A multi-objective optimization configuration method for energy storage power stations with multiple controllable loads

Abstract

The present invention discloses a multi-objective optimization configuration method for an energy storage power station including multiple controllable loads, relating to the technical field of power grid energy storage planning. The power grid model includes temperature-controlled loads and industrial loads. The temperature-controlled load model is based on the thermal characteristics of the building, taking into account indoor and outdoor temperatures, air conditioning power, and wall heat capacity parameters. The industrial load model is based on the importance of the equipment and user preferences, and divides load levels according to energy consumption and adjustability. By incorporating controllable loads into the energy storage power station model and fully exploring the coordination capabilities of multiple resources, the present invention proposes an energy storage power station model based on the flexibility of controllable loads and power grid balance. While meeting user needs and the balance state of the power grid, it achieves efficient utilization of the energy storage system and local consumption of new energy.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid energy storage planning, and in particular to a multi-objective optimization configuration method for an energy storage power station including multiple controllable loads. Background Art

[0002] With the increasing strain on global energy supply and worsening environmental pollution, energy efficiency has become a core issue in modern society. Energy storage power stations, as a key technology for improving energy efficiency, are gaining widespread attention. However, traditional grid energy storage planning often overlooks the impact of controllable loads, particularly temperature-controlled and industrial loads, posing new challenges for the optimal configuration of energy storage power stations. Controllable loads refer to loads within the power system that can flexibly adjust their power consumption based on grid demand, market prices, or other factors. Proper management of controllable loads can effectively increase energy flexibility and reduce fluctuations in power demand. In recent years, the widespread adoption of renewable energy sources such as wind and solar power has further increased grid instability due to their intermittent and volatile nature.

[0003] Therefore, optimizing the configuration of energy storage power stations to increase renewable energy consumption and ensure grid balance has become a key research topic. While traditional methods for optimizing the configuration of grid energy storage systems, such as mixed-integer linear programming (MILP), can solve nonlinear problems, they suffer from high computational complexity and limited accuracy. Convex programming methods, particularly second-order cone programming (SOCP), have emerged as a promising approach to solving these problems due to their speed and accuracy.

[0004] Therefore, the present invention proposes a multi-objective optimization configuration method for energy storage power stations that include multiple controllable loads. The method aims to achieve efficient utilization of energy storage and local consumption of renewable energy by improving the solution algorithm, and optimize the grid operation cost and energy storage power station configuration cost while ensuring user demand and grid balance.

[0005] 1. Patent document CN116345507B discloses a method and system for multi-objective optimization configuration of energy storage power station capacity that adapts to variable energy storage cycles. The above patent determines the weights of elements in a multi-objective Pareto optimization set based on a fuzzy set function, and outputs the multi-objective optimization capacity values of the energy storage power station in an orderly manner, providing an effective method and decision-making support for the multi-objective optimization configuration of energy storage power station capacity. However, the above patent cannot incorporate controllable loads into the energy storage power station model.

[0006] 2. Patent document CN111539086B discloses a multi-point layout method and system for energy storage power stations. The above patent achieves the goal of maximizing the optimization effect of the energy storage power station layout, comprehensively considers factors such as the constraints of the multi-point energy storage layout, and meets the different configuration requirements of the power system for energy storage power stations. However, the above patent cannot transform the original problem into a second-order cone programming problem through relaxation and linearization methods.

[0007] 3. Patent document CN113922396B discloses a method and terminal for configuring an energy storage power station to enhance the immunity of the power grid to voltage sags. The above patent combines the optimization of the configuration of the energy storage power station with the control of voltage sags to construct an optimal energy storage power station location and sizing plan to enhance the immunity of the power grid to voltage sags and improve voltage quality. However, the above patent cannot propose an algorithm for the multi-objective optimization configuration of energy storage power stations.

[0008] 4. Patent document CN113792911B discloses a method and system for dual-layer collaborative optimization configuration of the energy storage capacity of a photovoltaic storage system. The above patent realizes the reasonable and effective configuration of the energy storage capacity of the photovoltaic storage system under the influence of dual uncertainties, but the above patent cannot realize the improvement of the optimization strategy and response mechanism.

[0009] In summary, the aforementioned patents fail to incorporate controllable loads into the energy storage power station model, transform the original problem into a second-order cone programming problem through relaxation and linearization, propose an algorithm for multi-objective optimization configuration of energy storage power stations, and improve the optimization strategy and response mechanism. This results in high computational complexity, insufficient accuracy, an inability to improve the renewable energy absorption rate, and high grid operation costs.

[0010] To this end, this application proposes a multi-objective optimization configuration method for energy storage power stations that includes multiple controllable loads and can incorporate controllable loads into the energy storage power station model, transform the original problem into a second-order cone programming problem through relaxation and linearization methods, propose an algorithm for multi-objective optimization configuration of energy storage power stations, and improve the optimization strategy and response mechanism. Summary of the Invention

[0011] The purpose of the present invention is to provide a multi-objective optimization configuration method for an energy storage power station including multiple controllable loads, so as to solve the technical problems proposed in the above-mentioned background technology, namely, the inability to incorporate controllable loads into the energy storage power station model, converting the original problem into a second-order cone programming problem through relaxation and linearization methods, proposing an algorithm for multi-objective optimization configuration of energy storage power stations and improving the optimization strategy and response mechanism, resulting in high computational complexity, insufficient accuracy, inability to improve the absorption rate of renewable energy and high grid operation costs.

[0012] To achieve the above objectives, the present invention provides the following technical solution: a multi-objective optimization configuration method for an energy storage power station including multiple controllable loads, the configuration method comprising the following steps:

[0013] S1. Establish a power grid model and an energy storage power station model;

[0014] S2. Determine the multi-objective optimization goal: minimize the grid operation cost and the energy storage power station configuration cost;

[0015] S3. Set constraints: grid node voltage and line current constraints, power balance constraints, grid branch flow constraints, energy storage battery capacity and energy rate constraints, energy storage battery state of charge constraints, and voltage offset constraints;

[0016] S4. Convert the non-convex optimization problem into a second-order cone programming (SOCP) problem;

[0017] S4. Use POA-GWO-CSO optimization algorithm for optimization solution.

[0018] Preferably, the power grid model includes temperature control load and industrial load, wherein the temperature control load model is based on the thermal characteristics of the building, taking into account indoor and outdoor temperatures, air conditioning power and wall heat capacity parameters; the industrial load model is based on the importance of the equipment and user preferences, and divides the load levels according to energy consumption and adjustability;

[0019] The energy storage power station model is based on energy storage equipment. It takes into account the charging and discharging power, charging and discharging efficiency, and capacity operating parameters of the energy storage equipment to establish a full life cycle cost model, including the purchase cost, installation cost, operation and maintenance cost, and residual value recovery cost of the energy storage power station.

[0020] Preferably, the grid operation cost includes electricity purchase cost, network loss cost, power generation cost and load cost; the energy storage power station cost includes purchase cost, installation cost, operation and maintenance cost and residual value recovery.

[0021] Preferably, the electricity purchase cost in the power grid operation cost is calculated based on the electricity purchase price and interactive power between the system and the upper-level power grid;

[0022] The network loss cost is calculated based on the voltage difference between grid nodes and line conductance;

[0023] The power generation cost is calculated based on the fuel cost and operation and maintenance cost of the microturbine;

[0024] The load cost is dynamically calculated based on the regulation status of the controllable load.

[0025] Preferably, the purchase cost of the energy storage power station is amortized through time value, taking into account the benchmark interest rate and economic life, to optimize the comprehensive configuration cost within the planning period;

[0026] The installation cost and operation and maintenance cost are calculated by the unit capacity installation fee and operation and maintenance coefficient respectively, and the average annual decline coefficient of the equipment is taken into account.

[0027] Preferably, the second-order cone programming (SOCP) problem uses a second-order cone relaxation method (SOCR) to convert non-convex nonlinear constraints into a second-order cone programming (SOCP) problem, and the calculation accuracy and speed are guaranteed by relaxation processing. The second-order cone programming (SOCP) model is used to optimize the configuration scheme of the energy storage power station and the power balance constraint, flow constraint and energy storage battery state of charge constraint of the power grid under the premise of ensuring the global optimal solution. The second-order cone relaxation method (SOCR) relaxes the power grid flow equation and calculates the error gap to ensure that the optimal solution is consistent with the accuracy requirements of the original problem. When the error gap reaches the set accuracy, the optimal solution is considered to be equivalent to the optimal solution of the original problem.

[0028] Preferably, the POA-GWO-CSO optimization algorithm principle is as follows:

[0029] POA simulates the local search behavior of pelicans, updating the population position through the phases of moving toward prey and skimming the water surface;

[0030] GWO introduces the gray wolf group strategy to perform global search to avoid falling into local optimality;

[0031] CSO optimizes the population position through horizontal and vertical cross-updates to avoid premature convergence.

[0032] Preferably, the specific implementation steps of the POA-GWO-CSO optimization algorithm are as follows:

[0033] Step 1: Set the population size and maximum number of iterations;

[0034] Step 2: Collect instantaneous power system flow data, input it into the model, calculate the fitness value of the pelican population, and thus determine the initial value of the objective function;

[0035] Step 3: Randomly generate prey;

[0036] Step 4: Use the improved pelican position formula to update the pelican position in the first stage;

[0037] Step 5: Substitute the position in step 4 into the fitness function to determine whether the fitness value at this position is improved, that is, whether the objective function is improved;

[0038] Step 6: Update the position of the i-th pelican in the j-th dimension in the second stage;

[0039] Step 7: Substitute the position in step 6 into the fitness function to determine whether the fitness value at this position is improved, that is, whether the objective function is improved;

[0040] Step 8: Determine whether the maximum number of iterations is met. If yes, jump to the next step; otherwise, jump to step 3.

[0041] Step 9: Determine the pelican's position value based on the final fitness function value.

[0042] Preferably, the optimization solution steps are as follows:

[0043] a. Initialize the algorithm population size and number of iterations, collect instantaneous power flow data, and calculate fitness;

[0044] b. Update the location of the pelican population, perform local and global searches, and gradually approach the optimal solution through iteration;

[0045] c. Determine the improvement of the fitness function until the number of iterations is met or the optimal solution is found.

[0046] Preferably, during the execution of the optimization method, the charging and discharging constraints of the energy storage battery are taken into account to ensure that the charge state of the energy storage battery during the scheduling period is not lower than the set lower limit value; the voltage offset constraints of the power grid nodes are taken into account, the deviation of the node voltage is kept within the allowable range, and the average voltage offset degree of the power grid meets the set maximum allowable value.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] 1. This invention incorporates controllable loads into the energy storage power station model and fully exploits the coordination capabilities of multiple resources. It proposes an energy storage power station model that takes into account the flexibility of controllable loads and grid balance. This model achieves efficient utilization of the energy storage system and local consumption of new energy while meeting user needs and grid balance.

[0049] 2. This invention transforms the original problem into a second-order cone programming problem through relaxation and linearization methods, significantly reducing the difficulty of solving the model and improving the efficiency and accuracy of the solution;

[0050] 3. This invention proposes an algorithm for multi-objective optimization of energy storage power stations, solving the problems of traditional algorithms in multi-objective optimization of energy storage power stations, such as premature convergence, insufficient optimization range, low solution accuracy, and slow response speed.

[0051] 4. The present invention uses the POA-GWO-CSO algorithm to improve the optimization strategy and response mechanism, and quickly obtains the accurate solution with the lowest grid operation cost and the lowest energy storage power station configuration cost. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a schematic diagram of the optimization configuration process of the present invention. DETAILED DESCRIPTION

[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0054] See also Figure 1 The present invention provides an embodiment of a multi-objective optimization configuration method for an energy storage power station including multiple controllable loads, the configuration method comprising the following steps:

[0055] S1. Establish a power grid model and an energy storage power station model;

[0056] S2. Determine the multi-objective optimization goal: minimize the grid operation cost and the energy storage power station configuration cost;

[0057] S3. Set constraints: grid node voltage and line current constraints, power balance constraints, grid branch flow constraints, energy storage battery capacity and energy rate constraints, energy storage battery state of charge constraints, and voltage offset constraints;

[0058] S4. Convert the non-convex optimization problem into a second-order cone programming (SOCP) problem;

[0059] S4, using POA-GWO-CSO optimization algorithm for optimization solution;

[0060] The optimization solution steps are as follows:

[0061] a. Initialize the algorithm population size and number of iterations, collect instantaneous power flow data, and calculate fitness;

[0062] b. Update the location of the pelican population, perform local and global searches, and gradually approach the optimal solution through iteration;

[0063] c. Determine the improvement of the fitness function until the number of iterations is met or the optimal solution is found;

[0064] During the execution of the optimization method, the charging and discharging constraints of the energy storage battery are taken into account to ensure that the state of charge of the energy storage battery within the scheduling cycle is not lower than the set lower limit; the voltage offset constraints of the grid nodes are taken into account to ensure that the deviation of the node voltage is kept within the allowable range and the average voltage offset degree of the grid meets the set maximum allowable value;

[0065] Furthermore, the following conditions are included when setting constraints:

[0066] (1) Grid node voltage and line current constraints

[0067] During normal operation of the power grid, the voltage at each node cannot exceed the specified upper and lower limits. At the same time, due to the limited cross-section of the line, in order to ensure line safety, the allowable current carrying capacity is subject to certain restrictions:

[0068]

[0069] Where: U max 、U min are the upper and lower limits of node voltage respectively; U min It represents the maximum current carrying capacity of the line between node i and node j. The allowable current carrying capacity of the line can be uniformly set to a fixed value.

[0070] (2) Power balance constraints

[0071] ∑(P ES +P DG +P ESS )=∑(P load +P loss )

[0072] Where: P ES 、P DG 、P ESS are the grid power, distributed power and energy storage system grid-connected power respectively; P load 、P loss They are system load power and system active power loss respectively.

[0073] (3) Power grid branch flow constraints

[0074] The data model of the branch power flow model is as follows:

[0075]

[0076]

[0077]

[0078]

[0079] Where: v m，t With v n，t are the squares of the voltage amplitudes at nodes m and n respectively; i mn，t is the square of the current amplitude of line mn; r nm and X mn are the resistance and reactance of line mn respectively; P in，n，t With Q in，n，t are the active / reactive power injected into node n respectively; P mn，t With Q mn，t are the active / reactive power on the node m side respectively; l is the child node of node n.

[0080] (4) Energy storage battery capacity and energy rate constraints

[0081]

[0082] Where: They are the upper and lower limits of the rated capacity of the energy storage battery respectively; are the upper and lower limits of the charging and discharging power of the energy storage battery, respectively, and β is the energy rate of the energy storage battery.

[0083] (5) Energy storage battery state of charge constraints

[0084] First, to prevent deep charge and discharge of batteries from affecting the life of the energy storage battery, the SOC range of the energy storage battery must be constrained; and the state of charge of the energy storage battery at the beginning and end of a scheduling cycle should be equal to ensure the regulation capability of the energy storage in the next cycle:

[0085]

[0086] Where: The upper and lower limits of the charge and discharge depth of the energy storage battery; They are the charge states at the beginning and end of the energy storage battery operation cycle.

[0087] (6) Voltage offset constraint

[0088] One of the important indicators for measuring grid stability is node voltage fluctuation. To ensure grid stability, the node voltage deviation should be kept within a certain range, as shown below:

[0089]

[0090]

[0091] Where: U lev is the average voltage deviation degree during the grid operation cycle; N is the number of load nodes; T is the statistical duration of the calculation cycle; U i，(t) 、 are the voltage and reference voltage of the i-th node at time t; ΔU i，max is the maximum allowable voltage deviation of the i-th node; It is the maximum value allowed for the average voltage deviation during the grid operation cycle.

[0092] See also Figure 1The present invention provides an embodiment of a multi-objective optimization configuration method for an energy storage power station including multiple controllable loads, wherein the power grid model includes temperature-controlled loads and industrial loads. The temperature-controlled load model is based on the thermal characteristics of the building, taking into account indoor and outdoor temperatures, air conditioning power, and wall heat capacity parameters; the industrial load model divides load levels according to energy consumption and adjustability based on the importance of the equipment and user preferences.

[0093] The energy storage power station model is based on energy storage equipment and takes into account the charging and discharging power, charging and discharging efficiency, and capacity operating parameters of the energy storage equipment to establish a full life cycle cost model, including the purchase cost, installation cost, operation and maintenance cost, and residual value recovery cost of the energy storage station;

[0094] The grid operation costs include electricity purchase costs, network loss costs, power generation costs and load costs; the energy storage power station costs include purchase costs, installation costs, operation and maintenance costs and residual value recovery;

[0095] The electricity purchase cost in the grid operation cost is calculated based on the electricity purchase price and interactive power between the system and the upper-level grid;

[0096] The network loss cost is calculated based on the voltage difference between grid nodes and line conductance;

[0097] The power generation cost is calculated based on the fuel cost and operation and maintenance cost of the microturbine;

[0098] The load cost is dynamically calculated based on the regulation status of the controllable load;

[0099] The purchase cost of the energy storage power station is amortized through time value, taking into account the benchmark interest rate and economic life, to optimize the comprehensive configuration cost within the planning period;

[0100] The installation cost and operation and maintenance cost are calculated by the unit capacity installation cost and operation and maintenance coefficient respectively, and the average annual decline coefficient of the equipment is taken into account;

[0101] Furthermore, we first construct a power grid temperature control load model, as shown in the following formula:

[0102] T min ≤T t ≤T max

[0103] 0≤P Tem，t ≤P max

[0104]

[0105] Where, T min and T max Indicates the lower and upper limits of the comfortable temperature; T t represents the indoor temperature; C represents the wall heat capacity; Touut，t Indicates outdoor temperature; C GP Indicates the energy efficiency ratio of air conditioner; P TEM，t represents the power consumption of air conditioner at time t; P max Indicates the rated power of the air conditioner; R wa and R w represents the thermal resistance of the wall and window; k represents the window transmittance; Indicates the intensity of sunlight; Q in Indicates the heating power of the internal heat source; F w Indicates the exterior window area;

[0106] Secondly, construct the industrial load model as shown below:

[0107] ψ1=(1-a)ζ

[0108] Where: a is the importance of the equipment, which is related to the actual process; ζ is the willingness of the user in the processing plant;

[0109]

[0110]

[0111] Where: L low , L high In order to distinguish the minimum and maximum values of the levels of low, medium and high importance of equipment in the process, the equipment is divided into L1-L according to its importance in the process. 10 There are 10 levels in total, L1 is the device that can be interrupted at any time, L 10 For non-interruptible devices; P low , P high In order to distinguish the minimum and maximum energy consumption values of low-energy-consuming equipment, medium-energy-consuming equipment and high-energy-consuming equipment, L con The monthly energy consumption of the equipment can be obtained from actual questionnaire surveys;

[0112] The operating costs of the power grid include electricity purchase costs, network loss costs, power generation costs, and load costs, as follows:

[0113] (1) Electricity purchase cost C grid

[0114]

[0115] Where: It represents the electricity price purchased by the system from the upper power grid during period t; It represents the interaction power between the system and the upper power grid during period t; Δt represents the length of the segmented scheduling time;

[0116] (2) Network loss cost C loss

[0117]

[0118] Where: N node is the total number of nodes; is the voltage amplitude of nodes j and k during period t; is the set of tail nodes with node j as the first node; G jk is the conductance between node j and node k; is the phase angle difference between the voltages at nodes j and k during period t;

[0119] (3) Power generation cost C DG

[0120]

[0121] Where: Ω MG Assemble for micro gas turbine unit; and Unit fuel for MG

[0122] Cost and unit operation and maintenance cost; is the power generation power of micro gas turbine i during period t;

[0123] η i is the conversion efficiency of micro gas turbine i;

[0124] (4) Load cost C L0

[0125]

[0126] Where: P in，t represents the energy consumption of industrial load at time t;

[0127] Energy storage power station model

[0128] Among the various equipment available for energy storage power stations, battery (SB) energy storage technology is mature and widely used, and is the most common energy storage device in the power grid. The remaining capacity (SOC) of the battery is an important parameter that characterizes the operating status of the battery. The remaining capacity SOC of the battery at time t is the value of the battery. SB (t) and the remaining power S at time t-1 SB The relationship between (t-1) in the charging and discharging cases is as follows. This relationship needs to be taken into account in the optimization model, and the energy storage output and energy storage capacity are first regarded as control variables;

[0129] When the energy storage station is in charging state:

[0130] S SB (t) = S SB (t-1)+η ch Pch (t)Δt-λQ SB When the energy storage power station is in the discharging state:

[0131]

[0132] Where: P ch (t) is the charging power of the battery at time t; P dis (t) is the discharge power of the battery at time t; η ch is the charging efficiency of the battery; η dis is the discharge efficiency of the battery; λ is the self-discharge rate of the battery; Q SB is the capacity of the battery; Δt is the time interval;

[0133] (1) Purchase cost of energy storage power station

[0134] The purchase cost of an energy storage power station, as a major component of the overall configuration cost, directly impacts the economic viability of the optimized configuration model. Using a full lifecycle cost model for energy storage power stations to measure the overall configuration cost of the system allows the cost to be amortized over the planning cycle, making the model more objective. The purchase cost of an energy storage power station is shown in the following formula, which takes into account the time value of capital costs:

[0135]

[0136] Where: D is the planned life cycle of the energy storage station; N is the number of energy storage stations, each of which is connected to a node in the power grid and operated in parallel; r is the benchmark interest rate, which uses the social average investment rate of return to measure the time value of money; Q i is the configuration capacity of the i-th energy storage power station, c i is the unit capacity investment cost of the i-th energy storage power station, n i is the number of replacements of the i-th energy storage power station within the planned period, represented by n i =floor(D / d), where d is the economic life of the energy storage power station.

[0137] (2) Energy storage power station installation cost

[0138] Generally speaking, the purchasing manufacturer and equipment installation manufacturer of an energy storage power station may belong to different manufacturers. Even if they belong to the same manufacturer, the installation of an energy storage power station is a large project, and its installation costs need to be settled separately. Therefore, the equipment installation cost in the comprehensive optimization configuration cost needs to be considered in a model. The equipment installation cost of an energy storage power station during the planning period is shown in the following formula:

[0139]

[0140] Where: j = 0, 1, ..., ni Indicates the number of installations; is the j-th installation cost of the i-th energy storage power station, a i represents the average annual reduction coefficient of the installation cost of the i-th energy storage power station, C rep,i is the installation cost per unit capacity of the i-th energy storage power station.

[0141] (3) Operation and maintenance costs of energy storage power stations

[0142] The daily operation of an energy storage power station requires investment in operation and maintenance costs, including personnel, management, and parts replacement. The operation and maintenance costs of an energy storage power station over its entire life cycle are shown in the following formula:

[0143]

[0144] Where: P SB,i (t) is the charging and discharging power of the i-th energy storage station during period t, P SB,i When (t)≥0, the energy storage power station is in the discharging state, P SB,i (t) < 0, the energy storage power station is in the charging state; k ch,i is the operation and maintenance cost coefficient of the i-th energy storage station when charging, u i,1 (t) is the energy storage charging coefficient, u 2.i (t) is the energy storage discharge coefficient. When the energy storage is charged, u i,1 (t)=1 and u 2,i (t)=0, when the energy storage is discharged, u i,1 (t)=0 and u 2,i (t) = 1; k dis,i is the operation and maintenance cost coefficient of the i-th energy storage power station during discharge. The specific calculation method of this coefficient is as follows:

[0145]

[0146]

[0147] Where: N(x) is the maximum number of cycles, which is determined by x, where x is the charge and discharge depth; C in it is the initial fixed investment cost of energy storage; S SB，start is the initial state of charge of energy storage; S SB，end It is the state of charge at the end of energy storage; is the maximum remaining power, which is 0.9 times the capacity of the energy storage station; c ch and c dis are the charging and discharging influencing factors respectively. Assuming that each energy storage power station in the optimized configuration uses the same type of energy storage equipment, the above coefficients are all constants.

[0148] (4) Equipment residual value recovery

[0149] When an energy storage power station completes its life cycle, the remaining equipment has a certain residual value that can be recovered. This residual value can be included in the comprehensive optimization configuration cost as a benefit. The calculation method is as follows:

[0150]

[0151] Where: C 4，i is the residual value of the i-th energy storage station, which can be considered to be related to the capacity of the energy storage station, and C 4，i The calculation method is as follows:

[0152] C 4，i =k val Q i

[0153] Where: k val is the residual value coefficient of the energy storage power station.

[0154] See also Figure 1 The present invention provides an embodiment: a multi-objective optimization configuration method for an energy storage power station including multiple controllable loads, wherein the second-order cone programming (SOCP) problem is converted into a second-order cone programming (SOCP) problem by using a second-order cone relaxation method (SOCR), and the calculation accuracy and speed are guaranteed by relaxation processing. The second-order cone programming (SOCP) model is used to optimize the configuration scheme of the energy storage power station, optimize the power balance constraint, power flow constraint and energy storage battery state of charge constraint of the power grid, and relax the power flow equation of the power grid by using the second-order cone relaxation method (SOCR). The error gap is calculated to ensure that the optimal solution is consistent with the accuracy requirement of the original problem. When the error gap reaches the set accuracy, the optimal solution is considered to be equivalent to the optimal solution of the original problem.

[0155] Furthermore, we obtain the solution model with the lowest grid operation cost and the lowest energy storage power station configuration cost:

[0156]

[0157] The grid-side modeling of the above model primarily involves the power flow model of the power grid branches. However, the model contains non-convex nonlinear constraints, making it difficult to guarantee a globally optimal solution for mathematical optimization models containing such constraints. Therefore, the constraints can be addressed using the second-order cone relaxation method (SOCR). The second-order cone programming (SOCP) model after SOCR can be considered a further generalization of the linear programming model. It is essentially a convex programming model, with the standard form shown below:

[0158] F socp =min{cT x|Ax=b,x∈H}

[0159] Where: x is the n-dimensional decision variable; b, c, and A refer to coefficient constants; c T x is a linear function about x; H is a second-order cone or a rotated second-order cone, and its specific form is as follows:

[0160] Second-order cone:

[0161] Rotate a second-order cone:

[0162] Among them, R N Represents an N-dimensional real number, N represents the dimension, x i Represents the i-th dimension variable;

[0163] The SOCP model can obtain better solution results while ensuring calculation speed and accuracy. Based on the characteristics of the SOCP model, the mathematical model of the branch power flow model is transformed into the following form:

[0164] ||[2 Pmn，t 2Q mn，t i mn，t -v m，t ] T ||2≤i mn，t +v m，t

[0165] Before SOCP, C original is the original non-convex feasible region. After SOCR, C original will be expanded to include the original C original The convex feasible region C SOC , thus transforming the original power flow equation into a convex form. C SOC The optimal solution S obtained is a lower bound solution to the original problem, and if the optimal solution S is also the original feasible domain C original , then it is the optimal solution to the original problem. Then, the branch and bound method or cutting plane method in algorithm toolboxes such as GUROBI, CPLEX, and MOSEK can be used to solve the proposed problem, further ensuring the accuracy and efficiency of the model solution.

[0166] To verify the accuracy of the above model, it is necessary to further calculate the error gap of SOCR. The calculation method of the error gap after relaxation is as follows:

[0167]

[0168] When the error gap reaches the set accuracy, the corresponding optimal solution can be considered equivalent to the actual optimal solution.

[0169] See also Figure 1 The present invention provides an embodiment of a multi-objective optimization configuration method for an energy storage power station including multiple controllable loads. The principles of the POA-GWO-CSO optimization algorithm are as follows:

[0170] POA simulates the local search behavior of pelicans, updating the population position through the phases of moving toward prey and skimming the water surface;

[0171] GWO introduces the gray wolf group strategy to perform global search to avoid falling into local optimality;

[0172] CSO optimizes the population position through horizontal and vertical cross-updates to avoid premature convergence;

[0173] The specific implementation steps of the POA-GWO-CSO optimization algorithm are as follows:

[0174] Step 1: Set the population size and maximum number of iterations;

[0175] Step 2: Collect instantaneous power system flow data, input it into the model, calculate the fitness value of the pelican population, and thus determine the initial value of the objective function;

[0176] Step 3: Randomly generate prey;

[0177] Step 4: Use the improved pelican position formula to update the pelican position in the first stage;

[0178] Step 5: Substitute the position in step 4 into the fitness function to determine whether the fitness value at this position is improved, that is, whether the objective function is improved;

[0179] Step 6: Update the position of the i-th pelican in the j-th dimension in the second stage;

[0180] Step 7: Substitute the position in step 6 into the fitness function to determine whether the fitness value at this position is improved, that is, whether the objective function is improved;

[0181] Step 8: Determine whether the maximum number of iterations is met. If yes, jump to the next step; otherwise, jump to step 3.

[0182] Step 9: Determine the position value of the pelican based on the final fitness function value;

[0183] Furthermore, the algorithm implementation first initializes the pelican population, then updates the pelican positions in two phases. The first phase uses the POA algorithm and an introduced nonlinear inertia weighting factor for updates, while the second phase uses the CSO algorithm for cross-reference updates. Through continuous iteration, the optimal optimization solution is ultimately found, achieving user temperature comfort while promoting efficient energy storage utilization and local consumption of new energy.

[0184] The specific implementation steps based on the POA-GWO-CSO optimization algorithm are as follows:

[0185] Step 1: Set the population size N and the maximum number of iterations L;

[0186] Step 2: Collect instantaneous power system flow data, input it into the model, calculate the fitness value of the pelican population, and thus determine the initial value of the objective function;

[0187] Step 3: Randomly generate prey;

[0188] Step 4: Use the improved pelican position formula to calculate the pelican position in the first stage Make updates;

[0189] Step 5: Substitute this position into the fitness function to determine whether the fitness value is improved at this position, that is, whether the objective function is improved;

[0190] Step 6: Update the position of the i-th pelican in the j-th dimension in the second phase

[0191] Step 7: Substitute this position into the fitness function to determine whether the fitness value is improved at this position, that is, whether the objective function is improved;

[0192] Step 8: Determine whether the maximum number of iterations is met. If yes, jump to the next step; otherwise, jump to step 3.

[0193] Step 9: Determine the position value of the pelican based on the final fitness function value.

[0194] Working principle: First, considering the power balance of the power grid, a controllable load model of the power grid is constructed, and a storage power station model that comprehensively considers factors such as energy storage economy is established; secondly, a multi-objective optimization strategy for energy storage power stations is proposed with the lowest grid operation cost and the lowest energy storage power station configuration cost as the optimization goals; then, the original problem is transformed into a second-order cone problem through relaxation and linearization methods; finally, the POA-GWO-CSO algorithm is used to complete the solution of the multi-objective optimization configuration method.

[0195] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. A multi-objective optimization configuration method for an energy storage power station including multiple controllable loads, characterized by: The configuration method comprises the following steps: S1. Establish a power grid model and an energy storage power station model; the power grid model includes temperature control loads and industrial loads. The temperature control load model is based on the thermal characteristics of the building, taking into account indoor and outdoor temperatures, air conditioning power, and wall heat capacity parameters. The industrial load model is based on the importance of the equipment and user preferences, and divides the load levels according to energy consumption and adjustability. S2. Determine the multi-objective optimization goal: minimize the grid operation cost and the energy storage power station configuration cost; S3. Set constraints: grid node voltage and line current constraints, power balance constraints, grid branch flow constraints, energy storage battery capacity and energy rate constraints, energy storage battery state of charge constraints, and voltage offset constraints; S4. Convert the non-convex optimization problem into a second-order cone programming (SOCP) problem; S4. Use POA-GWO-CSO optimization algorithm for optimization solution.

2. The multi-objective optimization configuration method for an energy storage power station including multiple controllable loads according to claim 1, characterized in that: The energy storage power station model is based on energy storage equipment, taking into account the charging and discharging power, charging and discharging efficiency and capacity operating parameters of the energy storage equipment, and establishing a full life cycle cost model, including the purchase cost, installation cost, operation and maintenance cost and residual value recovery cost of the energy storage power station.

3. The multi-objective optimization configuration method for an energy storage power station including multiple controllable loads according to claim 1, characterized in that: The grid operation costs include electricity purchase costs, network loss costs, power generation costs and load costs; the energy storage power station costs include purchase costs, installation costs, operation and maintenance costs and residual value recovery.

4. The multi-objective optimization configuration method for an energy storage power station including multiple controllable loads according to claim 3, characterized in that: The electricity purchase cost in the grid operation cost is calculated based on the electricity purchase price and interactive power between the system and the upper-level grid; The network loss cost is calculated based on the voltage difference between grid nodes and line conductance; The power generation cost is calculated based on the fuel cost and operation and maintenance cost of the microturbine; The load cost is dynamically calculated based on the regulation status of the controllable load.

5. The multi-objective optimization configuration method for an energy storage power station including multiple controllable loads according to claim 3, characterized in that: The purchase cost of the energy storage power station is amortized through time value, taking into account the benchmark interest rate and economic life, to optimize the comprehensive configuration cost within the planning period; The installation cost and operation and maintenance cost are calculated by the unit capacity installation fee and operation and maintenance coefficient respectively, and the average annual decline coefficient of the equipment is taken into account.

6. The multi-objective optimization configuration method for an energy storage power station including multiple controllable loads according to claim 1, characterized in that: The second-order cone programming (SOCP) problem is to transform the non-convex nonlinear constraints into a second-order cone programming (SOCP) problem by using the second-order cone relaxation method (SOCR). The calculation accuracy and speed are guaranteed by relaxation processing. The second-order cone programming (SOCP) model is used to optimize the configuration scheme of the energy storage power station and the power balance constraint, flow constraint and state of charge constraint of the energy storage battery under the premise of ensuring the global optimal solution. The second-order cone relaxation method (SOCR) relaxes the power flow equation of the power grid and calculates the error gap to ensure that the optimal solution is consistent with the accuracy requirements of the original problem. When the error gap reaches the set accuracy, the optimal solution is considered to be equivalent to the optimal solution of the original problem.

7. The multi-objective optimization configuration method for an energy storage power station including multiple controllable loads according to claim 1, characterized in that: The principles of the POA-GWO-CSO optimization algorithm are as follows: POA simulates the local search behavior of pelicans, updating the population position through the phases of moving toward prey and skimming the water surface; GWO introduces the gray wolf group strategy to perform global search to avoid falling into local optimality; CSO optimizes the population position through horizontal and vertical cross-updates to avoid premature convergence.

8. The multi-objective optimization configuration method for an energy storage power station including multiple controllable loads according to claim 1, characterized in that: The specific implementation steps of the POA-GWO-CSO optimization algorithm are as follows: Step 1: Set the population size and maximum number of iterations; Step 2: Collect instantaneous power system flow data, input it into the model, calculate the fitness value of the pelican population, and thus determine the initial value of the objective function; Step 3: Randomly generate prey; Step 4: Use the improved pelican position formula to update the pelican position in the first stage; Step 5: Substitute the position in step 4 into the fitness function to determine whether the fitness value at this position is improved, that is, whether the objective function is improved; Step 6: Update the position of the i-th pelican in the j-th dimension in the second stage; Step 7: Substitute the position in step 6 into the fitness function to determine whether the fitness value at this position is improved, that is, whether the objective function is improved; Step 8: Determine whether the maximum number of iterations is met. If yes, jump to the next step; otherwise, jump to step 3. Step 9: Determine the pelican's position value based on the final fitness function value.

9. The multi-objective optimization configuration method for an energy storage power station including multiple controllable loads according to claim 1, characterized in that: The optimization solution steps are as follows: a. Initialize the algorithm population size and number of iterations, collect instantaneous power flow data, and calculate fitness; b. Update the location of the pelican population, perform local and global searches, and gradually approach the optimal solution through iteration; c. Determine the improvement of the fitness function until the number of iterations is met or the optimal solution is found.

10. The multi-objective optimization configuration method for an energy storage power station including multiple controllable loads according to claim 1, characterized in that: During the execution of the optimization method, the charging and discharging constraints of the energy storage battery are taken into account to ensure that the charge state of the energy storage battery within the scheduling cycle is not lower than the set lower limit value; the voltage offset constraints of the power grid nodes are taken into account, the deviation of the node voltage is kept within the allowable range, and the average voltage offset degree of the power grid meets the set maximum allowable value.

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