Distributed energy storage output scheduling optimization method, medium and system

Abstract

The invention provides a distributed energy storage output scheduling optimization method, medium and system, and belongs to the technical field of distributed energy storage output scheduling, and the method comprises the steps: firstly, collecting the electrical parameters, load parameters, renewable energy power generation parameters and energy storage equipment parameters of a transformer area, and building an energy storage grid-connected equation set which comprehensively considers the factors; and predicting a load, renewable energy output and energy storage demand curve in a period of time in the future according to historical data. Then, a multi-time-period energy storage scheduling optimization model is constructed, and objectives comprise operation cost minimization, renewable energy consumption maximization and power grid fluctuation minimization; and solving the multi-objective optimization problem by adopting an improved particle swarm optimization algorithm to obtain a plurality of candidate schemes. And finally, comprehensively evaluating the candidate schemes by using a multi-objective decision-making method, and selecting an optimal energy storage scheduling scheme. The technical problem that in the prior art, only parameters of an energy storage system are considered, and influences of power grid input electrical parameters and load parameters are ignored is solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of distributed energy storage output scheduling, and in particular, relates to a distributed energy storage output scheduling optimization method, medium and system. Background Art

[0002] Renewable energy generation technology has developed rapidly in recent years and has become an indispensable part of the power system. However, renewable energy generation is intermittent and uncertain, which brings certain challenges to the safe and stable operation of the power grid. In order to effectively absorb renewable energy power and improve its utilization efficiency, distributed energy storage technology has emerged and has 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 regulation, frequency regulation, voltage support and other services for the power grid. By reasonably scheduling the charging and discharging of distributed energy storage systems, power balance within the power grid can be achieved, the level of renewable energy consumption can be improved, and the operating cost of the power grid can be reduced. However, the existing distributed energy storage scheduling optimization methods still have the technical problem that they often only consider the parameters of the energy storage system itself, while ignoring the influence of other key factors such as the input power parameters and load parameters of the power grid, resulting in the optimization results cannot fully reflect the actual situation of the power grid operation. Summary of the invention

[0003] In view of this, the present invention provides a distributed energy storage output scheduling optimization method, medium and system, which can solve the technical problem that the prior art only considers the parameters of the energy storage system itself, but ignores the influence of other key factors such as grid input power parameters and load parameters.

[0004] The present invention is achieved in that: A first aspect of the present invention provides a distributed energy storage output dispatch optimization method, which includes the following steps: S10, collecting input power parameters, load parameters, renewable energy generation parameters of the substation, and energy storage parameters of each energy storage device in the distributed energy storage system; S20, establishing and fitting a group of energy storage grid-connected equations that take into account input power parameters, load parameters, renewable energy generation parameters, and energy storage parameters; S30, based on the historical data and current status of the input power parameters and load parameters, predicting the load change curve, renewable energy output curve and energy storage demand curve of continuous time segments in the future; S40, based on the predicted load change curve, renewable energy output curve and energy storage demand curve, combined with the energy storage grid-connected equation group, establish a multi-time period energy storage scheduling optimization model to describe the charging and discharging plan of each energy storage device in multiple time periods in the future, including charging and discharging time and power size; S50, determining multiple objectives of the multi-time period energy storage scheduling optimization model, including minimizing system operating costs, maximizing renewable energy consumption, minimizing grid fluctuations, and setting constraints to establish a multi-objective optimization problem; S60, using an improved particle swarm optimization algorithm to solve the multi-objective optimization problem and obtain multiple candidate optimization solutions; S70. Utilize a multi-objective decision-making method, including a Pareto optimal set or a hierarchical analysis method or a fuzzy comprehensive evaluation method, to comprehensively evaluate the multiple candidate optimization solutions, select the best energy storage scheduling solution and output it.

[0005] Specifically, the step S10 specifically includes: collecting input power parameters, load parameters and energy storage parameters of each energy storage device in the distributed energy storage system through real-time measurement and monitoring equipment. Among them, input power parameters include voltage, frequency, active power, reactive power and power factor, etc.; load parameters include peak and valley load, load type, load curve and power factor, etc.; renewable energy generation parameters include generation type, installed capacity, real-time output and output forecast curve, etc.; 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, etc. Through real-time monitoring and analysis, these key parameter data are obtained to lay the foundation for subsequent optimization and scheduling.

[0006] Wherein, the step S20 specifically includes: establishing a group of energy storage grid-connected equations that consider input power parameters, load parameters, renewable energy generation parameters, and energy storage parameters. The group of equations includes a power balance equation, an energy storage response process voltage change equation, a voltage constraint equation, a current constraint equation, an energy storage capacity constraint equation, and an energy storage life constraint equation. Wherein, 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; the energy storage response process voltage change equation describes the impact of the output power change of the energy storage system on the voltage; the voltage constraint equation and the current constraint equation ensure the safety of the 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 operating characteristics and constraints of the energy storage system in the power grid are described, providing a mathematical model basis for subsequent optimization and scheduling.

[0007] Wherein, the step S30 specifically includes: based on the historical data and current status of the input power 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. Wherein, the load forecast takes into account the characteristics of the load such as periodicity, trend and sudden changes; the renewable energy output forecast comprehensively considers factors such as meteorological data; and the energy storage demand curve is calculated based on the power balance equation. These forecast results reflect the actual demand changes in the operation of the power grid, laying the foundation for the establishment of subsequent optimization problems.

[0008] Wherein, the step S40 specifically includes: according to the predicted load change curve, renewable energy output curve and energy storage demand curve, combined with the energy storage grid-connected equations established above, a multi-time period energy storage scheduling optimization model is established. The decision variables of the optimization model are the charge and discharge power of each energy storage device in multiple time periods in the future; the optimization objectives include minimizing the system operation cost, maximizing the consumption of renewable energy and minimizing the grid fluctuation; the optimization constraints include voltage constraints, current constraints, energy storage capacity constraints, energy storage life constraints, etc., as well as various operation restrictions of energy storage devices. By establishing this multi-time period optimization model, the optimal charge and discharge plan of each energy storage device can be obtained, taking into account multiple optimization objectives.

[0009] Among them, the step S50 specifically includes: based on the multi-time period energy storage scheduling optimization model established above, determine its multiple objective functions, including minimizing system operating costs, maximizing renewable energy consumption and minimizing grid fluctuations; at the same time, set various constraints, such as voltage constraints, current constraints, energy storage capacity constraints, energy storage life constraints, etc., as well as various operating restrictions of energy storage equipment, to establish a multi-objective optimization problem. Through the establishment of this multi-objective optimization model, it is possible to seek a balance between objectives such as system cost, renewable energy consumption and grid stability, and obtain the optimal energy storage scheduling solution that meets various constraints.

[0010] Wherein, the step S60 specifically includes: using an improved particle swarm optimization algorithm to solve the aforementioned multi-objective optimization problem and obtain multiple candidate optimization solutions. The algorithm initializes a group of particles representing the energy storage scheduling scheme by simulating the foraging behavior of a flock of birds, and performs constraint checking, objective function calculation, individual optimal solution and global optimal solution update, and iterative update of speed and position on each particle. In order to increase population diversity, a mutation operation is also introduced. Through multiple iterations, multiple candidate optimization solutions that meet various constraints can be obtained.

[0011] Wherein, the step S70 specifically includes: using the analytic hierarchy process (AHP) to comprehensively evaluate the multiple candidate optimization solutions obtained in step S60 and select the best energy storage scheduling solution. First, a hierarchical model including objectives, evaluation criteria and candidate solutions is constructed, and then a judgment matrix is ​​constructed based on expert evaluation and the weight vector of each criterion is calculated to perform a consistency test. Next, the comprehensive score of each candidate solution is calculated, taking into account multiple objectives such as cost, renewable energy consumption and grid fluctuations. Finally, the solution 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 solution and obtain the optimal energy storage scheduling result.

[0012] 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.

[0013] Furthermore, 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.

[0014] Furthermore, the renewable energy power generation parameters include power generation type, installed capacity, real-time output, and output prediction curve.

[0015] Furthermore, the improved particle swarm optimization algorithm initializes a group of particles representing the energy storage scheduling scheme by simulating the foraging behavior of bird flocks, and performs constraint checking, objective function calculation, individual optimal solution and global optimal solution update, and iterative update steps of speed and position on each particle. In order to increase the diversity of the population, a mutation operation is also introduced. Through multiple iterations, multiple candidate optimization solutions that meet various constraints are obtained.

[0016] Each equation in the energy storage grid-connected equations is described in detail below: 1. Power balance equation: The power balance equation is specifically expressed as follows: ; In the formula, is the grid input power at time t; is the power generated by renewable energy at time t; is the output power of the energy storage system at time t (negative for charging and positive for discharging); is the load power at time t; is the system power loss at time t.

[0017] Parameter acquisition method: and Obtained through real-time measurement; Obtained through real-time monitoring of renewable energy power generation equipment; Obtained through real-time monitoring of the energy storage system; It can be estimated by the following formula: ; in, is the number of lines in the system, is the resistance of the ith circuit, is the current of the i-th line at time t.

[0018] 2. Voltage change equation of energy storage response process: The voltage variation equation of the energy storage response process is specifically expressed as follows: ; In the formula, is the voltage change at time t; and are the sensitivity coefficients of voltage to active power and reactive power respectively; and are the changes of active power and reactive power of the energy storage system at time t respectively; is the error term.

[0019] Parameter acquisition method: Sensitivity coefficient and It can be obtained by following the steps below: Step 1: Use power flow calculation software to establish a system model; Step 2: Under different operating conditions, slightly change the active and reactive power of the energy storage system and record the voltage changes; Step 3: Use the least squares method to fit and obtain the sensitivity coefficient.

[0020] and Obtained through real-time monitoring of the energy storage system.

[0021] 3. Voltage constraint equation: The voltage constraint equation is specifically expressed as follows: ; In the formula, is the voltage of the i-th node at time t; and are the minimum and maximum voltages allowed, respectively; is the number of system nodes.

[0022] Parameter acquisition method: Obtained through real-time measurement; and Determined according to the power system operation standards.

[0023] 4. Current constraint equation: The current constraint equation is specifically expressed as follows: ; In the formula, is the complex current of the jth line at time t; is the maximum allowable current of the jth line; is the number of lines in the system.

[0024] Parameter acquisition method: Obtained through real-time measurement; Determined according to line design parameters.

[0025] 5. Energy storage capacity constraint equation: The energy storage capacity constraint equation is specifically expressed as follows: ; In the formula, is the energy state of the energy storage system at time t; and are the minimum and maximum energies allowed by the energy storage system, respectively; and are the charging and discharging efficiencies, respectively; and are the charging and discharging powers at time t respectively; For the time interval.

[0026] Parameter acquisition method: Obtained through real-time monitoring of the energy storage system; and Determined according to the design parameters of the energy storage system; and It is obtained through experimental testing. The specific steps are as follows: Step 1: Charge and discharge the energy storage system at constant power; Step 2: Measure the total energy input / output; Step 3: Calculate efficiency: .

[0027] 6. Energy storage life constraint equation: The energy storage life constraint equation is specifically expressed as follows: ; In the formula, is the remaining life of the energy storage system at time t; is the initial lifespan; is the life attenuation coefficient; is the output power of the energy storage system at time k; is the rated power; is the minimum allowable remaining life.

[0028] Parameter acquisition method: and Determined according to the design parameters of the energy storage system; Obtained through accelerated life testing, the specific steps are as follows: Step 1: Cycle charge and discharge of the energy storage system at different depths and frequencies; Step 2: Regularly measure the capacity decay of the energy storage system; Step 3: Use the least squares method to fit the life attenuation coefficient .

[0029] These equations together constitute the energy storage grid-connected equation group, which describes the operating characteristics and constraints of the energy storage system in the power grid. By solving these equations, the optimal dispatch strategy of the energy storage system can be obtained.

[0030] S60. Improved particle swarm optimization algorithm for solving multi-objective optimization problems Step 1: Initialize the particle swarm Initialize a set of particles, each particle represents a possible energy storage scheduling scheme. The position vector of the particle is expressed as: ; In the formula, is the position vector of the i-th particle; is the output power of the energy storage system at time t; The number of time periods in the scheduling cycle.

[0031] Step 2: Check constraints For each particle, check whether the constraints are satisfied: 1) Voltage constraint: ; 2) Current Constraint: ; 3) Energy storage capacity constraints: ; 4) Energy storage life constraints: ; 5) Charge state restrictions of energy storage equipment: ; 6) Charge and discharge power limit: ; 7) Charge and discharge times limit: ; 8) Grid voltage and frequency limitations: ; 9) Renewable energy output limits: ; 10) Load demand limitation: .

[0032] If a particle does not satisfy the constraints, it is made to satisfy them through projection or repair operations.

[0033] Step 3: Calculate the objective function value For each particle, calculate the values ​​of multiple objective functions: ; ; ; In the formula, The cost of running the system; It is a negative value of renewable energy consumption; The fluctuation degree of power grid.

[0034] Step 4: Update individual optimal solutions and global optimal solutions For each particle, update its individual optimal solution . Use dominance relations to compare solutions in multi-objective optimization problems.

[0035] Update the global optimal solution set , which contains all non-dominated solutions.

[0036] Step 5: Update particle velocity and position Update the velocity and position of each particle: ; ; In the formula, is the velocity of the ith particle at time t; is the inertia weight; and is the acceleration constant; and is a random number between [0,1]; From the global optimal solution set A solution is randomly selected from .

[0037] Step 6: Introduce mutation operation In order to increase population diversity, mutation operations are introduced: ; In the formula, is the position of the particle after mutation; is the variation intensity; is a standard normal distribution.

[0038] Step 7: Repeat steps 2-6 until the maximum number of iterations is reached or the convergence condition is met.

[0039] S70. Multi-objective decision-making method to select the best energy storage scheduling scheme The analytic hierarchy process (AHP) is used to comprehensively evaluate the candidate optimization solutions. The steps are as follows: Step 1: Build a hierarchical model The decision-making objectives, evaluation criteria and candidate solutions are constructed into a hierarchical structure. The evaluation criteria include system operation cost, renewable energy consumption and grid fluctuation degree.

[0040] Step 2: Construct a judgment matrix For the three evaluation criteria, construct the judgment matrix A: ; In the formula, It indicates the importance of criterion i relative to criterion j, determined by expert evaluation or decision maker preference.

[0041] Step 3: Calculate the weight vector Calculate the maximum eigenvalue of the judgment matrix A And the corresponding eigenvector W: ; ; In the formula, is the weight vector.

[0042] Step 4: Consistency Check Calculate the consistency index CI and consistency ratio CR: ; ; Where RI is the random consistency index. When , the judgment matrix is ​​considered to have satisfactory consistency.

[0043] Step 5: Calculate the overall score of the candidate solutions For each candidate , calculate its comprehensive score: ; In the formula, and are the maximum and minimum values ​​of the i-th objective function among all candidate solutions, respectively.

[0044] Step 6: Choose the best solution Select the solution with the highest comprehensive score as the best energy storage scheduling solution: .

[0045] Through the above steps, an optimal energy storage scheduling scheme that comprehensively considers multiple objectives and constraints can be obtained. This method closely combines the particle swarm optimization algorithm with various equations and parameters in the present invention, ensuring that the optimization result satisfies all constraints and strikes a balance between multiple objectives.

[0046] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are executed in a computer, they are used to execute the above-mentioned distributed energy storage output scheduling optimization method.

[0047] A third aspect of the present invention provides a distributed energy storage output scheduling optimization system, which includes the above-mentioned computer-readable storage medium.

[0048] The present invention proposes a distributed energy storage output dispatch optimization method, which establishes an optimization model including multiple optimization objectives and various constraints, uses an improved particle swarm optimization algorithm to solve, and finally obtains the optimal energy storage dispatch scheme that meets the power grid operation requirements. Compared with the prior art, the scheme of the present invention has the following obvious advantages: First, the present invention simultaneously considers multiple optimization objectives such as minimizing system operation costs, maximizing renewable energy consumption, and minimizing grid fluctuations, taking into account the interests of all aspects of grid operation. By adopting a multi-objective optimization method, the best balance can be sought between these objectives to meet the needs of all stakeholders in the grid.

[0049] Secondly, when establishing the optimization model, the present invention fully considers the input power parameters, load parameters, renewable energy generation parameters and various parameters of the energy storage device itself, and fully reflects the actual operation status of the power grid. This ensures that the optimization results can better adapt to the actual operation needs of the power grid.

[0050] Furthermore, the present invention adopts an improved particle swarm optimization algorithm to solve multi-objective optimization problems, 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 the optimal solution that meets various constraints.

[0051] In general, the distributed energy storage output scheduling optimization method proposed in the present invention, by establishing a multi-objective optimization model and using advanced optimization algorithms to solve it, can better play the role of distributed energy storage systems in peak load regulation and supporting power grids, improve the level of renewable energy consumption, and solve the technical problem that the existing technology only considers the parameters of the energy storage system itself, while ignoring the influence of other key factors such as power grid input power parameters and load parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A flow chart of the method provided by the present invention; Figure 2 It is a forecast curve diagram of renewable energy output; Figure 3 This is a curve chart of the 24-hour operation of the energy storage system. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the embodiments of the present invention more clear, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0054] like Figure 1 FIG. 1 is a flow chart of a distributed energy storage output dispatch optimization method provided by the present invention. The method comprises the following steps: S10, collecting input power parameters, load parameters, renewable energy generation parameters of the substation, and energy storage parameters of each energy storage device in the distributed energy storage system; S20, establishing and fitting a group of energy storage grid-connected equations that take into account input power parameters, load parameters, renewable energy generation parameters, and energy storage parameters; S30, based on the historical data and current status of the input power parameters and load parameters, predict the load change curve, renewable energy output curve and energy storage demand curve of continuous time segments in the future; S40, based on the predicted load change curve, renewable energy output curve and energy storage demand curve, combined with the energy storage grid-connected equation group, a multi-time period energy storage scheduling optimization model is established to describe the charging and discharging plan of each energy storage device in multiple time periods in the future, including charging and discharging time and power size; S50, determining multiple objectives of the multi-time period energy storage scheduling optimization model, including minimizing system operating costs, maximizing renewable energy consumption, minimizing grid fluctuations, and setting constraints to establish a multi-objective optimization problem; S60, using an improved particle swarm optimization algorithm to solve the multi-objective optimization problem and obtain multiple candidate optimization solutions; S70. Use multi-objective decision-making methods, including Pareto optimal set or hierarchical analysis method or fuzzy comprehensive evaluation method, to comprehensively evaluate multiple candidate optimization solutions, select the best energy storage scheduling solution and output it.

[0055] The specific implementation methods of the above steps are described in detail below: The specific implementation method of step S10 is to collect the input power parameters, load parameters of the substation and the energy storage parameters of each energy storage device in the distributed energy storage system. Among them, the input power parameters include voltage, frequency, active power, reactive power and power factor, etc.; the load parameters include peak and valley load, load type, load curve and power factor, etc.; the renewable energy generation parameters include power generation type, installed capacity, real-time output and output forecast curve, etc.; 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, etc.

[0056] Through real-time measurement and monitoring equipment, input power parameters and load parameters can be obtained. Voltage, frequency, active power and reactive power can be directly obtained in real time through power measurement instruments; load type and load curve can be obtained through analysis of load user power consumption characteristics; peak and valley load, power factor, etc. can be obtained through statistical analysis of historical data.

[0057] The power generation type and installed capacity in the renewable energy power generation parameters can be obtained through field surveys and technical parameter consultation of renewable energy power generation equipment; the real-time output can be obtained through real-time monitoring of power generation equipment; and the output prediction curve requires the use of meteorological data and machine learning algorithms for prediction.

[0058] 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, charge and discharge efficiency, maximum charge and discharge power, and service life, can be obtained by consulting the manufacturer's technical parameters of the energy storage device and conducting on-site tests. and It can be calculated by conducting a constant power charge and discharge test on the energy storage device and measuring the total energy input and output.

[0059] In summary, the purpose of step S10 is to comprehensively collect various key parameters of the energy storage system to lay the foundation for subsequent optimization and scheduling. These parameter data are obtained through real-time measurement and analysis to ensure the reliability and accuracy of the input data.

[0060] The specific implementation of step S20 is to establish and fit a set of energy storage grid-connected equations that take into account input electrical parameters, load parameters, renewable energy generation parameters, and energy storage parameters. This energy storage grid-connected equation set includes a power balance equation, an energy storage response process voltage change equation, a voltage constraint equation, a current constraint equation, an energy storage capacity constraint equation, and an energy storage life constraint equation.

[0061] First, the power balance equation describes the balance between grid input power, renewable energy generation power, energy storage system output power, and load power. This equation ensures the power balance within the system and is the basis for optimizing energy storage scheduling. Among them, grid input power and load power can be obtained through real-time monitoring; renewable energy generation power can be obtained through real-time monitoring or prediction curves; and energy storage system output power can be obtained through real-time monitoring. System power loss can be estimated based on line resistance and real-time measured current.

[0062] Secondly, the voltage variation equation of the energy storage response process describes the impact of the 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 coefficient of voltage to active power and reactive power can be obtained through power flow calculation and least squares fitting.

[0063] Then, the voltage constraint equation and the current constraint equation ensure the safety of voltage and current in the operation of the power grid. These constraints are derived from the power system operation standards and need to be determined specifically according to the actual system.

[0064] 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 allowable range. Among them, the charging and discharging efficiency of the energy storage system also needs to be obtained through experimental testing.

[0065] 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 during the optimization process, resulting in premature exhaustion of its life. It also needs to be obtained through accelerated life testing.

[0066] In general, step S20 establishes a set of equations that fully describes the characteristics of energy storage grid connection. This set of equations provides a mathematical model basis for subsequent optimization scheduling, ensuring that the optimization results meet various constraints for safe operation of the power grid.

[0067] 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 of input power parameters and load parameters and the current status.

[0068] For the prediction of 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 characteristics of load, an accurate load prediction model can be established.

[0069] For the prediction of renewable energy output curve, meteorological data, machine learning and other means can be used to predict the output of renewable energy in the future. By analyzing the impact of weather conditions, sunshine time, wind speed and other factors on renewable energy power generation, an output prediction model is established.

[0070] For the prediction of energy storage demand curve, it is necessary to comprehensively consider the prediction results of load changes and renewable energy output. According to the power balance equation, the charging and discharging power that the energy storage system needs to provide in the future period can be calculated, thus obtaining the energy storage demand curve.

[0071] The above method predicts the trend of key parameter changes in the future, providing important input data for subsequent optimization and dispatch. These prediction results reflect the actual demand changes in power grid operation and lay the foundation for the establishment of optimization problems.

[0072] The specific implementation method of step S40 is to establish a multi-time period energy storage scheduling optimization model based on the predicted load change curve, renewable energy output curve and energy storage demand curve, combined with the energy storage grid-connected equations established above. This optimization model is used to describe the charging and discharging plan of each energy storage device in multiple time periods in the future, including charging and discharging time and power size.

[0073] Specifically, the decision variables of the optimization model are the charging and discharging power of each energy storage device in multiple time periods in the future, namely . The optimization objectives include minimizing system operating costs, maximizing renewable energy consumption, minimizing grid fluctuations, and other objectives. The optimization constraints include the various equation constraints established above, such as voltage constraints, current constraints, energy storage capacity constraints, energy storage life constraints, etc., as well as the state of charge restrictions of energy storage equipment, charging and discharging power restrictions, charging and discharging times restrictions, grid voltage and frequency restrictions, renewable energy output restrictions, and load demand restrictions.

[0074] By establishing such a multi-time period optimization model, the optimal charging and discharging plan for each energy storage device in the future can be obtained, while taking into account multiple goals such as system operation cost, renewable energy consumption and grid stability. This optimization model is the basis for the subsequent step S50 optimization solution.

[0075] The specific implementation method of step S50 is to determine multiple objectives of the multi-time period energy storage scheduling optimization model, including minimizing system operating costs, maximizing renewable energy consumption, minimizing grid fluctuations, and setting constraints to establish a multi-objective optimization problem.

[0076] The objective function of system operation cost is , taking into account the weighted sum of grid input power and energy storage system charging and discharging power. and the charging and discharging costs of energy storage systems It needs to be determined based on actual conditions.

[0077] Objective function for renewable energy consumption , directly take renewable energy output The purpose is to maximize the consumption of renewable energy.

[0078] Objective function for power grid fluctuations ,The sum of the change amplitudes of the grid input power at adjacent moments is used to describe it.,This indicator reflects the stability of the grid and needs to be minimized.

[0079] In addition to these three optimization objectives, various constraints need to be set when establishing the optimization problem, including the various constraints in the energy storage grid-connected equation group established above, such as voltage constraints, current constraints, energy storage capacity constraints, energy storage life constraints, etc., as well as the charge state restrictions of energy storage equipment, charging and discharging power restrictions, charging and discharging times restrictions, grid voltage and frequency restrictions, renewable energy output restrictions, and load demand restrictions.

[0080] By establishing such a multi-objective optimization problem, we can seek a balance between objectives such as system operating costs, renewable energy consumption, and grid stability, and obtain the optimal energy storage scheduling plan that meets various constraints.

[0081] The specific implementation method of step S60 is to use an improved particle swarm optimization algorithm to solve the above multi-objective optimization problem and obtain multiple candidate optimization solutions.

[0082] Particle swarm optimization algorithm is an optimization algorithm based on swarm intelligence, which finds the optimal solution by simulating the foraging behavior of bird flocks or fish schools. The main steps of the algorithm are as follows: First, a group of particles are initialized, each particle represents a feasible energy storage scheduling scheme, that is, the charging and discharging power sequence of each energy storage device in the future time period. The position vector of each particle Represents this scheduling plan.

[0083] Then, check whether each particle meets the aforementioned constraints, 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 required to make them meet the constraints.

[0084] Next, multiple objective function values ​​are calculated for each particle, including the system operation cost , Renewable energy consumption and power grid fluctuations .

[0085] Based on these objective function values, update the individual optimal solution of each particle , and update the global optimal solution set , this set contains all non-dominated solutions.

[0086] Then, update the velocity and position of each particle, including using the inertia weight , learning factor and And random numbers and And other parameters.

[0087] In order to increase the diversity of the population, a mutation operation is introduced, that is, the position of the particles is randomly perturbed with a certain probability.

[0088] The above steps will be repeated until the maximum number of iterations is reached or the convergence condition is met, and finally multiple candidate optimization solutions are obtained.

[0089] This improved particle swarm optimization algorithm can effectively solve multi-objective optimization problems and obtain a balanced solution among objectives such as cost, renewable energy consumption and grid fluctuations while ensuring various constraints.

[0090] The specific implementation method of step S70 is to use the analytic hierarchy process (AHP) to comprehensively evaluate the multiple candidate optimization solutions obtained in step S60 and select the best energy storage scheduling solution.

[0091] First, a hierarchical model is constructed, including three layers: decision-making objectives, evaluation criteria, and candidate solutions. The decision-making objective is to select the best energy storage scheduling solution; the evaluation criteria include system operation cost, , Renewable energy consumption and power grid fluctuations Three indicators; the candidate solutions are the multiple optimized solutions obtained in step S60 .

[0092] Then, based on expert evaluation or decision maker preference, a judgment matrix is ​​constructed. , reflecting the relative importance of each evaluation criterion. The weight vector of each criterion is 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.

[0093] Next, for each candidate solution , calculate its comprehensive score The score is the sum of the objective function values ​​after linear normalization according to the weight vector The objective function value is normalized by using the extreme value method, so that each objective function value is within the interval [0,1].

[0094] Finally, choose the solution with the highest comprehensive score As the best energy storage scheduling solution, this multi-objective decision-making method using the analytic hierarchy process can comprehensively evaluate the balance performance of each candidate solution among multiple objectives and obtain the optimal energy storage scheduling strategy.

[0095] In summary, the distributed energy storage output dispatch optimization method proposed in the present invention makes full use of input power parameters, load parameters, renewable energy generation parameters, and energy storage equipment parameters, and establishes various equations that describe the operating characteristics of the energy storage system in the power grid. On this basis, the improved particle swarm optimization algorithm is used to solve the multi-objective optimization problem, and the optimal energy storage dispatch scheme that meets various constraints is obtained. Finally, the candidate schemes are comprehensively evaluated using the hierarchical analysis method to select the best energy storage dispatch strategy. This method can give full play to the peak-shaving and power grid-supporting role of the distributed energy storage system, improve the level of renewable energy consumption, and reduce the operating cost and fluctuation of the power grid.

[0096] A second aspect of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores program instructions, and when the program instructions are executed in a computer, they are used to execute the above-mentioned distributed energy storage output scheduling optimization method.

[0097] A third aspect of the present invention provides a distributed energy storage output scheduling optimization system, which includes the above-mentioned computer-readable storage medium.

[0098] Specifically, the principle of the present invention is to establish a mathematical model that describes the operating characteristics of the energy storage system in the power grid, and use advanced optimization algorithms to solve the model to obtain the optimal dispatching strategy that meets various constraints. This method conforms to the basic logic of power system optimization dispatching.

[0099] First, by comprehensively collecting the input power parameters, load parameters, renewable energy generation parameters and various parameters of the distributed energy storage system, a group of energy storage grid-connected equations was established, including power balance equation, voltage change equation of energy storage response process, voltage constraint equation, current constraint equation, energy storage capacity constraint equation and 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 and scheduling.

[0100] Secondly, based on the above equations, 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 was established. This optimization model takes the charging and discharging plan of each energy storage device in the future time period as the decision variable, and sets multiple optimization goals such as minimizing system operating costs, maximizing renewable energy consumption and minimizing grid fluctuations, and considers various constraints. This multi-objective optimization model can seek a balance between multiple goals and obtain the optimal scheduling plan that is more in line with the overall interests of the power grid.

[0101] Finally, the improved particle swarm optimization algorithm is used to solve the above multi-objective optimization problem. Compared with 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. By iteratively updating the position and velocity of particles and introducing mutation operations to increase population diversity, the algorithm can eventually converge to the optimal solution that meets various constraints.

[0102] Compared with the prior art, the method of the present invention is more in line with the requirements of power system optimization dispatch in the following aspects: 1. It takes into account multiple optimization objectives and the needs of all stakeholders in the power grid, and can obtain a more balanced optimal scheduling plan.

[0103] 2. It fully reflects the actual operation status of the power grid, including input power parameters, load parameters, renewable energy generation parameters and energy storage equipment parameters, ensuring the feasibility and applicability of the optimization results.

[0104] 3. The use of advanced particle swarm optimization algorithm can effectively deal with complex multi-objective optimization problems and obtain the optimal solution that meets various constraints.

[0105] In order to better understand and implement the present invention, a specific embodiment 1 of the method of the present invention is provided below: The steps of this embodiment 1 are described in detail as follows: The specific implementation of step S10 is to collect the input power parameters, load parameters of the substation and the energy storage parameters of each energy storage device in the distributed energy storage system.

[0106] First, for the input electrical parameters, including voltage ,frequency , Active Power , reactive power And power factor Etc. These parameters can be directly obtained through power measuring instruments, namely: ; ; ; ; ; Secondly, the load parameters include peak and valley loads and , load type, load curve And power factor The peak and valley loads can be obtained through statistical analysis of historical load data; the load type can be determined based on the characteristics of the electrical equipment; the load curve needs to be modeled and predicted using methods such as time series analysis on historical load data; the power factor can also be calculated from historical data.

[0107] In addition, renewable energy generation parameters include generation type , installed capacity , Real-time output And the output prediction curve The power generation type and installed capacity can be obtained through field surveys and technical parameter consultation of renewable energy power generation equipment; real-time output can be obtained through real-time monitoring of power generation equipment; and 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 random simulation.

[0108] 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 Among them, the charge and discharge efficiency can be calculated by performing a constant power charge and discharge test on the energy storage device and measuring the total input and output energy: ; ; Through the above method, the input power parameters, load parameters, renewable energy generation parameters and various parameter data of the distributed energy storage system of the substation can be comprehensively collected and obtained, laying the foundation for subsequent optimization and scheduling.

[0109] The specific implementation of step S20 is to establish and fit a set of energy storage grid-connected equations that take into account input electrical parameters, load parameters, renewable energy generation parameters, and energy storage parameters. This set of equations includes a power balance equation, an energy storage response process voltage change equation, a voltage constraint equation, a current constraint equation, an energy storage capacity constraint equation, and an energy storage life constraint equation.

[0110] First, the power balance equation describes the grid input power , renewable energy power generation , Energy storage system output power And load power The balance between: ; in, is the system power loss, which can be calculated based on the line resistance and real-time measurement of current Make an estimate: ; Secondly, the voltage change equation of the energy storage response process describes the active and reactive power changes of the energy storage system. and The impact on voltage is: ; in, and is the sensitivity coefficient of voltage to active and reactive power, which can be obtained through power flow calculation and least squares fitting. is the error term.

[0111] Then, the voltage constraint equation ensures that the voltage at each node of the power grid is At the minimum allowed and maximum In scope: ; Similarly, the current constraint equation ensures that the current in each line Not exceeding its maximum allowed value : ; In addition, the energy storage capacity constraint equation describes the charging and discharging process of the energy storage system, ensuring its energy state At the minimum allowed and maximum In scope: ; Finally, the energy storage life constraint equation describes the life characteristics of the energy storage system and ensures its remaining life Not less than the minimum requirement : ; in, is the life attenuation coefficient and can be obtained through accelerated life testing.

[0112] By establishing the above-mentioned energy storage grid-connected equations, the operating characteristics and constraints of the energy storage system in the power grid can be fully described, providing a mathematical model basis for subsequent optimal scheduling.

[0113] The specific implementation method of step S30 is to predict the load change curve in the future based on the historical data of the input electrical parameters and load parameters and the current state. , Renewable energy output curve and energy storage demand curve .

[0114] For the prediction of load change curve, the prediction model can be established by using time series analysis and other methods: ; This model can capture the characteristics of load periodicity, trend and sudden changes, so as to predict the future load change trend.

[0115] For the prediction of renewable energy output curve, a prediction model based on support vector machine can be established in combination with meteorological data, which is recorded as: ; The energy storage demand curve It can be calculated based on the power balance equation: ; Through the above method, the actual demand changes of power grid operation in the future can be predicted, laying the foundation for the subsequent optimization and scheduling problems.

[0116] The specific implementation of step S40 is to calculate the load change curve according to the prediction. , Renewable energy output curve and energy storage demand curve ,Combined with the energy storage grid-connected equations established above, a multi-time period energy storage scheduling optimization model is established.

[0117] The decision variables of this optimization model are the charging and discharging power sequences of each energy storage device in multiple time periods in the future. ,in To optimize the length of the time period. The optimization objectives include: 1. Minimize system operating costs : ; 2. Maximize renewable energy consumption : ; 3. Minimize grid fluctuations : ; The optimization constraints include the various equation constraints established above, such as voltage constraints, current constraints, energy storage capacity constraints, energy storage life constraints, etc., as well as various operating restrictions of energy storage equipment, such as charge state restrictions, charge and discharge power restrictions, charge and discharge times restrictions, grid voltage and frequency restrictions, renewable energy output restrictions, and load demand restrictions.

[0118] By establishing such a multi-time period optimization model, the optimal charging and discharging plan for each energy storage device in the future can be obtained, seeking a balance between multiple objectives such as system cost, renewable energy consumption and grid stability.

[0119] The specific implementation method of step S50 is to determine multiple objective functions based on the multi-time period energy storage scheduling optimization model established above, and set various constraints to construct a multi-objective optimization problem.

[0120] First, determine the optimization objective function, including: 1. Minimize system operating costs ; 2. Maximize the consumption of renewable energy

[0121] 3. Minimize grid fluctuations ; Secondly, various constraints are set, including the constraints in the energy storage grid-connected equations established above, such as: Voltage Constraints: ; Current Constraint: ; Energy storage capacity constraints: ; Energy storage life constraints: ; In addition, it also includes charge state restrictions on energy storage equipment, charging and discharging power restrictions, charging and discharging number restrictions, grid voltage and frequency restrictions, renewable energy output restrictions, and load demand restrictions.

[0122] By establishing such a multi-objective optimization problem, we can balance and optimize multiple objectives such as system cost, renewable energy consumption and grid stability, and obtain the optimal energy storage scheduling plan that meets various constraints.

[0123] The specific implementation method of step S60 is to use an improved particle swarm optimization algorithm to solve the above multi-objective optimization problem and obtain multiple candidate optimization solutions.

[0124] Particle swarm optimization algorithm is an optimization algorithm based on swarm intelligence, and its main steps are as follows: 1. Initialize a set of particles , each particle Represents a feasible energy storage dispatch solution.

[0125] 2. Check whether each particle meets the constraints, such as voltage constraints, current constraints, energy storage capacity constraints, energy storage life constraints, etc. For particles that do not meet the constraints, projection or repair operations are required to make them meet the constraints.

[0126] 3. Calculate multiple objective function values ​​for each particle .

[0127] 4. Update the individual optimal solution of each particle , and update the global optimal solution set , this set contains all non-dominated solutions.

[0128] 5. Update the speed and position of each particle: ; ; in, is the inertia weight, and is the acceleration constant, and is a random number between [0,1], From the global optimal solution set A solution is randomly selected from .

[0129] 6. To increase population diversity, introduce mutation operations: ; in, is the position of the particle after mutation, is the variation intensity, is a standard normally distributed random vector.

[0130] The above steps will be repeated until the maximum number of iterations is reached or the convergence condition is met, and finally multiple candidate optimization solutions are obtained.

[0131] This improved particle swarm optimization algorithm can effectively solve multi-objective optimization problems and obtain a balanced solution among objectives such as cost, renewable energy consumption and grid fluctuations while ensuring various constraints.

[0132] The specific implementation method of step S70 is to use the analytic hierarchy process (AHP) to comprehensively evaluate the multiple candidate optimization solutions obtained in step S60 and select the best energy storage scheduling solution.

[0133] First, a hierarchical model including decision objectives, evaluation criteria and candidate solutions is constructed. The decision objective is to select the best energy storage scheduling solution; the evaluation criteria include system operation cost, , Renewable energy consumption and power grid fluctuations Three indicators; the candidate solutions are the multiple optimized solutions obtained in step S60 .

[0134] Then, based on expert evaluation or decision maker preference, a judgment matrix is ​​constructed. , reflecting the relative importance of each evaluation criterion:

[0135] The weight vector of each criterion is calculated using the eigenvalue decomposition method. ,At the same time, a consistency check is needed to ensure that the consistency of the ,judgment matrix reaches an acceptable level.

[0136] Next, for each candidate solution , calculate its comprehensive score : ; in, and Respectively The maximum and minimum values ​​of the objective function among all candidate solutions. The score comprehensively considers the performance of each objective function.

[0137] Finally, choose the solution with the highest comprehensive score As the best energy storage dispatch strategy: ; By adopting the hierarchical analysis method, the balance performance of each candidate solution among multiple objectives can be comprehensively evaluated to obtain the optimal energy storage scheduling strategy.

[0138] In order to further better understand and implement the present invention, the following provides an embodiment 2 of a specific application scenario of the present invention: There are a large number of distributed renewable energy power generation equipment and distributed energy storage systems in a regional power grid. In order to improve the level of renewable energy consumption and reduce the operating costs and fluctuations of the power grid, the power grid company decides to use the distributed energy storage output scheduling optimization method proposed in the present invention to optimize the scheduling of the energy storage system. The following is the specific implementation process of the method.

[0139] First, the company collected key parameter data of the regional power grid through on-site surveys and monitoring equipment. Input power parameters include: voltage kV, frequency Hz, Active Power MW, reactive power MVar, power factor The load parameters include: peak and valley loads are 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, output prediction curve is shown in Figure 2 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 and discharge efficiency and , Maximum charge and discharge power MW, initial life Years, lifespan attenuation coefficient .

[0140] Table 1 Load curve

[0141] Table 2 Real-time output of renewable energy

[0142] Based on the above data, the company established a mathematical model to describe the operating characteristics of the energy storage system in the power grid, including the power balance equation, the voltage change equation during the energy storage response process, the voltage constraint equation, the current constraint equation, the energy storage capacity constraint equation, and the energy storage life constraint equation.

[0143] The power balance equation is: ; in, is the grid input power at time t, which can be obtained through real-time measurement; is the renewable energy power generation at time t, which can be obtained through real-time monitoring; is the output power of the energy storage system at time t, which can be obtained through real-time monitoring; is the load power at time t, which can be obtained through real-time measurement; is the system power loss at time t, which can be estimated based on the line resistance and the current measured in real time.

[0144] The voltage change equation of the energy storage response process is: ; in, is the voltage change at time t; kV / MW and kV / MVar is the sensitivity coefficient of voltage to active and reactive power, obtained by power flow calculation and least squares fitting; and are the changes in active and reactive power of the energy storage system at time t, which can be obtained through real-time monitoring; is the error term.

[0145] The voltage constraint equation is: ; The current constraint equation is: A, j = 1, 2, …, 20; The energy storage capacity constraint equation is: ; The energy storage life constraint equation is: ; Next, the company established a multi-time period energy storage scheduling optimization model based on the predicted load change curve, renewable energy output curve and energy storage demand curve. The optimization objectives include: 1. Minimize system operating costs : ; 2. Maximize the consumption of renewable energy : ; 3. Minimize grid fluctuations : ; in, It is the charging and discharging power sequence of each energy storage device in 24 time periods.

[0146] The optimization constraints include the constraints of the equations established above, as well as the charging and discharging power limits of the energy storage equipment. MW, state of charge limit and charge and discharge times limit times / day, etc.

[0147] To solve this multi-objective optimization problem, the company adopted an improved particle swarm optimization algorithm. The specific steps are as follows: 1. Initialize a group of 50 particles, each particle represents a feasible energy storage scheduling solution .

[0148] 2. Check whether each particle meets the constraints. For particles that do not meet the constraints, make them meet the constraints through projection or repair operations.

[0149] 3. Calculate the three objective function values ​​for each particle .

[0150] 4. Update the individual optimal solution of each particle , and update the global optimal solution set .

[0151] 5. Update the speed and position of each particle: ; ; 6. Perform mutation operation on particles with a probability of 10%: ; 7. Repeat steps 2-6 until the maximum number of iterations, 100, is reached.

[0152] Finally, the algorithm converged to obtain the following candidate optimal solutions: Solution 1: ; Solution 2: ; Solution 3: ; in All are vectors, and double lines are used to avoid the inability to display the entire vector due to its length. Next, the enterprise uses the analytic hierarchy process (AHP) to conduct a comprehensive evaluation of the three candidate solutions. First, the following evaluation index system is constructed: Target layer: Select the optimal energy storage scheduling solution Criteria layer: system operation cost, renewable energy consumption, grid fluctuation Solution layer: Solution 1, Solution 2, Solution 3 According to the expert evaluation, the following judgment matrix is ​​obtained: ; calculate The maximum eigenvalue of and its corresponding eigenvector , satisfies the consistency check .

[0153] Finally, the comprehensive scores of the three solutions are calculated: Solution 1: ; Solution 2: ; Solution 3: ; It can be seen that Scheme 3 has the highest comprehensive score and is therefore selected as the best energy storage scheduling scheme. This scheme performs well in multiple objectives such as system operating costs, renewable energy consumption, and grid fluctuations, meets various constraints, and can maximize the role of distributed energy storage systems in peak load regulation and supporting the grid.

[0154] By implementing the distributed energy storage output dispatch optimization method proposed in the present invention, the operation status of the energy storage system of the regional power grid in the next 24 hours is as follows: Figure 3 As shown in the figure, it can be seen that the energy storage system reasonably allocates the charging and discharging power according to the changes in load and renewable energy output, while minimizing the fluctuation of grid input power, maximizing the absorption of renewable energy power, and maintaining the safe operation of grid voltage, current and other indicators.

[0155] The above description is only a specific implementation mode of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. A distributed energy storage output dispatch optimization method, characterized in that: The following steps are involved: S10, collecting input power parameters, load parameters, renewable energy generation parameters of the substation, and energy storage parameters of each energy storage device in the distributed energy storage system; S20, establishing and fitting a group of energy storage grid-connected equations that take into account input power parameters, load parameters, renewable energy generation parameters, and energy storage parameters; S30, based on the historical data and current status of the input power parameters and load parameters, predicting the load change curve, renewable energy output curve and energy storage demand curve of continuous time segments in the future; S40, based on the predicted load change curve, renewable energy output curve and energy storage demand curve, combined with the energy storage grid-connected equation group, establish a multi-time period energy storage scheduling optimization model to describe the charging and discharging plan of each energy storage device in multiple time periods in the future, including charging and discharging time and power size; S50, determining multiple objectives of the multi-time period energy storage scheduling optimization model, including minimizing system operating costs, maximizing renewable energy consumption, minimizing grid fluctuations, and setting constraints to establish a multi-objective optimization problem; S60, using an improved particle swarm optimization algorithm to solve the multi-objective optimization problem and obtain multiple candidate optimization solutions; S70. Utilize a multi-objective decision-making method, including a Pareto optimal set or a hierarchical analysis method or a fuzzy comprehensive evaluation method, to comprehensively evaluate the multiple candidate optimization solutions, select the best energy storage scheduling solution and output it.

2. A distributed energy storage output dispatch 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 and discharge efficiency, maximum charge and discharge power, and service life.

3. A distributed energy storage output dispatch 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. A distributed energy storage output dispatch 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 distributed energy storage output dispatch optimization method according to claim 4, characterized in that: The energy storage grid-connected equation group includes a power balance equation, an energy storage response process voltage change equation, a voltage constraint equation, a current constraint equation, an energy storage capacity constraint equation, and an energy storage life constraint equation.

6. A distributed energy storage output dispatch optimization method according to claim 5, characterized in that: The constraints include voltage constraint equations, current constraint equations, energy storage capacity constraint equations, energy storage life constraint equations, as well as charge state restrictions, charge and discharge power restrictions, charge and discharge times restrictions, grid voltage and frequency restrictions, renewable energy output restrictions, and load demand restrictions for energy storage devices.

7. A distributed energy storage output dispatch optimization method according to claim 6, characterized in that: The power balance equation describes the balance relationship between the grid input power, renewable energy power generation, energy storage system output power and load power; the energy storage response process voltage change equation describes the impact of the output power change of the energy storage system on the voltage; the voltage constraint equation and the current constraint equation ensure the safety of the grid voltage and current; the energy storage capacity constraint equation describes the charging and discharging characteristics of the energy storage system; the energy storage life constraint equation limits the service life of the energy storage equipment.

8. A distributed energy storage output dispatch optimization method according to claim 7, characterized in that: The improved particle swarm optimization algorithm initializes a group of particles representing the energy storage scheduling scheme by simulating the foraging behavior of bird flocks, and performs constraint checking, objective function calculation, individual optimal solution and global optimal solution update, and iterative update steps of speed and position on each particle. In order to increase the diversity of the population, a mutation operation is also introduced. Through multiple iterations, multiple candidate optimization solutions that meet various constraints are obtained.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program instructions, and when the program instructions are executed in a computer, they are used to execute a distributed energy storage output scheduling optimization method according to any one of claims 1 to 8.

10. A distributed energy storage output dispatch optimization system, characterized in that: A computer-readable storage medium comprising the computer-readable storage medium of claim 9.

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