A planning method for optimal site selection and capacity determination of energy storage systems and distributed generation.

Abstract

This invention discloses a planning method for optimal site selection and capacity determination of energy storage systems and distributed generation, comprising: representing the installation of energy storage and distributed power sources at each node in the power system topology diagram using decision vectors; establishing an admittance matrix based on the power system topology diagram, and then transforming it to obtain the impedance matrix of the node; using the lowest operating cost as the objective function and combining it with constraints, optimizing the decision vectors through sensitivity analysis of the impedance matrix and simulated annealing algorithm to obtain the optimal site selection and capacity determination scheme for the energy storage system and distributed generation. The method gives greater consideration to the overall operating environment of the power grid and the physical limitations of the equipment, including consideration of transformer capacity, ensuring that the safe operating range of the transformer is not exceeded while increasing BESS and DG; it provides greater flexibility, allowing dynamic adjustment of the number and scale of energy storage and generation facilities.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a planning method for optimal site selection and capacity determination of energy storage systems and distributed generation. Background Technology

[0002] The integration of battery energy storage systems (BESS) and distributed generation (DG, citing photovoltaics as an example) into power systems is becoming increasingly widespread because they improve grid efficiency and reliability. These technologies enhance the flexibility and stability of power supply by improving voltage curves, reducing technical power losses, and alleviating network congestion. Distributed generation typically generates electricity near the point of demand, reducing the need for long-distance transmission, improving energy utilization efficiency, and minimizing transmission losses.

[0003] However, despite the significant benefits of BESS and DG, existing site selection and capacity optimization techniques encounter several challenges in practical deployment. Firstly, most models optimize only for a fixed number of energy storage and distributed generation configurations, lacking sufficient flexibility and scalability to address evolving grid demands and market conditions. Secondly, existing technologies often overlook transformer operating limitations, failing to predict and prevent potential transformer overload problems caused by increased generation and dispatch operations. Particularly under high load conditions, without adequately considering transformer overload, increasing distributed generation and dispatchable energy storage may reduce grid stability.

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

[0005] To address the problems in the background art, the present invention provides a planning method for optimal site selection and capacity determination of energy storage systems and distributed generation, which can solve problems such as insufficient flexibility and high transformer overload in the prior art.

[0006] In a first aspect, the present invention provides a method for planning the optimal site selection and capacity allocation of energy storage systems and distributed generation, comprising:

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

[0008] S2: Establish the admittance matrix based on the topology 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 location and capacity scheme corresponding to the decision vector as the objective function and combining it with constraints, the decision vector is optimized by combining impedance matrix sensitivity analysis and simulated annealing algorithm to obtain the optimal location and capacity scheme for energy storage system and distributed generation. The constraints include: energy storage charging and discharging constraints, energy storage capacity upper and lower limit constraints, photovoltaic power 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 as follows:

[0012]

[0013] in, Admittance matrix The element in row i and column j; Let be the conductance of the branch between node i and node j; Let be the susceptance of the branch between nodes i and j;

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

[0015]

[0016] in, Here is the impedance matrix of the node; Let be the admittance matrix of the node.

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

[0018]

[0019] in, Operating costs include network loss costs and scheduling costs; branch road The square of the current; Let be the resistance value of branch l; Let t be the network loss cost for time period t; The number of branches; The scheduling period; The number of energy storage units installed; Let be the amount of charge generated during the i-th energy storage period t; Let be the discharge amount during the i-th energy storage period t; The depreciation cost per unit of electricity charged and discharged for energy storage is calculated by comprehensively considering factors such as the installation cost and maintenance cost of energy storage. The number of photovoltaic devices installed; The output power during the i-th photovoltaic time period t; The unit cost of photovoltaic power output is calculated by combining the installation and maintenance costs of photovoltaic systems.

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

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

[0022]

[0023]

[0024]

[0025] In the formula, This represents the discharge state of energy storage; it is set to 1 when energy storage is discharging and 0 when it is charging. The charging state of energy storage; P dc P represents the discharge amount of stored energy, which is 0 during discharge and 1 during charging. dc P is the discharge power of the stored energy. ch The charging power for energy storage; P max This represents the maximum charging and discharging power of the energy storage.

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

[0027]

[0028] In the formula, S min S represents the minimum energy storage capacity; S represents the existing energy storage capacity; S max This represents the maximum energy storage capacity.

[0029] The energy storage capacity update expression is as follows:

[0030]

[0031] In the formula, S t Let t be the energy storage capacity during time period t; η be the energy storage charge / discharge efficiency, where the charge / discharge efficiency is the same.

[0032] For ease of scheduling, the initial and final capacity of energy storage must be equal, that is, the capacity of energy storage must be consistent at the beginning and end of each day, as expressed below.

[0033]

[0034] in, This represents the initial energy storage capacity. This refers to the energy storage capacity during the final period.

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

[0036]

[0037] In the formula, P pv,max,t This represents the maximum output limit of photovoltaic power during time period t. The photovoltaic output for time period t;

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

[0039]

[0040] In the formula, P g This is the active power output of the generator at this node; To provide photovoltaic power for this node; The amount of energy discharged from the stored energy; The amount of charge for energy storage; P load P represents the active power load of this node. in The expression for injecting active power into this node is as follows:

[0041]

[0042] in,, Let be the injected power at node i; Let be the active power of branch ij; Let the square of the current in branch ij be ; Let ij be all branches with starting point j and ending point i; Let be the resistance value of branch ij; The active power of branch circuit ik; Let ik be all branches with starting point i and ending point k;

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

[0044]

[0045] Among them, Q g Q represents the reactive power output of the generator at this node. load This represents the reactive load of the node; Q in The reactive power injected into this node is expressed as follows:

[0046]

[0047] in, Inject reactive power into node i; Let be the reactive power of branch ij; Let be the square of branch ij; The reactance of branch ij;

[0048] The Ohm's law constraint on the network is expressed as follows:

[0049]

[0050] in, The square of the voltage at node i is given, and the power flow direction is j->i; The square of the voltage at node j; Let be the resistance value of branch ij; Let be the active power of branch ij; The reactance of branch ij; Let be the reactive power of branch ij; Let the square of the current in branch ij be ;

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

[0052]

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

[0054]

[0055] In the formula, This is the lower limit of the squared voltage at node i; Let be the upper limit of the square of the voltage at node i;

[0056] The generator power constraint is expressed as follows:

[0057]

[0058] In the formula, P g,max This represents the upper limit of the generator's active power. Q represents the lower limit of the generator's active power. g,max This represents the upper limit of the generator's reactive power output. This represents the lower limit of the generator's reactive power output.

[0059] Branch power constraints are expressed as follows:

[0060]

[0061] In the formula, branch road The maximum active power that can flow through; power. branch road The minimum active power that can flow through; branch road The maximum reactive power that can flow through; branch road The minimum reactive power that can flow through.

[0062] Furthermore, in step 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 addressing and capacity determination of the energy storage system and distributed generation is as follows:

[0063] S31: Set the initial temperature, cooling rate, stopping temperature, and initial decision vector for the simulated annealing algorithm; wherein, the initial decision vector contains the initial configuration of energy storage and distributed generation for 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]

[0066]

[0067] Where X is the decision vector before the disturbance, i.e., the current decision vector; The decision vector after perturbation, i.e., the new decision vector; x i The optimal node for reducing network loss is obtained using impedance matrix sensitivity analysis; The expression for the disturbance value generated based on the predetermined disturbance range is as follows:

[0068]

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

[0070] S33: Based on constraints, use a solver to calculate the operating cost of the location and sizing scheme corresponding to the new decision vector. In practice, the solver can be adjusted according to the actual scenario. In this embodiment, the Cplex solver is used.

[0071] S34: Calculate the 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, expressed as:

[0072]

[0073]

[0074]

[0075]

[0076] in, 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; The total cost of the location and sizing scheme corresponding to the current decision vector; σ% represents the total cost of the site selection and capacity allocation scheme corresponding to the new decision vector; W represents the total installation cost corresponding to the site selection and capacity allocation scheme; σ% represents the discount rate, typically 5%; and N represents the equipment lifespan. Operating costs include network loss costs and scheduling costs; branch road The square of the current; Let be the resistance value of branch l; Let t be the network loss cost for time period t; The number of branches; The scheduling period; The number of energy storage units installed; Let be the amount of charge generated during the i-th energy storage period t; Let be the discharge amount during the i-th energy storage period t; The depreciation cost per unit of electricity charged and discharged for energy storage; The number of photovoltaic devices installed; The output power during the i-th photovoltaic time period t; The unit cost of powering photovoltaic systems;

[0077] If the total cost difference If the value is greater than or equal to 0, then the total cost of the location and capacity sizing scheme corresponding to the new decision vector decreases. In other words, the location and capacity sizing scheme corresponding to the new decision vector is better than or equal to the location and capacity sizing scheme corresponding to the current decision vector. Therefore, the new decision vector is accepted. If the total cost difference If the value is less than 0, then the location and sizing scheme corresponding to the new decision vector is chosen based on probability:

[0078] Randomly select a random number from a preset range;

[0079] If the random number is less than the probability, then the addressing and sizing scheme corresponding to the new decision vector is accepted;

[0080] If the random number is greater than or equal to the probability, the location and capacity scheme corresponding to the new decision vector is not accepted, and proceed to S35;

[0081] S35: After completing one annealing cycle, reduce the temperature and determine whether the current temperature has converged or reached a preset temperature threshold. If yes, output the final decision vector, i.e., the optimal addressing and sizing scheme for energy storage and photovoltaics; if no, return to S32. The expression for reducing the temperature is as follows:

[0082]

[0083] Among them, T' em The temperature after annealing is denoted as 'a', and 'a' is the annealing coefficient.

[0084] Furthermore, in S32, the decision value corresponding to the optimal node for reducing network loss is obtained using impedance matrix sensitivity analysis. The process of determining is as follows:

[0085] S321: Calculate network loss using the impedance matrix, the expression is:

[0086]

[0087] in, This is the network loss matrix; It is a current matrix;

[0088] S322: Determine active network loss based on the network loss matrix, the expression is:

[0089]

[0090] in, For active network losses; The number of nodes; Let be the resistance of branch ij; Let be the current at node i; Let be the current at node j; Let be the voltage phase angle at node i; Let be the voltage phase angle at node j;

[0091] S323: Active network losses right Taking the derivative, we obtain the sensitivity analysis result, expressed as:

[0092]

[0093] in, Let be the voltage phase angle at node j;

[0094] S324: Select the node with the smallest sensitivity analysis result among all nodes as the point to be disturbed.

[0095] Furthermore, the probability calculation formula in S34 is as follows:

[0096]

[0097] In the formula, For probability; The current temperature is exp; exp is an exponential function. This represents the total cost difference between the original decision vector and the new decision vector.

[0098] This invention proposes a planning method for optimal site selection and capacity determination of energy storage systems and distributed generation, which has the following advantages:

[0099] (1) It has a certain degree of flexibility and scalability in decision vector design. By using decision vectors to represent whether each node is equipped with energy storage or distributed generation, the number and location of energy storage and generation equipment can be dynamically adjusted. The number is no longer fixed and can be configured entirely according to 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 optimal configuration under different load and market conditions.

[0100] (2) The simulated annealing algorithm effectively solves the global search problem in high-dimensional optimization problems. The simulated annealing algorithm avoids getting trapped in local optima through a random but controlled search process, ensuring that a global optimum or a solution close to the global optimum can be found. Combined with the sensitivity analysis of the Zbus matrix, the impact of different configurations on power grid performance, such as voltage stability and power loss, can be accurately evaluated, thereby further guiding the optimization process.

[0101] (3) By combining simulated annealing algorithm and second-order cone programming (power flow second-order cone constraint, so second-order cone programming is used) for the site selection, capacity determination and operation optimization of energy storage and distributed generation, the shortcomings of existing technologies such as insufficient flexibility and high transformer overload are overcome, and the efficiency, flexibility and comprehensiveness of power system optimization are realized. Whether in improving grid operation efficiency, reducing costs or enhancing system reliability, this invention has shown certain advantages and provided technical support for the development of modern power systems. Attached Figure Description

[0102] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0103] Figure 1 This is a flowchart of a planning method for optimal site selection and capacity determination of an energy storage system and distributed generation provided by an embodiment of the present invention. Detailed Implementation

[0104] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0105] Example 1

[0106] like Figure 1 As shown, this embodiment provides a planning method for optimal site selection and capacity allocation of energy storage systems and distributed generation, including:

[0107] S1: The decision vector represents the installation status of energy storage and distributed generation at each node in the power system topology diagram; when the decision variable value of a node is non-zero, it indicates that the node has installed energy storage or distributed generation; when the decision variable value of a node is zero, it indicates that the node has not installed energy storage or distributed generation.

[0108] A decision vector is used to represent the installation status of energy storage and distributed generation at each node. A non-zero decision variable value for a node indicates that it has installed either energy storage or distributed generation; a zero decision variable indicates that it has not. Planning can be performed on a variable number of energy storage and distributed generation installation sites by simply adding both potential energy storage and distributed generation installation sites to the decision vector. For example, with 10 potential energy storage installation sites and 5 potential distributed generation installation sites (unless otherwise specified, all sites except transformers can be considered potential sites), a 15-dimensional decision vector can be set to represent the installation status. By using the decision vector, all nodes can be designated as potential installation sites, with a decision value of 0 representing no installation and a non-zero decision value representing installation. The number of non-zero decision values ​​represents the number of installations. This addresses the problem that most models only optimize for a fixed number of energy storage and distributed generation configurations, lacking sufficient flexibility and scalability to cope with constantly changing grid demands and market conditions.

[0109] S2: Establish the admittance matrix based on the topology of the power system, and then transform it to obtain the impedance matrix of the node.

[0110] Specifically, S21: Obtain the topology diagram of the power system and establish the admittance matrix of each node, expressed as:

[0111]

[0112] in, Admittance matrix The element in row i and column j; Let be the conductance of the branch between node i and node j; Let be the susceptance of the branch between nodes i and j;

[0113] S22: Perform an inverse transformation on the admittance matrix of the node to obtain the impedance matrix of the node, expressed as:

[0114]

[0115] in, The impedance matrix (i.e., Zbus matrix) of the node. Let be the admittance matrix of the node.

[0116] S3: Taking the lowest operating cost of the location and capacity scheme corresponding to the decision vector as the objective function and combining it with constraints, the decision vector is optimized by combining impedance matrix sensitivity analysis and simulated annealing algorithm to obtain the optimal location and capacity scheme for energy storage system and distributed generation. The constraints include: energy storage charging and discharging constraints, energy storage capacity upper and lower limit constraints, photovoltaic power 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.

[0117] Impedance matrices provide an effective means of calculating the electrical effects between nodes, enabling the assessment of the impact of different configurations on grid performance, such as voltage stability and losses. Simulated annealing, through a random but controlled search process, explores the possible configuration space to find a cost-effective solution. Combining simulated annealing with impedance matrix sensitivity analysis allows for more efficient solutions to high-dimensional optimization problems on electrical topologies.

[0118] Specifically, the objective function in S3 is:

[0119]

[0120] in, Operating costs include network loss costs and scheduling costs; branch road The square of the current; Let be the resistance value of branch l; Let t be the network loss cost for time period t; The number of branches; The scheduling period; The number of energy storage units installed; Let be the amount of charge generated during the i-th energy storage period t; Let be the discharge amount during the i-th energy storage period t; The depreciation cost per unit of electricity charged and discharged for energy storage is calculated by comprehensively considering factors such as the installation cost and maintenance cost of energy storage. The number of photovoltaic devices installed; The output power during the i-th photovoltaic time period t; The unit cost of photovoltaic power output is calculated by combining the installation and maintenance costs of photovoltaic systems.

[0121] Specifically, the constraints in S3 are as follows:

[0122] The charging and discharging constraints for energy storage are expressed as follows:

[0123]

[0124]

[0125]

[0126] In the formula, This represents the discharge state of energy storage; it is set to 1 when energy storage is discharging and 0 when it is charging. This represents the charging state of energy storage; it is 0 when discharging and 1 when charging. dc P is the discharge power of the stored energy. ch The charging power for energy storage; P max This represents the maximum charging and discharging power of the energy storage.

[0127] The upper and lower limits of energy storage capacity are expressed as follows:

[0128]

[0129] In the formula, S min S represents the minimum energy storage capacity; S represents the existing energy storage capacity; S max This represents the maximum energy storage capacity.

[0130] The energy storage capacity update expression is as follows:

[0131]

[0132] In the formula, S t Let t be the energy storage capacity during time period t; η be the energy storage charge / discharge efficiency, where the charge / discharge efficiency is assumed to be the same.

[0133] For ease of scheduling, the initial and final capacity of energy storage must be equal, that is, the capacity of energy storage must be consistent at the beginning and end of each day, as expressed below.

[0134]

[0135] In the formula, This represents the initial energy storage capacity. This refers to the energy storage capacity during the final period.

[0136] The upper and lower limits of photovoltaic output constraints are expressed as follows:

[0137]

[0138] In the formula, P pv,max,t This represents the maximum output limit of photovoltaic power during time period t. Let t represent the photovoltaic output during time period t; the lower limit indicates no output, in which case 0 is taken.

[0139] The active power balance constraint at each node is expressed as follows:

[0140]

[0141] In the formula, P g This is the active power output of the generator at this node; To provide photovoltaic power for this node; The amount of energy discharged from the stored energy; The amount of charge for energy storage; P load P represents the active power load of this node. in The expression for injecting active power into this node is as follows:

[0142]

[0143] in, Let be the injected power at node i; Let be the active power of branch ij; Let the square of the current in branch ij be ; Let ij be all branches with starting point j and ending point i; Let be the resistance value of branch ij; The active power of branch circuit ik; Let ik be all branches with starting point i and ending point k;

[0144] The reactive power balance constraint at nodes is expressed as follows:

[0145]

[0146] Among them, Q g Q represents the reactive power output of the generator at this node. load This represents the reactive load of the node; Q in The reactive power injected into this node is expressed as follows:

[0147]

[0148] in, Inject reactive power into node i; Let be the reactive power of branch ij; Let be the square of branch ij; The reactance of branch ij;

[0149] The Ohm's law constraint on the network is expressed as follows:

[0150]

[0151] in, The square of the voltage at node i is given, and the power flow direction is j->i; The square of the voltage at node j; Let be the resistance value of branch ij; Let be the active power of branch ij; The reactance of branch ij; Let be the reactive power of branch ij; Let the square of the current in branch ij be ;

[0152] The second-order cone constraint for network power flow is expressed as follows:

[0153]

[0154] The node voltage constraint is expressed as follows:

[0155]

[0156] In the formula, This is the lower limit of the squared voltage at node i; Let be the upper limit of the square of the voltage at node i;

[0157] The generator power constraint is expressed as follows:

[0158]

[0159] In the formula, P g,max This represents the upper limit of the generator's active power output. The lower limit of generator active power output is generally considered to be the point where the generator is connected to the grid, and the upper limit is the maximum transmission power of the grid connection point. Nodes without generators are considered to have 0. g,max This represents the upper limit of the generator's reactive power output. This represents the lower limit of the generator's reactive power output.

[0160] Branch power constraints are expressed as follows:

[0161]

[0162] In the formula, branch road The maximum active power that can flow through; branch road The minimum active power that can flow through; branch road The maximum reactive power that can flow through; branch road The minimum reactive power that can flow through.

[0163] By modeling transformer capacity constraints as branch power constraints, it is possible to ensure that there is no overload. This solves the problem that existing technologies often neglect the operating limitations of transformers and fail to predict and prevent transformer overload problems that may be caused by increased generation and dispatch operations. In particular, it solves the problem that under high load conditions, transformer overload may not be fully considered, and that increasing distributed generation and dispatch energy storage may reduce the stability of grid operation.

[0164] Furthermore, in step 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 addressing and capacity determination of the energy storage system and distributed generation is as follows:

[0165] S31: Set the initial temperature, cooling rate, stopping temperature, and initial decision vector for the simulated annealing algorithm; wherein, the initial decision vector contains the initial configuration of energy storage and distributed generation for each node; the initial decision vector can be randomly selected for initialization or a known decision can be input as the initial value of the decision.

[0166] S32: Randomly perturb the current decision vector. The expression of the decision vector before and after the perturbation is as follows:

[0167]

[0168]

[0169] Where X is the decision vector before the perturbation; These are the decision vectors before and after the perturbation, respectively; x i The decision value corresponding to the optimal node for reducing network loss is obtained using impedance matrix sensitivity analysis. The expression for the small disturbance value generated according to the predetermined disturbance range is as follows:

[0170]

[0171] In the formula, d is the maximum amplitude of the disturbance; rand(0,1) is a random number uniformly distributed between 0 and 1, therefore, 2rand(0,1)-1 is a random number between -1 and 1.

[0172] More specifically, the process of determining the optimal node i for reducing network loss, obtained using impedance matrix sensitivity analysis, is as follows:

[0173] S321: Calculate network loss using the impedance matrix, the expression is:

[0174]

[0175] in, This is the network loss matrix; It is a current matrix;

[0176] S322: Determine active network loss based on the network loss matrix, the expression is:

[0177]

[0178] in, For active network losses; The number of nodes; Let be the resistance of branch ij; Let be the current at node i; Let be the current at node j; Let be the voltage phase angle at node i; Let be the voltage phase angle at node j;

[0179] S323: Active network losses right Taking the derivative, we obtain the sensitivity analysis result, expressed as:

[0180]

[0181] in, Let be the voltage phase angle at node j;

[0182] 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 a new decision vector can be generated using it as the point to be disturbed. It can optimize the high-dimensional performance of the model and accelerate convergence.

[0183] S33: Based on the constraints, the Cplex solver is used to solve for the optimal power flow of the location and capacity-limited scheme corresponding to the new decision vector, thereby calculating the operating cost of the new decision scheme. .

[0184] In practice, simply input the objective function and constraints into the solver, and the optimal result will be automatically obtained. Since solving the location and capacity stabilization scheme is a second-order cone programming problem for optimal power flow, the optimal power flow of the location and capacity stabilization scheme corresponding to the new decision vector is solved based on the costs of energy storage and photovoltaic installation and operation, as well as various constraints, including network topology constraints, node voltage constraints, and branch power constraints. If there is no solution, it means that the proposed decision vector cannot meet the constraints, and a large value is returned as a penalty; if there is a solution, the objective function value should be returned.

[0185] S34: Based on operating costs The discounted annual cost of total installation cost corresponding to the site selection and sizing scheme The total cost difference between the location and capacity schemes corresponding to the current decision vector and the location and capacity schemes corresponding to the new decision vector is calculated using the following expression:

[0186]

[0187]

[0188]

[0189]

[0190] in, 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; The total cost of the location and sizing scheme corresponding to the current decision vector; σ is the total cost of the site selection and capacity allocation scheme corresponding to the new decision vector; W is the total installation cost corresponding to the site selection and capacity allocation scheme; σ% is the discount rate; N is the equipment lifespan; Operating costs include network loss costs and scheduling costs; branch road The square of the current; Let be the resistance value of branch l; Let t be the network loss cost for time period t; The number of branches; The scheduling period; The number of energy storage units installed; Let be the amount of charge generated during the i-th energy storage period t; Let be the discharge amount during the i-th energy storage period t; The depreciation cost per unit of electricity charged and discharged for energy storage; The number of photovoltaic devices installed; The output power during the i-th photovoltaic time period t; The unit cost of powering photovoltaic systems;

[0191] If the total cost difference If the value is greater than or equal to 0, then the cost of the location and capacity sizing scheme corresponding to the new decision vector is reduced. That is, the location and capacity sizing scheme corresponding to the new decision vector is better than or equal to the location and capacity sizing scheme corresponding to the current decision vector, and the new decision vector is accepted. If the total cost difference If the value is less than 0, then the location and sizing scheme corresponding to the new decision vector is chosen based on probability:

[0192] Randomly select a random number within the range [0, 1];

[0193] If the random number is less than the probability, then the addressing and sizing scheme corresponding to the new decision vector is accepted;

[0194] If the random number is greater than or equal to the probability, the addressing and grading scheme corresponding to the new decision vector is not accepted, and the process proceeds to S35. For example, if the calculated probability is 0.3, a random number between 0 and 1 is selected. If it is less than 0.3, the solution is accepted; otherwise, it is rejected. The decision of whether to accept the decision vector is a random process. Thus, the simulated annealing algorithm can generate a new solution in each iteration. Based on this solution, the power grid performance can be evaluated, increasing the probability of finding the global optimum.

[0195] S35: After completing one annealing cycle, reduce the temperature and determine whether the current temperature has converged or reached a preset temperature threshold. If yes, output the final decision vector, i.e., the optimal addressing and sizing scheme for energy storage and photovoltaics; if no, return to S32. The expression for reducing the temperature is as follows:

[0196]

[0197] Among them, T' em This refers to the temperature after annealing. This is the annealing coefficient, which can generally be taken as a value between 0.8 and 0.99.

[0198] More specifically, the probability expression for accepting the new solution is as follows:

[0199]

[0200] In the formula, The probability of accepting a new solution; The current temperature is exp; exp is an exponential function. This represents 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 different location and capacity schemes. This formula indicates that even if the new solution is worse, there is still a certain probability of accepting it. This probability decreases as the temperature decreases, and the worse the new solution is compared to the original solution, the lower the probability.

[0201] After the upper-level planning of energy storage and distributed power generation installation schemes is completed, the location and capacity balancing schemes corresponding to the decision vectors are obtained through simulated annealing. The lower-level algorithm then uses second-order cone programming (SOCP) to solve the optimal power flow problem of the location and capacity balancing schemes, ensuring the safety and efficiency of the power grid in actual operation. The lower-level optimization considers voltage constraints, power balance, transformer capacity limitations, and line capacity limitations, accurately calculating the voltage, current, and power distribution at each node. By combining the operating cost obtained from the lower-level optimization results with the discounted annual cost of the total installation cost corresponding to the upper-level location and capacity balancing scheme, the final optimization function value is formed. This optimization function value serves as the objective function of the upper-level simulated annealing algorithm, enabling the algorithm to further optimize the installation schemes and ensure the system's economy and operational efficiency.

[0202] It should be understood that, in the embodiments of the present invention, the processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. The memory may include read-only memory and random access memory, and provides instructions and data to the processor. A portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.

[0203] The readable storage medium is a computer-readable storage medium, which can be an internal storage unit of the controller described in any of the foregoing embodiments, such as the controller's hard drive or memory. The readable storage medium can also be an external storage device of the controller, such as a plug-in hard drive, Smart Media Card (SMC), Secure Digital (SD) card, or Flash Card equipped on the controller. Further, the readable storage medium can include both the controller's internal storage unit and external storage devices. The readable storage medium is used to store the computer program and other programs and data required by the controller. The readable storage medium can also be used to temporarily store data that has been output or will be output.

[0204] Based on this understanding, the technical solution of the present invention, in essence, or 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned readable storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0205] It is understood that the same or similar parts in the above embodiments can be referred to each other, and the contents not described in detail in some embodiments can be referred to the same or similar contents in other embodiments.

[0206] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A planning method for optimal site selection and capacity determination of energy storage systems and distributed generation, characterized in that, include: S1: The decision vector represents the installation status of energy storage and distributed generation at each node in the power system topology diagram; when the decision variable value of a node is non-zero, it indicates that the node has installed energy storage or distributed generation; when the decision variable value of a node is zero, it indicates that the node has not installed energy storage or distributed generation. S2: Establish the admittance matrix based on the topology of the power system, and then transform it to obtain the impedance matrix of the node; S3: Taking the lowest operating cost of the location and capacity scheme corresponding to the decision vector as the objective function and combining it with constraints, the decision vector is optimized by combining impedance matrix sensitivity analysis and simulated annealing algorithm to obtain the optimal location and capacity scheme for energy storage system and distributed generation. The constraints include: energy storage charging and discharging constraints, energy storage capacity upper and lower limit constraints, photovoltaic power 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. 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 addressing and capacity determination of the energy storage system and distributed generation is as follows: S31: Set the initial temperature, cooling rate, stopping temperature, and initial decision vector for the simulated annealing algorithm; where the initial decision vector includes the initial configuration of energy storage and distributed generation for each node; S32: Randomly perturb the current decision vector. The expression of the decision vector before and after the perturbation is as follows: ； ； Where X is the decision vector before the disturbance, i.e., the current decision vector; The decision vector after perturbation, i.e., the new decision vector; x i The decision value corresponding to the optimal node for reducing network loss is obtained using impedance matrix sensitivity analysis. The expression for the disturbance value generated based on the preset disturbance range is as follows: ； In the formula, The maximum amplitude of the disturbance; 2rand(0,1)-1 is a random number between -1 and 1; S33: Based on constraints and the objective function, use a solver to calculate the operating cost of the location and sizing scheme corresponding to the new decision vector. ; S34: Based on operating costs The discounted annual cost of total installation cost corresponding to the site selection and sizing scheme The total cost difference between the location and capacity schemes corresponding to the current decision vector and the location and capacity schemes corresponding to the new decision vector is calculated using the following expression: ； ； ； ； in, 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; The total cost of the location and sizing scheme corresponding to the current decision vector; σ is the total cost of the site selection and capacity allocation scheme corresponding to the new decision vector; W is the total installation cost corresponding to the site selection and capacity allocation scheme; σ% is the discount rate; N is the equipment lifespan; Operating costs include network loss costs and scheduling costs; branch road The square of the current; Let be the resistance value of branch l; Let t be the network loss cost for time period t; The number of branches; The scheduling period; The number of energy storage units installed; Let be the amount of charge generated during the i-th energy storage period t; Let be the discharge amount during the i-th energy storage period t; The depreciation cost per unit of electricity charged and discharged for energy storage; The number of photovoltaic devices installed; The output power during the i-th photovoltaic time period t; The unit cost of powering photovoltaic systems; If the total cost difference If the value is greater than or equal to 0, then accept the new decision vector. If cost difference If the value is less than 0, then the location and sizing scheme corresponding to the new decision vector is chosen based on probability: Randomly select a random number within the range [0, 1]; If the random number is less than the probability, then the addressing and sizing scheme corresponding to the new decision vector is accepted; If the random number is greater than or equal to the probability, the location and capacity scheme corresponding to the new decision vector is not accepted, and proceed to S35; S35: After completing one annealing cycle, reduce the temperature and determine whether the current temperature has converged or reached a preset temperature threshold. If yes, output the final decision vector, i.e., the optimal addressing and sizing scheme for energy storage and photovoltaics; if no, return to S32. The expression for reducing the temperature is as follows: ； Among them, T em The temperature before annealing is denoted as α, and α is the annealing coefficient.

2. The optimal site selection and capacity planning method for energy storage systems and distributed generation according to claim 1, characterized in that, The specific process of S2 is as follows: S21: Obtain the topology of the power system and establish the admittance matrix of each node. The expression is as follows: ； in, Admittance matrix The element in row i and column j; Let be the conductance of the branch between node i and node j; Let be the susceptance of the branch between nodes i and j; S22: Perform an inverse transformation on the admittance matrix of the node to obtain the impedance matrix of the node, expressed as: ； in, Here is the impedance matrix of the node; Let be the admittance matrix of the node.

3. The optimal site selection and capacity planning method for energy storage systems and distributed generation according to claim 1, characterized in that, The objective function in S3 is: ； in, Operating costs include network loss costs and scheduling costs; branch road The square of the current; Let be the resistance value of branch l; Let t be the network loss cost for time period t; The number of branches; The scheduling period; The number of energy storage units installed; Let be the amount of charge generated during the i-th energy storage period t; Let be the discharge amount during the i-th energy storage period t; The depreciation cost per unit of electricity charged and discharged for energy storage; The number of photovoltaic devices installed; The output power during the i-th photovoltaic time period t; The unit cost of powering photovoltaic systems.

4. The optimal site selection and capacity planning method for energy storage systems and distributed generation according to claim 1, characterized in that, The specific constraints in S3 are as follows: The charging and discharging constraints for energy storage are expressed as follows: ； ； ； In the formula, This represents the discharge state of energy storage; it is set to 1 when energy storage is discharging and 0 when it is charging. This represents the charging state of energy storage; it is 0 when discharging and 1 when charging. dc The discharge power of the stored energy; P ch The charging power for energy storage; P max This represents the maximum charging and discharging power of the energy storage. The upper and lower limits of energy storage capacity are expressed as follows: ； In the formula, S min S represents the minimum energy storage capacity; S represents the existing energy storage capacity; S max This represents the maximum energy storage capacity. The energy storage capacity update expression is as follows: ； In the formula, S t Let t be the energy storage capacity during time period t; η be the energy storage charging and discharging efficiency, where the charging and discharging efficiency is the same. In this case, the initial capacity and the final capacity of the energy storage are equal, as expressed in the following expression: ； In the formula, This represents the initial energy storage capacity. This refers to the energy storage capacity during the final period. The upper and lower limits of photovoltaic output constraints are expressed as follows: ； In the formula, P pv,max,t This represents the maximum output limit of photovoltaic power during time period t. The photovoltaic output for time period t; The active power balance constraint at each node is expressed as follows: ； In the formula, P g This refers to the active power output of the generator at this node; To provide photovoltaic power for this node; The amount of energy discharged for this node; The amount of charge stored at this node; P load This represents the active power load of the node; P in The expression for injecting active power into this node is as follows: ； in, Let be the injected power at node i; Let be the active power of branch ij; Let the square of the current in branch ij be ; Let ji be all branches with starting point j and ending point i; Let be the resistance value of branch ij; The active power of branch circuit ik; Let ik be all branches with starting point i and ending point k; The reactive power balance constraint at nodes is expressed as follows: ； Among them, Q g Q represents the reactive power output of the generator at this node. load This represents the reactive load of the node; Q in The reactive power injected into this node is expressed as follows: ； in, Inject reactive power into node i; Let be the reactive power of branch ij; Let be the square of branch ij; The reactance of branch ij; The Ohm's law constraint on the network is expressed as follows: ； in, The voltage at node i is the square of the voltage at node i, and the power flow direction is j->i; The square of the voltage at node j; Let be the resistance value of branch ij; Let be the active power of branch ij; The reactance of branch ij; Let be the reactive power of branch ij; Let the square of the current in branch ij be ; The second-order cone constraint for network power flow is expressed as follows: ； The node voltage constraint is expressed as follows: ； In the formula, This is the lower limit of the squared voltage at node i; Let be the upper limit of the square of the voltage at node i; The generator power constraint is expressed as follows: ； In the formula, P g,max This represents the upper limit of the generator's active power output. Q represents the lower limit of the generator's active power output. g,max This represents the upper limit of the generator's reactive power output. This represents the lower limit of the generator's reactive power output. Branch power constraints are expressed as follows: ； In the formula, branch road The maximum active power that can flow through; branch road The minimum active power that can flow through; branch road The maximum reactive power that can flow through; branch road The minimum reactive power that can flow through.

5. The optimal site selection and capacity planning method for energy storage systems and distributed generation according to claim 1, characterized in that, The process for determining the optimal node for reducing network loss using impedance matrix sensitivity analysis in S32 is as follows: S321: Calculate network loss using the impedance matrix, the expression is: ； in, This is the network loss matrix; It is a current matrix; S322: Determine active network loss based on the network loss matrix, the expression is: ； in, For active network losses; The number of nodes; Let be the resistance of branch ij; Let be the current at node i; Let be the current at node j; Let be the voltage phase angle at node i; Let be the voltage phase angle at node j; S323: Active network losses right Taking the derivative, we obtain the sensitivity analysis result, expressed as: ； in, Let be the voltage phase angle at node j; S324: Select the node with the smallest sensitivity analysis result among all nodes as the point to be disturbed.

6. The optimal site selection and capacity planning method for energy storage systems and distributed generation according to claim 1, characterized in that, In S34, the probability is calculated using the following formula: ； In the formula, For probability; The current temperature is exp; exp is an exponential function. This represents the total cost difference between the original decision vector and the location and sizing schemes corresponding to the new decision vector.

Images