Distributed energy storage resource aggregator optimization scheduling method and system

Abstract

The invention discloses a distributed energy storage resource aggregator optimization scheduling method and system, and relates to the technical field of energy storage optimization, and the method comprises the steps: building a distributed energy storage resource aggregator model according to the characteristic parameters and charging and discharging characteristics of distributed energy storage in combination with the characteristics of a resource aggregator; establishing a multi-target distributed energy storage resource aggregator optimization scheduling model, and setting constraint conditions according to the characteristics of the resource aggregator; unit information, characteristic parameters of distributed energy storage and initial operation parameters of a distributed energy storage resource aggregator are obtained, and parameters of a multi-target genetic programming algorithm are initialized; and searching an optimal solution for the multi-target distributed energy storage resource aggregator optimal scheduling model by using a multi-target genetic programming algorithm, and performing distributed energy storage resource aggregator optimal scheduling. According to the method, the utilization efficiency of adjustable resources in a resource aggregator is improved, distributed resources can be utilized more efficiently, and the shortage of power duration auxiliary service resources is relieved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy storage optimization, and in particular to a method and system for optimizing and scheduling distributed energy storage resource aggregators. Background Art

[0002] Distributed energy, as a new type of energy, is more flexible, sustainable and efficient than traditional centralized energy. Distributed energy uses advanced technologies such as solar photovoltaic, wind power generation, biomass energy, etc., combined with smart grid, energy storage and intelligent control system, to achieve local production, consumption and management of energy, improve energy utilization efficiency, reduce energy loss and emissions, and promote energy transformation and sustainable development. With the large-scale grid connection of distributed energy, the randomness of its output poses a great challenge to the stable operation of the power grid. In addition, as the peak-to-valley difference increases with the increase in power load, it also puts pressure on power grid regulation. In addition to using load demand response methods, energy storage technology can be used to fundamentally solve the above problems. With the development of energy storage technology and the decline in cost, the formation of a distributed energy storage pattern is an inevitable trend in the development of smart grids in the future. On the grid side, it can balance the fluctuations between supply and demand of the grid, stabilize the operation of the grid by storing excess power during high-load periods and releasing power during low-load periods; provide backup power and emergency support in response to emergencies, failures and natural disasters to ensure the stability of power supply. In addition, grid-side energy storage can also participate in system auxiliary services, shaving peaks and filling valleys, and improving the frequency response characteristics of low-inertia systems after high-penetration distributed energy is connected to the grid. In summary, distributed energy storage can not only balance the difference between energy supply and demand, improve energy utilization efficiency, and reduce energy waste, but also enhance the stability and reliability of the energy system. It is an important part of the intelligent and sustainable development of the power grid.

[0003] However, the access to distributed energy storage also brings certain challenges and problems to the power grid: 1. The connection of decentralized distributed energy storage systems to the grid may increase the number of access points in the system and change the topology of the power grid, thereby affecting the power grid's response to faults and the speed of recovery. If the location and scale of the energy storage system are unreasonable, it may make it difficult to locate and isolate power grid faults, affecting the reliability of the power grid. 2. The access to large-scale distributed energy storage systems brings challenges to power grid management, including the monitoring, control and operation and maintenance of energy storage equipment, as well as the coordination and scheduling with other power grid resources. Corresponding management policies and technical specifications need to be formulated.

[0004] As the problem becomes more serious, resource aggregators have emerged and become an important role in the energy market. Resource aggregators "aggregate" various distributed energy and energy storage resources and optimize their management, solving the problem that distributed energy cannot directly participate in grid regulation, improving the efficiency of the energy system, and alleviating the problem of insufficient auxiliary service resources in the power market. Therefore, studying the optimized scheduling of distributed energy storage after connecting to the grid from the perspective of resource aggregators can realize the intelligence, flexibility and efficiency of the energy system, and provide important support and guarantee for grid operation and energy transformation. Summary of the invention

[0005] In view of the problems existing in the existing distributed energy storage resource aggregator optimization scheduling method, the present invention is proposed. Therefore, the problem to be solved by the present invention is how to provide a distributed energy storage resource aggregator optimization scheduling method and system.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a distributed energy storage resource aggregator optimization scheduling method, which includes: establishing a distributed energy storage resource aggregator model according to the characteristic parameters and charging and discharging characteristics of the distributed energy storage, combined with the characteristics of the resource aggregator; establishing a multi-objective distributed energy storage resource aggregator optimization scheduling model, and setting constraints according to the characteristics of the resource aggregator; obtaining unit information, characteristic parameters of the distributed energy storage and initial operating parameters of the distributed energy storage resource aggregator, and initializing multi-objective genetic programming algorithm parameters; using the multi-objective genetic programming algorithm to search for the optimal solution of the multi-objective distributed energy storage resource aggregator optimization scheduling model, and performing optimal scheduling of the distributed energy storage resource aggregator.

[0008] As a preferred solution of the distributed energy storage resource aggregator optimization scheduling method described in the present invention, the characteristic parameters of the distributed energy storage include the maximum storage capacity of the energy storage device, power limit, charging and discharging efficiency, cycle life and response time, historical charging and discharging records, charging and discharging cycles, and charging and discharging rates.

[0009] As a preferred solution of the distributed energy storage resource aggregator optimization scheduling method of the present invention, the establishment of the distributed energy storage resource aggregator model includes the following steps: collecting characteristic parameters of different types of energy storage equipment and the operation mode of the aggregator, analyzing the role of the aggregator in the power market; combining energy storage characteristic parameters, considering the mutual influence between the energy storage system and the aggregator business, and establishing the distributed energy storage resource aggregator model. The relevant expressions are as follows:

[0010]

[0011]

[0012] In the formula, They represent the charging and discharging power of the dth distributed energy storage respectively; D represents the set of distributed energy storage; and They represent the efficiency formula of the charging and discharging power of the dth distributed energy storage respectively; is the energy of the resource aggregator at time t-1; and They represent the charging and discharging power of the resource aggregator at time t-1 respectively; are the maximum charging and discharging power of the dth distributed energy storage at time t; is the maximum value of the energy of the dth distributed energy storage at the moment; To limit the number of charge and discharge cycles; conduct model testing and verification, use historical data to simulate the model, check whether the model can accurately reflect the dynamic behavior of the energy storage system and aggregator, monitor model performance, and adjust model parameters according to simulation results.

[0013] As a preferred solution of the distributed energy storage resource aggregator optimization scheduling method of the present invention, the multi-objective distributed energy storage resource aggregator optimization scheduling model includes optimizing scheduling with the goal of minimizing cost and maximizing power supply flexibility, and considering the impact of new energy. The objective function expression is as follows:

[0014]

[0015] MaxmizeF flexibility

[0016]

[0017] In the formula, They represent the operating cost of conventional units, the operating cost of distributed energy storage resource aggregators, and the cost of wind turbine abandonment respectively; N represents the number of time periods, Δ represents the length of the time period, and F flexibility Indicates the rate of change of charging and discharging power during the energy storage period, that is, maximizing the flexibility of energy storage power supply; is the operating cost per unit output of conventional units g; is the fixed operating cost of the conventional unit g; is the operating output of conventional unit g at time t; b t,g is the operating status of the conventional unit; t,g Normal unit startup state; t,g It is the normal unit shutdown state; Sdc g is the startup cost of conventional unit g; Suc g is the shutdown cost of conventional unit g; is the day-ahead time-of-use electricity price; c wppUnit wind curtailment cost of wind turbines; Predict power for wind turbines; Contribute to the wind turbine dispatching plan.

[0018] As a preferred solution of the distributed energy storage resource aggregator optimization scheduling method of the present invention, the constraint conditions include power balance constraint, unit operation time and logic constraint, unit operation constraint, resource aggregator main operation constraint and wind turbine unit scheduling output constraint; the power balance constraint is shown in the following formula:

[0019]

[0020] In the formula, is the predicted value of load demand; the unit operation time and logical constraints are as follows:

[0021]

[0022] y t,g +z t,g ≤1

[0023] y t,g -z t,g =b t,g -b t-1,g

[0024] In the formula, Ut g Dt is the start-up time; g is the unit downtime; the unit operation constraint is as follows:

[0025]

[0026] P t,g -P t-1,g ≤Ru g

[0027] P t-1,g -P t,g ≤Rd g

[0028] In the formula, is the upper limit of the output of conventional units. Conventional unit g output lower limit; Rd g is the upward climbing constraint of unit g, Ru g is the downward climbing constraint of unit g; the resource aggregator main operation constraint is as follows:

[0029]

[0030] In the formula, It is the upper and lower limits of the distributed energy storage aggregation charging and discharging power. is the lower limit of the distributed energy storage aggregate charging and discharging power; the wind turbine dispatch output constraint is as follows:

[0031]

[0032] In the formula, Predict power for wind turbines; Contribute to the wind turbine dispatching plan.

[0033] As a preferred solution of the distributed energy storage resource aggregator optimization scheduling method of the present invention, wherein: the distributed energy storage resource aggregator optimal scheduling includes the following steps: obtaining the optimization objective function and constraint conditions of the distributed energy storage resource aggregator, initializing the genetic algorithm parameters, selecting the population size to ensure genetic diversity, adjusting the crossover probability and mutation probability, selecting the strategy and the termination condition; initializing the population, randomly generating the initial distributed energy storage resource aggregator scheduling strategy, forming an initial population, the initial population size is N, the individuals represent the energy storage scheduling strategy, and the vector X is used i If , then the population is expressed as:

[0034] Population = {X1,X2,...,X N}

[0035] Encode the decision variables and encode the charging and discharging plan of each energy storage device into chromosomes; evaluate the fitness function, define the fitness function for each target, use the Pareto dominance principle to evaluate individuals, and add penalty items to reduce the fitness of solutions that violate the constraints; calculate the fitness values ​​of individuals in the population on multiple objective functions. According to the objective function, assuming that the objective function set is {F1, F2, …, Fm}, then the individual X i The fitness vector is:

[0036] F i =(F i1 ,F i2 ,...,F im )

[0037] Perform non-dominated sorting and selection, use the non-dominated sorting algorithm to sort the individuals in the population, and select a group of excellent individuals as parents according to the ranking, and select N s As the parent population; perform genetic operations, select individuals from the current population for reproduction according to the fitness function, perform crossover and mutation operations on the selected parent individuals to generate offspring individuals, and the crossover and mutation operations generate N c and N m The expression is:

[0038]

[0039] Update the population, merge the offspring produced by genetic operations with part or all of the old population, combine the parent individuals with the offspring individuals to generate the next generation population, and repeat the steps until the stopping condition is met. The expression is:

[0040]

[0041] Perform Pareto sorting, sort the new population according to the Pareto dominance principle, select the best individual to enter the next round of iteration, and stop executing the algorithm when the preset maximum number of iterations is reached and the termination condition is met; perform result analysis and verification, collect all non-dominated solutions to form a Pareto optimal solution set for sensitivity analysis; implement the scheduling plan, select the optimal solution from the Pareto optimal solution set as the actual scheduling plan, and convey the selected scheduling plan to the energy storage resource aggregator for execution.

[0042] As a preferred solution of the distributed energy storage resource aggregator optimization scheduling method of the present invention, the Pareto sorting includes: if all target f i , there exists f i (x)≤f i (y), and there is at least one target f j , so that f j (x)≤f j (y), mark all individuals as unclassified; for each individual x, find all individuals dominated by x and increase the domination count of x, remove all individuals not dominated by any individual and mark them as first-level Pareto optimal, and repeat the steps until all individuals are classified.

[0043] In a second aspect, the present invention provides a distributed energy storage resource aggregator optimization scheduling system, which includes: a construction module, which is used to establish a distributed energy storage resource aggregator model according to the characteristic parameters and charging and discharging characteristics of the distributed energy storage, combined with the characteristics of the resource aggregator; a constraint module, which is used to establish an objective function, constrain the objective function through constraint conditions, and establish an optimized scheduling model for the distributed energy storage resource aggregator; a parameter acquisition module, which is used to obtain information on conventional units of the regional power grid, characteristic parameters of the distributed energy storage and initial operating parameter information of the distributed energy storage resource aggregator; a scheduling operation module, which is used to use a multi-objective genetic programming algorithm to search for the Pareto optimal solution of the multi-objective optimization problem, and determine the convergence of the algorithm according to the threshold set by the convergence criterion.

[0044] In a third aspect, the present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, the steps of the distributed energy storage resource aggregator optimization scheduling method are implemented.

[0045] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, the steps of a method for optimizing scheduling of distributed energy storage resource aggregators are implemented.

[0046] The beneficial effects of the present invention are to achieve effective management and optimization of distributed energy storage resources, enhance the flexibility and response speed of resource aggregators in complex market environments, and improve the overall efficiency and stability of energy systems. Reduce the consumption of computing resources and accelerate the optimization process. Realize intelligent resource scheduling and energy utilization efficiency, and also enhance the market competitiveness and profitability of resource aggregators, which is of great significance to promoting the sustainable development of the energy industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0048] Figure 1 Flowchart of an optimized dispatching method for distributed energy storage resource aggregators. DETAILED DESCRIPTION

[0049] In order to make the above-mentioned purposes, features and advantages of the present invention more understandable, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0050] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0051] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0052] Example 1

[0053] Reference Figure 1 , which is the first embodiment of the present invention, and provides a distributed energy storage resource aggregator optimization scheduling method, including:

[0054] S1: Based on the characteristic parameters and charging and discharging characteristics of distributed energy storage and the characteristics of resource aggregators, a distributed energy storage resource aggregator model is established;

[0055] Specifically, the characteristic parameters of distributed energy storage include the maximum storage capacity of the energy storage device, power limit, charging and discharging efficiency, cycle life and response time, historical charging and discharging records, charging and discharging cycles, and charging and discharging rates.

[0056] Collect technical parameters of different types of energy storage devices (such as lithium-ion batteries, flywheel energy storage, compressed air energy storage, etc.), including maximum storage capacity, power limit, charge and discharge efficiency, cycle life, response time, etc. Obtain historical charge and discharge records, including charge and discharge cycles, depth, rate, etc., to understand the actual performance of energy storage devices.

[0057] Study the operating model of aggregators, including asset ownership structure, revenue model, risk management strategy, etc. Analyze the role of aggregators in the electricity market, such as participation in frequency regulation services, peak load reduction, backup power supply, etc.

[0058] Conduct characteristic parameter and feature analysis to evaluate the performance and economic feasibility of different energy storage technologies based on the collected data. Use statistical methods or machine learning techniques to establish a mathematical model of charge and discharge characteristics to reflect the behavior of energy storage devices under different conditions.

[0059] Defines how aggregators select, combine and dispatch energy storage resources to maximize revenue or meet specific objectives.

[0060] Combined with energy storage characteristic parameters, the energy storage system model and the aggregator decision model are integrated to create a resource aggregator model. Considering the mutual influence between the energy storage system and the aggregator business, a distributed energy storage resource aggregator model is established. The expression is as follows:

[0061]

[0062] In the formula, They represent the charging and discharging power of the dth distributed energy storage respectively; D represents the set of distributed energy storage; and They represent the efficiency formula of the charging and discharging power of the dth distributed energy storage respectively; is the energy of the resource aggregator at time t-1; and They represent the charging and discharging power of the resource aggregator at time t-1 respectively; are the maximum charging and discharging power of the dth distributed energy storage at time t; is the maximum value of the energy of the dth distributed energy storage at the moment; The limit number of charge and discharge cycles;

[0063] Conduct model testing and validation, simulate models using historical data or hypothetical scenarios, evaluate the model’s predictive power and stability, analyze simulation results, and check whether the model accurately reflects the dynamic behavior of the energy storage system and aggregator.

[0064] Adjust model parameters based on simulation results, improve model accuracy and reliability, monitor model performance, collect user feedback, and adjust the model to adapt to market and technology changes.

[0065] S2: Establish a multi-objective distributed energy storage resource aggregator optimization scheduling model and set constraints based on the characteristics of the resource aggregator;

[0066] Specifically, a multi-objective distributed energy storage resource aggregator optimization scheduling model is established to minimize costs and maximize power supply flexibility, and constraints are set according to the characteristics of the resource aggregator;

[0067] The multi-objective distributed energy storage resource aggregator optimization scheduling model optimizes scheduling with the goal of minimizing costs and maximizing power supply flexibility, and takes into account the impact of new energy. The scheduling cycle is 24 hours, which is divided into 24 scheduling periods. The objective function expression is as follows:

[0068]

[0069] MaxmizeF flexibility

[0070]

[0071] In the formula, They represent the operating cost of conventional units, the operating cost of distributed energy storage resource aggregators, and the cost of wind turbine abandonment respectively; N represents the number of time periods, Δ represents the length of the time period, and F flexibility Indicates the rate of change of charging and discharging power during the energy storage period, that is, maximizing the flexibility of energy storage power supply; is the operating cost per unit output of conventional units g; is the fixed operating cost of the conventional unit g; is the operating output of conventional unit g at time t; b t,g is the operating status of the conventional unit; t,g Normal unit startup state; t,g It is the normal unit shutdown state; Sdc g is the startup cost of conventional unit g; Suc g is the shutdown cost of conventional unit g; is the day-ahead time-of-use electricity price; c wpp Unit wind curtailment cost of wind turbines; Predict power for wind turbines; Contribute to the wind turbine dispatching plan.

[0072] The constraints include power balance constraints, unit operation time and logic constraints, unit operation constraints, resource aggregator operation constraints, and wind turbine dispatch output constraints; the power balance constraints are shown in the following formula:

[0073]

[0074] In the formula, is the predicted value of load demand; the unit operation time and logical constraints are as follows:

[0075]

[0076] y t,g +z t,g ≤1

[0077] y t,g -z t,g =b t,g -b t-1,g

[0078] In the formula, Ut g Dt is the start-up time; g is the unit downtime; the unit operation constraint is as follows:

[0079]

[0080] P t,g -P t-1,g ≤Ru g

[0081] P t-1,g -P t,g ≤Rd g

[0082] In the formula, is the upper limit of the output of conventional units. Conventional unit g output lower limit; Rd g is the upward climbing constraint of unit g, Ru g is the downward climbing constraint of unit g; the resource aggregator main operation constraint is as follows:

[0083]

[0084] In the formula, It is the upper and lower limits of the distributed energy storage aggregation charging and discharging power. is the lower limit of the distributed energy storage aggregate charging and discharging power; the wind turbine dispatch output constraint is as follows:

[0085]

[0086] In the formula, Predict power for wind turbines; Contribute to the wind turbine dispatching plan.

[0087] This method does not consider the phenomenon that the charging and discharging power of distributed energy storage is limited due to line overload. It only sets 0-1 variables and logical constraints in the distributed energy storage resource aggregator to solve the minimum operating cost of the resource aggregator and avoid the energy storage offsetting the resource aggregator utilization value when in the charging and discharging state.

[0088] S3: Obtain basic information of the unit, characteristic parameters of distributed energy storage and initial operating parameters of distributed energy storage resource aggregators, and initialize multi-objective genetic programming algorithm parameters;

[0089] Specifically, the basic information of conventional units and distributed energy units of the regional power grid is obtained, the characteristic parameters of distributed energy storage and the initial operating parameters of distributed energy storage resource aggregators are obtained, and the parameters of the multi-objective genetic programming algorithm are initialized;

[0090] S4: Use the multi-objective genetic programming algorithm to search for the optimal solution of the multi-objective distributed energy storage resource aggregator optimization scheduling model and perform optimal scheduling of distributed energy storage resource aggregators.

[0091] Specifically, the distributed energy storage resource aggregator optimization scheduling based on MOGP, MOGP is an optimization algorithm based on genetic programming, which can be used to solve multi-objective optimization problems. Its basic principle is to simulate the genetic and natural selection mechanism in the biological evolution process, randomly select a population, and use evolutionary operations (such as crossover and mutation) to search for the Pareto optimal solution of multiple objectives. The present invention takes minimizing cost and maximizing power supply flexibility as the objective function, and seeks the optimal scheduling of distributed energy storage resource aggregators participating in the grid side.

[0092] Obtain the optimization objective function and constraints of the distributed energy storage resource aggregator, initialize the genetic algorithm parameters, select the population size to ensure genetic diversity, adjust the crossover probability and mutation probability, select the strategy and termination conditions;

[0093] Initialize the population, randomly generate the initial distributed energy storage resource aggregator dispatch strategy, form the initial population, the initial population size is N, each individual represents an energy storage dispatch strategy, and the vector X i If , then the population is expressed as:

[0094] Population = {X1,X2,...,XN}

[0095] Encode the decision variables and encode the charging and discharging plan of each energy storage device into chromosomes;

[0096] Perform fitness function evaluation, define a fitness function for each goal, use the Pareto dominance principle to evaluate individuals, and add penalty items to reduce the fitness of solutions that violate constraints;

[0097] For each individual in the population, calculate its fitness value on multiple objective functions. According to the objective function, assuming that the objective function set is {F1, F2, …, Fm}, then individual X i The fitness vector is:

[0098] F i =(F i1 ,F i2 ,...,F im )

[0099] Perform non-dominated sorting and selection. Use the non-dominated sorting algorithm to sort the individuals in the population and select a group of excellent individuals as parents according to the ranking. Suppose N s individuals as the parent population;

[0100] Execute genetic operations, select individuals from the current population for reproduction according to the fitness function, perform crossover and mutation operations on the selected parent individuals, generate offspring individuals, and the crossover and mutation operations generate N c and N m The expression is:

[0101]

[0102] Update the population, merge the offspring produced by genetic operations with part or all of the old population, combine the parent individuals with the offspring individuals to generate the next generation population, and repeat the steps until the stopping condition is met. The expression is:

[0103]

[0104] Perform Pareto sorting, sort the new population according to the Pareto dominance principle, select the best individual to enter the next round of iteration, and stop executing the algorithm when the preset maximum number of iterations is reached and the termination condition is met;

[0105] Analyze and verify the results, collect all non-dominated solutions to form a Pareto optimal solution set for sensitivity analysis;

[0106] Implement the dispatch plan, select the optimal solution from the Pareto optimal solution set as the actual dispatch plan, and communicate the selected dispatch plan to the energy storage resource aggregator for execution.

[0107] Furthermore, the present embodiment also provides a distributed energy storage resource aggregator optimization scheduling system, including: a construction module, which is used to establish a distributed energy storage resource aggregator model according to the characteristic parameters and charging and discharging characteristics of the distributed energy storage, combined with the characteristics of the resource aggregator; a constraint module, which is used to establish an objective function, constrain the objective function through constraint conditions, and establish an optimized scheduling model for the distributed energy storage resource aggregator; a parameter acquisition module, which is used to obtain information on conventional units of the regional power grid, characteristic parameters of the distributed energy storage and initial operating parameter information of the distributed energy storage resource aggregator; a scheduling operation module, which is used to use a multi-objective genetic programming algorithm to search for the Pareto optimal solution of the multi-objective optimization problem, and determine the convergence of the algorithm according to the threshold set by the convergence criterion.

[0108] This embodiment also provides a computer device, which is suitable for the case of optimizing the scheduling method of distributed energy storage resource aggregators, including: a memory and a processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement all or part of the steps of the method described in the embodiment of the present invention as proposed in the above embodiment.

[0109] This embodiment also provides a storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the method in any optional implementation of the above embodiment is executed. Wherein, the storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk.

[0110] The storage medium proposed in this embodiment and the data storage method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be found in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0111] Example 2

[0112] Referring to Table 1, which is a second embodiment of the present invention, this embodiment provides a distributed energy storage resource aggregator optimization scheduling method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0113] Table 1 Technical characteristics comparison table

[0114] Technical features This method Traditional methods Model building Modeling based on comprehensive energy storage characteristics and resource aggregator characteristics Build a model based only on energy storage device parameters Goal Setting Multi-objective optimization, integrating economic, environmental and social goals Single objective optimization Constraints Contains detailed constraints on physics and market rules Simple or absent constraints Parameter initialization Comprehensive information support, refined parameter initialization Parameter initialization may be incomplete or rough Algorithm selection Multi-objective genetic programming algorithm to avoid local optimality Single-objective optimization algorithm, prone to local optimality Data Utilization Make full use of information about units, energy storage and aggregators Inadequate or poorly targeted data Adaptive Design Consider the adaptability to market changes and technological advances Lack of adaptability to market and technological changes Optimizing accuracy High-precision optimization, finding the Pareto optimal solution Low accuracy, missing the optimal solution

[0115] From the above, it can be seen that this method can achieve effective management and optimization of distributed energy storage resources, enhance the flexibility and response speed of resource aggregators in complex market environments, and improve the overall efficiency and stability of the energy system. Through multi-objective optimization, efficient use of resources can be achieved, operating costs can be reduced, the convergence speed of the algorithm and the accuracy of the solution can be improved, the consumption of computing resources can be reduced, and the optimization process can be accelerated. Intelligent resource scheduling and energy utilization efficiency can be achieved, while also improving the market competitiveness and profitability of resource aggregators, which is of great significance to promoting the sustainable development of the energy industry.

[0116] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should be included in the scope of the claims of the present invention.

Claims

1. A distributed energy storage resource aggregator optimization scheduling method, characterized by: include, According to the characteristic parameters and charging and discharging characteristics of distributed energy storage, a distributed energy storage resource aggregator model is established in combination with the characteristics of resource aggregators; Establish a multi-objective distributed energy storage resource aggregator optimization scheduling model and set constraints based on the characteristics of the resource aggregator; Obtain unit information, characteristic parameters of distributed energy storage and initial operating parameters of distributed energy storage resource aggregators, and initialize multi-objective genetic programming algorithm parameters; A multi-objective genetic programming algorithm is used to search for the optimal solution of the multi-objective distributed energy storage resource aggregator optimization scheduling model and perform optimal scheduling of distributed energy storage resource aggregators.

2. The distributed energy storage resource aggregator optimization scheduling method according to claim 1, characterized in that: The characteristic parameters of the distributed energy storage include the maximum storage capacity of the energy storage device, power limit, charge and discharge efficiency, cycle life and response time, historical charge and discharge records, charge and discharge cycles, and charge and discharge rates.

3. The distributed energy storage resource aggregator optimization scheduling method according to claim 2, characterized in that: The establishment of a distributed energy storage resource aggregator model includes the following steps: Collect characteristic parameters of different types of energy storage equipment and the operating models of aggregators, and analyze the role of aggregators in the electricity market; Combined with energy storage characteristic parameters, considering the mutual influence between energy storage system and aggregator business, a distributed energy storage resource aggregator model is established. The relevant expressions are as follows: In the formula, They represent the charging and discharging power of the dth distributed energy storage respectively; D represents the set of distributed energy storage; and They represent the efficiency formula of the charging and discharging power of the dth distributed energy storage respectively; is the energy of the resource aggregator at time t-1; and They represent the charging and discharging power of the resource aggregator at time t-1 respectively; are the maximum charging and discharging power of the dth distributed energy storage at time t; is the maximum value of the energy of the dth distributed energy storage at the moment; The limit number of charge and discharge cycles; Conduct model testing and verification, use historical data to simulate the model, check whether the model can accurately reflect the dynamic behavior of the energy storage system and aggregator, monitor model performance, and adjust model parameters based on simulation results.

4. The distributed energy storage resource aggregator optimization scheduling method according to claim 3, characterized in that: The multi-objective distributed energy storage resource aggregator optimization scheduling model includes optimizing scheduling with the goal of minimizing costs and maximizing power supply flexibility, and considering the impact of new energy. The objective function expression is as follows: MaxmizeF flexibility In the formula, They represent the operating cost of conventional units, the operating cost of distributed energy storage resource aggregators, and the cost of wind turbine abandonment respectively; N represents the number of time periods, Δ represents the length of the time period, and F flexibility Indicates the rate of change of charging and discharging power during the energy storage period, that is, maximizing the flexibility of energy storage power supply; is the operating cost per unit output of conventional units g; is the fixed operating cost of the conventional unit g; is the operating output of conventional unit g at time t; b t,g is the operating status of the conventional unit; t,g Normal unit startup state; t,g It is the normal unit shutdown state; Sdc g is the startup cost of conventional unit g; Suc g is the shutdown cost of conventional unit g; is the day-ahead time-of-use electricity price; c wpp Unit wind curtailment cost of wind turbines; Predict power for wind turbines; Contribute to the wind turbine dispatching plan.

5. The distributed energy storage resource aggregator optimization scheduling method according to claim 4, characterized in that: The constraints include power balance constraints, unit operation time and logic constraints, unit operation constraints, resource aggregator entity operation constraints, and wind turbine unit dispatch output constraints; the power balance constraints are shown in the following formula: In the formula, is the predicted value of load demand; the unit operation time and logical constraints are as follows: y t,g +z t,g ≤1 y t,g -z t,g =b t,g -b t-1,g In the formula, Ut g Dt is the start-up time; g is the unit downtime; the unit operation constraint is as follows: P t,g -P t-1,g ≤Ru g P t-1,g -P t,g ≤Rd g In the formula, is the upper limit of the output of conventional units. Conventional unit g output lower limit; Rd g is the upward climbing constraint of unit g, Ru g is the downward climbing constraint of unit g; the resource aggregator main operation constraint is as follows: In the formula, It is the upper and lower limits of the distributed energy storage aggregation charging and discharging power. is the lower limit of the distributed energy storage aggregate charging and discharging power; the wind turbine dispatch output constraint is as follows: In the formula, Predict power for wind turbines; Contribute to the wind turbine dispatching plan.

6. The distributed energy storage resource aggregator optimization scheduling method according to claim 5, characterized in that: The optimal scheduling of distributed energy storage resource aggregators includes the following steps: Obtain the optimization objective function and constraints of the distributed energy storage resource aggregator, initialize the genetic algorithm parameters, select the population size to ensure genetic diversity, adjust the crossover probability and mutation probability, select the strategy and termination conditions; Initialize the population, randomly generate the initial distributed energy storage resource aggregator dispatch strategy, form the initial population, the initial population size is N, the individual represents the energy storage dispatch strategy, and the vector X i If , then the population is expressed as: Population={X1,X2,...,X N } Encode the decision variables and encode the charging and discharging plan of each energy storage device into chromosomes; Perform fitness function evaluation, define a fitness function for each goal, use the Pareto dominance principle to evaluate individuals, and add penalty items to reduce the fitness of solutions that violate constraints; For individuals in the population, the fitness values ​​on multiple objective functions are calculated. According to the objective function, assuming that the objective function set is {F1, F2, …, Fm}, the individual X i The fitness vector is: F i =(F i1 ,F i2 ,...,F im ) Perform non-dominated sorting and selection, use the non-dominated sorting algorithm to sort the individuals in the population, and select a group of excellent individuals as parents according to the ranking, and select N s as the parent population; Execute genetic operations, select individuals from the current population for reproduction according to the fitness function, perform crossover and mutation operations on the selected parent individuals, generate offspring individuals, and the crossover and mutation operations generate N c and N m The expression is: Update the population, merge the offspring produced by genetic operations with part or all of the old population, combine the parent individuals with the offspring individuals to generate the next generation population, and repeat the steps until the stopping condition is met. The expression is: Perform Pareto sorting, sort the new population according to the Pareto dominance principle, select the best individual to enter the next round of iteration, and stop executing the algorithm when the preset maximum number of iterations is reached and the termination condition is met; Analyze and verify the results, collect all non-dominated solutions to form a Pareto optimal solution set for sensitivity analysis; Implement the dispatch plan, select the optimal solution from the Pareto optimal solution set as the actual dispatch plan, and communicate the selected dispatch plan to the energy storage resource aggregator for execution.

7. The distributed energy storage resource aggregator optimization scheduling method according to claim 6, characterized in that: The Pareto sorting includes if all targets f i , there exists f i (x)≤f i (y), and there is at least one target f j , so that f j (x)≤f j (y), all individuals are marked as unclassified; For each individual x, find all individuals dominated by x and increase the domination count of x. Remove all individuals that are not dominated by any individual and mark them as first-level Pareto optimal. Repeat the steps until all individuals are classified.

8. A distributed energy storage resource aggregator optimization scheduling system, based on the distributed energy storage resource aggregator optimization scheduling method according to any one of claims 1 to 7, characterized in that: include, A construction module is used to establish a distributed energy storage resource aggregator model based on characteristic parameters and charging and discharging characteristics of distributed energy storage and combined with resource aggregator characteristics; The constraint module is used to establish the objective function, constrain the objective function through constraint conditions, and establish the distributed energy storage resource aggregator optimization scheduling model; Parameter acquisition module, used to obtain information about conventional units in the regional power grid, characteristic parameters of distributed energy storage, and initial operating parameter information of distributed energy storage resource aggregators; The scheduling operation module is used to search for the Pareto optimal solution of the multi-objective optimization problem using the multi-objective genetic programming algorithm, and determine the convergence of the algorithm according to the threshold set by the convergence criterion.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the distributed energy storage resource aggregator optimization scheduling method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the distributed energy storage resource aggregator optimization scheduling method according to any one of claims 1 to 7 are implemented.

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