuration method in the prior art, the energy storage system configuration method provided by the embodiment of the invention has the advantages that the power loss is low and is reduced by about 20%, and compared with the loss sensitivity configuration method in the prior art, the power loss is reduced by 5%. The voltage does not exceed the acceptable range and the voltage deviation is smoother than the other two methods, 22% lower than the weak bus configuration method and 10% lower than the loss sensitivity configuration method. The address problem of the present invention is performed in a very short time. The siting scheme requires a load-carrying capacity calculation that can be completed in a few seconds, even for large power systems. The overall computational burden of the present invention is to compute the specifications of the ESSs, and the present invention uses the HPSO TVAC to handle the size problem. Compared with the existing method, the algorithm has obvious effect in specification calculation. The calculation results shown in table 1 are as follows:
TABLE 1
As can be seen from table 1, the different methods give the same optimum value but the execution times are different. The HPSO TVAC is the best choice in terms of execution time and number of iterations to aggregate.
As shown in fig. 3, the present invention also provides an energy storage system configuration apparatus, including:
an obtaining module 301, configured to obtain system contingency event data of each network bus of a current distributed energy system;
an index generating module 302, configured to generate an accidental sensitivity index of each network bus based on the system accidental event data of each network bus;
a configuration parameter determining module 303, configured to determine the number, installation location, and specification of the energy storage systems to be configured based on the accidental sensitivity index;
and the configuration module 304 is configured to execute energy storage system configuration of the current distributed energy system according to the number, the installation location, and the specification of the energy storage systems to be configured.
In some examples, the index generation module 302 includes:
a matrix generation submodule 3021 configured to generate an contingency sensitivity matrix based on system contingency event data of each network bus of the network buses, where a row of the contingency sensitivity matrix represents a system contingency event and a column represents a network bus;
an index generation submodule 3022, configured to generate an accidental sensitivity index for each network bus based on the accidental sensitivity matrix and the number of accidental events.
In some examples, the system contingency data comprises:
data on the effects of the system contingency on voltage deviations, line current variations and generator output power variations.
In some examples, the configuration parameter determining module 303 includes:
the quantity determination submodule 3031 is used for determining the base number of the network bus higher than the set accidental sensitivity index threshold value, and the base number is the quantity of the energy storage systems to be configured;
and the position determining submodule 3032 is used for preferentially selecting the network bus with a higher accidental sensitivity index in each network bus as the installation position of the energy storage system to be configured.
In some examples, the configuration parameter determining module 303 further includes:
the specification determining submodule 3033 is configured to determine, according to a heuristic-Particle-Swarm Optimization (HPSO) Time-varying Acceleration Coefficient (TVAC), a specification of the energy storage system to be configured based on the contingency sensitivity index.
In a third aspect of the embodiments of the present invention, a storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the method for configuring an energy storage system according to the embodiments of the present invention is implemented.
The energy storage system configuration method provided by the embodiment of the invention provides an optimal allocation strategy based on an accidental sensitivity index, and the optimal allocation strategy is used for determining the optimal position and the optimal specification for installing ESSs (emergency service systems) so as to improve the voltage stability and minimize the system power consumption in a cost-effective mode.
Compared with the traditional method, the method is technically and computationally effective, and allows the reliability of the smart grid in an emergency state to be improved through a heuristic strategy. The heuristic is based on a Contingency Sensitivity Index (CSI) that evaluates the impact of system contingencies on the network bus (i.e., nodes) so that the most vulnerable bus becomes the best place to install the ESSs. Such heuristic strategies allow to circumvent the combinatorial nature of the addressing problem, thereby having advantages in terms of complexity and reduced computational burden.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention 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, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (8)
1. A method of configuring an energy storage system, comprising:
acquiring system contingency event data of each network bus of the current distributed energy system;
generating an accidental sensitivity index of each network bus based on the system accidental event data of each network bus;
determining the number, the installation positions and the specifications of the energy storage systems to be configured based on the accidental sensitivity index;
executing energy storage system configuration of the current distributed energy system according to the number, the installation position and the specification of the energy storage systems to be configured;
the generating of the contingency sensitivity index of each network bus based on the system contingency event data of each network bus comprises:
generating an accidental sensitivity matrix based on system accidental event data of each network bus of the network buses, wherein rows of the accidental sensitivity matrix represent system accidental events, and columns represent network buses;
generating an accidental sensitivity index of each network bus based on the accidental sensitivity matrix and the number of the accidental events;
the system contingency event data comprising:
data on the effects of the system contingency on voltage deviations, line current variations and generator output power variations.
2. The method of claim 1, wherein determining the number, installation locations, and specifications of energy storage systems to be deployed based on the contingency sensitivity index comprises:
determining the cardinal number of the network buses higher than the set accidental sensitivity index threshold value, and determining the cardinal number as the number of the energy storage systems to be configured;
and preferentially selecting the network bus with higher accidental sensitivity index in all the network buses as the installation position of the energy storage system to be configured.
3. The method of claim 1, wherein determining the number, installation locations, and specifications of energy storage systems to be deployed based on the contingency sensitivity index comprises:
and determining the specification of the energy storage system to be configured based on the accidental sensitivity index according to a heuristic particle swarm optimization HPSO time-varying acceleration coefficient TVAC.
4. An energy storage system deployment apparatus, comprising:
the acquisition module is used for acquiring system contingency event data of each network bus of the current distributed energy system;
the index generation module is used for generating an accidental sensitivity index of each network bus based on the system accidental event data of each network bus;
the configuration parameter determining module is used for determining the number, the installation position and the specification of the energy storage systems to be configured based on the accidental sensitivity index;
and the configuration module is used for executing the energy storage system configuration of the current distributed energy system according to the number, the installation position and the specification of the energy storage systems to be configured.
5. The apparatus of claim 4, wherein the index generation module comprises:
the matrix generation submodule is used for generating an accidental sensitivity matrix based on system accidental event data of each network bus, rows of the accidental sensitivity matrix represent system accidental events, and columns represent the network buses;
and the index generation submodule is used for generating the accidental sensitivity index of each network bus based on the accidental sensitivity matrix and the number of the accidental events.
6. The apparatus of claim 4, wherein said system contingency event data comprises:
data on the effects of the system contingency on voltage deviations, line current variations and generator output power variations.
7. The apparatus of claim 4, wherein the configuration parameter determination module comprises:
the quantity determination submodule is used for determining the cardinal number of the network bus higher than the set accidental sensitivity index threshold value, and the cardinal number is the quantity of the energy storage systems to be configured;
and the position determining submodule is used for preferentially selecting the network bus with higher accidental sensitivity index in each network bus as the installation position of the energy storage system to be configured.
8. A storage medium, on which a computer program is stored, which, when being executed by a processor, carries out a method of configuring an energy storage system according to any one of claims 1 to 3.
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