CN117748555B - Multi-objective optimal configuration method and device for distributed photovoltaic energy storage system - Google Patents
Multi-objective optimal configuration method and device for distributed photovoltaic energy storage system Download PDFInfo
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Abstract
The embodiment of the invention relates to a multi-objective optimal configuration method and device for a distributed photovoltaic energy storage system, wherein the method comprises the following steps: establishing a photovoltaic power generation optimization model, and obtaining photovoltaic power generation equipment configuration parameters and energy storage equipment configuration parameters based on the photovoltaic power generation optimization model; using the photovoltaic power generation equipment configuration parameters and the energy storage equipment configuration parameters as input data, establishing a power distribution network operation optimization model, and obtaining power distribution network operation parameters based on the power distribution network operation optimization model; and establishing a multi-target collaborative optimization model to obtain system optimization parameters. According to the technical scheme provided by the embodiment of the invention, the objective function is built step by considering the utilization efficiency of new energy sources in the distributed photovoltaic energy storage system, the running stability of the power distribution network, the power supply reliability optimization index, the environmental benefit optimization index and other factors, so that the optimal configuration index of the system is obtained, and the multi-objective real-time optimal configuration of the distributed photovoltaic energy storage system is realized.
Description
Technical Field
The embodiment of the invention relates to the technical field of power system optimal configuration, in particular to a multi-objective optimal configuration method and device for a distributed photovoltaic energy storage system.
Background
Distributed photovoltaic energy storage systems typically include photovoltaic power generation equipment, energy storage equipment, and are connected to a distribution network. Due to the complexity and uncertainty of the distribution network, how to implement reasonable configuration of the system is a problem to be solved.
In the prior art configuration schemes, only some local problems of the power distribution network, such as network loss reduction, voltage stability improvement and the like, are usually focused, and a global optimization view is lacked. In practical applications, there may be conflicts between multiple targets, requiring trade-offs and decisions between multiple targets and optimization. The running state of the power distribution network is changed at any time, and the running state of the power distribution network is difficult to adapt to the change of the running state of the power distribution network in the configuration scheme in the prior art, so that the real-time dynamic optimal configuration is realized.
Disclosure of Invention
Based on the above situation in the prior art, an object of the embodiments of the present invention is to provide a method and an apparatus for multi-objective optimization configuration of a distributed photovoltaic energy storage system, which implement multi-objective real-time optimization configuration of the distributed photovoltaic energy storage system by establishing an optimization objective function associated with each other step by step.
In order to achieve the above object, according to a first aspect of the present invention, there is provided a multi-objective optimization configuration method for a distributed photovoltaic energy storage system, including the steps of:
Establishing a photovoltaic power generation optimization model, and obtaining photovoltaic power generation equipment configuration parameters and energy storage equipment configuration parameters based on the photovoltaic power generation optimization model;
using the photovoltaic power generation equipment configuration parameters and the energy storage equipment configuration parameters as input data, establishing a power distribution network operation optimization model, and obtaining power distribution network operation parameters based on the power distribution network operation optimization model;
And establishing a multi-objective collaborative optimization model based on the photovoltaic power generation equipment configuration parameters, the energy storage equipment configuration parameters and the power distribution network operation parameters to obtain system optimization parameters.
Further, the building of the photovoltaic power generation optimization model includes building a first objective function F 1:
F1＝Max(Ppv*(Pgrid+Pbat)/(Ppv+Pgrid+Pbat))
Wherein, P pv represents the actual output power of the photovoltaic power generation equipment in the system, P grid represents the power obtained by the power distribution network from the photovoltaic energy storage system, and P bat represents the power for charging and discharging the energy storage equipment.
Further, the solving the constraint condition satisfied by the first objective function F 1 includes:
Ppv≤Ppv_max
Pbat≤Pbat_max
Ebat≤Ebat_max
Pgrid+Pbat＝Pload
Wherein, P pv_max represents the maximum output power of the photovoltaic power generation device, P bat_max represents the maximum charge and discharge power of the energy storage device, E bat represents the output capacity of the energy storage device, E bat_max represents the maximum capacity of the energy storage device, and P load represents the load demand of the power distribution network.
Further, the establishing a power distribution network operation optimization model includes establishing a second objective function F 2:
F2＝Min(w1*ΔUpv+w2*ΔUbat+w3*ΔUload)
Wherein Δu pv represents the voltage deviation of the photovoltaic power generation device access point in the system, Δu bat represents the voltage deviation of the energy storage device access point in the system, and Δu load represents the voltage deviation of the load center node. w 1、w2 and w 3 represent the weight coefficients of the respective voltage deviations.
Further, the solving the constraint condition satisfied by the second objective function F 2 includes:
Ppvmin≤Ppv≤Ppvmax
Pbatmin≤Pbat≤Pbatmax
ΔUmin≤ΔUpv≤ΔUmax
ΔUmin≤ΔUbat≤ΔUmax
ΔUmin≤ΔUload≤ΔUmax
Ppv+Pbat＝Pl+Ploss
Wherein P pvmin represents the minimum allowable configuration power of the photovoltaic power generation device, and P pvmax represents the maximum allowable configuration power of the photovoltaic power generation device; p batmin represents the minimum allowed configuration power of the energy storage device, and P batmax represents the maximum allowed configuration power of the energy storage device; Δu min represents the system minimum allowable voltage deviation, Δu max represents the system maximum allowable voltage deviation; p l represents the distribution network load power and P loss represents the system lost power.
Further, the position of the load center node is determined according to the following steps:
Calculating the load moment of each node;
And selecting the node with the smallest load moment as a load center node.
Further, the load moment is calculated by the following formula:
M＝Pl*L*μ
Where P l represents the load power of the node, L represents the electrical distance from the node to the candidate load center, and μ represents the weight coefficient of the node.
Further, the weight coefficient is determined according to the following formula:
Wherein, alpha 1 represents the load type coefficient connected with the node; α 2 represents a load level coefficient to which the node is connected; beta 1 represents the voltage class coefficient of the node; gamma 1 denotes the short-circuit capacity of the node; gamma 2 denotes the impedance coefficient of the node.
Further, the establishing the multi-objective collaborative optimization model includes establishing a third objective function F 3:
MinF3＝v1*LP―v2*R
LP＝∑i∈NP(LPi)
Wherein LP represents the probability of no-load, N represents the set of load nodes, and P (LP i) represents the probability of no-load for node i; r represents the photovoltaic power generation utilization rate, E pv represents the electric energy generated by photovoltaic power generation equipment, and E total represents the total electric energy of the system; v 1 and v 2 represent weight coefficients;
The solving of the satisfied constraint condition by the third objective function F 3 further includes:
Vmin≤Vi≤Vmax
Iij≤Imax
Rmin≤R≤Rmax
Wherein V i represents a node voltage, V min represents a node voltage minimum allowable value, and V max represents a node voltage maximum allowable value; i ij denotes line current, I max denotes line current threshold; r min represents a minimum allowable value of the photovoltaic power generation utilization rate, and R max represents a maximum allowable value of the photovoltaic power generation utilization rate.
According to another aspect of the present invention, there is provided a multi-objective optimal configuration device for a distributed photovoltaic energy storage system, the device comprising:
the photovoltaic power generation optimizing model building module is used for building a photovoltaic power generation optimizing model and obtaining photovoltaic power generation equipment configuration parameters and energy storage equipment configuration parameters based on the photovoltaic power generation optimizing model;
The power distribution network operation optimization model building module is used for building a power distribution network operation optimization model by using the photovoltaic power generation equipment configuration parameters and the energy storage equipment configuration parameters as input data, and obtaining power distribution network operation parameters based on the power distribution network operation optimization model;
the multi-target collaborative optimization model building module is used for building a multi-target collaborative optimization model based on the photovoltaic power generation equipment configuration parameters, the energy storage equipment configuration parameters and the power distribution network operation parameters to obtain system optimization parameters.
In summary, the embodiment of the invention provides a multi-objective optimization configuration method and device for a distributed photovoltaic energy storage system, wherein the method comprises the following steps: establishing a photovoltaic power generation optimization model, and obtaining photovoltaic power generation equipment configuration parameters and energy storage equipment configuration parameters based on the photovoltaic power generation optimization model; using the photovoltaic power generation equipment configuration parameters and the energy storage equipment configuration parameters as input data, establishing a power distribution network operation optimization model, and obtaining power distribution network operation parameters based on the power distribution network operation optimization model; and establishing a multi-objective collaborative optimization model based on the photovoltaic power generation equipment configuration parameters, the energy storage equipment configuration parameters and the power distribution network operation parameters to obtain system optimization parameters. According to the technical scheme provided by the embodiment of the invention, the photovoltaic power generation optimization model, the power distribution network operation optimization model and the multi-objective collaborative optimization model are established, so that the utilization efficiency of new energy sources in the distributed photovoltaic energy storage system, the operation stability of the power distribution network, the power supply reliability optimization index, the environmental benefit optimization index and other factors are considered, and the objective function is established step by step, so that the optimal configuration index of the system is obtained, and the multi-objective real-time optimal configuration of the distributed photovoltaic energy storage system is realized.
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Fig. 1 is a flowchart of a multi-objective optimization configuration method of a distributed photovoltaic energy storage system provided by an embodiment of the present invention.
Detailed Description
The objects, technical solutions and advantages of the present invention will become more apparent by the following detailed description of the present invention with reference to the accompanying drawings. It should be understood that the description is only illustrative and is not intended to limit the scope of the invention. In addition, in the following description, descriptions of well-known structures and techniques are omitted so as not to unnecessarily obscure the present invention.
It is to be noted that unless otherwise defined, technical or scientific terms used in one or more embodiments of the present invention should be taken in a general sense as understood by one of ordinary skill in the art to which the present invention belongs. The use of the terms "first," "second," and the like in one or more embodiments of the present invention does not denote any order, quantity, or importance, but rather the terms "first," "second," and the like are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect.
The technical scheme of the invention is described in detail below with reference to the accompanying drawings. The embodiment of the invention provides a multi-target optimal configuration method of a distributed photovoltaic energy storage system, a flow chart of the method is shown in fig. 1, and the multi-target optimal configuration method of the distributed photovoltaic energy storage system provided by the embodiment of the invention comprises the following steps:
S202, a photovoltaic power generation optimizing model is established, and photovoltaic power generation equipment configuration parameters and energy storage equipment configuration parameters are obtained based on the photovoltaic power generation optimizing model. Establishing the photovoltaic power generation optimization model comprises the steps of establishing a first objective function F 1:
F1＝Max(Ppv*(Pgrid+Pbat)/(Ppv+Pgrid+Pbat))
Wherein, P pv represents the actual output power of the photovoltaic power generation equipment in the system, P grid represents the power obtained by the power distribution network from the photovoltaic energy storage system, and P bat represents the power for charging and discharging the energy storage equipment. In the first objective function F 1, P pv*(Pgrid+Pbat) represents a portion of the power actually generated by the photovoltaic power generation device that is commonly utilized by the power distribution network and the photovoltaic power generation device, and may represent how much of the power generation amount of the photovoltaic power generation device is effectively utilized.
The constraint conditions that the first objective function F 1 needs to satisfy are:
Ppv≤Ppv_max
Pbat≤Pbat_max
Ebat≤Ebat_max
Pgrid+Pbat＝Pload
Wherein, P pv_max represents the maximum output power of the photovoltaic power generation device, P bat_max represents the maximum charge and discharge power of the energy storage device, E bat represents the output capacity of the energy storage device, E bat_max represents the maximum capacity of the energy storage device, and P load represents the load demand of the power distribution network.
Based on the constraint conditions, the first objective function F 1 is solved, a genetic algorithm or a particle swarm algorithm can be adopted, and an optimal solution meeting the constraint conditions is found through iterative search, so that the configuration power of the photovoltaic power generation equipment and the configuration power of the energy storage equipment are obtained.
S204, using the photovoltaic power generation equipment configuration parameters and the energy storage equipment configuration parameters as input data, establishing a power distribution network operation optimization model, and obtaining power distribution network operation parameters based on the power distribution network operation optimization model. The building of the power distribution network operation optimization model comprises the building of a second objective function F 2:
F2＝Min(w1*ΔUpv+w2*ΔUbat+w3*ΔUload)
Wherein Δu pv represents the voltage deviation of the photovoltaic power generation device access point in the system, Δu bat represents the voltage deviation of the energy storage device access point in the system, and Δu load represents the voltage deviation of the load center node. w 1、w2 and w 3 respectively represent weight coefficients of the voltage deviations, and can be set according to practical requirements by considering factors such as importance of nodes, influence on system stability and the like. In the embodiment, voltage deviation is quantified by selecting three important nodes in the system, so that the running stability of the power grid can be estimated and optimized more accurately.
The above voltage deviations can be calculated by the following formula:
Wherein, U pv represents an actual voltage value of the photovoltaic power generation device access point, U pv_rate represents a rated voltage value of the photovoltaic power generation device access point, U bat represents an actual voltage value of the energy storage device access point, U bat_rate represents a rated voltage value of the energy storage device access point, and U load represents a load center node. The actual voltage value at the point, U load_rate, represents the rated voltage value at the load center node. The load center node is a node bearing a larger load in the power system, is a key node in the system, and has important significance on the stability and safety of the system. The position of the load center node can be used for calculating the load moment of each node, and the node with the minimum load moment is selected as the load center node. The load moment can be calculated by the following formula:
M＝Pl*L*μ
where P l represents the load power of the node, L represents the electrical distance from the node to the candidate load center, and μ represents the weight coefficient of the node. The weight coefficient is selected by considering the factors of load type, load grade, voltage grade coefficient, short circuit capacity and impedance, so as to more accurately represent the importance degree of the corresponding node. The weight coefficient may be determined according to the following formula:
Wherein alpha 1 represents the load type coefficient connected with the node, and is divided into important industrial load, commercial load and resident load, and the value is from high to low; alpha 2 represents a load grade coefficient connected with the node, and is divided into a primary load, a secondary load and a tertiary load, the value is from high to low, and the higher the load grade is, the higher the requirement on the power supply reliability is; beta 1 represents the voltage class coefficient of the node, which is divided into high voltage, medium voltage and low voltage, and the higher the voltage class is, the stronger the power supply capability of the node is, and the higher the value is; gamma 1 represents the short-circuit capacity of the node, the short-circuit capacity reflects the electrical strength and power supply capacity of the node, and the larger the short-circuit capacity is, the higher the power supply reliability of the node is, so that the coefficient is correspondingly higher; gamma 2 represents the impedance coefficient of the node, and the smaller the impedance, the better the electrical performance of the node, the higher the power supply quality, and therefore the coefficient thereof is correspondingly higher.
The second objective function F 2 is solved, comprising the steps of:
S2041, according to the solving result of the first objective function, obtaining the configuration power of the photovoltaic power generation equipment and the configuration power of the energy storage equipment, wherein the configuration power and the configuration power of the energy storage equipment are respectively used as the power initial values of the photovoltaic power generation equipment and the energy storage equipment in the second objective function.
S2042, setting constraint conditions, including photovoltaic power generation equipment configuration power constraint conditions, energy storage equipment configuration power constraint conditions, voltage deviation constraint conditions and system power balance constraint conditions:
Ppvmin≤Ppv≤Ppvmax
Pbatmin≤Pbat≤Pbatmax
ΔUmin≤ΔUpv≤ΔUmax
ΔUmin≤ΔUbat≤ΔUmax
ΔUmin≤ΔUload≤ΔUmax
Ppv+Pbat＝Pl+Ploss
Wherein P pvmin represents the minimum allowable configuration power of the photovoltaic power generation device, and P pvmax represents the maximum allowable configuration power of the photovoltaic power generation device; p batmin represents the minimum allowed configuration power of the energy storage device, and P batmax represents the maximum allowed configuration power of the energy storage device; Δu min represents the system minimum allowable voltage deviation, Δu max represents the system maximum allowable voltage deviation; p l represents the distribution network load power and P loss represents the system lost power.
S2043, according to the initial value and the constraint condition, adopting an optimization algorithm to iterate the configuration power value of the photovoltaic power generation equipment and the configuration power value of the energy storage equipment to obtain an optimal solution meeting the constraint condition, wherein the optimal solution comprises the configuration power value of the re-optimized photovoltaic power generation equipment, the configuration power value of the energy storage equipment, the voltage deviation value of an access point of the optimized photovoltaic power generation equipment, the voltage deviation value of the access point of the energy storage equipment and the voltage deviation value of a load center node; the system power balance parameters can be obtained, and the power balance relation among the photovoltaic, the energy storage and the load and the power loss in the system can be obtained by meeting the system power balance constraint conditions.
S206, utilizing the photovoltaic power generation equipment configuration parameters, the energy storage equipment configuration parameters and the power distribution network operation parameters as input data, and establishing a multi-objective collaborative optimization model to obtain system optimization parameters. And establishing a multi-objective collaborative optimization model by taking the power supply reliability optimization index and the environmental benefit optimization index as optimization targets by utilizing parameters obtained by solving the second objective function so as to realize multi-objective optimization of the power distribution network. Establishing the multi-objective collaborative optimization model includes establishing a third objective function F 3:
MinF3＝v1*LP―v2*R
LP＝∑i∈NP(LPi)
Wherein LP represents the probability of no-load, N represents the set of load nodes, and P (LP i) represents the probability of no-load for node i; r represents the photovoltaic power generation utilization rate, E pv represents the electric energy generated by photovoltaic power generation equipment, and E total represents the total electric energy of the system; v 1 and v 2 represent weight coefficients for balancing the importance of the two optimization objectives, which are translated into minimization problems with a negative sign, since R is the maximization objective. In solving the third objective function, in addition to the relevant constraints referred to above, the following constraints need to be satisfied:
Vmin≤Vi≤Vmax
Iij≤Imax
Rmin≤R≤Rmax
Wherein V i represents a node voltage, V min represents a node voltage minimum allowable value, and V max represents a node voltage maximum allowable value; i ij denotes line current, I max denotes line current threshold; r min represents a minimum allowable value of the photovoltaic power generation utilization rate, and R max represents a maximum allowable value of the photovoltaic power generation utilization rate.
And solving the third objective function F 3 based on the constraint condition, wherein parameters obtained by solving the second objective function are introduced into the third objective function as constraint conditions or decision variables in the solving process. And taking the configuration power value of the optimal photovoltaic power generation equipment and the configuration power value of the optimal energy storage equipment obtained by solving the second objective function as known conditions, for example, taking the configuration power value of the optimal photovoltaic power generation equipment and the configuration power value of the optimal energy storage equipment as constraint conditions in a third objective function, so as to ensure that the configuration power of the photovoltaic power generation equipment and the configuration power of the optimal energy storage equipment are kept near the optimal values when the power supply reliability and the environmental benefit are optimized. And introducing the voltage deviation value obtained by solving the second objective function into a third objective function as a constraint condition. By limiting the range of voltage deviations, it can be ensured that the voltage quality of the distribution network is maintained or improved during the optimization process. And solving the obtained system power balance parameter by using the second objective function, wherein the power balance constraint can be considered in the third objective function. By ensuring a power balance between the photovoltaic, energy storage and load, a stable operation of the grid can be maintained. The third objective function is a multi-objective function, taking into account two optimization objectives: power supply reliability optimization metrics and environmental benefit optimization metrics, where there may be a conflict, require trade-offs and decisions between the two goals. When solving the multi-objective optimization function, a plurality of solutions exist, namely, the pareto optimal solution set, and the multi-objective optimization function can be solved by adopting a multi-objective optimization algorithm, such as NSGA-II, MOPSO and other algorithms. By solving the third objective function, a group of pareto optimal solution sets can be obtained, wherein the solution sets comprise different trade-off relations between power supply reliability and environmental benefits. According to actual conditions and requirements, a proper solution can be selected from the pareto optimal solution set to serve as a final decision scheme, so that a balanced decision of power supply reliability and environmental benefit optimization is realized.
The embodiment of the invention also provides a multi-objective optimal configuration device of the distributed photovoltaic energy storage system, which comprises:
the photovoltaic power generation optimizing model building module is used for building a photovoltaic power generation optimizing model and obtaining photovoltaic power generation equipment configuration parameters and energy storage equipment configuration parameters based on the photovoltaic power generation optimizing model.
The power distribution network operation optimization model building module is used for building a power distribution network operation optimization model by using the photovoltaic power generation equipment configuration parameters and the energy storage equipment configuration parameters as input data, and obtaining power distribution network operation parameters based on the power distribution network operation optimization model.
The multi-target collaborative optimization model building module is used for building a multi-target collaborative optimization model based on the photovoltaic power generation equipment configuration parameters, the energy storage equipment configuration parameters and the power distribution network operation parameters to obtain system optimization parameters.
The process of realizing the functions of each module in the multi-target optimal configuration device for the distributed photovoltaic energy storage system provided by the embodiment of the invention is the same as each step in the multi-target optimal configuration method for the distributed photovoltaic energy storage system provided by the embodiment of the invention, and repeated description thereof will be omitted.
In summary, the embodiment of the invention relates to a multi-objective optimal configuration method and device for a distributed photovoltaic energy storage system, wherein the method comprises the following steps: establishing a photovoltaic power generation optimization model, and obtaining photovoltaic power generation equipment configuration parameters and energy storage equipment configuration parameters based on the photovoltaic power generation optimization model; using the photovoltaic power generation equipment configuration parameters and the energy storage equipment configuration parameters as input data, establishing a power distribution network operation optimization model, and obtaining power distribution network operation parameters based on the power distribution network operation optimization model; and establishing a multi-objective collaborative optimization model based on the photovoltaic power generation equipment configuration parameters, the energy storage equipment configuration parameters and the power distribution network operation parameters to obtain system optimization parameters. According to the technical scheme provided by the embodiment of the invention, the photovoltaic power generation optimization model, the power distribution network operation optimization model and the multi-objective collaborative optimization model are established, so that the utilization efficiency of new energy sources in the distributed photovoltaic energy storage system, the operation stability of the power distribution network, the power supply reliability optimization index, the environmental benefit optimization index and other factors are considered, and the objective function is established step by step, so that the optimal configuration index of the system is obtained, and the multi-objective real-time optimal configuration of the distributed photovoltaic energy storage system is realized.
It should be understood that the above discussion of any of the embodiments is exemplary only and is not intended to suggest that the scope of the invention (including the claims) is limited to these examples; the technical features of the above embodiments or in the different embodiments may also be combined within the idea of the invention, the steps may be performed in any order of battery units and many other variations exist in the different aspects of one or more embodiments of the invention as described above, which are not provided in detail for the sake of brevity. The above detailed description of the present invention is merely illustrative or explanatory of the principles of the invention and is not necessarily intended to limit the invention. Accordingly, any modification, equivalent replacement, improvement, etc. made without departing from the spirit and scope of the present invention should be included in the scope of the present invention. Furthermore, the appended claims are intended to cover all such changes and modifications that fall within the scope and boundary of the appended claims, or equivalents of such scope and boundary.
Claims (2)
1. A multi-target optimal configuration method of a distributed photovoltaic energy storage system is characterized by comprising the following steps:
establishing a photovoltaic power generation optimization model, including establishing a first objective function ：
Wherein,Representing the actual output power of photovoltaic power generation equipment in the system,/>Representing power obtained from photovoltaic energy storage system by power distribution network,/>Representing the power of charging and discharging the energy storage device;
The first objective function Solving the satisfied constraint conditions includes:
Wherein, Represents the maximum output power of the photovoltaic power generation equipment,/>Representing the maximum charge-discharge power of the energy storage device,/>Representing the output capacity of the energy storage device,/>Representing the maximum capacity of the energy storage device,/>Representing the load demand of the distribution network;
obtaining photovoltaic power generation equipment configuration parameters and energy storage equipment configuration parameters based on the photovoltaic power generation optimization model;
The photovoltaic power generation equipment configuration parameters and the energy storage equipment configuration parameters are used as input data to build a power distribution network operation optimization model, wherein the power distribution network operation optimization model comprises a second objective function ：
Wherein,Representing voltage deviation of photovoltaic power generation equipment access point in system,/>Representing voltage deviation of energy storage device access point in system,/>Representing the voltage deviation of the load center node,/>、/>And/>Weight coefficients respectively representing the voltage deviations; the position of the load center node is determined according to the following steps: calculating the load moment of each node; selecting a node with the smallest load moment as a load center node; the load moment is calculated by the following formula:
Wherein, Representing load power of node,/>Representing the electrical distance of a node to a candidate load center node,/>A weight coefficient representing the node; the weight coefficient/>The determination is made according to the following equation:
Wherein, Representing the load type coefficient to which the node is connected; /(I)Representing the load level coefficient to which the node is connected; /(I)Representing the voltage class coefficient of the node; /(I)Representing the short circuit capacity of the node; /(I)Representing the impedance coefficient of the node;
the second objective function Solving the satisfied constraint conditions includes:
Wherein, Representing the minimum allowed configuration power of a photovoltaic power plant,/>Representing a maximum allowable configuration power of the photovoltaic power generation device; /(I)Representing a minimum allowable configuration power of the energy storage device,/>Representing a maximum allowable configuration power of the energy storage device; /(I)Representing the minimum allowable voltage deviation of the system,/>Representing a maximum allowable voltage deviation of the system; /(I)Representing load power of distribution network,/>Indicating that the system is losing power;
obtaining power distribution network operation parameters based on the power distribution network operation optimization model;
Based on the photovoltaic power generation equipment configuration parameters, the energy storage equipment configuration parameters and the power distribution network operation parameters, establishing a multi-objective collaborative optimization model to obtain system optimization parameters, wherein the establishing of the multi-objective collaborative optimization model comprises the steps of establishing a third objective function ：
Wherein,Representing probability of no-load,/>Representing a set of load nodes,/>Representing nodes/>Probability of load loss; /(I)Representing the utilization rate of photovoltaic power generation,/>Representing the electrical energy produced by a photovoltaic power plant,/>Representing the total power of the system; /(I)AndRepresenting the weight coefficient;
The third objective function Solving the satisfied constraint further includes:
Wherein, Representing node voltage,/>Representing the minimum allowable value of node voltage,/>Representing the maximum allowable value of the node voltage; Representing line current,/> Representing a line current threshold; /(I)Represents the minimum allowable value of the photovoltaic power generation utilization rate,/>And the maximum allowable value of the photovoltaic power generation utilization rate is indicated.
2. A distributed photovoltaic energy storage system multi-objective optimal configuration device for optimal configuration using the distributed photovoltaic energy storage system multi-objective optimal configuration method according to claim 1, the device comprising:
the photovoltaic power generation optimizing model building module is used for building a photovoltaic power generation optimizing model and obtaining photovoltaic power generation equipment configuration parameters and energy storage equipment configuration parameters based on the photovoltaic power generation optimizing model;
The power distribution network operation optimization model building module is used for building a power distribution network operation optimization model by using the photovoltaic power generation equipment configuration parameters and the energy storage equipment configuration parameters as input data, and obtaining power distribution network operation parameters based on the power distribution network operation optimization model;
the multi-target collaborative optimization model building module is used for building a multi-target collaborative optimization model based on the photovoltaic power generation equipment configuration parameters, the energy storage equipment configuration parameters and the power distribution network operation parameters to obtain system optimization parameters.
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