Optimal locating and sizing planning method for energy storage system and distributed power generation

Abstract

The invention discloses an optimal locating and sizing planning method for an energy storage system and distributed power generation. The method comprises the following steps: representing the condition of installing an energy storage and distributed power supply in each node in a topological graph of a power system through a decision vector; establishing an admittance matrix according to the topological graph of the power system, and then performing conversion to obtain an impedance matrix of the nodes; and by taking the lowest operation cost as an objective function and combining constraint conditions, optimizing decision vectors by combining sensitivity analysis of an impedance matrix and a simulated annealing algorithm to obtain an optimal locating and sizing scheme of the energy storage system and distributed power generation. According to the method, the overall operation environment of a power grid and physical limitations of equipment are more considered, including consideration of the capacity of a transformer, and it is ensured that the safe operation range of the transformer is not exceeded while BESS and DG are increased; greater flexibility is provided, and the number and scale of energy storage and power generation facilities are allowed to be dynamically adjusted.

Description

Technical Field

[0001] The present invention relates to the technical field of power systems, and in particular to a planning method for optimal location and capacity of energy storage systems and distributed generation. Background Art

[0002] The integration of battery energy storage systems (BESS) and distributed generation (DG, such as photovoltaics) in power systems is becoming more and more common because they can improve the efficiency and reliability of the power grid. These technologies improve the flexibility and stability of power supply by improving the voltage curve, reducing technical power losses, and alleviating network congestion. Distributed generation usually generates electricity near the location where electricity is needed, reducing the need for long-distance transmission, improving energy efficiency and reducing transmission losses.

[0003] However, despite the significant benefits of BESS and DG, existing site selection and sizing optimization technologies encounter some problems in actual deployment. On the one hand, most models only optimize for a fixed amount of energy storage and distributed generation configurations, lacking sufficient flexibility and scalability to cope with changing grid demands and market conditions. On the other hand, existing technologies often ignore the operating limitations of transformers and fail to predict and prevent transformer overload problems that may be caused by increased generation and dispatch operations. Especially under high load conditions, adding distributed generation and dispatching energy storage may reduce the stability of grid operation if transformer overload is not fully considered.

[0004] In order to better respond to the actual operation and safety needs of the power grid, more consideration needs to be given to the overall operating environment of the power grid and the physical limitations of the equipment, including consideration of the capacity of the transformer, to ensure that the safe operating range of the transformer is not exceeded while adding BESS and DG; greater flexibility should be provided to allow dynamic adjustment of the number and scale of energy storage and power generation facilities to adapt to changing power grid and market needs. Summary of the invention

[0005] In order to solve the problems in the background technology, the present invention provides a planning method for optimal location and capacity of energy storage systems and distributed power generation, which can solve the problems of insufficient flexibility and high overload of transformers in the prior art.

[0006] In a first aspect, the present invention provides a planning method for optimal location and capacity of an energy storage system and distributed generation, comprising:

[0007] S1: The installation of energy storage and distributed power generation at each node in the power system topology diagram is represented by a decision vector; when the decision variable value corresponding to the node is non-zero, it indicates that the node has installed energy storage or distributed power generation; when the decision variable corresponding to the node is zero, it indicates that the node has not installed energy storage and distributed power generation;

[0008] S2: Establish an admittance matrix based on the topological diagram of the power system, and then transform it to obtain the impedance matrix of the node;

[0009] S3: Taking the lowest operating cost of the site selection and sizing scheme corresponding to the decision vector as the objective function and combining it with the constraints, the decision vector is optimized by combining the sensitivity analysis of the impedance matrix and the simulated annealing algorithm to obtain the optimal site selection and sizing scheme for the energy storage system and distributed generation; the constraints include: energy storage charging and discharging constraints, energy storage capacity upper and lower limit constraints, photovoltaic output upper and lower limit constraints, photovoltaic output upper and lower limit constraints, node active power balance constraints, node reactive power balance constraints, network Ohm's law constraints, network power flow second-order cone constraints, node voltage constraints, generator power constraints, and branch power constraints.

[0010] Furthermore, the specific process of S2 is as follows:

[0011] S21: Obtain the topology of the power system and establish the admittance matrix of each node. The expression is:

[0012] Y ij =g ij +jb ij

[0013] Among them, Y ij is the element in row i and column j of the admittance matrix Y; g ij is the conductance of the branch between node i and node j; b ij is the susceptance of the branch between nodes i and j;

[0014] S22: Perform an inverse transformation on the node's admittance matrix to obtain the node's impedance matrix, which is expressed as:

[0015] Z=Y -1

[0016] Among them, Z is the impedance matrix of the node; Y is the admittance matrix of the node.

[0017] Furthermore, the objective function in S3 is:

[0018]

[0019] Among them, C is the operating cost, including network loss cost and dispatching cost; is the square of the current in branch l; r l is the resistance value of branch l; ρ t is the network loss cost in time period t; m is the number of branches; T is the scheduling period; n s is the amount of energy storage installed; P ch,t,i is the charging capacity of the i-th energy storage period t; P dc,t,iis the discharge amount of the i-th energy storage period t; ρ s The depreciation cost of energy storage per unit of electricity is calculated based on the installation cost and maintenance cost of energy storage. pv is the number of installed photovoltaic devices; p pv,t,i is the output of the ith photovoltaic period t; ρ pv It is the unit cost of photovoltaic power output, which is calculated comprehensively from the installation cost and maintenance cost of photovoltaic power.

[0020] Furthermore, the constraints in S3 are specifically:

[0021] The charging and discharging constraints of energy storage are expressed as follows:

[0022] μ dc +μ ch ≤1

[0023] 0≤P dc ≤μ dc ×P max

[0024] 0≤P ch ≤μ ch ×P max

[0025] In the formula, μ dc is the discharge state of the energy storage, which takes 1 when the energy storage is discharged and 0 when it is charged; μ ch is the charging state of energy storage; P dc is the discharge amount of energy storage, which is 0 when discharging and 1 when charging; P dc is the discharge power of energy storage; P ch is the charging power of energy storage; P max is the maximum charging and discharging power of energy storage;

[0026] The upper and lower limits of energy storage capacity are expressed as follows:

[0027] S min ≤S≤S max

[0028] In the formula, S min is the minimum capacity of energy storage; S is the existing capacity of energy storage; S max is the maximum capacity of energy storage;

[0029] The capacity of energy storage is updated, and the expression is as follows:

[0030] S t+1 =S t +ηP ch,t -ηP dc,t

[0031] In the formula, S tis the energy storage capacity in time period t; η is the energy storage charging and discharging efficiency, where the charging and discharging efficiency are the same.

[0032] For the convenience of scheduling, the initial capacity and final capacity of energy storage are required to be equal, that is, to ensure that the capacity of energy storage is consistent at the beginning and end of each day. The expression is as follows.

[0033] S1=S T

[0034] Among them, S1 is the energy storage capacity in the initial period; S T is the energy storage capacity during the final period;

[0035] The upper and lower limits of photovoltaic output are as follows:

[0036] 0≤p pv,t ≤P pv,max,t

[0037] Where P pv,max,t is the maximum output limit of photovoltaic power during period t; p pv,t is the photovoltaic output during period t;

[0038] The node active power balance constraint is expressed as follows:

[0039] P g +p pv +P dc -P ch +P in =P load

[0040] Where P g is the active output of the generator at this node; p pv is the photovoltaic output of the node; P dc is the discharge amount of stored energy; P ch is the charge amount of energy storage; P load is the active load of the node; P in The active power injected into the node is expressed as follows:

[0041]

[0042] Among them, P in,i is the injected power of node i; P ij is the active power of branch ij; is the square of the current in branch ij; j:j->i is all branches ij with starting point j and end point i; r ij is the resistance value of branch ij; P ik is the active power of branch ik; k:i->k is all branches ik with starting point i and end point k;

[0043] The node reactive power balance constraint is expressed as follows:

[0044] Q g +Q in =Q load

[0045] Among them, Q g is the reactive power output of the generator at this node; Q load is the reactive load of the node; Q in Inject reactive power into the node, the expression is as follows:

[0046]

[0047] Among them, Q in,i Inject reactive power into node i; Q ij is the reactive power of branch ij; is the square of branch ij; x ij is the reactance of branch ij;

[0048] The network is constrained by Ohm's law, and the expression is as follows:

[0049]

[0050] in, is the square of the voltage at node i, and the power flow is j->i; is the square of the voltage at node j; r ij is the resistance value of branch ij; P ij is the active power of branch ij; ij is the reactance of branch ij; Q ij is the reactive power of branch ij; is the square of the current in branch ij;

[0051] The second-order cone constraint of the network power flow is expressed as follows:

[0052]

[0053] The node voltage constraint is expressed as follows:

[0054]

[0055] In the formula, is the lower limit of the square voltage of node i; is the upper limit of the voltage square of node i;

[0056] Generator power constraint, the expression is as follows:

[0057] P g,min ≤P g ≤P g,max ,Qg,min ≤Q g ≤Q g,max

[0058] Where P g,max The upper limit of the active power of the generator; -P g,max is the lower limit of the generator active power; Q g,max The upper limit of the reactive power output of the generator; -Q g,max It is the lower limit of reactive power output of the generator;

[0059] The branch power constraint is expressed as follows:

[0060] P l,min ≤P l ≤P l,max ,Q l,min ≤Q l ≤Q l,max

[0061] Where P l,max is the maximum active power that can flow through branch l; power; P l,min is the minimum active power that can flow through branch l; Q l,max is the maximum reactive power that can flow through branch l; Q l,min is the minimum reactive power that can flow through branch l.

[0062] Furthermore, in S3, the specific process of optimizing the decision vector by combining the sensitivity analysis of the impedance matrix and the simulated annealing algorithm to obtain the optimal location and capacity of the energy storage system and distributed generation is as follows:

[0063] S31: setting the initial temperature, cooling rate, stop temperature and initial decision vector of the simulated annealing algorithm; wherein the initial decision vector includes the initial configuration of energy storage and distributed generation of each node;

[0064] S32: Randomly perturb the current decision vector. The expression of the decision vector before and after the perturbation is as follows:

[0065] X=[x1,x2,...,x i ,...,x n ]

[0066] X'=[x1,x2,...,x i +Δx i ,...,x n ]

[0067] Among them, X is the decision vector before disturbance, that is, the current decision vector; X' is the decision vector after disturbance, that is, the new decision vector; x i is the optimal node for reducing network loss obtained by impedance matrix sensitivity analysis; Δx iis the disturbance value generated according to the predetermined disturbance range, and its expression is as follows:

[0068] Δx i =δ·(2·rand(0,1)-1)

[0069] Where d is the maximum amplitude of the disturbance; 2rand(0,1)-1 is a random number between -1 and 1;

[0070] S33: Based on the constraint conditions, use a solver to solve the operating cost C of the site selection and capacity determination scheme corresponding to the new decision vector; during specific implementation, the solver can be adjusted according to the actual scenario. In this embodiment, a Cplex solver is used;

[0071] S34: Calculate the cost difference between the location and capacity solution corresponding to the current decision vector and the location and capacity solution corresponding to the new decision vector. The expression is:

[0072] ΔZ=Z(X)-Z(X')

[0073] Z=C+W'

[0074]

[0075] Among them, ΔZ is the total cost difference between the site selection and capacity scheme corresponding to the current decision vector and the site selection and capacity scheme corresponding to the new decision vector; Z(X) is the total cost of the site selection and capacity scheme corresponding to the current decision vector; Z(X) is the total cost of the site selection and capacity scheme corresponding to the new decision vector; W is the total installation cost corresponding to the site selection and capacity scheme; σ% is the discount rate, which is generally 5%; N is the life of the equipment; C is the operating cost, including network loss cost and dispatch cost; is the square of the current in branch l; r l is the resistance value of branch l; ρ t is the network loss cost in time period t; m is the number of branches; T is the scheduling period; n s is the amount of energy storage installed; P ch,t,i is the charging capacity of the i-th energy storage period t; P dc,t,i is the discharge amount of the i-th energy storage period t; ρ s is the depreciation cost of energy storage charging and discharging per unit of electricity; n pv is the number of installed photovoltaic devices; p pv,t,i is the output of the ith photovoltaic period t; ρ pv is the unit cost of photovoltaic power output;

[0076] If the total cost difference ΔZ is greater than or equal to 0, the total cost of the location and capacity plan corresponding to the new decision vector is reduced, that is, the location and capacity plan corresponding to the new decision plan vector is better than or equal to the location and capacity plan corresponding to the current decision vector, and the new decision vector X' is accepted; if the total cost difference ΔZ is less than 0, whether to accept the location and capacity plan corresponding to the new decision vector is selected according to probability:

[0077] Randomly draw a random number within a preset range;

[0078] If the random number is less than the probability, the location and capacity solution corresponding to the new decision vector is accepted;

[0079] If the random number is greater than or equal to the probability, the location and capacity solution corresponding to the new decision vector is not accepted, and the process goes to S35;

[0080] S35: After completing one annealing, lower the temperature and determine whether the current temperature converges or reaches the preset temperature threshold: if so, output the final decision vector, that is, the optimal location and capacity solution for energy storage and photovoltaics; if not, return to S32; wherein the expression for lowering the temperature is as follows:

[0081] T' em =αT em

[0082] Among them, T' em is the temperature after annealing, and a is the annealing coefficient.

[0083] Furthermore, the decision value x corresponding to the optimal node for reducing network loss obtained by using impedance matrix sensitivity analysis in S32 is i The determination process is:

[0084] S321: Use the impedance matrix to calculate the network loss, the expression is:

[0085] S loss =(I bus ) T Z bus (I bus ) *

[0086] Among them, S loss is the network loss matrix; I bus is the current matrix;

[0087] S322: Determine the active network loss based on the network loss matrix, expressed as:

[0088]

[0089] Among them, P loss is the active network loss; n is the number of nodes; r ijis the resistance of branch ij; I i is the current at node i; I j is the current at node j; θ i is the voltage phase angle of node i; θ j is the voltage phase angle of node j;

[0090] S323: Active network loss P loss to I i Take the derivative and get the sensitivity analysis result, the expression is:

[0091]

[0092] Among them, θ j is the voltage phase angle of node j;

[0093] S324: Select the node with the smallest sensitivity analysis result among all nodes as the point to be disturbed.

[0094] Furthermore, the probability calculation formula in S34 is as follows:

[0095]

[0096] Where P ac is the probability; T em is the current temperature; exp is the exponential function; ΔZ is the total cost difference between the original decision vector and the new decision vector.

[0097] The present invention proposes a planning method for optimal location and capacity of energy storage systems and distributed generation, which has the following advantages:

[0098] (1) It has certain flexibility and scalability in decision vector design. By using decision vectors to indicate whether each node is equipped with energy storage or distributed generation, the number and location of energy storage and generation equipment can be adjusted dynamically. The number is no longer fixed, and the configuration is completely based on actual needs and optimization results. This makes the planning of energy storage and distributed generation more adaptable to the dynamic changes of the power grid and helps to achieve the optimal configuration under different load and market conditions.

[0099] (2) The simulated annealing algorithm is used to effectively solve the global search problem in high-dimensional optimization problems. The simulated annealing algorithm avoids falling into the local optimum through a random but controlled search process, ensuring that the global optimal solution or a solution close to the global optimal solution can be found. Combined with the sensitivity analysis of the Zbus matrix, the impact of different configurations on grid performance, such as voltage stability and power loss, can be accurately evaluated, thereby further guiding the optimization process.

[0100] (3) By combining simulated annealing algorithm and second-order cone programming (second-order cone constraints for power flow, so second-order cone programming is used) to optimize the site selection, capacity setting and operation of energy storage and distributed generation, the inflexibility of existing technologies and high transformer overload are overcome, and the efficiency, flexibility and comprehensiveness of power system optimization are achieved. Whether in improving the efficiency of power grid operation, reducing costs, or enhancing system reliability, the present invention has shown certain advantages and provided technical support for the development of modern power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0102] Figure 1 It is a flow chart of a planning method for optimal location and capacity determination of an energy storage system and distributed generation provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0103] To make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0104] Example 1

[0105] like Figure 1 As shown, this embodiment provides a planning method for optimal location and capacity of energy storage systems and distributed generation, including:

[0106] S1: The installation of energy storage and distributed power generation at each node in the power system topology diagram is represented by a decision vector; when the decision variable value corresponding to the node is non-zero, it indicates that the node has installed energy storage or distributed power generation; when the decision variable corresponding to the node is zero, it indicates that the node has not installed energy storage and distributed power generation.

[0107] A decision vector is used to represent the installation of energy storage and distributed power generation at each node. When the value of the decision variable corresponding to the node is non-zero, it means that the node has installed an energy storage or distributed power generation; when the decision variable corresponding to the node is zero, it means that the node has not installed energy storage and distributed power generation. It is only necessary to add the energy storage installation candidate points and the distributed generation installation candidate points to the decision vector to plan the number of unfixed energy storage and distributed power generation. For example, there are 10 energy storage installation candidate points and 5 distributed power generation installation candidate points (if the candidate points are not specifically specified, it can be considered that all points except the transformer are candidate points), and a 15-dimensional decision vector can be set to represent the installation of energy storage and distributed power generation. By using the decision vector, all nodes can be set as the points to be installed, the decision value of 0 represents not installed, the decision value of not 0 represents installation, and the number of final decision values ​​​​not 0 represents the number of installations, which solves the problem that most models only optimize for a fixed number of energy storage and distributed power generation configurations, and lack sufficient flexibility and scalability to cope with changing grid demands and market conditions.

[0108] S2: Establish the admittance matrix according to the topological diagram of the power system, and then transform it to obtain the impedance matrix of the node.

[0109] Specifically, S21: obtain the topology diagram of the power system and establish the admittance matrix of each node, which is expressed as:

[0110] Y ij =g ij +jb ij

[0111] Among them, Y ij is the element in row i and column j of the admittance matrix Y; g ij is the conductance of the branch between node i and node j; b ij is the susceptance of the branch between nodes i and j;

[0112] S22: Perform an inverse transformation on the node's admittance matrix to obtain the node's impedance matrix, which is expressed as:

[0113] Z=Y -1

[0114] Among them, Z is the impedance matrix of the node (ie, the Zbus matrix); Y is the admittance matrix of the node.

[0115] S3: Taking the lowest operating cost of the site selection and sizing scheme corresponding to the decision vector as the objective function and combining it with the constraints, the decision vector is optimized by combining the sensitivity analysis of the impedance matrix and the simulated annealing algorithm to obtain the optimal site selection and sizing scheme for the energy storage system and distributed generation; the constraints include: energy storage charging and discharging constraints, energy storage capacity upper and lower limit constraints, photovoltaic output upper and lower limit constraints, photovoltaic output upper and lower limit constraints, node active power balance constraints, node reactive power balance constraints, network Ohm's law constraints, network power flow second-order cone constraints, node voltage constraints, generator power constraints, and branch power constraints.

[0116] The impedance matrix provides an effective means of calculating the electrical impact between nodes and can evaluate the impact of different configurations on grid performance, such as voltage stability and losses. The simulated annealing algorithm explores the possible configuration space through a random but controlled search process to find the most cost-effective solution. The combination of the simulated annealing algorithm and impedance matrix sensitivity analysis can more effectively solve high-dimensional optimization problems on electrical topology graphs.

[0117] Specifically, the objective function in S3 is:

[0118]

[0119] Among them, C is the operating cost, including network loss cost and dispatching cost; is the square of the current in branch l; r l is the resistance value of branch l; ρ t is the network loss cost in time period t; m is the number of branches; T is the scheduling period; n s is the amount of energy storage installed; P ch,t,i is the charging capacity of the i-th energy storage period t; P dc,t,i is the discharge amount of the i-th energy storage period t; ρ s The depreciation cost of energy storage per unit of electricity is calculated based on the installation cost and maintenance cost of energy storage. pv is the number of installed photovoltaic devices; p pv,t,i is the output of the ith photovoltaic period t; ρ pv It is the unit cost of photovoltaic power output, which is calculated comprehensively from the installation cost and maintenance cost of photovoltaic power.

[0120] Specifically, the constraints in S3 are:

[0121] The charging and discharging constraints of energy storage are expressed as follows:

[0122] μ dc +μ ch ≤1

[0123] 0≤P dc ≤μdc ×P max

[0124] 0≤P ch ≤μ ch ×P max

[0125] In the formula, μ dc is the discharge state of the energy storage, which takes 1 when the energy storage is discharged and 0 when it is charged; μ ch is the charging state of the energy storage, which is 0 when the energy storage is discharged and 1 when it is charged; P dc is the discharge power of energy storage; P ch is the charging power of energy storage; P max is the maximum charging and discharging power of energy storage;

[0126] The upper and lower limits of energy storage capacity are expressed as follows:

[0127] S min ≤S≤S max

[0128] In the formula, S min is the minimum capacity of energy storage; S is the existing capacity of energy storage; S max is the maximum capacity of energy storage;

[0129] The capacity of energy storage is updated, and the expression is as follows:

[0130] S t+1 =S t +ηP ch,t -ηP dc,t

[0131] In the formula, S t is the energy storage capacity in period t; η is the energy storage charging and discharging efficiency, where the charging and discharging efficiency are assumed to be the same.

[0132] For the convenience of scheduling, the initial capacity and final capacity of energy storage are required to be equal, that is, to ensure that the capacity of energy storage is consistent at the beginning and end of each day. The expression is as follows.

[0133] S1=S T

[0134] Where S1 is the energy storage capacity in the initial period; S T is the energy storage capacity during the final period;

[0135] The upper and lower limits of photovoltaic output are as follows:

[0136] 0≤p pv,t ≤P pv,max,t

[0137] Where P pv,max,t is the maximum output limit of photovoltaic power during period t; ppv,t is the photovoltaic power output during period t; the lower limit refers to no power output, in which case it is taken as 0.

[0138] The node active power balance constraint is expressed as follows:

[0139] P g +p pv +P dc -P ch +P in =P load

[0140] Where P g is the active output of the generator at this node; p pv is the photovoltaic output of the node; P dc is the discharge amount of stored energy; P ch is the charge amount of energy storage; P load is the active load of the node; P in The active power injected into the node is expressed as follows:

[0141]

[0142] Among them, P in,i is the injected power of node i; P ij is the active power of branch ij; is the square of the current in branch ij; j:j->i is all branches ij with starting point j and end point i; r ij is the resistance value of branch ij; P ik is the active power of branch ik; k:i->k is all branches ik with starting point i and end point k;

[0143] The node reactive power balance constraint is expressed as follows:

[0144] Q g +Q in =Q load

[0145] Among them, Q g is the reactive power output of the generator at this node; Q load is the reactive load of the node; Q in Inject reactive power into the node, the expression is as follows:

[0146]

[0147] Among them, Q in,i Inject reactive power into node i; Q ij is the reactive power of branch ij; is the square of branch ij; x ij is the reactance of branch ij;

[0148] The network is constrained by Ohm's law, and the expression is as follows:

[0149]

[0150] in, is the square of the voltage at node i, and the power flow is j->i; is the square of the voltage at node j; r ij is the resistance value of branch ij; P ij is the active power of branch ij; ij is the reactance of branch ij; Q ij is the reactive power of branch ij; is the square of the current in branch ij;

[0151] The second-order cone constraint of the network power flow is expressed as follows:

[0152]

[0153] The node voltage constraint is expressed as follows:

[0154]

[0155] In the formula, is the lower limit of the square voltage of node i; is the upper limit of the voltage square of node i;

[0156] Generator power constraint, the expression is as follows:

[0157] P g,min ≤P g ≤P g,max ,Q g,min ≤Q g ≤Q g,max

[0158] Where P g,max P is the upper limit of the active output of the generator; g,min The lower limit of the active power output of the generator. The grid-connected point is generally regarded as the connected generator. The upper limit is the maximum transmission power of the grid-connected point. The node without a generator is considered to be 0; Q g,max is the upper limit of the reactive power output of the generator; Q g,min It is the lower limit of reactive power output of the generator;

[0159] The branch power constraint is expressed as follows:

[0160] P l,min ≤P l ≤P l,max ,Q l,min ≤Q l ≤Q l,max

[0161] Where P l,max is the maximum active power that can flow through branch l; P l,min is the minimum active power that can flow through branch l; Q l,max is the maximum reactive power that can flow through branch l; Q l,min is the minimum reactive power that can flow through branch l.

[0162] By modeling the transformer capacity constraint as a branch power constraint, overload can be guaranteed. This solves the problem that the existing technology often ignores the operating limitations of the transformer and fails to predict and prevent transformer overload problems that may be caused by increased power generation and scheduling operations. In particular, it solves the problem that transformer overload is not fully considered under high load conditions, and the increase of distributed generation and scheduling energy storage may reduce the stability of power grid operation.

[0163] Furthermore, in S3, the specific process of optimizing the decision vector by combining the sensitivity analysis of the impedance matrix and the simulated annealing algorithm to obtain the optimal location and capacity of the energy storage system and distributed generation is as follows:

[0164] S31: setting the initial temperature, cooling rate, stop temperature and initial decision vector of the simulated annealing algorithm; wherein the initial decision vector includes the initial configuration of energy storage and distributed generation of each node; the initial decision vector can be initialized by randomly selecting a decision vector or inputting a known decision as the initial value of the decision;

[0165] S32: Randomly perturb the current decision vector. The expression of the decision vector before and after the perturbation is as follows:

[0166] X=[x1,x2,...,x i ,...,x n ]

[0167] X'=[x1,x2,...,x i +Δx i ,...,x n ]

[0168] Among them, X is the decision vector before the disturbance; X' is the decision vector before and after the disturbance; x i is the decision value corresponding to the optimal node for reducing network loss obtained by using impedance matrix sensitivity analysis; Δx i is a small disturbance value generated according to a predetermined disturbance range, and its expression is as follows:

[0169] Δx i =δ·(2·rand(0,1)-1)

[0170] Where d is the maximum amplitude of the disturbance; rand(0,1) is a random number uniformly distributed between 0 and 1, so 2rand(0,1)-1 is a random number between -1 and 1.

[0171] More specifically, the process of determining the optimal node i for reducing network loss using impedance matrix sensitivity analysis is as follows:

[0172] S321: Use the impedance matrix to calculate the network loss, the expression is:

[0173] S loss =(I bus ) T Z bus (I bus ) *

[0174] Among them, S loss is the network loss matrix; I bus is the current matrix;

[0175] S322: Determine the active network loss based on the network loss matrix, expressed as:

[0176]

[0177] Among them, P loss is the active network loss; n is the number of nodes; r ij is the resistance of branch ij; I i is the current at node i; I j is the current at node j; θ i is the voltage phase angle of node i; θ j is the voltage phase angle of node j;

[0178] S323: Active network loss P loss to I i Take the derivative and get the sensitivity analysis result, the expression is:

[0179]

[0180] Among them, θ j is the voltage phase angle of node j;

[0181] S324: Select the node with the smallest sensitivity analysis result among all nodes as the point to be disturbed i. Based on this, the node i that can best reduce network loss can be found, and using it as the point to be disturbed to generate a new decision vector X' can optimize the high-dimensional performance of the model and accelerate convergence.

[0182] S33: Based on the constraints, the Cplex solver is used to solve the optimal power flow of the site selection and sizing scheme corresponding to the new decision vector, so as to calculate the operating cost C of the new decision scheme.

[0183] In specific implementation, you only need to input the objective function and constraints into the solver to automatically get the most effective result. Because the solution to the site selection and sizing scheme is a second-order cone programming solution to the optimal power flow problem of the site selection and sizing scheme, the optimal power flow of the site selection and sizing scheme corresponding to the new decision vector is solved based on the cost of energy storage and photovoltaic installation and operation, as well as various constraints, including network topology constraints, node voltage constraints, branch power constraints, etc. If there is no solution, it means that the proposed decision vector cannot meet the constraints, and a larger value is returned as a penalty; if there is a solution, the objective function value should be returned (is this objective function value the value of the annealing model? Or the objective function value of the underlying objective function?).

[0184] S34: Based on the operating cost C and the discounted annual cost W' of the total installation cost corresponding to the site selection and capacity determination scheme, the total cost difference between the site selection and capacity determination scheme corresponding to the current decision vector and the site selection and capacity determination scheme corresponding to the new decision vector is calculated, and the expression is:

[0185] ΔZ=Z(X)-Z(X')

[0186] Z=C+W'

[0187]

[0188] Among them, ΔZ is the total cost difference between the location and capacity scheme corresponding to the current decision vector and the location and capacity scheme corresponding to the new decision vector; Z(X) is the total cost of the location and capacity scheme corresponding to the current decision vector; Z(X) is the total cost of the location and capacity scheme corresponding to the new decision vector; W is the total installation cost corresponding to the location and capacity scheme; σ% is the discount rate; N is the life of the equipment; C is the operating cost, including network loss cost and dispatch cost; is the square of the current in branch l; r l is the resistance value of branch l; ρ t is the network loss cost in time period t; m is the number of branches; T is the scheduling period; n s is the amount of energy storage installed; P ch,t,i is the charging capacity of the i-th energy storage period t; P dc,t,i is the discharge amount of the i-th energy storage period t; ρ s is the depreciation cost of energy storage charging and discharging per unit of electricity; n pv is the number of installed photovoltaic devices; p pv,t,i is the output of the ith photovoltaic period t; ρ pv is the unit cost of photovoltaic power output;

[0189] If the total cost difference ΔZ is greater than or equal to 0, the cost of the location and capacity plan corresponding to the new decision vector is reduced, that is, the location and capacity plan corresponding to the new decision plan vector is better than or equal to the location and capacity plan corresponding to the current decision vector, and the new decision vector X' is accepted; if the total cost difference ΔZΔC is less than 0, then whether to accept the location and capacity plan corresponding to the new decision vector is selected according to probability:

[0190] Randomly draw a random number in [0, 1];

[0191] If the random number is less than the probability, the location and capacity solution corresponding to the new decision vector is accepted;

[0192] If the random number is greater than or equal to the probability, the location and capacity solution corresponding to the new decision vector is not accepted, and the process goes to S35; for example, if the calculated probability is 0.3, a random number between 0 and 1 is taken. If it is less than 0.3, the solution is accepted, otherwise it is not accepted. The process of choosing whether to accept the decision vector is random, so the simulated annealing algorithm can generate a new solution in each iteration, and the power grid performance can be evaluated based on this solution, which increases the possibility of finding the global optimal solution.

[0193] S35: After completing one annealing, lower the temperature and determine whether the current temperature converges or reaches the preset temperature threshold: if so, output the final decision vector, that is, the optimal location and capacity solution for energy storage and photovoltaics; if not, return to S32; wherein the expression for lowering the temperature is as follows:

[0194] T' em =αT em

[0195] Among them, T' em is the temperature after annealing; α is the annealing coefficient, which can generally take a value between 0.8-0.99.

[0196] More specifically, the probability of accepting a new solution is expressed as follows:

[0197]

[0198] Where P ac is the probability of accepting a new solution; T em is the current temperature; exp is the exponential function; ΔZ is the total cost difference between the original decision vector (i.e., the original solution) and the new decision vector (i.e., the new solution) corresponding to the location and capacity scheme. This formula indicates that even if the new solution is worse, there is a certain probability of accepting it, and this probability decreases as the temperature decreases. At the same time, the worse the new solution is compared to the original solution, the smaller the probability.

[0199] After the upper layer plans the installation plan for energy storage and distributed generation, the site selection and sizing plan corresponding to the decision vector is obtained through the simulated annealing algorithm. The lower layer solves the optimal power flow problem of the site selection and sizing plan through the second-order cone programming (SOCP), that is, it is solved by the solver to ensure the safety and efficiency of the power grid in actual operation. The lower layer optimization takes into account voltage constraints, power balance, transformer capacity limitations and line capacity limitations, and accurately calculates the voltage, current and power distribution of each node. The operating cost obtained by the lower layer optimization result is combined with the discounted annual cost of the total installation cost corresponding to the upper layer site selection and sizing plan to form the final optimization function value. This optimization function value is used as the objective function of the upper layer simulated annealing algorithm, so that the upper layer algorithm can further optimize the installation plan to ensure the economic and operational efficiency of the system.

[0200] It should be understood that in the embodiments of the present invention, the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The memory may include a read-only memory and a random access memory, and provide instructions and data to the processor. A portion of the memory may also include a non-volatile random access memory. For example, the memory may also store information about the type of device.

[0201] The readable storage medium is a computer-readable storage medium, which may be an internal storage unit of the controller described in any of the foregoing embodiments, such as a hard disk or memory of the controller. The readable storage medium may also be an external storage device of the controller, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the controller. Furthermore, the readable storage medium may also include both an internal storage unit of the controller and an external storage device. The readable storage medium is used to store the computer program and other programs and data required by the controller. The readable storage medium may also be used to temporarily store data that has been output or is to be output.

[0202] Based on this understanding, the technical solution of the present invention, in essence or in other words, the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned readable storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., various media that can store program codes.

[0203] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0204] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A planning method for optimal location and capacity of energy storage systems and distributed generation, characterized in that: include: S1: The installation of energy storage and distributed power generation at each node in the power system topology diagram is represented by a decision vector; when the decision variable value corresponding to the node is non-zero, it indicates that the node has installed energy storage or distributed power generation; when the decision variable corresponding to the node is zero, it indicates that the node has not installed energy storage and distributed power generation; S2: Establish an admittance matrix based on the topological diagram of the power system, and then transform it to obtain the impedance matrix of the node; S3: Taking the lowest operating cost of the site selection and sizing scheme corresponding to the decision vector as the objective function and combining it with the constraints, the decision vector is optimized by combining the sensitivity analysis of the impedance matrix and the simulated annealing algorithm to obtain the optimal site selection and sizing scheme for the energy storage system and distributed generation; the constraints include: energy storage charging and discharging constraints, energy storage capacity upper and lower limit constraints, photovoltaic output upper and lower limit constraints, photovoltaic output upper and lower limit constraints, node active power balance constraints, node reactive power balance constraints, network Ohm's law constraints, network power flow second-order cone constraints, node voltage constraints, generator power constraints, and branch power constraints.

2. The method for planning the optimal location and capacity of energy storage systems and distributed generation according to claim 1, characterized in that: The specific process of S2 is: S21: Obtain the topology of the power system and establish the admittance matrix of each node. The expression is: Y ij =g ij +jb ij Among them, Y ij is the element in row i and column j of the admittance matrix Y; g ij is the conductance of the branch between node i and node j; b ij is the susceptance of the branch between nodes i and j; S22: Perform an inverse transformation on the node's admittance matrix to obtain the node's impedance matrix, which is expressed as: Z=Y -1 Among them, Z is the impedance matrix of the node; Y is the admittance matrix of the node.

3. The method for planning the optimal location and capacity of energy storage systems and distributed generation according to claim 1, characterized in that: The objective function in S3 is: Among them, C is the operating cost, including network loss cost and dispatching cost; is the square of the current in branch l; r l is the resistance value of branch l; ρ t is the network loss cost in time period t; m is the number of branches; T is the scheduling period; n s is the amount of energy storage installed; P ch,t,i is the charging capacity of the i-th energy storage period t; P dc,t,i is the discharge amount of the i-th energy storage period t; ρ s is the depreciation cost of energy storage per unit of charge and discharge; n pv is the number of installed photovoltaic devices; p pv,t,i is the output of the ith photovoltaic period t; ρ pv is the unit cost of photovoltaic power output.

4. The method for planning the optimal location and capacity of energy storage systems and distributed generation according to claim 1, characterized in that: The constraints in S3 are specifically: The charging and discharging constraints of energy storage are expressed as follows: m dc +m ch ≤1 0≤P dc ≤μ dc ×P max 0≤P ch ≤μ ch ×P max In the formula, μ dc is the discharge state of the energy storage, which takes 1 when the energy storage is discharged and 0 when it is charged; μ ch is the charging state of the energy storage, which is 0 when the energy storage is discharged and 1 when it is charged; P dc is the discharge power of energy storage; P ch is the charging power of energy storage; P max is the maximum charging and discharging power of energy storage; The upper and lower limits of energy storage capacity are expressed as follows: S min ≤S≤S max In the formula, S min is the minimum capacity of energy storage; S is the existing capacity of energy storage; S max is the maximum capacity of energy storage; The capacity of energy storage is updated, and the expression is as follows: S t+1 =S t +ηP ch,t -ηP dc,t In the formula, S t is the energy storage capacity in time period t; η is the energy storage charging and discharging efficiency, where the charging and discharging efficiency is the same; Among them, the initial capacity and final capacity of energy storage are equal, and the expression is as follows: S1=S T Where S1 is the energy storage capacity in the initial period; S T is the energy storage capacity during the final period; The upper and lower limits of photovoltaic output are as follows: 0≤p pv,t ≤P pv,max,t Where P pv,max,t is the maximum output limit of photovoltaic power during period t; p pv,t is the photovoltaic output during period t; The node active power balance constraint is expressed as follows: P g,i +p pv,i +P dc,i -P ch,i +P in,i =P load,i Where P g is the active output of the generator at this node; p pv is the photovoltaic output of the node; P dc The discharge amount of energy stored in the node; P ch The amount of energy stored for the node; P load is the active load of the node; P in The active power injected into the node is expressed as follows: Among them, P in,i is the injected power of node i; P ij is the active power of branch ij; is the square of the current in branch ij; j:j->i is all branches ji with starting point j and end point i; r ij is the resistance value of branch ij; P ik is the active power of branch ik; k:i->k is all branches ik with starting point i and end point k; The node reactive power balance constraint is expressed as follows: Q g +Q in =Q load Among them, Q g is the reactive power output of the generator at this node; Q load is the reactive load of the node; Q in Inject reactive power into the node, the expression is as follows: Among them, Q in,i Inject reactive power into node i; Q ij is the reactive power of branch ij; is the square of branch ij; x ij is the reactance of branch ij; The network is constrained by Ohm's law, and the expression is as follows: in, is the square of the voltage at node i, and the power flow is j->i; is the square of the voltage at node j; r ij is the resistance value of branch ij; P ij is the active power of branch ij; x ij is the reactance of branch ij; Q ij is the reactive power of branch ij; is the square of the current in branch ij; The second-order cone constraint of the network power flow is expressed as follows: The node voltage constraint is expressed as follows: In the formula, is the lower limit of the square voltage of node i; is the upper limit of the voltage square of node i; Generator power constraint, the expression is as follows: P g,min ≤P g ≤P g,max ,Q g,min ≤Q g ≤Q g,max Where P g,max P is the upper limit of the active output of the generator; g,min The lower limit of the active output of the generator; Q g,max is the upper limit of the reactive power output of the generator; Q g,min It is the lower limit of reactive power output of the generator; The branch power constraint is expressed as follows: P l,min ≤P l ≤P l,max ,Q l,min ≤Q l ≤Q l,max Where P l,max is the maximum active power that can flow through branch l; P l,min is the minimum active power that can flow through branch l; Q l,max is the maximum reactive power that can flow through branch l; Q l,min is the minimum reactive power that can flow through branch l.

5. The method for planning the optimal location and capacity of energy storage systems and distributed generation according to claim 1, characterized in that: In S3, the specific process of optimizing the decision vector by combining the sensitivity analysis of the impedance matrix and the simulated annealing algorithm to obtain the optimal location and capacity of the energy storage system and distributed generation is: S31: setting the initial temperature, cooling rate, stop temperature and initial decision vector of the simulated annealing algorithm; wherein the initial decision vector includes the initial configuration of energy storage and distributed generation of each node; S32: Randomly perturb the current decision vector. The expression of the decision vector before and after the perturbation is as follows: X=[x1,x2,…,x i ,…,x n ] X'=[x1,x2,...,x i +Δx i ,...,x n ] Among them, X is the decision vector before disturbance, that is, the current decision vector; X' is the decision vector after disturbance, that is, the new decision vector; x i is the decision value corresponding to the optimal node for reducing network loss obtained by using impedance matrix sensitivity analysis; Δx i is the disturbance value generated according to the preset disturbance range, and its expression is as follows: Δx i =δ·(2·rand(0,1)-1) Where d is the maximum amplitude of the disturbance; 2rand(0,1)-1 is a random number between -1 and 1; S33: Based on the constraints and the objective function, use the solver to solve the operating cost C of the site selection and capacity determination scheme corresponding to the new decision vector; S34: Based on the operating cost C and the discounted annual cost W' of the total installation cost corresponding to the site selection and capacity determination scheme, the total cost difference between the site selection and capacity determination scheme corresponding to the current decision vector and the site selection and capacity determination scheme corresponding to the new decision vector is calculated, and the expression is: ΔZ=Z(X)-Z(X') Z=C+W' Among them, ΔZ is the total cost difference between the location and capacity scheme corresponding to the current decision vector and the location and capacity scheme corresponding to the new decision vector; Z(X) is the total cost of the location and capacity scheme corresponding to the current decision vector; Z(X) is the total cost of the location and capacity scheme corresponding to the new decision vector; W is the total installation cost corresponding to the location and capacity scheme; σ% is the discount rate; N is the life of the equipment; C is the operating cost, including network loss cost and dispatch cost; is the square of the current in branch l; r l is the resistance value of branch l; ρ t is the network loss cost in time period t; m is the number of branches; T is the scheduling period; n s is the amount of energy storage installed; P ch,t,i is the charging capacity of the i-th energy storage period t; P dc,t,i is the discharge amount of the i-th energy storage period t; ρ s is the depreciation cost of energy storage per unit of charge and discharge; n pv is the number of installed photovoltaic devices; p pv,t,i is the output of the ith photovoltaic period t; ρ pv is the unit cost of photovoltaic power output; If the total cost difference ΔZ is greater than or equal to 0, the new decision vector X' is accepted; if the cost difference ΔZ is less than 0, the location and capacity plan corresponding to the new decision vector is chosen according to probability: Randomly draw a random number in [0, 1]; If the random number is less than the probability, the location and capacity solution corresponding to the new decision vector is accepted; If the random number is greater than or equal to the probability, the location and capacity solution corresponding to the new decision vector is not accepted, and the process goes to S35; S35: After completing one annealing, lower the temperature and determine whether the current temperature converges or reaches the preset temperature threshold: if so, output the final decision vector, that is, the optimal location and capacity solution for energy storage and photovoltaics; if not, return to S32; wherein the expression for lowering the temperature is as follows: T' em =αT em Among them, T em is the temperature before annealing, and a is the annealing coefficient.

6. The method for planning the optimal location and capacity of the energy storage system and distributed generation according to claim 5, characterized in that: The process of determining the best node for reducing network loss by using impedance matrix sensitivity analysis in S32 is as follows: S321: Use the impedance matrix to calculate the network loss, the expression is: S loss =(I bus ) T Z bus (I bus ) * Among them, S loss is the network loss matrix; I bus is the current matrix; S322: Determine the active network loss based on the network loss matrix, expressed as: Among them, P loss is the active network loss; n is the number of nodes; r ij is the resistance of branch ij; I i is the current at node i; I j is the current at node j; θ i is the voltage phase angle of node i; θ j is the voltage phase angle of node j; S323: Active network loss P loss to I i Take the derivative and get the sensitivity analysis result, the expression is: Among them, θ j is the voltage phase angle of node j; S324: Select the node with the smallest sensitivity analysis result among all nodes as the point to be disturbed.

7. The method for planning the optimal location and capacity of energy storage systems and distributed generation according to claim 5, characterized in that: In S34, the probability calculation formula is as follows: Where P ac is the probability; T em is the current temperature; exp is the exponential function; ΔZ is the total cost difference between the site selection and capacity planning schemes corresponding to the original decision vector and the new decision vector.

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