A distributed energy storage output scheduling optimization method, medium and system

Abstract

The application provides a distributed energy storage output scheduling optimization method, medium and system, and belongs to the technical field of distributed energy storage output scheduling. First, the electric parameters, load parameters, renewable energy generation parameters and energy storage device parameters of a transformer area are collected, and an energy storage grid-connected equation set is established by comprehensively considering these factors. According to historical data, the load, renewable energy output and energy storage demand curves in a future period of time are predicted. Then, an energy storage scheduling optimization model of multiple time periods is constructed, and the targets include minimizing the operation cost, maximizing the renewable energy consumption and minimizing the power grid fluctuation. An improved particle swarm optimization algorithm is used to solve the multi-objective optimization problem, and multiple candidate schemes are obtained. Finally, a multi-objective decision method is used to comprehensively evaluate the candidate schemes, and the best energy storage scheduling scheme is selected. The technical problem that the prior art only considers the parameters of the energy storage system itself and ignores the influence of the input electric parameters and load parameters of the power grid is solved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of distributed energy storage output scheduling, and particularly relates to a distributed energy storage output scheduling optimization method, a medium and a system. BACKGROUND

[0002] In recent years, renewable energy generation technology has developed rapidly and has become an indispensable part of the power system. However, renewable energy generation has the characteristics of intermittency and uncertainty, which brings certain challenges to the safe and stable operation of the power grid. In order to effectively consume renewable energy power and improve its utilization efficiency, distributed energy storage technology has emerged as the times require and become an important means of flexible regulation of the power grid. At present, distributed energy storage has been widely used in power systems and can provide load peak shaving, frequency regulation, voltage support and other services for the power grid. By reasonably scheduling the charging and discharging of the distributed energy storage system, power balance within the power grid can be achieved, the consumption level of renewable energy can be improved, and the operation cost of the power grid can be reduced. However, the existing distributed energy storage scheduling optimization method often only considers the parameters of the energy storage system itself, and ignores the influence of other key factors such as grid input electrical parameters and load parameters, resulting in that the optimization result cannot comprehensively reflect the actual situation of the power grid operation. SUMMARY

[0003] Therefore, the application provides a distributed energy storage output scheduling optimization method, a medium and a system, which can solve the technical problem that the existing technology only considers the parameters of the energy storage system itself and ignores the influence of other key factors such as grid input electrical parameters and load parameters.

[0004] The application is implemented as follows:

[0005] The first aspect of the application provides a distributed energy storage output scheduling optimization method, which comprises the following steps:

[0006] S10, collecting input electrical parameters, load parameters, renewable energy generation parameters of a transformer area, and energy storage parameters of each energy storage device in a distributed energy storage system;

[0007] S20, establishing and fitting a set of energy storage grid-connected equations considering the input electrical parameters, load parameters, renewable energy generation parameters and energy storage parameters;

[0008] S30, based on the historical data and current state of the input electrical parameters and load parameters, predicting the load change curve, renewable energy output curve and energy storage demand curve in a continuous small time period in the future;

[0009] S40, according to the predicted load curve, the renewable energy output curve and the energy storage demand curve, combining the energy storage grid equation set, a multi-time period energy storage scheduling optimization model is established to describe the charging and discharging plan of each energy storage device in the future multiple time periods, including charging and discharging time, power size;

[0010] S50, a plurality of objectives of the multi-time period energy storage scheduling optimization model are determined, including minimizing system operation cost, maximizing renewable energy consumption, minimizing grid fluctuation, and setting constraint conditions to establish a multi-objective optimization problem;

[0011] S60, an improved particle swarm optimization algorithm is used to solve the multi-objective optimization problem to obtain a plurality of candidate optimization solutions;

[0012] S70, a multi-objective decision-making method is used to comprehensively evaluate the plurality of candidate optimization solutions, select the best energy storage scheduling scheme and output.

[0013] Specifically, the step S10 specifically comprises: collecting the input electrical parameters, load parameters and energy storage parameters of each energy storage device in the distributed energy storage system of the transformer area through real-time measurement and monitoring equipment. The input electrical parameters include voltage, frequency, active power, reactive power and power factor; the load parameters include peak-valley load, load type, load curve and power factor; the renewable energy generation parameters include generation type, installed capacity, real-time output and output prediction curve; the energy storage parameters include response speed, output change rate limit, rated capacity, current state of charge, charging and discharging efficiency, maximum charging and discharging power and service life. Through real-time monitoring and analysis, these key parameter data are obtained to lay a foundation for subsequent optimization scheduling.

[0014] The step S20 specifically comprises: establishing an energy storage grid equation set considering input electrical parameters, load parameters, renewable energy generation parameters and energy storage parameters. The equation set includes power balance equation, energy storage response process voltage variation equation, voltage constraint equation, current constraint equation, energy storage capacity constraint equation and energy storage life constraint equation. The power balance equation describes the balance relationship between grid input power, renewable energy generation power, energy storage system output power and load power; the energy storage response process voltage variation equation describes the influence of energy storage system output power change on voltage; the voltage constraint equation and the current constraint equation ensure the safety of grid voltage and current; the energy storage capacity constraint equation describes the charging and discharging characteristics of the energy storage system; and the energy storage life constraint equation limits the service life of the energy storage device. By establishing these equations, the operation characteristics and constraint conditions of the energy storage system in the grid are described to provide a mathematical model basis for subsequent optimization scheduling.

[0015] The step S30 specifically comprises: based on the historical data and current state of input electrical parameters and load parameters, using time series analysis, machine learning and other methods to predict the load change curve, renewable energy output curve and energy storage demand curve in the future period. The load prediction considers the periodicity, trend and sudden change of the load; the renewable energy output prediction considers factors such as meteorological data; and the energy storage demand curve is calculated according to the power balance equation. These prediction results reflect the actual demand changes of the power grid operation, laying a foundation for the subsequent optimization problem.

[0016] The step S40 specifically comprises: according to the predicted load change curve, renewable energy output curve and energy storage demand curve, combining the established energy storage grid connection equation set to establish a multi-time period energy storage scheduling optimization model. The decision variable of the optimization model is the charging and discharging power of each energy storage device in the future multiple time periods; the optimization objectives include minimizing the system operation cost, maximizing the renewable energy consumption and minimizing the power grid fluctuation; and the optimization constraint conditions include voltage constraint, current constraint, energy storage capacity constraint, energy storage life constraint and other operation limitations of the energy storage device. By establishing this multi-time period optimization model, the optimal charging and discharging plan of each energy storage device can be obtained, taking into account multiple optimization objectives.

[0017] The step S50 specifically comprises: based on the established multi-time period energy storage scheduling optimization model, determining multiple objective functions including minimizing the system operation cost, maximizing the renewable energy consumption and minimizing the power grid fluctuation; and setting various constraint conditions such as voltage constraint, current constraint, energy storage capacity constraint, energy storage life constraint and other operation limitations of the energy storage device, to establish a multi-objective optimization problem. Through the establishment of this multi-objective optimization model, a balance can be sought among the system cost, renewable energy consumption and power grid stability, and an optimal energy storage scheduling scheme that meets various constraint conditions can be obtained.

[0018] The step S60 specifically comprises: using an improved particle swarm optimization algorithm to solve the aforementioned multi-objective optimization problem to obtain multiple candidate optimization solutions. The algorithm simulates the foraging behavior of a bird swarm, initializes a group of particles representing energy storage scheduling schemes, and performs constraint checking, objective function calculation, individual optimal solution and global optimal solution updating, and iterative updating of speed and position for each particle. To increase population diversity, a mutation operation is also introduced. Through multiple iterations, multiple candidate optimization solutions that meet various constraint conditions can be obtained.

[0019] The step S70 specifically comprises: using an analytic hierarchy process (AHP) to comprehensively evaluate the multiple candidate optimal solutions obtained in the step S60, and selecting the best energy storage scheduling scheme. First, a hierarchical structure model including an objective, evaluation criteria and candidate schemes is constructed, then a judgment matrix is constructed according to expert evaluation and a weight vector of each criterion is calculated, and consistency test is performed. Next, the comprehensive score of each candidate scheme is calculated, which comprehensively considers multiple objectives such as cost, renewable energy consumption and power grid fluctuation. Finally, the scheme with the highest comprehensive score is selected as the optimal energy storage scheduling strategy. This multi-objective decision-making method can comprehensively evaluate the balance performance of each candidate scheme, and obtain the optimal energy storage scheduling result.

[0020] The energy storage parameters include response speed, output change rate limit, rated capacity, current state of charge, charge and discharge efficiency, maximum charge and discharge power and service life.

[0021] Further, the input electrical parameters include voltage, frequency, active power, reactive power and power factor; and the load parameters include peak-valley load, load type, load curve and power factor.

[0022] Further, the renewable energy generation parameters include generation type, installed capacity, real-time output and output prediction curve.

[0023] Further, the improved particle swarm optimization algorithm simulates the foraging behavior of a bird swarm to initialize a group of particles representing energy storage scheduling schemes, and performs steps of constraint checking, objective function calculation, individual optimal solution and global optimal solution updating, and iterative updating of speed and position for each particle. In order to increase population diversity, a mutation operation is also introduced. Through multiple iterations, multiple candidate optimal solutions that meet various constraint conditions are obtained.

[0024] Each equation in the energy storage grid-connected equation set will be described in detail below:

[0025] 1. Power balance equation:

[0026] The power balance equation is specifically represented as follows:

[0027] ;

[0028] In the formula, is the grid input power at time t; is the renewable energy generation power at time t; is the energy storage system output power at time t (charging is negative and discharging is positive); is the load power at time t; is the system loss power at time t.

[0029] Parameter acquisition method:

[0030] and obtained by real-time measurement;

[0031] obtained by real-time monitoring of the renewable energy power generation equipment;

[0032] obtained by real-time monitoring of the energy storage system;

[0033] can be estimated by the following formula:

[0034] ;

[0035] wherein, is the number of lines in the system, is the resistance of the i-th line, is the current of the i-th line at time t.

[0036] 2. Energy storage response process voltage variation equation:

[0037] The energy storage response process voltage variation equation is specifically represented as follows:

[0038] ;

[0039] wherein, is the voltage change amount at time t; and are the sensitivity coefficients of voltage to active power and reactive power, respectively; and are the change amounts of active power and reactive power of the energy storage system at time t, respectively; is an error term.

[0040] Parameter acquisition method:

[0041] Sensitivity coefficients and can be obtained by the following steps:

[0042] Step 1: Use power flow calculation software to establish a system model;

[0043] Step 2: Under different operating conditions, slightly change the active and reactive power of the energy storage system, and record the voltage change;

[0044] Step 3: Use the least squares method to fit to obtain the sensitivity coefficients.

[0045] and obtained by real-time monitoring of the energy storage system.

[0046] 3. Voltage constraint equation:

[0047] The voltage constraint equation is specifically expressed as follows:

[0048] ;

[0049] where, is the voltage of the i-th node at time t; and are the minimum and maximum allowed voltages, respectively; is the number of nodes in the system.

[0050] Parameter acquisition method:

[0051] obtained through real-time measurement;

[0052] and determined according to the power system operation standards.

[0053] 4. Current constraint equation:

[0054] The current constraint equation is specifically expressed as follows:

[0055] ;

[0056] where, is the complex current of the j-th line at time t; is the maximum allowed current of the j-th line; is the number of lines in the system.

[0057] Parameter acquisition method:

[0058] obtained through real-time measurement;

[0059] determined according to the line design parameters.

[0060] 5. Energy storage capacity constraint equation:

[0061] The energy storage capacity constraint equation is specifically expressed as follows:

[0062] ;

[0063] where, is the energy state of the energy storage system at time t; and are the minimum and maximum energy allowed by the energy storage system, respectively; and are the charging and discharging efficiencies, respectively; and charging and discharging power at time t, respectively; is a time interval.

[0064] Parameter acquisition method:

[0065] obtained through real-time monitoring of the energy storage system;

[0066] and determined according to the design parameters of the energy storage system;

[0067] and obtained through experimental tests, the specific steps being as follows:

[0068] Step 1: charge and discharge the energy storage system at a constant power;

[0069] Step 2: measure the total energy input / output;

[0070] Step 3: calculate the efficiency: .

[0071] 6. Energy storage life constraint equation:

[0072] The energy storage life constraint equation is specifically represented as follows:

[0073] ;

[0074] wherein, is the remaining life of the energy storage system at time t; is the initial life; is the life decay coefficient; is the output power of the energy storage system at time k; is the rated power; is the allowed minimum remaining life.

[0075] Parameter acquisition method:

[0076] and determined according to the design parameters of the energy storage system;

[0077] obtained through accelerated life tests, the specific steps being as follows:

[0078] Step 1: cyclically charge and discharge the energy storage system at different depths and frequencies;

[0079] Step 2: periodically measure the capacity decay of the energy storage system;

[0080] Step 3: obtain the life decay coefficient by least squares fitting .

[0081] These equations collectively form the energy storage grid-connected equation set, describing the operating characteristics and constraints of the energy storage system in the grid. By solving these equations, the optimal scheduling strategy of the energy storage system can be obtained.

[0082] S60. Improved particle swarm optimization algorithm for solving multi-objective optimization problems

[0083] Step 1: Initialize the particle swarm

[0084] Initialize a set of particles, each representing a possible energy storage scheduling scheme. The position vector of a particle is represented as:

[0085] ;

[0086] where, is the position vector of the i-th particle; is the output power of the energy storage system at time t; is the number of time periods in the scheduling period.

[0087] Step 2: Check the constraints

[0088] For each particle, check whether the constraints are met:

[0089] 1) Voltage constraint:

[0090] ;

[0091] 2) Current constraint:

[0092] ;

[0093] 3) Energy storage capacity constraint:

[0094] ;

[0095] 4) Energy storage life constraint:

[0096] ;

[0097] 5) State of charge limit of energy storage device:

[0098] ;

[0099] 6) Charge and discharge power limit:

[0100] ;

[0101] 7) Charge and discharge times limit:

[0102] ;

[0103] 8) Grid voltage and frequency limits:

[0104] ;

[0105] 9) Renewable energy output limits:

[0106] ;

[0107] 10) Load demand limits:

[0108] .

[0109] If a particle does not satisfy the constraints, it is made to satisfy the constraints through projection or repair operations.

[0110] Step 3: Calculate objective function values

[0111] For each particle, calculate the value of multiple objective functions:

[0112] ;

[0113] ;

[0114] ;

[0115] where, Csystemis the system operation cost; CRESis the negative value of renewable energy consumption; Cgridis the grid fluctuation level.

[0116] Step 4: Update individual optimal solution and global optimal solution

[0117] For each particle, update its individual optimal solution Dominance relation is used to compare solutions in multi-objective optimization problems.

[0118] Update the global optimal solution set , which contains all non-dominated solutions.

[0119] Step 5: Update particle velocity and position

[0120] Update the velocity and position of each particle:

[0121] ;

[0122] ;

[0123] where, vi(t) is the velocity of the i-th particle at time t; wiis the inertia weight; and is the acceleration constant; and is a random number between [0, 1]; is a randomly selected solution from the global optimal solution set .

[0124] Step 6: Introduce mutation operation

[0125] To increase population diversity, introduce mutation operation:

[0126] ;

[0127] where, is the mutated particle position; is the mutation strength; is the standard normal distribution.

[0128] Step 7: Repeat steps 2-6 until the maximum number of iterations is reached or the convergence condition is met.

[0129] S70. Multi-objective decision-making method to select the best energy storage scheduling scheme

[0130] Use the analytic hierarchy process (AHP) to comprehensively evaluate the candidate optimization solutions, with the following steps:

[0131] Step 1: Construct a hierarchical model

[0132] Construct a hierarchical structure of decision objectives, evaluation criteria, and candidate schemes. Evaluation criteria include system operation cost, renewable energy consumption, and grid fluctuation level.

[0133] Step 2: Construct a judgment matrix

[0134] For 3 evaluation criteria, construct a judgment matrix A: ;

[0135] where, represents the importance of criterion i relative to criterion j, determined by expert evaluation or decision-maker preference.

[0136] Step 3: Calculate the weight vector

[0137] Calculate the maximum eigenvalue and the corresponding eigenvector W of the judgment matrix A:

[0138] ;

[0139] ;

[0140] where, is the weight vector.

[0141] Step 4: Consistency check

[0142] Calculate the consistency index CI and the consistency ratio CR:

[0143]

[0144]

[0145] In the formula, RI is the random consistency index. When , it is considered that the judgment matrix has satisfactory consistency.

[0146] Step 5: Calculate the comprehensive score of the candidate scheme

[0147] For each candidate scheme , calculate its comprehensive score:

[0148]

[0149] In the formula, and are the maximum value and the minimum value of the ith objective function among all candidate schemes, respectively.

[0150] Step 6: Select the best scheme

[0151] Select the scheme with the highest comprehensive score as the best energy storage scheduling scheme:

[0152]

[0153] Through the above steps, a best energy storage scheduling scheme considering multiple objectives and constraint conditions can be obtained. This method closely combines the particle swarm optimization algorithm with various equations and parameters in the present application, ensuring that the optimization result meets all constraint conditions and balances among multiple objectives.

[0154] The second aspect of the present application provides a computer readable storage medium, wherein the computer readable storage medium stores program instructions, and the program instructions are used to execute the above-mentioned distributed energy storage output scheduling optimization method when running in a computer.

[0155] The third aspect of the present application provides a distributed energy storage output scheduling optimization system, which comprises the above-mentioned computer readable storage medium.

[0156] ​​​​The application provides a distributed energy storage output scheduling optimization method, which establishes an optimization model containing multiple optimization objectives and various constraint conditions, adopts an improved particle swarm optimization algorithm for solving, and finally obtains an optimal energy storage scheduling scheme meeting the requirements of power grid operation.

[0157] Firstly, the application simultaneously considers multiple optimization objectives such as minimization of system operation cost, maximization of renewable energy consumption and minimization of power grid fluctuation, and takes into account all aspects of interests of power grid operation. By adopting a multi-objective optimization method, the best balance between these objectives can be sought to meet the needs of all stakeholders of the power grid.

[0158] Secondly, when establishing the optimization model, the application fully considers input electrical parameters, load parameters, renewable energy generation parameters and various parameters of the energy storage device itself, and comprehensively reflects the actual situation of power grid operation. This ensures that the optimization result can better adapt to the actual operation requirements of the power grid.

[0159] Thirdly, the application adopts an improved particle swarm optimization algorithm to solve the multi-objective optimization problem, which can effectively deal with complex optimization problems. Compared with existing linear programming, dynamic programming and other methods, the particle swarm optimization algorithm has stronger search ability and convergence speed, and can obtain an optimal solution meeting various constraint conditions.

[0160] In summary, the distributed energy storage output scheduling optimization method provided by the application can better play the roles of peak shaving and power grid support of the distributed energy storage system, improve the renewable energy consumption level, and solve the technical problem of the prior art that only considers the parameters of the energy storage system itself and ignores the influences of other key factors such as input electrical parameters and load parameters of the power grid. BRIEF DESCRIPTION OF DRAWINGS

[0161] Figure 1 The flowchart of the method provided by the application is shown in the figure.

[0162] Figure 2 The renewable energy output prediction curve is shown in the figure.

[0163] Figure 3 The 24-hour operation condition curve of the energy storage system is shown in the figure. DETAILED DESCRIPTION

[0164] In order to make the purpose, technical scheme and advantages of the embodiments of the application clearer, the technical scheme in the embodiments of the application will be described clearly and completely below with reference to the drawings in the embodiments of the application.

[0165] As Figure 1As shown, the present application provides a distributed energy storage output scheduling optimization method flow chart, the method comprises the following steps:

[0166] S10, collecting input electrical parameters, load parameters, renewable energy generation parameters of the transformer area, and energy storage parameters of each energy storage device in the distributed energy storage system;

[0167] S20, establishing and fitting an energy storage grid-connected equation group considering input electrical parameters, load parameters, renewable energy generation parameters and energy storage parameters;

[0168] S30, based on historical data and current state of input electrical parameters and load parameters, predicting load change curve, renewable energy output curve and energy storage demand curve in a continuous time period in the future;

[0169] S40, according to the predicted load change curve, renewable energy output curve and energy storage demand curve, combining the energy storage grid-connected equation group, establishing a multi-time period energy storage scheduling optimization model for describing the charging and discharging plan of each energy storage device in the future, including charging and discharging time and power size;

[0170] S50, determining multiple objectives of the multi-time period energy storage scheduling optimization model, including minimizing system operation cost, maximizing renewable energy consumption, minimizing grid fluctuation, and setting constraint conditions to establish a multi-objective optimization problem;

[0171] S60, using an improved particle swarm optimization algorithm to solve the multi-objective optimization problem to obtain multiple candidate optimization solutions;

[0172] S70, using a multi-objective decision-making method, including Pareto optimal set or analytic hierarchy process or fuzzy comprehensive evaluation method, to comprehensively evaluate the multiple candidate optimization solutions, select the best energy storage scheduling scheme and output.

[0173] The specific implementation of the above steps is described in detail as follows:

[0174] The specific implementation of step S10 is to collect input electrical parameters, load parameters and energy storage parameters of each energy storage device in the distributed energy storage system of the transformer area. Among them, the input electrical parameters include voltage, frequency, active power, reactive power and power factor, etc.; the load parameters include peak-valley load, load type, load curve and power factor, etc.; the renewable energy generation parameters include generation type, installed capacity, real-time output and output prediction curve, etc.; the energy storage parameters include response speed, output change rate limit, rated capacity, current state of charge, charging and discharging efficiency, maximum charging and discharging power and service life, etc.

[0175] The input electrical parameters and load parameters can be obtained through real-time measurement and monitoring equipment. Among them, voltage, frequency, active power and reactive power, etc. can be directly obtained through real-time power measurement instrument; load type and load curve, etc. can be obtained through analysis of the power characteristics of the load user; peak-valley load, power factor, etc. can be obtained through historical data statistical analysis.

[0176] The generation type and installed capacity in the renewable energy generation parameter can be obtained through on-site investigation and technical parameter consultation of the renewable energy generation equipment; the real-time output can be obtained through real-time monitoring of the generation equipment; the output prediction curve needs to be predicted by using meteorological data and machine learning algorithm.

[0177] The parameters of each energy storage device in the distributed energy storage system, including response speed, output change rate limit, rated capacity, current state of charge, charging and discharging efficiency, maximum charging and discharging power, and service life, etc. can be obtained through consultation of technical parameters of energy storage equipment manufacturers and on-site testing. Among them, the charging and discharging efficiency and can be calculated by constant power charging and discharging test of the energy storage equipment and measuring the total energy input and output.

[0178] In summary, the purpose of step S10 is to comprehensively collect various key parameters of the energy storage system, laying a foundation for subsequent optimization scheduling. Through real-time measurement and analysis means to obtain these parameter data, the reliability and accuracy of the input data are ensured.

[0179] The specific implementation of step S20 is to establish and fit the energy storage grid connection equation set considering the input electrical parameters, load parameters, renewable energy generation parameters and energy storage parameters. This energy storage grid connection equation set includes power balance equation, energy storage response process voltage variation equation, voltage constraint equation, current constraint equation, energy storage capacity constraint equation and energy storage life constraint equation, etc.

[0180] Firstly, the power balance equation describes the balance relationship between the grid input power, renewable energy generation power, energy storage system output power and load power. This equation ensures the power balance in the system and is the basis of energy storage scheduling optimization. Among them, the grid input power and load power can be obtained through real-time monitoring; the renewable energy generation power can be obtained through real-time monitoring or prediction curve; the energy storage system output power can be obtained through real-time monitoring. The system loss power can be estimated according to the line resistance and the real-time measured current.

[0181] Secondly, the voltage variation equation of the energy storage response process describes the impact of active and reactive power changes of the energy storage system on the voltage. This equation can be used to predict the impact of energy storage scheduling on the grid voltage. Among them, the sensitivity coefficients of voltage to active power and reactive power can be obtained by power flow calculation and least squares fitting.

[0182] Then, the voltage constraint equation and the current constraint equation ensure the safety of voltage and current in the operation of the grid. These constraint conditions come from the operation standards of the power system and need to be determined according to the actual system.

[0183] In addition, the energy storage capacity constraint equation describes the charging and discharging process of the energy storage system, ensuring that the energy state of the energy storage system is within the allowed range. Among them, the charging and discharging efficiency of the energy storage system also needs to be obtained through experimental test.

[0184] Finally, the energy storage life constraint equation describes the life characteristics of the energy storage system, ensuring that the energy storage equipment will not be overused and cause premature depletion of life in the process of optimal scheduling. The life attenuation coefficient in this constraint equation also needs to be obtained through accelerated life test.

[0185] In summary, step S20 establishes a set of equations that comprehensively describe the characteristics of energy storage grid connection. This equation set provides a mathematical model basis for subsequent optimal scheduling, ensuring that the optimization results meet various constraint conditions for safe operation of the grid.

[0186] The specific implementation of step S30 is to predict the load change curve, renewable energy output curve and energy storage demand curve in the future period based on the historical data and current state of input electrical parameters and load parameters.

[0187] For the prediction of the load change curve, time series analysis, machine learning and other methods can be used to model and predict historical load data. By analyzing the periodicity, trend and sudden change of load, an accurate load prediction model can be established.

[0188] For the prediction of the renewable energy output curve, meteorological data, machine learning and other means can be used to predict the renewable energy output in the future period. By analyzing the influence of weather conditions, sunshine time, wind speed and other factors on renewable energy generation, an output prediction model is established.

[0189] For the prediction of the energy storage demand curve, the prediction results of load change and renewable energy output need to be considered comprehensively. According to the power balance equation, the charging and discharging power required by the energy storage system in the future period can be calculated, and thus the energy storage demand curve is obtained.

[0190] The key parameter change trend in the future period is predicted by the above method, which provides important input data for subsequent optimal scheduling. These prediction results reflect the actual demand change of power grid operation, and lay the foundation for the establishment of optimization problem.

[0191] The specific implementation of step S40 is to establish a multi-time period energy storage scheduling optimization model according to the predicted load change curve, renewable energy output curve and energy storage demand curve, combined with the previously established energy storage grid connection equation set. This optimization model is used to describe the charging and discharging plan of each energy storage device in the future multiple time periods, including charging and discharging time and power size.

[0192] Specifically, the decision variable of this optimization model is the charging and discharging power of each energy storage device in the future multiple time periods, that is, The optimization objectives include minimizing system operation cost, maximizing renewable energy consumption, minimizing grid fluctuation and other multiple objectives. The optimization constraints include various equation constraints established before, such as voltage constraint, current constraint, energy storage capacity constraint, energy storage life constraint, etc., as well as state of charge limit of energy storage device, charging and discharging power limit, charging and discharging times limit, grid voltage and frequency limit, renewable energy output limit and load demand limit, etc.

[0193] By establishing such a multi-time period optimization model, the optimal charging and discharging plan of each energy storage device in the future period can be obtained, while considering multiple objectives such as system operation cost, renewable energy consumption and grid stability. This optimization model is the basis for subsequent step S50 optimization solution.

[0194] The specific implementation of step S50 is to determine multiple objectives of the multi-time period energy storage scheduling optimization model, including minimizing system operation cost, maximizing renewable energy consumption, minimizing grid fluctuation, and setting constraint conditions to establish a multi-objective optimization problem.

[0195] For the objective function of system operation cost , the weighted sum of grid input power and energy storage system charging and discharging power is considered. Among them, the grid input price and the charging and discharging cost of the energy storage system need to be determined according to the actual situation.

[0196] For the objective function of renewable energy consumption , the negative value of renewable energy output is directly taken, the purpose is to maximize the consumption of renewable energy.

[0197] For the objective function of grid fluctuation The sum of the variation amplitudes of the grid input power at adjacent time instants is adopted to describe the grid fluctuation. This index reflects the grid stability, which needs to be minimized.

[0198] In addition to the three optimization objectives, various constraints need to be set when establishing the optimization problem, including the constraints in the established grid-connected equations of the energy storage, such as voltage constraints, current constraints, energy storage capacity constraints, energy storage life constraints, and the like, as well as state-of-charge limits of the energy storage devices, charge and discharge power limits, charge and discharge frequency limits, grid voltage and frequency limits, renewable energy output limits, and load demand limits.

[0199] By establishing such a multi-objective optimization problem, a balance can be sought among the system operation cost, renewable energy consumption, and grid stability, and an optimal energy storage scheduling scheme that satisfies various constraints can be obtained.

[0200] The specific implementation of step S60 is to solve the above multi-objective optimization problem using an improved particle swarm optimization algorithm to obtain multiple candidate optimization solutions.

[0201] The particle swarm optimization algorithm is a swarm intelligence-based optimization algorithm that finds the optimal solution by simulating the foraging behavior of a bird swarm or a fish swarm. The main steps of the algorithm are as follows:

[0202] First, a group of particles is initialized, each particle representing a feasible energy storage scheduling scheme, i.e., the charge and discharge power sequence of each energy storage device in the future time period. The position vector of each particle represents this scheduling scheme.

[0203] Then, it is checked whether each particle satisfies the various constraints described above, such as voltage constraints, current constraints, energy storage capacity constraints, energy storage life constraints, and the like. For particles that do not satisfy the constraints, projection or repair operations are needed to make them satisfy the constraints.

[0204] Next, the multiple objective function values of each particle are calculated, including the system operation cost , the renewable energy consumption , and the grid fluctuation .

[0205] Based on these objective function values, the individual optimal solution of each particle is updated, and the global optimal solution set is updated, which contains all non-dominated solutions.

[0206] Then, the speed and position of each particle are updated, including the use of inertia weight , learning factor and , and random numbers and Equal parameters.

[0207] In order to increase population diversity, a mutation operation is also introduced, that is, the position of the particle is randomly disturbed with a certain probability.

[0208] The above steps are repeated until the maximum number of iterations is reached or the convergence condition is met. Finally, a plurality of candidate optimal solutions are obtained.

[0209] The improved particle swarm optimization algorithm can effectively solve the multi-objective optimization problem, and obtain a balanced solution among the cost, renewable energy consumption and grid fluctuation under the premise of ensuring various constraint conditions.

[0210] The specific implementation of step S70 is to use the analytic hierarchy process (AHP) to comprehensively evaluate the plurality of candidate optimal solutions obtained in step S60, and select the best energy storage scheduling scheme.

[0211] Firstly, a hierarchical structure model is constructed, including three layers of decision objectives, evaluation criteria and candidate schemes. Among them, the decision objective is to select the best energy storage scheduling scheme; the evaluation criteria include three indexes of system operation cost , renewable energy consumption and grid fluctuation ; and the candidate scheme is the plurality of optimal solutions obtained in step S60 .

[0212] Then, according to the expert evaluation or the preference of the decision maker, a judgment matrix is constructed, reflecting the relative importance of each evaluation criterion. The weight vector of each criterion is calculated by using the eigenvalue decomposition method . At the same time, consistency check is needed to ensure that the consistency of the judgment matrix reaches an acceptable level.

[0213] Next, for each candidate scheme , the comprehensive score is calculated. The score is obtained by weighting and summing the linearly normalized values of each objective function according to the weight vector . Among them, the extreme value method is used for the normalization of the objective function value, so that the objective function value is in the interval [0, 1].

[0214] Finally, the scheme with the highest comprehensive score is selected as the best energy storage scheduling scheme. This multi-objective decision-making method using the analytic hierarchy process can comprehensively evaluate the balanced performance of each candidate scheme among multiple objectives, and obtain the optimal energy storage scheduling strategy.

[0215] In summary, the distributed energy storage output scheduling optimization method provided by the application fully utilizes input electrical parameters, load parameters, renewable energy generation parameters and energy storage device parameters, and establishes various equations describing the operation characteristics of the energy storage system in the power grid. On this basis, an improved particle swarm optimization algorithm is used to solve the multi-objective optimization problem, and the optimal energy storage scheduling scheme that meets various constraint conditions is obtained. Finally, the analytic hierarchy process is used to comprehensively evaluate the candidate schemes, and the best energy storage scheduling strategy is selected. The method can fully play the role of peak shaving and power grid support of the distributed energy storage system, improve the renewable energy consumption level, and reduce the power grid operation cost and fluctuation.

[0216] The second aspect of the application provides a computer readable storage medium, wherein the computer readable storage medium stores program instructions, and the program instructions are used to execute the distributed energy storage output scheduling optimization method when the computer readable storage medium is run in the computer.

[0217] The third aspect of the application provides a distributed energy storage output scheduling optimization system, wherein the distributed energy storage output scheduling optimization system comprises the computer readable storage medium.

[0218] Specifically, the principle of the application is to establish a mathematical model describing the operation characteristics of the energy storage system in the power grid, and to solve the model by using an advanced optimization algorithm to obtain the optimal scheduling strategy that meets various constraint conditions. This method conforms to the basic logic of power system optimization scheduling.

[0219] Firstly, by comprehensively collecting input electrical parameters, load parameters, renewable energy generation parameters and various parameters of the distributed energy storage system in the transformer area, an energy storage grid equation group is established, which comprises a power balance equation, an energy storage response process voltage variation equation, a voltage constraint equation, a current constraint equation, an energy storage capacity constraint equation and an energy storage life constraint equation. These equations describe the dynamic characteristics and operation constraints of the energy storage system in the power grid, and provide a mathematical model basis for subsequent optimization scheduling.

[0220] Secondly, based on the above equation group, combined with the predicted load change curve, renewable energy output curve and energy storage demand curve, a multi-time period energy storage scheduling optimization model is established. The optimization model takes the charging and discharging plan of each energy storage device in the future time period as a decision variable, sets multiple optimization objectives such as minimizing the system operation cost, maximizing renewable energy consumption and minimizing power grid fluctuation, and considers various constraint conditions. This multi-objective optimization model can balance among multiple objectives, and obtain an optimal scheduling scheme that is more in line with the overall interests of the power grid.

[0221] Finally, the improved particle swarm optimization algorithm is used to solve the above multi-objective optimization problem. Compared with the existing linear programming, dynamic programming and other methods, the particle swarm optimization algorithm has stronger search ability and convergence speed, and can effectively deal with complex optimization problems. Through iterative updating of the position and speed of the particles, and introduction of mutation operation to increase the population diversity, the algorithm can finally converge to the optimal solution that meets various constraint conditions.

[0222] Compared with the prior art, the method of the present application is more in line with the needs of power system optimization scheduling in the following aspects:

[0223] 1. Multiple optimization objectives are considered, the needs of all parties related to the power grid are taken into account, and a more balanced optimal scheduling scheme can be obtained.

[0224] 2. The actual situation of power grid operation is comprehensively reflected, including input electrical parameters, load parameters, renewable energy generation parameters and energy storage device parameters, ensuring the feasibility and applicability of the optimization results.

[0225] 3. The advanced particle swarm optimization algorithm is used, which can effectively deal with complex multi-objective optimization problems and obtain optimal solutions that meet various constraint conditions.

[0226] In order to better understand and implement the present application, a specific embodiment 1 of the method of the present application is provided as follows:

[0227] The specific implementation of step S10 is to collect the input electrical parameters, load parameters and energy storage parameters of each energy storage device in the distributed energy storage system of the transformer area.

[0228] Firstly, for the input electrical parameters, including voltage , frequency , active power , reactive power and power factor , etc. These parameters can be directly obtained by power measurement instrument, i.e.

[0229] ;

[0230] ;

[0231] ;

[0232] ;

[0233] ;

[0234] Secondly, the load parameters include peak-valley load and Load type, load curve and power factor Peak and valley loads can be obtained through statistical analysis of historical load data; load type can be determined based on the characteristics of electrical equipment; load curves require modeling and prediction of historical load data using methods such as time series analysis; and power factor can also be calculated from historical data.

[0235] In addition, renewable energy generation parameters include generation type. Installed capacity Real-time output and output prediction curve The type of power generation and installed capacity can be obtained through on-site surveys and technical parameter reviews of renewable energy power generation equipment; real-time output can be obtained through real-time monitoring of the power generation equipment; while the output prediction curve needs to be predicted using meteorological data and machine learning algorithms, such as prediction models based on support vector machines (SVM). There are currently a large number of related existing technologies, such as CN202210916336.7 A wind power generation prediction system and method based on the Internet of Things, and CN201810296773.7 A wind power prediction method based on historical meteorological data and stochastic simulation.

[0236] Finally, the parameters of each energy storage device in the distributed energy storage system include response speed. Output change rate limit Rated capacity Current state of charge Charge and discharge efficiency and Maximum charge and discharge power and and service life and The charge / discharge efficiency can be calculated by conducting a constant power charge / discharge test on the energy storage device and measuring the total input and output energy.

[0237] ;

[0238] ;

[0239] Using the above methods, we can comprehensively collect and obtain input electrical parameters, load parameters, renewable energy generation parameters, and various parameter data of distributed energy storage systems in the distribution area, laying the foundation for subsequent optimized scheduling.

[0240] The detailed implementation of step S20 is to establish and fit a grid-connected energy storage equation set considering input electrical parameters, load parameters, renewable energy generation parameters, and energy storage parameters. This equation set includes power balance equations, energy storage response process voltage variation equations, voltage constraint equations, current constraint equations, energy storage capacity constraint equations, and energy storage life constraint equations, etc.

[0241] First, the power balance equation describes the balance relationship between the grid input power , the renewable energy generation power , the energy storage system output power , and the load power , i.e.:

[0242] ;

[0243] wherein is the system loss power, which can be estimated according to the line resistance and the real-time measured current :

[0244] ;

[0245] Second, the energy storage response process voltage variation equation describes the influence of the active and reactive power changes and of the energy storage system on the voltage, i.e.:

[0246] ;

[0247] wherein and are the voltage sensitivity coefficients to active and reactive power, which can be obtained by power flow calculation and least square fitting. is the error term.

[0248] Then, the voltage constraint equation ensures that the voltage of each node of the grid is within the allowed minimum and maximum range:

[0249] ;

[0250] Similarly, the current constraint equation ensures that the current of each line does not exceed its maximum allowed value :

[0251] ;

[0252] In addition, the energy storage capacity constraint equation describes the charging and discharging process of the energy storage system, and ensures that the energy state of the energy storage system is within the allowed range.The minimum allowed range is and the maximum allowed range is :

[0253] ;

[0254] Finally, the energy storage life constraint equation describes the life characteristics of the energy storage system, ensuring that its remaining life is not less than the minimum requirement :

[0255] ;

[0256] wherein is the life attenuation coefficient, which can be obtained through accelerated life testing.

[0257] By establishing the above energy storage grid-connected equation set, the operation characteristics and constraint conditions of the energy storage system in the power grid can be comprehensively described, providing a mathematical model basis for subsequent optimization scheduling.

[0258] The specific implementation of step S30 is to predict the load change curve , the renewable energy output curve and the energy storage demand curve in the future period of time based on the historical data of input electrical parameters and load parameters and the current state.

[0259] For the prediction of the load change curve, a prediction model can be established using time series analysis and other methods:

[0260] ;

[0261] This model can capture the periodicity, trend and sudden changes of the load, thereby predicting the future load change trend.

[0262] For the prediction of the renewable energy output curve, a support vector machine-based prediction model can be established in combination with meteorological data, denoted as: ;

[0263] The energy storage demand curve can be calculated according to the power balance equation:

[0264] ;

[0265] Through the above methods, the actual demand change of the power grid operation in the future period of time can be predicted, laying a foundation for the subsequent optimization scheduling problem.

[0266] The specific implementation of step S40 is to determine the optimal scheduling strategy according to the predicted load change curve , the renewable energy output curve and energy storage demand curve , combined with the previously established energy storage grid-connected equation set, a multi-time period energy storage scheduling optimization model is established.

[0267] The decision variable of the optimization model is the charge and discharge power sequence of each energy storage device in the future multiple time periods , wherein is the length of the optimization time period. The optimization objectives include:

[0268] 1. Minimize system operation cost :

[0269] ;

[0270] 2. Maximize renewable energy consumption :

[0271] ;

[0272] 3. Minimize grid fluctuation :

[0273] ;

[0274] The optimization constraints include the previously established various equation constraints, such as voltage constraints, current constraints, energy storage capacity constraints, energy storage life constraints, etc., as well as various operation limits of energy storage devices, such as state of charge limits, charge and discharge power limits, charge and discharge frequency limits, grid voltage and frequency limits, renewable energy output limits, and load demand limits, etc.

[0275] By establishing such a multi-time period optimization model, the optimal charge and discharge plan of each energy storage device in the future period of time can be obtained, and a balance between system cost, renewable energy consumption, and grid stability, etc. multiple objectives.

[0276] The specific implementation of step S50 is based on the previously established multi-time period energy storage scheduling optimization model, to determine its multiple objective functions, and to set various constraint conditions, to construct a multi-objective optimization problem.

[0277] First, determine the optimization objective function, including:

[0278] 1. Minimize system operation cost ;

[0279] 2. Maximize renewable energy consumption

[0280] 3. Minimize grid fluctuation ;

[0281] Secondly, various constraints are set, including the constraints in the established grid-connected equations of energy storage, such as:

[0282] Voltage constraint:

[0283] ;

[0284] Current constraint:

[0285] ;

[0286] Energy storage capacity constraint:

[0287] ;

[0288] Energy storage life constraint:

[0289] ;

[0290] In addition, it also includes the state of charge limit of energy storage device, charge and discharge power limit, charge and discharge times limit, grid voltage and frequency limit, renewable energy output limit, and load demand limit, etc.

[0291] By establishing such a multi-objective optimization problem, the optimal energy storage scheduling scheme that meets various constraints can be obtained by balancing and optimizing multiple objectives such as system cost, renewable energy consumption and grid stability.

[0292] The specific implementation of step S60 is to solve the above multi-objective optimization problem by using an improved particle swarm optimization algorithm to obtain multiple candidate optimization solutions.

[0293] The particle swarm optimization algorithm is a swarm intelligence-based optimization algorithm, and its main steps are as follows:

[0294] 1. Initialize a set of particles , each particle represents a feasible energy storage scheduling scheme.

[0295] 2. Check whether each particle meets the constraint conditions, such as voltage constraint, current constraint, energy storage capacity constraint, energy storage life constraint, etc. For particles that do not meet the constraints, projection or repair operations are needed to make them meet the constraints.

[0296] 3. Calculate the multiple objective function values of each particle.

[0297] 4. Update the individual optimal solution of each particle, and update the global optimal solution set , which contains all non-dominated solutions.

[0298] 5. Update the velocity and position of each particle:

[0299] ;

[0300] ;

[0301] wherein, is the inertia weight, and are acceleration constants, and are random numbers between 0 and 1, is a randomly selected solution from the global optimal solution set .

[0302] 6. To increase the population diversity, introduce a mutation operation:

[0303] ;

[0304] wherein, is the mutated particle position, is the mutation strength, is a standard normal distribution random vector.

[0305] The above steps will be repeated until the maximum number of iterations is reached or the convergence condition is met. Finally, multiple candidate optimal solutions are obtained.

[0306] The improved particle swarm optimization algorithm can effectively solve the multi-objective optimization problem, and obtain a balanced solution among the cost, renewable energy consumption and grid fluctuation under the premise of ensuring various constraint conditions.

[0307] The specific implementation of step S70 is to use the analytic hierarchy process (AHP) to comprehensively evaluate the multiple candidate optimal solutions obtained in step S60, and select the best energy storage scheduling scheme.

[0308] First, a hierarchical structure model including decision objectives, evaluation criteria and candidate schemes is constructed. The decision objective is to select the best energy storage scheduling scheme; the evaluation criteria include three indexes of system operation cost , renewable energy consumption and grid fluctuation ; and the candidate schemes are the multiple optimal solutions obtained in step S60 .

[0309] Then, according to expert evaluation or decision maker preference, a judgment matrix is constructed to reflect the relative importance of each evaluation criterion:

[0310]

[0311] The weight vectors of each criterion are calculated using the eigenvalue decomposition method. At the same time, a consistency check is required to ensure that the consistency of the judgment matrix reaches an acceptable level.

[0312] Next, for each candidate solution Calculate its overall score :

[0313] ;

[0314] in, and The first The score is calculated by taking into account the maximum and minimum values ​​of each objective function across all candidate solutions.

[0315] Finally, the option with the highest overall score is selected. As the optimal energy storage dispatch strategy:

[0316] ;

[0317] By employing the analytic hierarchy process (AHP), we can comprehensively evaluate the balance performance of each candidate scheme among multiple objectives, thereby obtaining the optimal energy storage scheduling strategy.

[0318] To further understand and implement this invention, a specific application scenario, Example 2, is provided below: A regional power grid contains a large number of distributed renewable energy generation devices and distributed energy storage systems. To improve the absorption of renewable energy and reduce grid operating costs and fluctuations, the power grid company decides to adopt the distributed energy storage output scheduling optimization method proposed in this invention for the optimized scheduling of the energy storage system. The specific implementation process of this method is described below.

[0319] First, the company collected key parameter data of the regional power grid through on-site surveys and monitoring equipment. Input electrical parameters included: voltage. kV, frequency Hz, active power MW, reactive power MVar, power factor Load parameters include: peak and valley loads respectively. MW and MW, load curve as shown in Table 1, power factor Renewable energy generation parameters include: installed capacity. MW, real-time output is shown in Table 2, and the output prediction curve is shown in Table 3. Figure 2 As shown. The parameters of each device in the distributed energy storage system are as follows: response speed s. Output change rate limit MW / s, rated capacity MWh, current state of charge , charge-discharge efficiency and , maximum charge-discharge power MW, initial life years, life attenuation coefficient .

[0320] Table 1 Load curve

[0321]

[0322] Table 2 Real-time output of renewable energy

[0323]

[0324] Based on the above data, the enterprise established a mathematical model describing the operating characteristics of the energy storage system in the power grid, including power balance equation, energy storage response process voltage variation equation, voltage constraint equation, current constraint equation, energy storage capacity constraint equation and energy storage life constraint equation, etc.

[0325] The power balance equation is:

[0326] ;

[0327] wherein, is the grid input power at time t, which can be obtained by real-time measurement; is the renewable energy power generation at time t, which can be obtained by real-time monitoring; is the energy storage system output power at time t, which can be obtained by real-time monitoring; is the load power at time t, which can be obtained by real-time measurement; is the system loss power at time t, which can be estimated according to the line resistance and the real-time measured current.

[0328] The energy storage response process voltage variation equation is:

[0329] ;

[0330] wherein, is the voltage change at time t; kV / MW and kV / MVar are the sensitivity coefficients of voltage to active and reactive power, which are obtained by power flow calculation and least squares fitting; and are the change amounts of active and reactive power of the energy storage system at time t, which can be obtained by real-time monitoring; is the error term.

[0331] The voltage constraint equation is:

[0332] ;

[0333] The current constraint equation is:

[0334] A, j = 1, 2, …, 20;

[0335] The energy storage capacity constraint equation is:

[0336] ;

[0337] The energy storage lifetime constraint equation is:

[0338] ;

[0339] Next, based on the predicted load change curve, renewable energy output curve, and energy storage demand curve, the company established a multi-time period energy storage dispatch optimization model. The optimization objectives include:

[0340] 1. Minimize system operating costs :

[0341] ;

[0342] 2. Maximize the absorption of renewable energy :

[0343] ;

[0344] 3. Minimize power grid fluctuations :

[0345] ;

[0346] in, This is a sequence of charge and discharge power for each energy storage device over 24 time periods.

[0347] The optimization constraints include the constraints of the aforementioned equations, as well as the charging and discharging power limits of the energy storage device. MW, state of charge limitation and charge / discharge cycle limit times / day, etc.

[0348] To solve this multi-objective optimization problem, the company adopted an improved particle swarm optimization algorithm. The specific steps are as follows:

[0349] 1. Initialize a swarm of 50 particles, each particle representing a feasible energy storage scheduling scheme. .

[0350] 2. Check if each particle satisfies the constraints, and if not, make it satisfy by projection or repair operation.

[0351] 3. Calculate the three objective function values for each particle .

[0352] 4. Update the individual best solution for each particle , and update the global best solution set .

[0353] 5. Update the velocity and position of each particle:

[0354] ;

[0355] ;

[0356] 6. With a probability of 10%, perform mutation operation on the particle:

[0357] ;

[0358] 7. Repeat steps 2-6 until the maximum number of iterations is reached, which is 100 times.

[0359] Finally, the algorithm converges to the following several candidate optimal solutions:

[0360] Scheme 1:

[0361] ;

[0362] Scheme 2:

[0363] ;

[0364] Scheme 3:

[0365] ;

[0366] where are vectors, and the double-line method is used to avoid displaying the complete vector due to its length. Next, the enterprise uses the analytic hierarchy process (AHP) to comprehensively evaluate the three candidate schemes. First, the following evaluation index system is constructed:

[0367] Target layer: Select the optimal energy storage scheduling scheme

[0368] Criteria layer: System operation cost, renewable energy consumption, grid fluctuation

[0369] Scheme layer: Scheme 1, Scheme 2, Scheme 3

[0370] According to expert evaluation, the following judgment matrix is obtained ;

[0371] The maximum eigenvalue of the calculation and its corresponding eigenvector , meet the consistency test .

[0372] Finally, the comprehensive score of the three schemes is calculated:

[0373] Scheme 1: ;

[0374] Scheme 2: ;

[0375] Scheme 3: ;

[0376] As can be seen, the comprehensive score of scheme 3 is the highest, so it is selected as the best energy storage scheduling scheme. This scheme performs well in system operation cost, renewable energy consumption and power grid fluctuation and other multiple targets, meets various constraint conditions, and can maximize the peak shaving and support of the distributed energy storage system.

[0377] By implementing the distributed energy storage output scheduling optimization method proposed in the present application, the operation of the energy storage system of the regional power grid within the next 24 hours is as shown in Figure 3 It can be seen that the energy storage system reasonably allocates the charging and discharging power according to the changes of load and renewable energy output, maximizes the consumption of renewable energy power while minimizing the input power fluctuation of the power grid, and maintains the safe operation of the power grid voltage, current and other indicators.

[0378] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of changes or replacements within the technical range disclosed in the present application, which should be covered within the protection scope of the present application.​

Claims

1. A method for optimizing the scheduling of distributed energy storage output, characterized in that, Includes the following steps: S10, collect the input electrical parameters, load parameters, renewable energy generation parameters of the data acquisition area, and the energy storage parameters of each energy storage device in the distributed energy storage system; S20. Establish and fit a set of energy storage grid-connected equations that consider input electrical parameters, load parameters, renewable energy generation parameters and energy storage parameters, including power balance equations, voltage variation equations during energy storage response, voltage constraint equations, current constraint equations, energy storage capacity constraint equations and energy storage lifetime constraint equations. S30. Based on the historical data and current status of the input electrical parameters and load parameters, predict the load change curve, renewable energy output curve, and energy storage demand curve for continuous time segments in the future. S40. Based on the predicted load change curve, renewable energy output curve and energy storage demand curve, and combined with the energy storage grid connection equation set, establish a multi-time period energy storage scheduling optimization model to describe the charging and discharging plan of each energy storage device in multiple future time periods, including charging and discharging time and power. S50. Determine multiple objectives of the multi-time period energy storage scheduling optimization model, including minimizing system operating costs, maximizing renewable energy consumption, and minimizing grid fluctuations, and set constraints to establish a multi-objective optimization problem; S60. The improved particle swarm optimization algorithm is used to solve the multi-objective optimization problem to obtain multiple candidate optimization solutions; S70. Using multi-objective decision-making methods, including Pareto optimal set, analytic hierarchy process, or fuzzy comprehensive evaluation method, comprehensively evaluate the multiple candidate optimization solutions, select the best energy storage scheduling scheme, and output it. The power balance equation describes the balance between grid input power, renewable energy generation power, energy storage system output power, and load power. The voltage variation equation during the energy storage response process describes the impact of changes in the energy storage system's output power on voltage. Voltage and current constraint equations ensure the safety of grid voltage and current. The energy storage capacity constraint equation describes the charging and discharging characteristics of the energy storage system. The energy storage lifetime constraint equation limits the lifespan of the energy storage device. These constraints include voltage, current, energy storage capacity, and energy storage lifetime constraints, as well as limitations on the energy storage device's state of charge, charging and discharging power, number of charging and discharging cycles, grid voltage and frequency, renewable energy output, and load demand. The voltage variation equation during the energy storage response process is specifically expressed as follows: ; In the formula, Let t be the voltage change at time t; and These are the sensitivity coefficients of voltage to active power and reactive power, respectively. and These represent the changes in active and reactive power of the energy storage system at time t, respectively. This is the error term; The energy storage lifetime constraint equation is specifically expressed as follows: ; In the formula, Let t be the remaining lifetime of the energy storage system at time t; Initial lifespan; This is the lifetime degradation coefficient; Let k be the output power of the energy storage system at time k; Rated power; The minimum allowable remaining lifespan; This was obtained through accelerated life testing, and the specific steps are as follows: Step 1: Cycle charge and discharge the energy storage system at different depths and frequencies; Step 2: Periodically measure the capacity degradation of the energy storage system; Step 3: Obtain the lifetime decay coefficient by fitting using the least squares method. ; The improved particle swarm optimization algorithm simulates the foraging behavior of bird flocks, initializes a set of particles representing energy storage scheduling schemes, and performs constraint checks, objective function calculations, updates of individual optimal solutions and global optimal solutions, and iterative updates of velocity and position for each particle. To increase population diversity, mutation operations are also introduced. Through multiple iterations, multiple candidate optimization solutions that satisfy various constraints are obtained. The power balance equation is specifically expressed as follows: ; In the formula, Let t be the power input to the power grid. Let t be the renewable energy power generation capacity at time t; Let t be the output power of the energy storage system. Let be the load power at time t; Let be the system power loss at time t; where, ;in, The number of lines in the system. Let be the resistance of the i-th line. Let be the current of the i-th line at time t; The voltage variation equation for the energy storage response process is specifically expressed as follows: ; In the formula, Let t be the voltage change at time t; and These are the sensitivity coefficients of voltage to active power and reactive power, respectively. and These represent the changes in active and reactive power of the energy storage system at time t, respectively. This is the error term; The voltage constraint equation is specifically expressed as follows: ; In the formula, Let be the voltage of the i-th node at time t; and These are the minimum and maximum allowable voltages, respectively; This represents the number of system nodes. The current constraint equation is specifically expressed as follows: ; In the formula, Let be the complex current of the j-th line at time t; The maximum allowable current for the j-th line; This refers to the number of lines in the system. The energy storage capacity constraint equation is specifically expressed as follows: ; In the formula, Let t be the energy state of the energy storage system. and These are the minimum and maximum allowable energy levels for the energy storage system, respectively. and These are the charging and discharging efficiencies, respectively. and Let be the charging and discharging power at time t, respectively; For time intervals; The energy storage lifetime constraint equation is specifically expressed as follows: ; In the formula, Let t be the remaining lifetime of the energy storage system at time t; Initial lifespan; This is the lifetime degradation coefficient; Let k be the output power of the energy storage system at time k; Rated power; The minimum allowable remaining lifetime.

2. The distributed energy storage output scheduling optimization method according to claim 1, characterized in that, The energy storage parameters include response speed, output change rate limit, rated capacity, current state of charge, charge / discharge efficiency, maximum charge / discharge power, and service life.

3. The distributed energy storage output scheduling optimization method according to claim 2, characterized in that, The input electrical parameters include voltage, frequency, active power, reactive power, and power factor; the load parameters include peak and valley load, load type, load curve, and power factor.

4. The distributed energy storage output scheduling optimization method according to claim 3, characterized in that, The renewable energy power generation parameters include power generation type, installed capacity, real-time output, and output prediction curve.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores program instructions, which, when executed in a computer, are used to perform the distributed energy storage output scheduling optimization method according to any one of claims 1-4.

6. A distributed energy storage output scheduling and optimization system, characterized in that, It includes the computer-readable storage medium of claim 5.

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