Source-load-storage scheduling method and device considering energy storage life, equipment and storage medium

Abstract

The invention relates to the technical field of source network load storage multi-objective optimization scheduling, in particular to a source load storage scheduling method and device considering energy storage life, electronic equipment and a storage medium. A particle swarm optimization algorithm adopting sine adaptive inertia weight is provided, scheduling plans of all units can be arranged, preference solutions approaching optimal values of all targets can be found more efficiently, various resources in the power distribution network can be utilized more fully, and the optimization effect is the best. According to the method, the optimal scheduling model of the active power distribution network is comprehensively established, indexes such as maintenance resource consumption of the power distribution network, energy storage life loss, scheduling resource consumption of a load aggregator and network loss of the power distribution network are analyzed, energy storage resource calling resource consumption can be greatly reduced, the service life of the system is prolonged, and the system performance is improved. And the optimization scheduling efficiency of the power distribution network is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of multi-objective optimization scheduling of sources, networks, loads and storage, and in particular to a source-load-storage scheduling method, device, electronic equipment and storage medium taking into account the life of energy storage. Background Art

[0002] To address the challenges posed by the large-scale integration of renewable energy into the grid, the combined operation of renewable energy and energy storage has become a key measure. In an integrated power system encompassing "source, grid, load, and storage," energy storage plays a key role in peak load shaving, serving as a backup energy source, and smoothing out fluctuations in renewable energy, providing crucial support for the economic dispatch and safe, stable operation of the power system.

[0003] While much research has been conducted on optimized operation and economic dispatch strategies for power sources, grids, loads, and storage, few studies have precisely analyzed the correlation between energy storage lifespan and economic dispatch outcomes. On the one hand, the factors influencing energy storage battery lifespan degradation are numerous and complex, making it difficult to estimate. On the other hand, the diversification of load types within distribution networks has led to a gradual shift from traditional unidirectional power flow to active bidirectional power flow, placing greater pressure on distribution networks to regulate peak loads. The frequent involvement of energy storage in power system optimization and dispatch has further complicated the estimation of energy storage lifespan.

[0004] Therefore, in the context of the energy internet, where "source, grid, load, and storage" interact collaboratively, research on optimal control of sources, grids, loads, and storage has overlooked the important factor of energy storage life loss. This has prevented energy storage from fully playing its role in improving the reliability of power supply in the power system, and has also prevented the effective and accurate assessment of the overall resource consumption of distribution network optimization scheduling. Therefore, there is an urgent need for an optimized scheduling strategy that considers the life loss of energy storage in the "source-load-storage" collaborative interaction. By considering the factor of energy storage life loss and combining it with the participation of controllable loads in system scheduling, the rationality of distribution network optimization scheduling can be further improved while reducing the scheduling pressure of the distribution network and improving energy utilization.

[0005] Based on this, it is necessary to develop and design a source-load-storage scheduling method that takes into account the energy storage life. Summary of the Invention

[0006] The embodiments of the present invention provide a source-load-storage scheduling method, device, electronic device and storage medium that take into account the life of energy storage, which is used to solve the problem of large overall resource consumption caused by scheduling strategies in the prior art not considering the life of energy storage equipment.

[0007] In a first aspect, an embodiment of the present invention provides a source-load-storage scheduling method taking into account energy storage life, comprising:

[0008] Build output and / or energy storage models for each component of the source, grid, load, and storage based on their dynamic characteristics and mutual influence, and build a load aggregator response output model and load aggregator dispatch strategy in combination with a controllable load response strategy.

[0009] With the goal of maximizing the reliability of the distribution network and minimizing the resource consumption of its dispatching and operation, a multi-objective optimization dispatch model for source, grid, load, and storage is established, taking into account the lifespan loss of energy storage, based on multiple constraints constructed based on network balance, distributed generation output power, and the charge and discharge power of the energy storage system.

[0010] Based on the ideal point method multi-objective decision model, the highest power supply reliability goal of the distribution network and the minimum resource consumption decision goal of the distribution network scheduling operation are transformed into single objectives;

[0011] The source-grid-load-storage multi-objective optimization scheduling model is solved based on the optimization algorithm, and the scheduling strategy is determined according to the obtained solution.

[0012] In a second aspect, an embodiment of the present invention provides a source-load-storage scheduling device that takes into account energy storage life, which is used to implement the source-load-storage scheduling method that takes into account energy storage life as described in the first aspect or any possible implementation of the first aspect. The source-load-storage scheduling device that takes into account energy storage life includes:

[0013] The source-grid-load-storage model construction module is used to construct the output and / or energy storage model of each source-grid-load-storage component based on the dynamic characteristics and mutual influence of each component, and to construct the load aggregator response output model and load aggregator scheduling strategy in combination with the controllable load response strategy;

[0014] The dispatch model construction module is used to establish a multi-objective optimization dispatch model for source, grid, load, and storage, taking into account the life loss of energy storage, with the goals of maximizing the power supply reliability of the distribution network and minimizing the consumption of dispatch and operation resources of the distribution network, based on multiple constraints constructed according to network balance, distributed power output, and the charge and discharge power of the energy storage system;

[0015] The target conversion module is used to convert the target of maximizing the power supply reliability of the distribution network and the target of minimizing the resource consumption of the distribution network's dispatching operation into a single target based on the multi-target decision model of the ideal point method;

[0016] as well as,

[0017] The scheduling strategy determination module is used to solve the source-grid-load-storage multi-objective optimization scheduling model based on the optimization algorithm and determine the scheduling strategy according to the obtained solution.

[0018] In a third aspect, an embodiment of the present invention provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, it implements the steps of the method described in the first aspect or any possible implementation of the first aspect.

[0019] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps of the method described in the first aspect or any possible implementation of the first aspect.

[0020] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0021] The present invention provides an implementation method for a source-load-storage scheduling method that takes into account the life of energy storage. First, the method constructs an output and / or energy storage model of each source-grid-load-storage component based on the dynamic characteristics and mutual influence of each component, and constructs a load aggregator response output model and a load aggregator scheduling strategy in combination with a controllable load response strategy. Then, with the highest power supply reliability of the distribution network and the lowest dispatching and operating resource consumption of the distribution network as the goals, a source-grid-load-storage multi-objective optimization scheduling model that takes into account the life loss of energy storage is established based on multiple constraints constructed according to network balance, distributed power output power, and charging and discharging power of the energy storage system. Then, based on the ideal point method multi-objective decision model, the highest power supply reliability goal of the distribution network and the lowest dispatching and operating resource consumption decision goal of the distribution network are converted into single objectives. Finally, the source-grid-load-storage multi-objective optimization scheduling model is solved based on an optimization algorithm, and a scheduling strategy is determined based on the obtained solution.

[0022] The present invention introduces an ideal point method based on an evaluation function to solve multi-objective decision-making problems, and proposes a particle swarm optimization algorithm using sinusoidal adaptive inertia weights. This algorithm can arrange the scheduling plans of each unit, more efficiently find the preferred solutions that approach the optimal values ​​of each objective, make more effective use of various resources in the distribution network, and achieve the best optimization effect.

[0023] The present invention proposes a multi-objective optimization scheduling strategy for sources, grids, loads and storage that takes into account the loss of energy storage life. It establishes a relatively comprehensive optimization scheduling model for the active distribution network and analyzes indicators such as distribution network maintenance resource consumption, energy storage life loss, load aggregator scheduling resource consumption, and distribution network network loss. It can significantly reduce the consumption of energy storage resource call resources, extend the system service life, and effectively improve the efficiency of distribution network optimization scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or descriptions of the prior art. 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 paying any creative labor.

[0025] Figure 1 This is a flow chart of a source-load-storage scheduling method taking into account energy storage life provided by an embodiment of the present invention;

[0026] Figure 2 This is a flow chart of a load aggregator response output dispatch strategy provided by an embodiment of the present invention;

[0027] Figure 3 This is a flow chart of a multi-objective optimization scheduling model for source, grid, load and storage that takes into account energy storage life loss based on a particle swarm optimization algorithm according to an embodiment of the present invention;

[0028] Figure 4 This is a functional block diagram of a source-load-storage scheduling device taking into account energy storage life provided by an embodiment of the present invention;

[0029] Figure 5 This is a functional block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0030] In the following description, specific details such as particular system structures and techniques are provided for purposes of illustration, not limitation, to facilitate a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in alternative embodiments without these specific details. In other instances, detailed descriptions of well-known systems, devices, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0031] In order to make the objectives, technical solutions and advantages of the present invention more clear, the following will be described through specific implementation methods in conjunction with the accompanying drawings.

[0032] The following is a detailed description of an embodiment of the present invention. This example is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiment.

[0033] Figure 1 Flowchart of a source-load-storage scheduling method taking into account energy storage life provided in an embodiment of the present invention.

[0034] like Figure 1 As shown, it shows a flow chart of the implementation of the source-load-storage scheduling method taking into account the energy storage life provided by an embodiment of the present invention, which is detailed as follows:

[0035] In step 101, the output and / or energy storage model of each source, grid, load and storage component is constructed based on the dynamic characteristics and mutual influence of each component, and the load aggregator response output model and load aggregator scheduling strategy are constructed in combination with the controllable load response strategy.

[0036] In some embodiments, the source, grid, load and storage components include: photovoltaic power generation, wind power generation and / or energy storage systems;

[0037] The photovoltaic power generation output model is:

[0038] P pv,t =rAη

[0039] Where, P pv,t is the output power of photovoltaic power generation at time t, r is the radiation intensity of sunlight absorbed by the photovoltaic panel, A is the installation area of ​​the photovoltaic panel, and η is the conversion efficiency of the photovoltaic panel assembly;

[0040] The wind power output model is:

[0041]

[0042] Where, v in 、v out and v rated are cut-in wind speed, cut-out wind speed and rated wind speed respectively; n w is the number of fans, P rated is the rated power of a single fan, P w,t is the output power of the wind turbine generator set at time t;

[0043] The energy storage model is:

[0044]

[0045] Where S t is the state of charge of the energy storage system at time t; E t is the energy in the energy storage system at time t; E is the rated capacity of the energy storage system.

[0046] In some embodiments, the load aggregator response output model is:

[0047]

[0048] Where U s,t is the response status of user s at time t, N is the total number of users who can participate in demand response, P s,t is the response power of user s at time t.

[0049] Exemplarily, the present invention first considers in detail the dynamic characteristics and mutual influence of each unit in the distribution network, establishes a photovoltaic output model, a wind output model, and an energy storage model; and combines the response strategy of the controllable load to establish a load aggregator response output model and a scheduling strategy model.

[0050] (1) Photovoltaic output model

[0051] As a preferred embodiment of the present invention, the photovoltaic output power is mainly affected by factors such as light intensity, photovoltaic panel surface temperature, and area. The output power mathematical model is expressed as:

[0052] P pv,t =rAη

[0053] Where, P pv,t is the output power of photovoltaic power generation at time t, r is the radiation intensity of sunlight absorbed by the photovoltaic panel, A is the installation area of ​​the photovoltaic panel, and η is the conversion efficiency of the photovoltaic panel assembly.

[0054] (2) Wind output model

[0055] As a preferred embodiment of the present invention, the active power output of a wind turbine generator set is related to natural factors such as wind speed and wind direction, as well as human control. The most influential factor is the real-time wind speed. The wind power output model is as follows:

[0056]

[0057] Where, v in 、v out and v rated are cut-in wind speed, cut-out wind speed and rated wind speed respectively; n w is the number of fans, P rated is the rated power of a single fan, P w,t is the wind turbine's output power at time t. When the real-time wind speed is lower than the cut-in speed, no power is output. When the wind speed is higher than the rated wind speed, the output active power no longer increases with wind speed. When the wind speed exceeds the cut-in speed, the wind turbine is disconnected from the grid and ceases operation.

[0058] (3) Energy storage model

[0059] The energy storage system has the functions of load smoothing and peak-shaving. The charging and discharging capacity of the energy storage system at any time is mainly determined by measuring the state of charge of the battery in the energy storage device at that time. Its mathematical model expression is:

[0060]

[0061] Where S t is the state of charge of the energy storage system at time t; E tis the energy in the energy storage system at time t; E is the rated capacity of the energy storage system.

[0062] (4) Load aggregator response output model

[0063] A load aggregator (LA) is defined as an intermediary between the providers and demanders of demand response resources. It clusters and analyzes user electricity usage characteristics and response capabilities, aggregates controllable resources, and participates in grid dispatch, thereby achieving the multi-party goals of the grid company, the load aggregator, and power end users, and minimizing resource consumption. Because the load response capabilities and capacities within the load groups managed by the load aggregator vary, the load aggregator allocates specific output plans to each user. The load aggregator's response output per unit time is:

[0064]

[0065] Where U s,t is the response status of user s at time t, N is the total number of users who can participate in demand response, P s,t is the response power of user s at time t.

[0066] (5) Load aggregator response output dispatch strategy model

[0067] Load aggregators can utilize demand-side resources to integrate user demand response resources and introduce them into market transactions, making idle load resources play a role while also bringing benefits to other power system participants. The load aggregator response output dispatch strategy model is as follows: Figure 2 As shown in the figure, first, the flexible loads are classified and aggregated, and then the distribution network optimizes the dispatching to issue demand instructions to the load aggregator. The load aggregator formulates a response plan and then determines whether the user response constraints are met. If so, a response instruction is issued to the user. Otherwise, a message to adjust the load capacity is sent to the distribution network and a new response plan is formulated.

[0068] In step 102, with the goal of maximizing the power supply reliability of the distribution network and minimizing the dispatching and operating resource consumption of the distribution network, a multi-objective optimization dispatching model of source, grid, load and storage is established that takes into account the life loss of energy storage based on multiple constraints constructed according to network balance, distributed power output and charging and discharging power of the energy storage system.

[0069] In some embodiments, the source-grid-load-storage multi-objective optimization scheduling model includes a first objective function representing the power supply reliability target of the distribution network and a second objective function representing the scheduling operation resource consumption of the distribution network, wherein the first objective function is:

[0070]

[0071] Where, and are the maximum and minimum values ​​of the daily load power of the distribution network respectively; and P g (t) are the average daily load and the momentary load of the distribution network, ω1 and ω2 are the weight coefficients of the two evaluation indicators, and min f1 is the first objective function;

[0072] The second objective function is:

[0073]

[0074] Where C1 is the daily maintenance resource consumption of the distribution network, S1 is the daily peak-valley resource consumption difference of energy storage, is the daily life loss of energy storage participating in the distribution network dispatching operation, C LA is the daily expenditure cost of the load aggregator, C loss is the daily network loss of the distribution network, minf2 is the second objective function;

[0075] The daily maintenance cost model of the distribution network is:

[0076]

[0077] Where T is the operating time of the distribution network in one day, is the energy storage operation and maintenance resource consumption per unit capacity, P ess,t is the output power of the energy storage system at time t, is the photovoltaic operation and maintenance resource consumption per unit capacity, is the photovoltaic output power at the tth moment, is the unit capacity wind turbine operation and maintenance resource consumption, P w,t is the wind turbine output power at the tth moment;

[0078] The daily peak-valley resource consumption difference model of energy storage is:

[0079]

[0080] Where p t is the resource consumption per unit of electricity in period t, P c,t is the actual charging power of the energy storage system in period t, P dis,t is the actual discharge power of the energy storage system in period t, B c,t is the actual charging state of the energy storage system during period t. c,t =1 means the energy storage system is in charging state, B c,t The energy storage system is not in charging state, B dis,t is the actual discharge state of the energy storage system in period t. dis,t =1 means the energy storage system is in the discharge state, B dis,t =0: the energy storage system is not in charging state;

[0081] The energy storage life loss model is:

[0082]

[0083] is the daily life loss of the energy storage system participating in the distribution network dispatching operation, ε is the life loss of the energy storage battery per unit charge and discharge capacity, λ(S t ) is the life loss coefficient, S t is the energy storage charge state at time t.

[0084] In some embodiments, the source-grid-load-storage multi-objective optimization scheduling model includes: distributed power output constraints, node voltage constraints, branch power constraints, total power conservation constraints, and flexible load response constraints, wherein the distributed power output constraints are:

[0085]

[0086] Where, P DG,j (t) is the actual active power output of distributed generation j in the distribution network, and are the upper and lower limits of the power output of distributed power source j respectively;

[0087]

[0088] Where λ is the prediction error coefficient of distributed power output, 0≤|λ|≤λ max , is the day-ahead forecast value of the distributed generation output;

[0089] The node voltage constraint is:

[0090]

[0091] Where U i is the voltage amplitude of the i-th node in the distribution network; and are the lower and upper limits of the node voltage amplitude respectively;

[0092] The branch power constraint is:

[0093]

[0094] Where, P L,i is the power delivered by the Lth line in the distribution network, and are the lower and upper limits of branch power respectively;

[0095] The total power conservation constraint is:

[0096]

[0097] Where, P TG,t is the total power obtained by the distribution network from the main grid at time t, P pv,t is the output power of photovoltaic power generation, P w,t is the output power of the wind turbine, P cl,t is the active power required by the uncontrollable load at time t, P ess,t and P LA,t are the active output of energy storage and load aggregation at time t, P DG,t is the total active power output of distributed generation at time t, P load,t is the total power load in the distribution network at time t;

[0098] The flexible load response constraint is:

[0099]

[0100] Where Δt is the simulation time step within the scheduling period, M s is the maximum number of responses of the reducible user s in a cycle, P s,t is the response power of user s at time t.

[0101] Exemplarily, the present invention constructs a target optimization model with the goals of maximizing power supply reliability of the distribution network and minimizing dispatching and operating costs of the distribution network. The target optimization model includes an objective function min f1, an objective function min f2, and several constraints.

[0102] The objective function minf1 is the highest power supply reliability of the distribution network. The power supply reliability is mainly reflected in the fluctuation of the comprehensive load curve on the distribution network side, which is jointly evaluated by the power supply load fluctuation amplitude and fluctuation rate.

[0103]

[0104] Where, and are the maximum and minimum values ​​of the daily load power of the distribution network respectively; and P g (t) are the average daily load and momentary load of the distribution network, ω1 and ω2 are the weight coefficients of the two evaluation indicators, and min f1 is the first objective function.

[0105] The objective function min f2 is to minimize the dispatching and operating resource consumption of the distribution network. The minimum operating resource consumption is composed of the distribution network maintenance resource consumption, the energy storage life loss resource consumption, the load aggregator dispatching resource consumption, and the distribution network network loss resource consumption.

[0106]

[0107] Where C1 is the daily maintenance resource consumption of the distribution network, S1 is the daily peak-valley resource consumption difference of energy storage, is the daily life loss of energy storage participating in the distribution network dispatching operation, C LA is the daily expenditure cost of the load aggregator, C loss is the daily network loss of the distribution network, minf2 is the second objective function

[0108] (1) Distribution network maintenance cost model

[0109] As a preferred embodiment of the present invention, the distribution network daily maintenance cost model C1 is as follows:

[0110]

[0111] Where T is the daily operation time of the distribution network, which is usually set to 24 hours. is the energy storage operation and maintenance resource consumption per unit capacity, P ess,t is the output power of the energy storage system at time t, which means the actual charge / discharge power P of the energy storage system in time period t ch,t 、P dis,t The collection of is the photovoltaic operation and maintenance resource consumption per unit capacity, is the photovoltaic output power at the tth moment, is the unit capacity wind turbine operation and maintenance resource consumption, P w,t is the wind turbine output power at the tth moment.

[0112] (2) Energy storage benefit model

[0113] As a preferred embodiment of the present invention, energy storage can play a role in load balancing and peak-to-valley shifting. By adjusting the energy storage device to charge during low-load valleys and discharge during peak-load peaks, peak-valley arbitrage can be achieved by utilizing the peak-valley electricity price difference. The daily peak-valley resource consumption difference S1 of energy storage can be expressed as:

[0114]

[0115] Where p t is the resource consumption per unit of electricity in period t, P c,t is the actual charging power of the energy storage system in period t, P dis,t is the actual discharge power of the energy storage system in period t, B c,t is the actual charging state of the energy storage system during period t. c,t =1 means the energy storage system is in charging state, B c,t The energy storage system is not in charging state, B dis,tis the actual discharge state of the energy storage system in period t. dis,t =1 means the energy storage system is in the discharge state, B dis,t =0 means the energy storage system is not in charging state.

[0116] (3) Energy storage life loss model

[0117] As a preferred embodiment of the present invention, the energy storage life is related to the number of charge and discharge cycles and can be defined by the total amount of charge and discharge cycles. The energy storage life is proportional to the charge and discharge amount of the energy storage. At the same time, when the energy storage is discharged at a high state of charge, its service life is less affected. Conversely, its service life will be accelerated. This relationship is characterized by the life loss coefficient (S(t)):

[0118]

[0119] Let the life loss cost per unit charge and discharge of the energy storage battery be ε, then the life loss cost is expressed as the life loss cost per unit charge and discharge of the energy storage battery

[0120]

[0121] is the daily life loss of the energy storage system participating in the distribution network dispatching operation, ε is the life loss of the energy storage battery per unit charge and discharge capacity, λ(S t ) is the life loss coefficient, S t is the energy storage charge state at time t.

[0122] (4) Load aggregator response output cost model

[0123] As a preferred embodiment of the present invention, the resource consumption of different load types is different. The load aggregator needs to calculate the resource consumption according to the scheduling contract. The daily expenditure cost of the load aggregator is:

[0124]

[0125] Where C LA is the daily expenditure cost of the load aggregator, f s represents the incentive price for user s to participate in scheduling per unit time period, P s,t represents the response power of user s at time t.

[0126] (5) Network loss cost model

[0127] As a preferred embodiment of the present invention, the network loss cost represents the active power loss cost generated during the power transmission process, and is used to measure the energy utilization efficiency and economy of the distribution network. loss for:

[0128]

[0129] Among them, c loss is the unit network loss cost; N l is the total number of lines; I l,t is the current of the lth line at the tth moment obtained by the probabilistic power flow; R l is the resistance of the first line.

[0130] Step 2: The target optimization model includes the objective function min f1, the objective function min f2, and several constraints. The several constraints include network balance constraints, distributed power constraints, energy storage system charge and discharge power constraints, energy storage life loss constraints, etc.

[0131] (1) Distributed power generation output constraints

[0132]

[0133] Where, P DG,j (t) is the actual active power output of distributed generation j in the distribution network, and are the upper and lower limits of the power output of distributed power source j respectively.

[0134]

[0135] Where λ is the prediction error coefficient of distributed power output, 0≤|λ|≤λ max , is the day-ahead forecast value of the distributed generation output.

[0136] (2) Node voltage constraints

[0137]

[0138] Where U i is the voltage amplitude of the i-th node in the distribution network; and are the lower and upper limits of the node voltage amplitude respectively;

[0139] (3) Branch power constraints

[0140]

[0141] Where, P L,i is the power delivered by the Lth line in the distribution network, and are the lower and upper limits of branch power respectively;

[0142] (4) Total power conservation constraint

[0143] The total amount of electricity purchased by the distribution network from the main grid at each moment is conserved by the comprehensive load of the uncontrollable load, distributed generation output and controllable load scheduling in the distribution network.

[0144] The total power conservation constraint is:

[0145]

[0146] Where, P TG,t is the total power obtained by the distribution network from the main grid at time t, P pv,t is the output power of photovoltaic power generation, P w,t is the output power of the wind turbine, P cl,t is the active power required by the uncontrollable load at time t, P ess,t and P LA,t are the active output of energy storage and load aggregation at time t, P DG,t is the total active power output of distributed generation at time t, P load,t is the total power load in the distribution network at time t

[0147] (5) Flexible load response constraints

[0148] When calling on the flexible load resources of the distribution network, taking into account the user's electricity satisfaction when participating in the dispatch, both the transferable load and the curtailable load must meet the maximum response capacity constraint; for the transferable load, the user's normal electricity demand must be guaranteed and the load transfer balance constraint within a dispatch cycle must be met.

[0149] The flexible load response constraints are:

[0150]

[0151] Where Δt is the simulation time step in the scheduling period. To avoid frequent load calls, 1h is taken in the user response model. The user's load call is based on the response plan formulated at the time. s is the maximum number of responses of the reducible user s in a cycle, P s,t is the response power of user s at time t.

[0152] (6) Energy storage system response model

[0153] The energy storage system charges and discharges according to grid instructions. The amount of energy storage system charge and discharge is related to the system's installed power and operational charge and discharge efficiency. The state of charge of the energy storage system in adjacent time periods satisfies the following coupling relationship:

[0154]

[0155] Where: S trepresents the energy storage charge state at time t; E represents the energy storage rated capacity; η represents the charge and discharge efficiency of the energy storage; Δt represents the simulation time step within the scheduling period, which is set to 1h, and P ch,t 、P dis,t represents the energy storage charging and discharging power, and σ is the self-loss coefficient of the energy storage system.

[0156] The energy storage system cannot be in the charging and discharging states at the same time and must meet the following constraints:

[0157] State of charge (SOC) constraints:

[0158] S min ≤S t ≤S max

[0159] Where: S t Indicates the energy storage charge state at time t; S min and S max are the minimum and maximum state of charge, respectively.

[0160] Charge / discharge constraints:

[0161] B ch,t +B dis,t <=1

[0162] Where: t represents a 0-1 variable, where 1 indicates that the energy storage is in a charging state; 0 indicates that the energy storage is in a discharging state.

[0163] Charge / discharge power constraints:

[0164] During the charging and discharging process, the actual power and capacity of the energy storage should be within the rated value range. The specific constraints on the energy storage charging and discharging power are as follows:

[0165] 0≤P dis,t ≤B dis,t P

[0166] 0≤P ch,t ≤B ch,t P

[0167] In step 103, the goal of maximizing power supply reliability of the distribution network and the decision goal of minimizing dispatching and operating resource consumption of the distribution network are converted into single goals based on the ideal point method multi-objective decision model.

[0168] In some embodiments, the multi-objective decision model based on the ideal point method converts the goal of maximizing the power supply reliability of the distribution network and the decision goal of minimizing the dispatching and operation resource consumption of the distribution network into a single objective, including:

[0169] Calculate the maximum value of each point target within the value range;

[0170] The satisfaction function value of each single objective function is calculated according to the first formula, wherein the first formula is:

[0171]

[0172] Where ρ1 is the first satisfaction index, ρ2 is the second satisfaction index, f1 is the first objective function, is the minimum value of the first objective function, is the maximum value of the first objective function, f2 is the second objective function, is the minimum value of the second objective function, is the maximum value of the second objective function;

[0173] The multiple goals are converted into a single goal according to a second formula, the first satisfaction index, and the second satisfaction index. The second formula is:

[0174]

[0175] In the formula, x is the decision variable, X is its definition domain, min d(ρ) is the overall coordination evaluation function, is the optimal value of the first satisfaction index, is the optimal value of the second satisfaction index.

[0176] For example, the ideal point method multi-objective decision model based on the evaluation function transforms the two multi-objective decision problems of maximizing the power supply reliability of the distribution network and optimizing the dispatching and operating cost of the distribution network into single-objective optimization problems about the evaluation function.

[0177] For a multi-objective optimization scheduling model, it is necessary to first find the optimal value of each single objective, then calculate the satisfaction function of each single objective, and finally find the preferred solution closest to the satisfactory solution in the solution space. The specific steps are as follows:

[0178] (1) Calculate the maximum value of each point target within the range. and

[0179] (2) Calculate the satisfaction function value of each single objective function. The calculation formula of each single objective satisfaction function is:

[0180]

[0181] Where ρ1 is the first satisfaction index, ρ2 is the second satisfaction index, f1 is the first objective function, is the minimum value of the first objective function, is the maximum value of the first objective function, f2 is the second objective function, is the minimum value of the second objective function, is the maximum value of the second objective function and the optimal value of the satisfaction function

[0182] (3) The multi-objective function min f1 and min f2 decision problem is transformed into a single-objective optimization problem about the evaluation function, which can be expressed as follows:

[0183]

[0184] x is the decision variable, X is its domain, and min d(p) is the overall coordination evaluation function.

[0185] In step 104, the source-grid-load-storage multi-objective optimization scheduling model is solved based on an optimization algorithm, and a scheduling strategy is determined according to the obtained solution.

[0186] In some embodiments, solving the source-grid-load-storage multi-objective optimization scheduling model based on an optimization algorithm includes:

[0187] Get multiple particles and multiple velocities;

[0188] For each particle, the optimal position is found from all iterations as the individual extreme value, thereby obtaining multiple individual extreme values;

[0189] Finding an optimal position from the plurality of particles as a global extremum;

[0190] The velocity and position of the particle swarm are updated according to the third formula, multiple individual extreme values, and the global extreme value, wherein the third formula is:

[0191]

[0192] Where, v i,j (t+1) and x i,j (t+1) is the velocity and position of the jth particle in the i-th dimension at the t+1th iteration, j is the number of particles, c1 and c2 are the first learning factor and the second learning factor, ω is the inertia weight coefficient, r1 and r2 are the first constant and the second constant, r1 and r2 are uniform random numbers in the range [0, 1];

[0193] If the position change of the global extreme value is less than the threshold, jump to the step of finding the optimal position of each particle from all previous iterations as the individual extreme value, thereby obtaining multiple individual extreme values;

[0194] Otherwise, the global extreme value is used as the solution of the source-grid-load-storage multi-objective optimization scheduling model.

[0195] For example, the improved particle swarm algorithm is used to solve the multi-objective optimization problem of the source-grid-load-storage multi-objective optimization scheduling model taking into account the energy storage life loss. The steps of the improved particle swarm algorithm are as follows:

[0196] In a D-dimensional target search space, there is a community of N particles, and the i-th particle is represented as a D-dimensional vector, which is recorded as:

[0197] X i =(x i,1 ,x i,2 ,...,x i,D )i=1,2,...,N

[0198] The "flying" speed of the i-th particle is also a D-dimensional vector:

[0199] V i =(v i,1 ,v i,2 ,...,v i,D )i=1,2,...,N

[0200] The optimal position found by the i-th particle so far is called the individual extreme value, which is recorded as:

[0201] P best =(p i,1 ,p i,2 ,...,p i,D )i=1,2,...,N

[0202] The optimal position found by the entire particle swarm so far is called the global extreme value, which is recorded as:

[0203] g best =(g i,1 ,g i,2 ,...,g i,D )i=1,2,...,N

[0204] Update the velocity and position of the particle swarm:

[0205]

[0206] Where, v i,j (t+1) and x i,j (t+1) are the velocity and position of the jth particle in the i-th dimension in the t+1-th iteration, j is the number of particles, c1 and c2 are the first learning factor and the second learning factor, ω is the inertia weight coefficient, r1 and r2 are the first constant and the second constant, respectively, and r1 and r2 are uniform random numbers in the range [0, 1].

[0207] Since a larger inertia factor helps to escape from local minima and facilitate global search, while a smaller inertia factor facilitates accurate local search of the current search area, which facilitates algorithm convergence, most optimization algorithms usually use the linear decreasing weight method. However, if this method cannot find the best point in the early stages of the algorithm, the linear decrease of ω will prevent the algorithm from ultimately converging to the best point. Therefore, to overcome the shortcomings of the linear decrease, the sinusoidal adaptive inertia weight method is used for optimization to balance the global search capability and local improvement capability of the PSO algorithm:

[0208]

[0209] where ω max 、ω min They represent the maximum and minimum values ​​of ω respectively, f represents the current objective function value of the particle, and f avg and f min They represent the average target value and minimum target value of all current particles respectively.

[0210] In the above formula, the inertia weight automatically changes with the particle's objective function value, hence the name adaptive weight. T represents the total iteration time, and θ controls the period of the sine function. The sinusoidal adaptive weighting method adjusts the inertia weight in two ways: It periodically adjusts the inertia weight according to the sine function throughout the iteration process; while simultaneously, the inertia weight of each particle is adaptively adjusted based on its fitness. This method is more intelligent, boasting strong global and local search capabilities, resulting in better optimal solutions and faster convergence.

[0211] The specific process of solving the multi-objective optimization scheduling model of source, grid, load and storage taking into account the energy storage life loss based on the particle swarm optimization algorithm can be referred to Figure 3 .

[0212] The present invention provides an implementation method for a source-load-storage scheduling method that takes into account the life of energy storage. First, the method constructs an output and / or energy storage model of each source-grid-load-storage component based on the dynamic characteristics and mutual influence of each component, and constructs a load aggregator response output model and a load aggregator scheduling strategy in combination with a controllable load response strategy. Then, with the highest power supply reliability of the distribution network and the lowest dispatching and operating resource consumption of the distribution network as the goals, a source-grid-load-storage multi-objective optimization scheduling model that takes into account the life loss of energy storage is established based on multiple constraints constructed according to network balance, distributed power output power, and charging and discharging power of the energy storage system. Then, based on the ideal point method multi-objective decision model, the highest power supply reliability goal of the distribution network and the lowest dispatching and operating resource consumption decision goal of the distribution network are converted into single objectives. Finally, the source-grid-load-storage multi-objective optimization scheduling model is solved based on an optimization algorithm, and a scheduling strategy is determined based on the obtained solution.

[0213] The present invention introduces an ideal point method based on an evaluation function to solve multi-objective decision-making problems, and proposes a particle swarm optimization algorithm using sinusoidal adaptive inertia weights. This algorithm can arrange the scheduling plans of each unit, more efficiently find the preferred solutions that approach the optimal values ​​of each objective, make more effective use of various resources in the distribution network, and achieve the best optimization effect.

[0214] The present invention proposes a multi-objective optimization scheduling strategy for sources, grids, loads and storage that takes into account the loss of energy storage life. It establishes a relatively comprehensive optimization scheduling model for the active distribution network and analyzes indicators such as distribution network maintenance resource consumption, energy storage life loss, load aggregator scheduling resource consumption, and distribution network network loss. It can significantly reduce the consumption of energy storage resource call resources, extend the system service life, and effectively improve the efficiency of distribution network optimization scheduling.

[0215] It should be understood that the size of the serial numbers of each step in the above embodiment does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0216] The following is an embodiment of the device of the present invention. For details not described in detail, please refer to the corresponding method embodiment described above.

[0217] Figure 4 This is a functional block diagram of a source-load-storage scheduling device taking into account the energy storage life provided by an embodiment of the present invention, referring to Figure 4 The source-load-storage scheduling device taking into account the energy storage life includes: a source-grid-load-storage model construction module 401, a scheduling model construction module 402, a target conversion module 403 and a scheduling strategy determination module 404, wherein:

[0218] The source-grid-load-storage model construction module 401 is used to construct the output and / or energy storage model of each source-grid-load-storage component based on the dynamic characteristics and mutual influence of each component, and to construct the load aggregator response output model and load aggregator scheduling strategy in combination with the controllable load response strategy;

[0219] The scheduling model construction module 402 is used to establish a multi-objective optimization scheduling model for source, grid, load, and storage that takes into account the life loss of energy storage, with the goal of maximizing the power supply reliability of the distribution network and minimizing the consumption of scheduling and operation resources of the distribution network, based on multiple constraints constructed based on network balance, distributed power output, and charge and discharge power of the energy storage system;

[0220] The target conversion module 403 is used to convert the target of maximizing the power supply reliability of the distribution network and the target of minimizing the resource consumption of the dispatching operation of the distribution network into a single target based on the multi-target decision model of the ideal point method;

[0221] as well as,

[0222] The scheduling strategy determination module 404 is used to solve the source-grid-load-storage multi-objective optimization scheduling model based on an optimization algorithm and determine the scheduling strategy according to the obtained solution.

[0223] Figure 5 : is a functional block diagram of an electronic device provided by an embodiment of the present invention. Figure 5 As shown, the electronic device 5 of this embodiment includes: a processor 500 and a memory 501, wherein the memory 501 stores a computer program 502 that can be run on the processor 500. When the processor 500 executes the computer program 502, the steps in the above-mentioned source-load-storage scheduling method and embodiment taking into account the energy storage life are implemented, for example Figure 1 Steps 101 to 104 are shown.

[0224] Illustratively, the computer program 502 may be divided into one or more modules / units, and the one or more modules / units are stored in the memory 501 and executed by the processor 500 to implement the present invention.

[0225] The electronic device 5 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device 5 may include, but is not limited to, a processor 500 and a memory 501. Those skilled in the art will understand that Figure 5 It is only an example of the electronic device 5 and does not constitute a limitation of the electronic device 5. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device 5 may also include input and output devices, network access devices, buses, etc.

[0226] The processor 500 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0227] The memory 501 may be an internal storage unit of the electronic device 5, such as a hard disk or memory of the electronic device 5. The memory 501 may also be an external storage device of the electronic device 5, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 5. Furthermore, the memory 501 may include both an internal storage unit of the electronic device 5 and an external storage device. The memory 501 is used to store the computer program 502 and other programs and data required by the electronic device 5. The memory 501 may also be used to temporarily store data that has been output or is about to be output.

[0228] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the implementation method can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method implementation method, and will not be repeated here.

[0229] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0230] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0231] In the embodiments provided by the present invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0232] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of this embodiment.

[0233] In addition, the functional units in various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0234] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned implementation method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and when the computer program is executed by the processor, it can implement the steps of the above-mentioned various methods and device implementation methods. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc.

[0235] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A source-load-storage scheduling method taking into account the energy storage life, characterized in that: include: According to the dynamic characteristics and mutual influence of each part of the source, grid, load and storage, the output and / or energy storage model of each part of the source, grid, load and storage is constructed, and the load aggregator response output model and load aggregator dispatch strategy are constructed in combination with the controllable load response strategy; With the goal of maximizing the reliability of power supply of the distribution network and minimizing the resource consumption of the dispatching and operation of the distribution network, a multi-objective optimization dispatching model of source, grid, load and storage taking into account the life loss of energy storage is established based on multiple constraints constructed according to network balance, output power of distributed power sources and charging and discharging power of energy storage systems. Based on the ideal point method multi-objective decision model, the highest power supply reliability goal of the distribution network and the minimum resource consumption decision goal of the distribution network dispatching operation are transformed into a single objective; The source-grid-load-storage multi-objective optimization scheduling model is solved based on the optimization algorithm, and the scheduling strategy is determined according to the obtained solution.

2. The source-load-storage scheduling method taking into account the energy storage life according to claim 1 is characterized in that: The source, grid, load and storage components include: photovoltaic power generation, wind power generation and / or energy storage system; The photovoltaic power generation output model is: P pv,t =rAη Where P pv,t is the output power of photovoltaic power generation at time t, r is the radiation intensity of sunlight absorbed by the photovoltaic panel, A is the installation area of ​​the photovoltaic panel, and η is the conversion efficiency of the photovoltaic panel assembly; The wind power output model is: In the formula, v in 、v out and v rated are cut-in wind speed, cut-out wind speed and rated wind speed respectively; n w is the number of fans, P rated is the rated power of a single fan, P w,t is the output power of the wind turbine generator set at time t; The energy storage model is: In the formula, S t is the state of charge of the energy storage system at time t; E t is the energy in the energy storage system at time t; E is the rated capacity of the energy storage system.

3. The source-load-storage scheduling method taking into account the energy storage life according to claim 1 is characterized in that: The load aggregator response output model is: Where U s，t is the response status of user s at time t, N is the total number of users who can participate in demand response, P s，t is the response power of user s at time t.

4. The source-load-storage scheduling method taking into account the energy storage life according to claim 1 is characterized in that: The source-grid-load-storage multi-objective optimization scheduling model includes a first objective function representing the power supply reliability target of the distribution network and a second objective function representing the scheduling and operation resource consumption of the distribution network, wherein the first objective function is: In the formula, and are the maximum and minimum values ​​of the daily load power of the distribution network respectively; and P g (t) are the average daily load and time value of the distribution network, ω1 and ω2 are the weight coefficients of the two evaluation indicators, and min f1 is the first objective function; The second objective function is: In the formula, C1 is the daily maintenance resource consumption of the distribution network, S1 is the daily peak-valley resource consumption difference of energy storage, is the daily life loss of energy storage participating in the distribution network dispatching operation, C LA is the daily expenditure cost of the load aggregator, C loss is the daily network loss of the distribution network, min f2 is the second objective function; The daily maintenance cost model of the distribution network is: Where T is the operation time of the distribution network in one day, is the energy storage operation and maintenance resource consumption per unit capacity, P ess，t is the output power of the energy storage system at the tth moment, is the photovoltaic operation and maintenance resource consumption per unit capacity, is the photovoltaic output power at the tth moment, is the unit capacity wind turbine operation and maintenance resource consumption, P w，t is the wind turbine output power at the tth moment; The daily peak-valley resource consumption difference model of energy storage is: In the formula, p t is the resource consumption per unit of electricity in period t, P c，t is the actual charging power of the energy storage system in period t, P dis，t is the actual discharge power of the energy storage system in period t, B c，t is the actual charging state of the energy storage system in period t. c，t =1 means the energy storage system is in charging state, B c，t The energy storage system is not in charging state, B dis，t is the actual discharge state of the energy storage system in period t. dis，t =1 means the energy storage system is in the discharge state, B dis，t =0 means the energy storage system is not in charging state; The energy storage life loss model is: is the daily life loss of the energy storage system participating in the distribution network dispatching operation, ε is the life loss of the energy storage battery per unit charge and discharge capacity, λ(S t ) is the life loss coefficient, S t is the energy storage charge state at the tth moment.

5. The source-load-storage scheduling method taking into account the energy storage life according to claim 1 is characterized in that: The source-grid-load-storage multi-objective optimization scheduling model includes: distributed power output constraints, node voltage constraints, branch power constraints, total power conservation constraints and flexible load response constraints, wherein the distributed power output constraints are: Where P DG，j (t) is the actual active power output of distributed generation j in the distribution network, and are the upper and lower limits of power output of distributed generation j respectively; Where λ is the prediction error coefficient of the distributed generation output, 0≤|λ|≤λ max , It is the day-ahead forecast value of the distributed generation output; The node voltage constraint is: Where U i is the voltage amplitude of the ith node in the distribution network; and are the lower and upper limits of the node voltage amplitude respectively; The branch power constraint is: Where P L，i is the power transmitted by the Lth line in the distribution network, and are the lower and upper limits of branch power respectively; The total power conservation constraint is: Where P TG，t is the total power obtained by the distribution network from the main grid at time t, P pv，t is the output power of photovoltaic power generation, P w，t is the output power of the wind turbine, P cl，t is the active power required by the uncontrollable load at time t, P ess，t and P LA，t are the active output of energy storage and load aggregation at time t, P DG，t is the total active power output of distributed generation at time t, P load，t is the total power load in the distribution network at time t; The flexible load response constraint is: Where Δt is the simulation time step within the scheduling period, M s is the maximum number of responses of the reducible user s in a cycle, P s，t is the response power of user s at time t.

6. The source-load-storage scheduling method taking into account the energy storage life according to claim 1 is characterized in that: The multi-objective decision model based on the ideal point method transforms the highest power supply reliability goal of the distribution network and the minimum resource consumption decision goal of the distribution network into a single goal, including: Calculate the maximum value of each point target within the range; The satisfaction function value of each single objective function is calculated according to the first formula, wherein the first formula is: In the formula, ρ1 is the first satisfaction index, ρ2 is the second satisfaction index, f1 is the first objective function, is the minimum value of the first objective function, is the maximum value of the first objective function, f2 is the second objective function, is the minimum value of the second objective function, is the maximum value of the second objective function; The multiple goals are converted into a single goal according to a second formula, the first satisfaction index and the second satisfaction index, wherein the second formula is: In the formula, x is the decision variable, X is its definition domain, min d(ρ) is the overall coordination evaluation function, is the optimal value of the first satisfaction index, is the optimal value of the second satisfaction index.

7. The source-load-storage scheduling method taking into account the energy storage life according to any one of claims 1 to 6, characterized in that: The solving of the source-grid-load-storage multi-objective optimization scheduling model based on the optimization algorithm includes: Get multiple particles and multiple velocities; For each particle, find the optimal position from all iterations as the individual extreme value, thereby obtaining multiple individual extreme values; Finding the optimal position from the plurality of particles as a global extremum; The speed and position of the particle swarm are updated according to the third formula, multiple individual extreme values ​​and the global extreme value, wherein the third formula is: In the formula, v i,j (t+1) and x i,j (t+1) are the velocity and position of the jth particle in the i-th dimension at the t+1th iteration, j is the number of particles, c1 and c2 are the first learning factor and the second learning factor, ω is the inertia weight coefficient, r1 and r2 are the first constant and the second constant, r1 and r2 are uniform random numbers in the range of [0, 1]; If the position change of the global extreme value is less than the threshold, jump to the step of finding the optimal position of each particle from all iterations as the individual extreme value, thereby obtaining multiple individual extreme values; Otherwise, the global extreme value is used as the solution of the source-grid-load-storage multi-objective optimization scheduling model.

8. A source-load-storage scheduling device taking into account the energy storage life, characterized in that: The method for implementing the source-load-storage scheduling method taking into account the energy storage life as described in any one of claims 1 to 7, wherein the source-load-storage scheduling device taking into account the energy storage life comprises: The source-grid-load-storage model building module is used to build the output and / or energy storage model of each source-grid-load-storage component according to the dynamic characteristics and mutual influence of each component, and to build the load aggregator response output model and load aggregator dispatching strategy in combination with the controllable load response strategy; The dispatch model building module is used to establish a multi-objective optimization dispatch model of source, grid, load and storage that takes into account the life loss of energy storage, with the goal of maximizing the power supply reliability of the distribution network and minimizing the dispatch operation resource consumption of the distribution network, based on multiple constraints constructed according to network balance, distributed power output and the charging and discharging power of the energy storage system; The target conversion module is used to convert the highest target of power supply reliability of the distribution network and the minimum decision target of dispatching and operating resource consumption of the distribution network into a single target based on the ideal point method multi-target decision model; as well as, The scheduling strategy determination module is used to solve the source-grid-load-storage multi-objective optimization scheduling model based on the optimization algorithm, and determine the scheduling strategy according to the obtained solution.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method as claimed in any one of claims 1 to 7 are implemented.

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