Energy storage locating and sizing optimization method and system

Abstract

The invention relates to the technical field of power systems, and discloses an energy storage locating and sizing optimization method and system.The method comprises the steps that an energy storage locating and sizing double-layer optimization model is established, and the energy storage locating and sizing double-layer optimization model comprises an upper layer optimization model and a lower layer optimization model; the upper-layer optimization model takes the minimum life cycle cost of the energy storage system as a target, and the lower-layer optimization model takes the minimum network loss as a target; acquiring equipment information, equipment parameters and operating conditions of energy equipment, and solving the energy storage locating and sizing double-layer optimization model according to the equipment information, the equipment parameters and the operating conditions to obtain a locating and sizing optimization scheme of the energy storage system. According to the method, the energy storage locating and sizing double-layer optimization model is constructed and solved based on the genetic adaptive ant colony algorithm and the second-order cone programming, so that the locating and sizing configuration optimization scheme of the energy storage system is obtained, and the network loss cost and the energy storage investment and operation and maintenance cost of the energy storage system can be effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to an energy storage site selection and capacity optimization method and system. Background Art

[0002] As the proportion of renewable energy in the energy mix continues to increase, the operation of the power system faces unprecedented challenges. Traditional power systems face uncertainties from multiple aspects, such as the output volatility of renewable energy, the uncertainty of grid load, and changes in the power market, which make the operation of the power system more complicated. Among them, the line loss problem of distributed generation (DG) access sites has become increasingly prominent and has become one of the key issues that need to be solved in the current power system operation.

[0003] Since the access location and capacity of the energy storage system will affect its ability to regulate the distribution of network power flow, especially different access locations and capacities will lead to differences in the energy storage's ability to regulate grid branch overloads; considering the high construction and maintenance costs of energy storage systems, it is necessary to conduct in-depth research on the access nodes and capacity of energy storage.

[0004] At present, many experts use various algorithms, compensation devices and ESS to reduce the line loss of the system from the perspective of electricity. SOCP can be used to improve the accuracy of the solution, and the solver can be used to increase the speed of the solution. However, most of the current algorithms have problems such as slow convergence time and easy to fall into local optimality.

[0005] Therefore, it is an urgent problem for those skilled in the art to propose a more optimized energy storage optimization configuration method to solve the difficulties existing in the prior art. Summary of the invention

[0006] The embodiments of the present invention provide a method and system for optimizing energy storage site selection and capacity determination to solve the above-mentioned technical problems in the prior art.

[0007] In order to have a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. This summary is not intended to be a general review, nor is it intended to identify key / important components or to delineate the scope of protection of these embodiments. Its only purpose is to present some concepts in a simple form as a preface to the detailed description that follows.

[0008] According to a first aspect of an embodiment of the present invention, a method for optimizing energy storage site selection and capacity determination is provided.

[0009] In one embodiment, the energy storage site selection and capacity optimization method includes:

[0010] Establishing a two-layer optimization model for energy storage site selection and capacity determination, wherein the two-layer optimization model for energy storage site selection and capacity determination includes an upper optimization model and a lower optimization model, wherein the upper optimization model aims to minimize the full life cycle cost of the energy storage system, and the lower optimization model aims to minimize network loss;

[0011] The equipment information, equipment parameters and operating conditions of the energy equipment are obtained, and the energy storage site selection and sizing double-layer optimization model is solved according to the equipment information, the equipment parameters and the operating conditions to obtain an energy storage system site selection and sizing optimization plan.

[0012] In one embodiment, the objective function of the upper optimization model is:

[0013] f1=minC=min(C sys +C rep +C dis +C ope )

[0014] C=C sys +C rep +C dis +C ope

[0015]

[0016] Where f1 represents the minimum life cycle cost of the energy storage system; C represents the life cycle cost of the energy storage system; C sys represents the initial installation cost coefficient of the energy storage system; C rep represents the replacement cost of the energy storage system; C dis represents the energy storage recovery cost of the energy storage system; C ope Represents the energy storage operation cost of the energy storage system; C E,sys Represents the initial installation cost coefficient of the rated capacity of the energy storage system; C P,sys E represents the initial installation cost coefficient of the configured power of the energy storage system; HESS Represents the rated capacity of the energy storage system; P HESS represents the rated power of the energy storage system; σ represents the discount rate; Y represents the planned life of the energy storage project; k represents the number of times the energy storage device is replaced; C E,rep Indicates the replacement cost coefficient of rated capacity; C P,rep represents the replacement cost coefficient of the configured power; G represents the number of operating days per year; Δt represents the charging and discharging time; C pr (t) represents the available electricity price in time period t; C E,dis Expressed as the recovery cost coefficient of rated capacity; C P,dis Expressed as the recovery cost coefficient of the configured power.

[0017] In one embodiment, the constraints of the upper optimization model are:

[0018]

[0019] Where P HESS Indicates the rated power of the energy storage system; P HESS,max , P HESS,min Respectively represent the upper and lower limits of the rated power of the energy storage system; E HESS Indicates the rated capacity of the energy storage system; E HESS,max 、E HESS,min Respectively represent the upper and lower limits of the rated capacity of the energy storage system; n HESS Indicates the number of installed configuration nodes of the energy storage system; n max Indicates the upper limit of the configuration nodes of the energy storage system.

[0020] In one embodiment, the objective function of the lower layer optimization model is:

[0021]

[0022] Where minf2 represents the minimum line network loss; i and j represent the configuration node numbers of the energy storage system, β ij,t represents the quadratic current of branch ij during period t, r ij Represents the resistance of branch ij; E is the set of distribution network branches; T is the total number of time periods throughout the day.

[0023] In one embodiment, the constraints of the lower layer optimization model are:

[0024] Q cimin ≤Q ci ≤Q cimax

[0025] Q Dimin ≤Q Di ≤Q Dimax

[0026]

[0027] 0≤P c (t)≤P max

[0028] -P max ≤P d (t)≤0

[0029] P c (t)P d (t) = 0

[0030] SOC min ≤SOC(t)≤SOC max

[0031]

[0032] In the formula, Q ci represents the capacity of the reactive power compensation device at the configuration node i, Q cimax , Q cimin They represent the upper and lower limits of the capacity of the reactive power compensation device at the configuration node i; Q Di represents the reactive power output of the DG connected to the configuration node i; Q Dimax , Q Dimin They represent the upper and lower limits of reactive power output of the DG connected to the configuration node i; α i,t represents the square of the voltage of the configuration node i during the period t; α j,t represents the square of the voltage of the configuration node j during the period t; β ij,t represents the quadratic current of branch ij during period t; They represent the upper and lower limits of the quadratic voltage of the configuration node i in the period t; P c (t), P d (t) represent the charging power and discharging power stored at time t respectively; P max Indicates the maximum charge and discharge power of the energy storage system; SOC(t) indicates the battery state of charge of the battery after the first optimization cycle; SOC max , SOC min They represent the upper and lower limits of the battery state of charge respectively; SOC0 represents the initial state of charge of the battery; E n represents the rated capacity of the battery; Δt represents the optimization time interval; T represents the number of optimization time periods; P ij,t , Q ij,t They represent the active power and reactive power at the head end of branch j respectively; P j,t represents the active injection power of configuration node j; k:j→k represents the set of child nodes with configuration node j as the parent node; q j,t represents the reactive injection power of configuration node j; r ij +jx ij is the impedance of branch ij, where r ij is the resistance of branch ij, where x ij is the reactance of branch ij; P jk,t and Q jk,t are the active and reactive powers at the head end of branch jk respectively.

[0033] In one embodiment, solving the energy storage site selection and capacity two-layer optimization model includes:

[0034] The upper optimization model of the double-layer optimization model of energy storage site selection and sizing is solved by using a genetic adaptive ant colony algorithm; the lower optimization model of the double-layer optimization model of energy storage site selection and sizing is solved by using a Cplex solver; when the network loss reaches the optimal state, the operation and maintenance cost under this state is fed back to the upper optimization model, and the genetic adaptive ant colony algorithm is continued to be used to optimize and solve the upper optimization model, so as to complete the solution of the double-layer optimization model of energy storage site selection and sizing.

[0035] In one embodiment, the genetic adaptive ant colony algorithm includes:

[0036] Encode and decode ant paths; and perform path selection and pheromone update based on the shortest path expression and pheromone update formula in the genetic adaptive ant colony algorithm;

[0037] After the ant search completes the cycle, based on the phenomenon of crossover and mutation in the genetic process of the biological world, starting from the ant colony, a new path is generated through probabilistic selection, crossover and mutation operations, so that the ant colony can search for a better area.

[0038] In one embodiment, the shortest path expression is:

[0039]

[0040] Where: represents the shortest path; τ ij represents the pheromone concentration value; α and β represent the control coefficients of pheromone concentration and visibility weight respectively; η ij Represents the visibility value. In the traveling salesman problem, η ij represents the distance between any two cities; i and j represent any two cities in the traveling salesman problem; k represents the kth ant; ω k represents the set of nodes to be visited by the kth ant; t represents the current number of iterations.

[0041] In one embodiment, the pheromone update formula is:

[0042]

[0043] In the formula, τ ij (t+1) represents the pheromone at time t+1; ρ represents the volatility coefficient of the pheromone, which depends on the change value Δτ of the pheromone concentration after the previous cycle ij ; represents a function that takes into account the number of convergences and the total cost of the current path; τ ij (t) represents the pheromone concentration at time t; Δτ ij (t+1) represents the pheromone increment at time t+1; Represents a function that reflects the impact of the total cost of the path on the superiority of the solution.

[0044] According to a second aspect of an embodiment of the present invention, a system for optimizing the site selection and capacity determination of energy storage is provided.

[0045] In one embodiment, the energy storage site selection and capacity optimization system includes:

[0046] A model building module, used to establish a two-layer optimization model for energy storage site selection and capacity determination, wherein the two-layer optimization model for energy storage site selection and capacity determination includes an upper optimization model and a lower optimization model, wherein the upper optimization model aims to minimize the full life cycle cost of the energy storage system, and the lower optimization model aims to minimize network losses;

[0047] The model optimization module is used to obtain the equipment information, equipment parameters and operating conditions of the energy equipment, and solve the energy storage site selection and sizing double-layer optimization model based on the equipment information, the equipment parameters and the operating conditions to obtain the site selection and sizing optimization plan for the energy storage system.

[0048] In one embodiment, the objective function of the upper optimization model is:

[0049] f1=minC=min(C sys +C rep +C dis +C ope )

[0050] C=C sys +C rep +C dis +C ope

[0051]

[0052] Where f1 represents the minimum life cycle cost of the energy storage system; C represents the life cycle cost of the energy storage system; C sys represents the initial installation cost coefficient of the energy storage system; C rep represents the replacement cost of the energy storage system; C dis represents the energy storage recovery cost of the energy storage system; C ope represents the energy storage operation cost of the energy storage system; C E,sys Represents the initial installation cost coefficient of the rated capacity of the energy storage system; C P,sys E represents the initial installation cost coefficient of the configured power of the energy storage system; HESS Represents the rated capacity of the energy storage system; P HESS represents the rated power of the energy storage system; σ represents the discount rate; Y represents the planned life of the energy storage project; k represents the number of times the energy storage device is replaced; C E,repIndicates the replacement cost coefficient of rated capacity; C P,rep represents the replacement cost coefficient of the configured power; G represents the number of operating days per year; Δt represents the charging and discharging time; C pr (t) represents the available electricity price in time period t; C E,dis Expressed as the recovery cost coefficient of rated capacity; C P,dis Expressed as the recovery cost coefficient of the configured power.

[0053] In one embodiment, the constraints of the upper optimization model are:

[0054]

[0055] Where P HESS Indicates the rated power of the energy storage system; P HESS,max , P HESS,min Respectively represent the upper and lower limits of the rated power of the energy storage system; E HESS Indicates the rated capacity of the energy storage system; E HESS,max 、E HESS,min Respectively represent the upper and lower limits of the rated capacity of the energy storage system; n HESS Indicates the number of installed configuration nodes of the energy storage system; n max Indicates the upper limit of the configuration nodes of the energy storage system.

[0056] In one embodiment, the objective function of the lower layer optimization model is:

[0057]

[0058] Where minf2 represents the minimum line network loss; i and j represent the configuration node numbers of the energy storage system, β ij,t represents the quadratic current of branch ij during period t, r ij Represents the resistance of branch ij; E is the set of distribution network branches; T is the total number of time periods throughout the day.

[0059] In one embodiment, the constraints of the lower layer optimization model are:

[0060] Q cimin ≤Q ci ≤Q cimax

[0061] Q Dimin ≤Q Di ≤Q Dimax

[0062]

[0063] 0≤P c (t)≤P max

[0064] -P max ≤P d (t)≤0

[0065] P c (t)P d (t) = 0

[0066] SOC min ≤SOC(t)≤SOC max

[0067]

[0068] In the formula, Q ci represents the capacity of the reactive power compensation device at the configuration node i, Q cimax , Q cimin They represent the upper and lower limits of the capacity of the reactive power compensation device at the configuration node i; Q Di represents the reactive power output of the DG connected to the configuration node i; Q Dimax , Q Dimin They represent the upper and lower limits of reactive power output of the DG connected to the configuration node i; α i,t represents the square of the voltage of the configuration node i during the period t; α j,t represents the square of the voltage of the configuration node j during the period t; β ij,t represents the quadratic current of branch ij during period t; They represent the upper and lower limits of the quadratic voltage of the configuration node i in the period t; P c (t), P d (t) represent the charging power and discharging power stored at time t respectively; P max Indicates the maximum charge and discharge power of the energy storage system; SOC(t) indicates the battery state of charge of the battery after the first optimization cycle; SOC max , SOC min They represent the upper and lower limits of the battery state of charge respectively; SOC0 represents the initial state of charge of the battery; E n represents the rated capacity of the battery; Δt represents the optimization time interval; T represents the number of optimization time periods; P ij,t , Q ij,t They represent the active power and reactive power at the head end of branch j respectively; P j,t represents the active injection power of configuration node j; k:j→k represents the set of child nodes with configuration node j as the parent node; q j,t represents the reactive injection power of configuration node j; r ij +jx ij is the impedance of branch ij, where r ij is the resistance of branch ij, where x ij is the reactance of branch ij; Pjk,t and Q jk,t are the active and reactive powers at the head end of branch jk respectively.

[0069] In one embodiment, when solving the double-layer optimization model for energy storage location and sizing, the model optimization module uses a genetic adaptive ant colony algorithm to solve the upper optimization model of the double-layer optimization model for energy storage location and sizing; uses a Cplex solver to solve the lower optimization model of the double-layer optimization model for energy storage location and sizing; when the network loss reaches the optimal state, the operation and maintenance cost in this state is fed back to the upper optimization model, and the genetic adaptive ant colony algorithm is continued to be used to optimize and solve the upper optimization model, thereby completing the solution of the double-layer optimization model for energy storage location and sizing.

[0070] In one embodiment, the genetic adaptive ant colony algorithm includes:

[0071] Encode and decode ant paths; and perform path selection and pheromone update based on the shortest path expression and pheromone update formula in the genetic adaptive ant colony algorithm;

[0072] After the ant search completes the cycle, based on the phenomenon of crossover and mutation in the genetic process of the biological world, starting from the ant colony, a new path is generated through probabilistic selection, crossover and mutation operations, so that the ant colony can search for a better area.

[0073] In one embodiment, the shortest path expression is:

[0074]

[0075] Where: represents the shortest path; τ ij represents the pheromone concentration value; α and β represent the control coefficients of pheromone concentration and visibility weight respectively; η ij Represents the visibility value. In the traveling salesman problem, η ij represents the distance between any two cities; i and j represent any two cities in the traveling salesman problem; k represents the kth ant; ω k represents the set of nodes to be visited by the kth ant; t represents the current number of iterations.

[0076] In one embodiment, the pheromone update formula is:

[0077]

[0078] In the formula, τ ij (t+1) represents the pheromone at time t+1; ρ represents the volatility coefficient of the pheromone, which depends on the change value Δτ of the pheromone concentration after the previous cycle ij ; represents a function that takes into account the number of convergences and the total cost of the current path; τ ij (t) represents the pheromone concentration at time t; Δτ ij (t+1) represents the pheromone increment at time t+1; Represents a function that reflects the impact of the total cost of the path on the superiority of the solution.

[0079] According to a third aspect of an embodiment of the present invention, a computer device is provided.

[0080] In some embodiments, the computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0081] According to a fourth aspect of embodiments of the present invention, a computer-readable storage medium is provided.

[0082] In one embodiment, the computer-readable storage medium stores a computer program, and the computer program implements the steps of the above method when executed by a processor.

[0083] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0084] The present invention constructs and solves a two-layer optimization model for energy storage site selection and sizing based on a genetic adaptive ant colony algorithm and second-order cone programming, thereby obtaining a configuration optimization scheme for site selection and sizing of the energy storage system, which can effectively reduce the network loss cost of the energy storage system as well as the energy storage investment and operation and maintenance costs.

[0085] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0087] Figure 1 It is a flow chart of a method for optimizing energy storage site selection and capacity determination according to an exemplary embodiment;

[0088] Figure 2 is a structural block diagram of an energy storage site selection and capacity optimization system according to an exemplary embodiment;

[0089] Figure 3 is a schematic diagram of a two-layer optimization model for energy storage site selection and capacity determination according to an exemplary embodiment;

[0090] Figure 4is a schematic diagram of the full life cycle cost of an energy storage system according to an exemplary embodiment;

[0091] Figure 5 is a schematic diagram of a second-order cone convex relaxation process for an optimal power flow model according to an exemplary embodiment;

[0092] Figure 6 is a schematic diagram of a single-point crossover of a genetic adaptive ant colony algorithm according to an exemplary embodiment;

[0093] Figure 7 is a schematic diagram of a variation of a genetic adaptive ant colony algorithm according to an exemplary embodiment;

[0094] Figure 8 is a flow chart of a genetic adaptive ant colony algorithm according to an exemplary embodiment;

[0095] Fig. 9 is a schematic diagram of an optimized configuration scheme for a 33-node power distribution system including distributed photovoltaic and wind power according to an exemplary embodiment;

[0096] Fig.10 is a 24-hour node voltage diagram according to an exemplary embodiment;

[0097] Fig.11 is a 24-hour active load diagram according to an exemplary embodiment;

[0098] Fig.12 is a 24-hour reactive load diagram according to an exemplary embodiment;

[0099] Fig.13 is a 24-hour photovoltaic wind turbine output diagram according to an exemplary embodiment;

[0100] Fig.14 is a 24-hour ESS battery charging and discharging power diagram according to an exemplary embodiment;

[0101] Fig.15 is a 24-hour ESS battery power SOC diagram according to an exemplary embodiment;

[0102] Fig.16 is a 24-hour network loss diagram of a distribution network according to an exemplary embodiment;

[0103] Fig.17 is a 24-hour reactive power compensation capacitor bank (CB) output diagram according to an exemplary embodiment;

[0104] Fig.18 is a 24-hour static (continuous) reactive power compensation capacitor SVC output diagram according to an exemplary embodiment;

[0105] Fig.19 The figure is a schematic diagram showing the structure of a computer device according to an exemplary embodiment. DETAILED DESCRIPTION

[0106] The following description and accompanying drawings fully illustrate the specific embodiments of this article so that those skilled in the art can practice them. Parts and features of some embodiments may be included in or replace parts and features of other embodiments. The scope of the embodiments of this article includes the entire scope of the claims, as well as all available equivalents of the claims. Herein, the terms "first", "second", etc. are only used to distinguish one element from another, without requiring or implying any actual relationship or order between these elements. In fact, the first element can also be called the second element, and vice versa. Moreover, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that the structure, device or equipment including a series of elements includes not only those elements, but also other elements that are not explicitly listed, or also include elements inherent to such structure, device or equipment. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the structure, device or equipment including the elements. Each embodiment is described in a progressive manner herein, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other.

[0107] The terms "longitudinal", "lateral", "upper", "lower", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc. in this document indicate the orientation or position relationship based on the orientation or position relationship shown in the drawings, and are only for the convenience of describing this document and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as a limitation on the present invention. In the description of this document, unless otherwise specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a mechanical connection or an electrical connection, it can also be the internal communication of two elements, it can be a direct connection, or it can be an indirect connection through an intermediate medium. For ordinary technicians in this field, the specific meanings of the above terms can be understood according to specific circumstances.

[0108] As used herein, the term "plurality" means two or more than two, unless otherwise specified.

[0109] In this document, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B means: A or B.

[0110] In this article, the term "and / or" is a description of the association relationship between objects, indicating that three relationships may exist. For example, A and / or B means: A or B, or, A and B.

[0111] It should be understood that, although the various steps in the flow chart are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps is not strictly limited in order, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0112] Each module in the device or system of the present application can be implemented in whole or in part by software, hardware, or a combination thereof. The above modules can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above modules.

[0113] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0114] Figure 1 An embodiment of an energy storage site selection and capacity optimization method of the present invention is shown.

[0115] In this optional embodiment, the energy storage site selection and capacity optimization method includes:

[0116] Step S101, establishing a two-layer optimization model for energy storage site selection and capacity determination, wherein the two-layer optimization model for energy storage site selection and capacity determination includes an upper optimization model and a lower optimization model, wherein the upper optimization model aims to minimize the full life cycle cost of the energy storage system, and the lower optimization model aims to minimize network loss;

[0117] Step S103, obtaining equipment information, equipment parameters and operating conditions of energy equipment, and solving the energy storage site selection and sizing double-layer optimization model based on the equipment information, the equipment parameters and the operating conditions to obtain an energy storage system site selection and sizing optimization plan.

[0118] Figure 2 An embodiment of an energy storage site selection and capacity optimization system of the present invention is shown.

[0119] In this optional embodiment, the energy storage site selection and capacity optimization system includes:

[0120] A model building module 201 is used to establish a two-layer optimization model for energy storage site selection and capacity determination, wherein the two-layer optimization model for energy storage site selection and capacity determination includes an upper optimization model and a lower optimization model, wherein the upper optimization model aims to minimize the full life cycle cost of the energy storage system, and the lower optimization model aims to minimize network loss;

[0121] The model optimization module 203 is used to obtain the equipment information, equipment parameters and operating conditions of the energy equipment, and solve the energy storage site selection and sizing double-layer optimization model based on the equipment information, the equipment parameters and the operating conditions to obtain the site selection and sizing optimization plan for the energy storage system.

[0122] In the above embodiment, the components of the two-layer optimization model for energy storage site selection and capacity determination are as follows: Figure 3 As shown in the figure, the economic cost of the energy storage system is established based on the life cycle theory, and its components are as follows: Figure 4 Based on the energy storage life cycle cost model, the objective function of the upper optimization model is expressed as follows:

[0123] f1=minC=min(C sys +C rep +C dis +C ope )

[0124] C=C sys +C rep +C dis +C ope

[0125]

[0126]

[0127] Where f1 represents the minimum life cycle cost of the energy storage system; C represents the life cycle cost of the energy storage system; C sys represents the initial installation cost coefficient of the energy storage system; C rep represents the replacement cost of the energy storage system; C dis represents the energy storage recovery cost of the energy storage system; C ope represents the energy storage operation cost of the energy storage system (C ope Mainly refers to the operation stage after energy storage configuration, which is composed of fuel costs, electricity costs, and carbon dioxide emission costs, and takes into account the operating costs under real-time electricity prices); C syEs , represents the initial installation cost coefficient of the rated capacity of the energy storage system; C P,sys E represents the initial installation cost coefficient of the configured power of the energy storage system; HESS Represents the rated capacity of the energy storage system; P HESSrepresents the rated power of the energy storage system; σ represents the discount rate; Y represents the planned life of the energy storage project; k represents the number of times the energy storage device is replaced, and its value is Y / Y HESS And when Y / Y HESS When it is a non-integer, k is rounded up to 1 (Y HESS is the life of the energy storage device); C E,rep Indicates the replacement cost coefficient of rated capacity; C P,rep represents the replacement cost coefficient of the configured power; G represents the number of operating days per year; Δt represents the charging and discharging time; C pr (t) represents the available electricity price in time period t; C E,dis Expressed as the recovery cost coefficient of rated capacity; C P,dis Expressed as the recovery cost coefficient of the configured power.

[0128] In the above embodiment, considering the actual installation condition restrictions of the energy storage device, the objective function of the upper optimization model needs to constrain the number of nodes of the energy storage configuration and the rated capacity and rated power of each node configuration. Therefore, the constraint conditions of the upper optimization model are:

[0129]

[0130] Where P HESS Indicates the rated power of the energy storage system; P HESS,max , P HESS,min Respectively represent the upper and lower limits of the rated power of the energy storage system; E HESS Indicates the rated capacity of the energy storage system; E HESS,max 、E HESS,min Respectively represent the upper and lower limits of the rated capacity of the energy storage system; n HESS Indicates the number of installed configuration nodes of the energy storage system; n max Indicates the upper limit of the configuration nodes of the energy storage system.

[0131] In the above embodiment, the lower layer optimization model aims to minimize network loss, and its objective function is:

[0132]

[0133] Where minf2 represents the minimum line network loss; i and j represent the configuration node numbers of the energy storage system, β ij,t represents the quadratic current of branch ij during period t, r ij Represents the resistance of branch ij; E is the set of distribution network branches; T is the total number of time periods throughout the day.

[0134] The existence of quadratic terms in the optimal power flow of the lower optimization model causes the model to be non-convex, which is not convenient for the existing solver to directly calculate. Therefore, the present invention uses a second-order cone convex relaxation to process the above optimal power flow model. The relaxation process diagram is shown in Figure 5As shown:

[0135] The lower constraint conditions after the second-order cone convex relaxation are:

[0136] Distributed generation reactive power output, static reactive power compensation device output and node voltage constraints:

[0137] Q cimin ≤Q ci ≤Q cimax

[0138] Q Dimin ≤Q Di ≤Q Dimax

[0139]

[0140] In the formula, Q ci represents the capacity of the reactive power compensation device at the configuration node i, Q cimax , Q cimin They represent the upper and lower limits of the capacity of the reactive power compensation device at the configuration node i; Q Di represents the reactive power output of the DG connected to the configuration node i; Q Dimax , Q Dimin They represent the upper and lower limits of reactive power output of the DG connected to the configuration node i; α i,t represents the square of the voltage of the configuration node i during the period t; They represent the upper and lower limits of the quadratic voltage of the configuration node i during time period t.

[0141] Output power constraints of energy storage devices:

[0142] 0≤P c (t)≤P max

[0143] -P max ≤P d (t)≤0

[0144] P c (t)P d (t) = 0

[0145] Where P c (t), P d (t) represent the charging power and discharging power stored at time t respectively; P max Indicates the maximum charging and discharging power of the energy storage system.

[0146] Energy storage capacity limitation constraints:

[0147] SOC min ≤SOC(t)≤SOC max

[0148] Where SOC(t) represents the battery state of charge of the battery after the first optimization cycle; SOC max , SOC min Respectively represent the upper and lower limits of the battery state of charge;

[0149]

[0150] In the formula, SOC0 represents the initial state of charge of the battery; E n represents the rated capacity of the battery; Δt represents the optimization time interval; T represents the number of optimization time periods; P c (t), P d (t) represent the charging power and discharging power stored at time t, respectively.

[0151] Battery charge and discharge balance constraints:

[0152]

[0153] Power flow constraints:

[0154]

[0155] Where P ij,t , Q ij,t They represent the active power and reactive power at the head end of branch j respectively; P j,t represents the active injection power of configuration node j; k:j→k represents the set of child nodes with configuration node j as the parent node; q j,t represents the reactive injection power of configuration node j; α i,t represents the square of the voltage of the configuration node i during the period t; α j,t represents the square of the voltage of the configuration node j during the period t; β ij,t represents the quadratic current of branch ij during period t; r ij +jx ij is the impedance of branch ij, where r ij is the resistance of branch ij, where x ij is the reactance of branch ij; P jk,t and Q jk,t are the active and reactive powers at the head end of branch jk respectively.

[0156] In the above embodiment, for the distribution system containing distributed power sources such as photovoltaic and wind power, a two-layer optimization model for energy storage site selection and sizing is proposed, with the full life cycle cost and network loss as the upper and lower optimization targets respectively. The upper optimization target is the full life cycle cost of the energy storage device, including the energy storage operation and maintenance cost represented by the product of the real-time electricity price and the daily operating power of the energy storage device; the lower optimization is based on the upper decision and branch flow model, and a mixed integer second-order cone programming model with the goal of minimizing the network loss of the distribution system is established; the upper model is solved using the genetic adaptive ant colony algorithm, and the lower second-order cone programming is calculated using the Cplex solver. When the network loss reaches the optimal state, the operation and maintenance cost in this state is fed back to the upper layer, and the optimization process of the upper adaptive ant colony algorithm is continued, thereby completing the iterative calculation of the two-layer optimization.

[0157] For energy storage systems, optimization algorithms need to take into account computational efficiency, convergence speed, and global optimization capabilities. Computational efficiency is used to ensure that the algorithm can quickly find a solution, convergence speed avoids invalid searches, and global optimization is to jump out of the local optimum and obtain the optimal solution. However, the basic ant colony algorithm has obvious shortcomings in these aspects and is difficult to meet the needs of energy storage systems.

[0158] For the genetic adaptive ant colony algorithm of the upper optimization model, the shortest path expression of the ant colony algorithm is:

[0159]

[0160] Where: represents the shortest path; τ ij represents the pheromone concentration value; α and β represent the control coefficients of pheromone concentration and visibility weight respectively; η ij Represents the visibility value. In the traveling salesman problem, η ij represents the distance between any two cities; i and j represent any two cities in the traveling salesman problem; k represents the kth ant; ω k represents the set of nodes to be visited by the kth ant; t represents the current number of iterations.

[0161] When the pheromone concentration control coefficient α = 0, the ants make choices based entirely on the city distance j. However, the solution composed of each local optimal solution is not the global optimal solution. When the visibility weight control coefficient β = 0, the ants make choices based entirely on the pheromone concentration τ ij The ants make a judgment, which causes them to converge quickly, but this is not the optimal solution. Therefore, in the practical application of the ant colony algorithm, it is necessary to select appropriate α, β and visibility value η ij .

[0162] Adaptive Ant Colony Optimization (ACA) is an improved ant colony algorithm that dynamically adjusts the algorithm's parameters and strategies to continuously optimize the algorithm's performance to adapt to different problems and practical application environments. Compared with the basic ant colony algorithm, this algorithm has better optimization effects and higher search speed.

[0163] The adaptive ant colony algorithm used in the present invention is based on the basic ant colony algorithm, introduces a certain degree of randomness and energy storage environment feedback mechanism, and adaptively adjusts relevant algorithm parameters to overcome some problems existing in the basic ant colony algorithm, such as local optimal solution traps, slow convergence speed and other problems.

[0164] The adaptive ant colony algorithm used in the present invention introduces heuristic pheromones. Because in the ant colony algorithm, the pheromone level on the path is one of the key factors used to guide ants to search. At the beginning of the search, ants will tend to take the path with a high pheromone concentration. However, the pheromone of the unsearched path will gradually decrease due to the volatility coefficient, which will affect the global search ability of the algorithm because it will reduce the probability of the searched path being selected again. In order to overcome this problem, we can balance the global search ability and search speed by adjusting the pheromone volatility coefficient.

[0165] The adaptive ant colony algorithm is a method of adaptively changing values. The expression of pheromone update at time t+1 is:

[0166]

[0167] Where: τ ij (t+1) represents the pheromone at time t+1; ρ represents the volatility coefficient of the pheromone, which depends on the change value Δτ of the pheromone concentration after the previous cycle ij ; represents a function that takes into account the number of convergences and the total cost of the current path; τ ij (t) represents the pheromone concentration at time t, which is usually determined by the path length of the ants of this generation; It represents a function that reflects the impact of the total cost of the path on the superiority of the solution, because a lower total cost may mean a higher superiority. This formula means that the new pheromone concentration is determined by the original pheromone natural volatilization and the new pheromone increment. Among them, the natural volatilization of pheromone and the new pheromone increment are both affected by the total cost of the path, which enables the algorithm to better consider the costs of different components when optimizing the energy storage system.

[0168] The adaptive ant colony algorithm introduces an adaptive mechanism to dynamically adjust the pheromone parameters, which improves the flexibility of the algorithm, but it may still face the problem of local optimality. The Genetic Adaptive Ant Colony Optimization (GACA) proposed in the present invention combines the crossover and mutation operations of the genetic algorithm, increases the diversity of the population, can better explore the solution space, avoids the local optimal problem that the ant colony algorithm may fall into, and helps to find the global optimal solution.

[0169] The genetic adaptive ant colony algorithm is inspired by the genetic algorithm. After the ant cycle iteration, it can perform crossover and mutation operations on the paths of each ant to generate a new path, and substitute it into the objective function to test whether a better solution can be generated. If the optimal solution is generated, the path through which the optimal solution passes is updated with pheromones.

[0170] First, we need to encode and decode the ant path. If we use a fixed-length binary symbol to represent the ant path, the path consists of only {0,1}, which means that the ant can only choose 0 or 1 at different nodes. For example, if we use a binary symbol of length k to represent the ant path, the path with all 0s is path U1, the path with the last bit being 1 and the rest being all 0s is path U2, and so on. The path with all 1s is path There are 2 k kind of path.

[0171]

[0172] After the ant path iterates, the objective function value needs to be calculated, which requires coding first. To encode, the variable value must first be converted into a binary string. The length of the string depends on the required accuracy. For example, suppose the interval of a variable x is [a, b], and the required accuracy is 4 decimal places. The number of bits of a binary string of a variable is represented by m, which is expressed by the following formula:

[0173] 2 m-1 <(ba)×10 4 <2 m -1

[0174] Returning a real value from binary can be done using the following formula:

[0175]

[0176] In the formula, decimal(substring) represents the variable x j The decimal value of .

[0177] Then, the path selection and pheromone update are carried out according to the shortest path expression and pheromone update formula in the adaptive ant colony algorithm.

[0178] After the ant search completes the cycle, in order to increase the types of solutions, according to the phenomenon of crossover and mutation in the genetic process of the biological world, new paths are generated through probabilistic selection, crossover, mutation and other operations, so that the ant colony can search for a better area. The selection operation uses the proportional selection operator. The proportional selection factor uses the objective function value of each ant path to determine its possibility of being selected. Assuming that the number of ants is M, the objective function value of the path represented by ant i is f i , then the probability of ant i being selected is:

[0179]

[0180] Where P i is the probability of ant i being selected, f i is the objective function value of the path represented by ant i, f k is the objective function value of the path represented by ant k, and M is the number of ants.

[0181] When the probability of ant i being selected is determined, a random number between 0 and 1 is generated to determine which ant's path will participate in the crossover. If the probability of individual selection is high, it can be selected multiple times, and its offspring will be re-evaluated for individual fitness to generate a better path. The crossover operation uses a single-point crossover operator, with only one crossover point. In any ant colony that has undergone a selection operation, the individual is the crossover object, and a crossover point position is randomly generated. Two individuals exchange paths at the crossover point to form a new individual, as shown in the schematic diagram. Figure 6 The mutation operator uses a basic mutation operator or a uniform mutation operator to achieve individual flipping for a binary individual population, that is, 0 becomes 1, and 1 becomes 0, as shown in Figure 7 shown.

[0182] The genetic adaptive ant colony algorithm proposed in this invention is used to solve the optimal configuration capacity of energy storage, taking the full life cycle cost of the energy storage system as the optimization target, and taking supply and demand balance, energy constraint and capacity limit as the constraint conditions. The flowchart of the genetic adaptive ant colony algorithm is shown in the figure. Figure 8 shown.

[0183] In the above embodiment, the upper optimization model is optimized by using a genetic adaptive ant colony algorithm, and the lower second-order cone programming can be directly calculated by using a cplex solver. When the network loss reaches the optimum, the operation and maintenance cost in this state is fed back to the upper layer, and the optimization process of the upper adaptive ant colony algorithm is continued, thereby completing the iterative calculation of the double-layer optimization and obtaining the optimal solution to the site selection and sizing problem of the distribution network energy storage system.

[0184] As one or more implementation methods, in order to verify the effectiveness of the optimization method proposed in this embodiment, an IEEE33-node distribution system containing distributed photovoltaic and wind power is built, and the following settings are made for the parameters of the distribution system and the energy storage device.

[0185] For the IEEE 33-node distribution system to be configured with energy storage, the system is connected to a distributed photovoltaic device at node 16, a distributed wind turbine device at node 17, a continuous reactive power compensation device (SVC) at nodes 5, 15, and 31, and a discrete reactive power compensation device (CB) at nodes 5 and 15. The points to be connected to the energy storage device are 1-32.

[0186] Based on the energy storage access locations obtained in this example, which are 15 and 32 nodes, the optimal configuration scheme of this example is as follows: Fig. 9 Taking a typical day of 24 hours as an example, the sampling interval is 1 hour, and the 24-hour node voltage diagram in the distribution network is as follows: Fig.10 As shown, the 24-hour active load diagram is as follows Fig.11 As shown, the 24-hour reactive load diagram is as follows Fig.12 As shown, the 24-hour photovoltaic wind turbine output diagram is as follows Fig.13 As shown, the 24-hour ESS battery charge and discharge power diagram is as follows Fig.14 As shown, the 24-hour ESS battery SOC diagram is as follows Fig.15 As shown, the 24-hour network loss diagram of the distribution network is as follows Fig.16 As shown, the 24-hour reactive power compensation capacitor bank (CB) output diagram is as follows Fig.17 As shown, the 24-hour static (continuous) reactive power compensation capacitor SVC output diagram is as follows Fig.18 shown.

[0187] Depend on Fig.15 and Fig.16 It can be obtained that, under the required energy storage configuration scheme, each energy storage device can operate safely and stably within the allowable range of its state of charge and active power.

[0188] This embodiment establishes a reasonable energy storage configuration optimization target, determines the life cycle cost and network loss as the objective functions of the two-layer optimization model respectively, and corrects the current configuration plan according to the energy storage operation cost under the minimum network loss operation state while considering the optimal economic cost. This can avoid a sharp increase in the network loss of the distribution system after the energy storage is configured, thereby comprehensively improving the economic performance and stability of the distribution system. The genetic adaptive ant colony algorithm and second-order cone convex relaxation are used to solve the two-layer optimization algorithm, and the linearization of the lower model is realized with the help of second-order cone relaxation. It is combined with the upper-layer adaptive ant colony algorithm to improve the convergence speed of the solution process.

[0189] Fig.19An embodiment of a computer device of the present invention is shown, which may be a server, and includes a processor, a memory and a network interface connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store static information and dynamic information data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, the steps in the above method embodiment are implemented.

[0190] Those skilled in the art will understand that Fig.19 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0191] In addition, the above-mentioned computer device provided by the present invention includes a memory and a processor, the memory stores a computer program, and the processor implements the steps in the above-mentioned method embodiment when executing the computer program.

[0192] In addition, the present invention further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps in the above method embodiment are implemented.

[0193] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0194] The present invention is not limited to the structures which have been described above and shown in the drawings, and various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for optimizing the location and capacity of energy storage, characterized in that: include: Establishing a two-layer optimization model for energy storage site selection and capacity determination, wherein the two-layer optimization model for energy storage site selection and capacity determination includes an upper optimization model and a lower optimization model, wherein the upper optimization model aims to minimize the full life cycle cost of the energy storage system, and the lower optimization model aims to minimize network loss; The equipment information, equipment parameters and operating conditions of the energy equipment are obtained, and the energy storage site selection and sizing double-layer optimization model is solved according to the equipment information, the equipment parameters and the operating conditions to obtain an energy storage system site selection and sizing optimization plan.

2. The energy storage site selection and capacity optimization method according to claim 1 is characterized in that: The objective function of the upper optimization model is: f1=minC=min(C sys +C rep +C dis +C ope ) C=C sys +C rep +C dis +C ope Where f1 represents the minimum life cycle cost of the energy storage system; C represents the life cycle cost of the energy storage system; C sys represents the initial installation cost coefficient of the energy storage system; C rep represents the replacement cost of the energy storage system; C dis represents the energy storage recovery cost of the energy storage system; C ope represents the energy storage operation cost of the energy storage system; C E,sys Represents the initial installation cost coefficient of the rated capacity of the energy storage system; C P,sys represents the initial installation cost coefficient of the configured power of the energy storage system; E HESS Represents the rated capacity of the energy storage system; P HESS represents the rated power of the energy storage system; σ represents the discount rate; Y represents the planned life of the energy storage project; k represents the number of times the energy storage device is replaced; C E,rep Indicates the replacement cost coefficient of rated capacity; C P,rep represents the replacement cost coefficient of the configured power; G represents the number of operating days per year; Δt represents the charging and discharging time; C pr (t) represents the available electricity price in time period t; C E,dis Expressed as the recovery cost coefficient of rated capacity; C P,dis Expressed as the recovery cost coefficient of the configured power.

3. The energy storage site selection and capacity optimization method according to claim 2 is characterized in that: The constraints of the upper optimization model are: Where P HESS Indicates the rated power of the energy storage system; P HESS,max , P HESS,min Respectively represent the upper and lower limits of the rated power of the energy storage system; E HESS Indicates the rated capacity of the energy storage system; E HESS,max 、E HESS,min Respectively represent the upper and lower limits of the rated capacity of the energy storage system; n HESS Indicates the number of installed configuration nodes of the energy storage system; n max Indicates the upper limit of the configuration nodes of the energy storage system.

4. The method for optimizing the location and capacity of energy storage according to claim 1, characterized in that: The objective function of the lower optimization model is: In the formula, minf2 represents the minimum line network loss; i, j represent the configuration node number of the energy storage system, β ij,t represents the quadratic current of branch ij during period t, r ij represents the resistance of branch ij; E is the set of distribution network branches; T is the total number of time periods throughout the day.

5. The method for optimizing the location and capacity of energy storage according to claim 4, characterized in that: The constraints of the lower optimization model are: Q cimin ≤Q ci ≤Q cimax Q Dimin ≤Q Di ≤Q Dimax 0≤P c (t)≤P max -P max ≤P d (t)≤0 P c (t)P d (t)=0 SOC min ≤SOC(t)≤SOC max In the formula, Q ci represents the capacity of the reactive power compensation device at the configuration node i, Q cimax , Q cimin They represent the upper and lower limits of the capacity of the reactive power compensation device at the configuration node i; Q Di represents the reactive power output of the DG connected to the configuration node i; Q Dimax , Q Dimin They represent the upper and lower limits of reactive power output of the DG connected to the configuration node i; α i,t represents the square of the voltage of the configuration node i during the period t; α j,t represents the square of the voltage of the configuration node j during the period t; β ij,t represents the quadratic current of branch ij during period t; They represent the upper and lower limits of the quadratic voltage of the configuration node i in the period t; P c (t), P d (t) represent the charging power and discharging power stored at time t respectively; P max Indicates the maximum charge and discharge power of the energy storage system; SOC(t) indicates the battery state of charge of the battery after the first optimization cycle; SOC max , SOC min Respectively represent the upper and lower limits of the battery state of charge; SOC0 represents the initial state of charge of the battery; E n represents the rated capacity of the battery; Δt represents the optimization time interval; T represents the number of optimization time periods; P ij,t , Q ij,t They represent the active power and reactive power at the head end of branch j respectively; P j,t represents the active injection power of configuration node j; k:j→k represents the set of child nodes with configuration node j as the parent node; q j,t represents the reactive injection power of configuration node j; r ij +jx ij is the impedance of branch ij, where r ij is the resistance of branch ij, where x ij is the reactance of branch ij; P jk,t and Q jk,t are the active and reactive powers at the head end of branch jk respectively.

6. The method for optimizing the location and capacity of energy storage according to claim 1, characterized in that: Solving the two-layer optimization model for energy storage site selection and capacity determination includes: The upper optimization model of the double-layer optimization model of energy storage site selection and sizing is solved by using a genetic adaptive ant colony algorithm; the lower optimization model of the double-layer optimization model of energy storage site selection and sizing is solved by using a Cplex solver; when the network loss reaches the optimal state, the operation and maintenance cost under this state is fed back to the upper optimization model, and the genetic adaptive ant colony algorithm is continued to be used to optimize and solve the upper optimization model, so as to complete the solution of the double-layer optimization model of energy storage site selection and sizing.

7. The method for optimizing the location and capacity of energy storage according to claim 6, characterized in that: The genetic adaptive ant colony algorithm comprises: Encode and decode ant paths; and perform path selection and pheromone update based on the shortest path expression and pheromone update formula in the genetic adaptive ant colony algorithm; After the ant search completes the cycle, based on the phenomenon of crossover and mutation in the genetic process of the biological world, starting from the ant colony, a new path is generated through probabilistic selection, crossover and mutation operations, so that the ant colony can search for a better area.

8. The method for optimizing the location and capacity of energy storage according to claim 6, characterized in that: The shortest path expression is: Where: represents the shortest path; τ ij represents the pheromone concentration value; α and β represent the control coefficients of pheromone concentration and visibility weight respectively; η ij Represents the visibility value. In the traveling salesman problem, η ij Represents the distance between any two cities; i and j represent any two cities in the traveling salesman problem; k represents the kth ant; ω k represents the set of nodes to be visited by the kth ant; t represents the current iteration number; The pheromone update formula is: In the formula, τ ij (t+1) represents the pheromone at time t+1; ρ represents the volatility coefficient of the pheromone, which depends on the change value Δτ of the pheromone concentration after the previous cycle ij ; represents a function that takes into account the number of convergences and the total cost of the current path; τ ij (t) represents the pheromone concentration at time t; Δτ ij (t+1) represents the pheromone increment at time t+1; Represents a function that reflects the impact of the total cost of the path on the superiority of the solution.

9. An energy storage site selection and capacity optimization system, characterized in that: include: A model building module, used to establish a two-layer optimization model for energy storage site selection and capacity determination, wherein the two-layer optimization model for energy storage site selection and capacity determination includes an upper optimization model and a lower optimization model, wherein the upper optimization model aims to minimize the full life cycle cost of the energy storage system, and the lower optimization model aims to minimize network losses; The model optimization module is used to obtain the equipment information, equipment parameters and operating conditions of the energy equipment, and solve the energy storage site selection and sizing double-layer optimization model based on the equipment information, the equipment parameters and the operating conditions to obtain the site selection and sizing optimization plan for the energy storage system.

10. The energy storage site selection and capacity optimization system according to claim 9, characterized in that: The objective function of the upper optimization model is: f1=minC=min(C sys +C rep +C dis +C ope ) C=C sys +C rep +C dis +C ope Where f1 represents the minimum life cycle cost of the energy storage system; C represents the life cycle cost of the energy storage system; C sys represents the initial installation cost coefficient of the energy storage system; C rep represents the replacement cost of the energy storage system; C dis represents the energy storage recovery cost of the energy storage system; C ope represents the energy storage operation cost of the energy storage system; C E,sys Represents the initial installation cost coefficient of the rated capacity of the energy storage system; C P,sys represents the initial installation cost coefficient of the configured power of the energy storage system; E HESS Represents the rated capacity of the energy storage system; P HESS represents the rated power of the energy storage system; σ represents the discount rate; Y represents the planned life of the energy storage project; k represents the number of times the energy storage device is replaced; C E,rep Indicates the replacement cost coefficient of rated capacity; C P,rep represents the replacement cost coefficient of the configured power; G represents the number of operating days per year; Δt represents the charging and discharging time; C pr (t) represents the available electricity price in time period t; C E,dis Expressed as the recovery cost coefficient of rated capacity; C P,dis Expressed as the recovery cost coefficient of the configured power.

11. The energy storage site selection and capacity optimization system according to claim 10, characterized in that: The constraints of the upper optimization model are: Where P HESS Indicates the rated power of the energy storage system; P HESS,max , P HESS,min Respectively represent the upper and lower limits of the rated power of the energy storage system; E HESS Indicates the rated capacity of the energy storage system; E HESS,max 、E HESS,min Respectively represent the upper and lower limits of the rated capacity of the energy storage system; n HESS Indicates the number of installed configuration nodes of the energy storage system; n max Indicates the upper limit of the configuration nodes of the energy storage system.

12. The energy storage site selection and capacity optimization system according to claim 9, characterized in that: The objective function of the lower optimization model is: In the formula, minf2 represents the minimum line network loss; i, j represent the configuration node number of the energy storage system, β ij,t represents the quadratic current of branch ij during period t, r ij represents the resistance of branch ij; E is the set of distribution network branches; T is the total number of time periods throughout the day.

13. The energy storage site selection and capacity optimization system according to claim 12, characterized in that: The constraints of the lower optimization model are: Q cimin ≤Q ci ≤Q cimax Q Dimin ≤Q Di ≤Q Dimax 0≤P c (t)≤P max -P max ≤P d (t)≤0 P c (t)P d (t)=0 SOC min ≤SOC(t)≤SOC max In the formula, Q ci represents the capacity of the reactive power compensation device at the configuration node i, Q cimax , Q cimin They represent the upper and lower limits of the capacity of the reactive power compensation device at the configuration node i; Q Di represents the reactive power output of the DG connected to the configuration node i; Q Dimax , Q Dimin They represent the upper and lower limits of reactive power output of the DG connected to the configuration node i; α i,t represents the square of the voltage of the configuration node i during the period t; α j,t represents the square of the voltage of the configuration node j during the period t; β ij,t represents the quadratic current of branch ij during period t; They represent the upper and lower limits of the quadratic voltage of the configuration node i in the period t; P c (t), P d (t) represent the charging power and discharging power stored at time t respectively; P max Indicates the maximum charge and discharge power of the energy storage system; SOC(t) indicates the battery state of charge of the battery after the first optimization cycle; SOC max , SOC min Respectively represent the upper and lower limits of the battery state of charge; SOC0 represents the initial state of charge of the battery; E n represents the rated capacity of the battery; Δt represents the optimization time interval; T represents the number of optimization time periods; P ij,t , Q ij,t They represent the active power and reactive power at the head end of branch j respectively; P j,t represents the active injection power of configuration node j; k:j→k represents the set of child nodes with configuration node j as the parent node; q j,t represents the reactive injection power of configuration node j; r ij +jx ij is the impedance of branch ij, where r ij is the resistance of branch ij, where x ij is the reactance of branch ij; P jk,t and Q jk,t are the active and reactive powers at the head end of branch jk respectively.

14. The energy storage site selection and capacity optimization system according to claim 9, characterized in that: When solving the double-layer optimization model for energy storage location and capacity, the model optimization module uses a genetic adaptive ant colony algorithm to solve the upper optimization model of the double-layer optimization model for energy storage location and capacity; uses a Cplex solver to solve the lower optimization model of the double-layer optimization model for energy storage location and capacity; when the network loss reaches the optimal state, the operation and maintenance cost in this state is fed back to the upper optimization model, and the genetic adaptive ant colony algorithm is continued to be used to optimize and solve the upper optimization model, so as to complete the solution of the double-layer optimization model for energy storage location and capacity.

15. The energy storage site selection and capacity optimization system according to claim 14, characterized in that: The genetic adaptive ant colony algorithm comprises: Encode and decode ant paths; and perform path selection and pheromone update based on the shortest path expression and pheromone update formula in the genetic adaptive ant colony algorithm; After the ant search completes the cycle, based on the phenomenon of crossover and mutation in the genetic process of the biological world, starting from the ant colony, a new path is generated through probabilistic selection, crossover and mutation operations, so that the ant colony can search for a better area.

16. The energy storage site selection and capacity optimization system according to claim 15, characterized in that: The shortest path expression is: Where: represents the shortest path; τ ij represents the pheromone concentration value; α and β represent the control coefficients of pheromone concentration and visibility weight respectively; η ij Represents the visibility value. In the traveling salesman problem, η ij Represents the distance between any two cities; i and j represent any two cities in the traveling salesman problem; k represents the kth ant; ω k represents the set of nodes to be visited by the kth ant; t represents the current iteration number; The pheromone update formula is: In the formula, τ ij (t+1) represents the pheromone at time t+1; ρ represents the volatility coefficient of the pheromone, which depends on the change value Δτ of the pheromone concentration after the previous cycle ij ; represents a function that takes into account the number of convergences and the total cost of the current path; τ ij (t) represents the pheromone concentration at time t; Δτ ij (t+1) represents the pheromone increment at time t+1; Represents a function that reflects the impact of the total cost of the path on the superiority of the solution.

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