A micro-grid energy storage configuration method and device, computer equipment and storage medium

Abstract

The present application relates to the technical field of micro-grid energy storage configuration, and discloses a micro-grid energy storage configuration method, device, computer equipment and storage medium, the method comprising: obtaining the full life cycle cost of the energy storage system in the micro-grid; obtaining the energy storage income and the wind and light utilization rate of the energy storage system within a preset time period, and determining a target function based on the energy storage income and the wind and light utilization rate; establishing an energy storage capacity planning model based on the full life cycle cost; solving the energy storage capacity planning model based on the target function and a preset constraint condition set, to obtain a target micro-grid energy storage configuration result. The present application considers the full life cycle cost of the energy storage system, takes the energy storage income and the wind and light utilization rate as the target function, optimizes the energy utilization rate of the energy storage system within the preset time period to obtain the target micro-grid energy storage configuration result, and guarantees the energy storage economy. Furthermore, under the configuration of the target micro-grid energy storage configuration result, the energy storage utilization rate of the micro-grid can be improved.

Description

Technical Field

[0001] This invention relates to the field of microgrid energy storage configuration technology, specifically to a microgrid energy storage configuration method, device, computer equipment, and storage medium. Background Technology

[0002] Energy storage systems are an important component of the power system's "collection-generation-transmission-distribution-consumption-storage" structure and form the foundation for building new energy microgrids. Wind power and photovoltaic power generation are intermittent energy sources, exhibiting significant instability and suffering greatly from issues such as "wind curtailment" and "power restriction."

[0003] As a solution for integrating new energy sources, the concept of microgrids has emerged. From a system perspective, a microgrid combines generators, loads, energy storage devices, and control devices into a single, controllable unit. Since the total power supply and load cannot always be in a state of supply-demand balance, energy storage systems are needed to absorb excess energy or release energy to compensate for insufficient system power. During off-grid and grid-connected operation, energy storage devices stabilize voltage and adjust frequency to achieve safe and reliable power supply. When integrating distributed power sources and supplying high-quality power to loads, they balance system power. Therefore, energy storage systems are essential in microgrids, solving the problem of power supply-demand imbalance. In the power system, they primarily play a role in peak shaving, improving the operational stability of microgrids, and enhancing power quality.

[0004] Currently, research on microgrid energy storage configuration planning mainly focuses on short- to medium-term configuration planning that considers economic objectives such as cost reduction and maximizing returns. Research on long-term configuration planning that considers the entire energy storage lifecycle is relatively limited. Therefore, there is an urgent need to propose a new microgrid energy storage configuration method that takes into account the entire energy storage lifecycle. Summary of the Invention

[0005] In view of this, the present invention provides a microgrid energy storage configuration method to address the problem that existing research on microgrid energy storage configuration planning mainly focuses on short- to medium-term configuration planning that considers economic objectives such as cost reduction and maximizing benefits, while there is a lack of research on long-term configuration planning that considers the entire energy storage life cycle.

[0006] In a first aspect, the present invention provides a microgrid energy storage configuration method, the microgrid energy storage configuration comprising:

[0007] Obtain the total lifecycle cost of the energy storage system in the microgrid; obtain the energy storage revenue and wind and solar utilization rate of the energy storage system within a preset time period, and determine the objective function based on the energy storage revenue and wind and solar utilization rate; establish an energy storage capacity planning model based on the total lifecycle cost; solve the energy storage capacity planning model based on the objective function and a preset set of constraints to obtain the energy storage configuration result of the target microgrid.

[0008] The microgrid energy storage configuration method provided by this invention considers the full life cycle cost of the energy storage system. With energy storage revenue and wind and solar utilization rate as objective functions, it optimizes the energy utilization rate of the energy storage system within a preset time period to obtain the target microgrid energy storage configuration result, thus ensuring the economic efficiency of energy storage. Furthermore, under the configuration of the target microgrid energy storage configuration result, the energy storage utilization rate of the microgrid can be improved.

[0009] In one alternative implementation, obtaining the total lifecycle cost of the energy storage system in the microgrid includes:

[0010] Obtain the target comprehensive cost and equivalent cycle number of the energy storage system in the microgrid; establish an energy storage lifetime model based on the equivalent cycle number; and determine the full life cycle cost of the energy storage system based on the energy storage lifetime model and the target comprehensive cost.

[0011] This invention establishes an energy storage life model based on the equivalent number of cycles of the energy storage system, ignoring the impact of other factors on the energy storage life. Furthermore, by combining the target comprehensive cost of the energy storage system, the full life cycle cost of the energy storage system is determined, thereby improving the accuracy of the full life cycle cost of the energy storage system.

[0012] In one optional implementation, obtaining the target overall cost and equivalent cycle count of the energy storage system in the microgrid includes:

[0013] Obtain the investment cost, replacement cost, operation and maintenance cost, processing cost, recycling cost, and energy storage discharge capacity of the energy storage system; determine the target comprehensive cost based on the investment cost, replacement cost, operation and maintenance cost, processing cost, and recycling cost; and determine the equivalent number of cycles based on the energy storage discharge capacity.

[0014] The target comprehensive cost provided by this invention takes into account the investment cost, replacement cost, operation and maintenance cost, processing cost and recycling cost of the energy storage system, and meets the "IEC60300-3-3 International Standard".

[0015] In one optional implementation, an energy storage lifetime model is established based on the equivalent number of cycles, including:

[0016] Obtain the first energy storage cycle number of the energy storage system; determine the second energy storage cycle number based on the energy storage discharge amount and the first energy storage cycle number; establish an energy storage lifetime model based on the second energy storage cycle number and the equivalent cycle number.

[0017] This invention converts the number of cycles at different depths of discharge into the number of cycles at 100% depth of discharge and establishes an energy storage life model. It uses only the depth of discharge as the core factor affecting battery life loss and ignores the influence of other factors on energy storage life.

[0018] In one optional implementation, the energy storage capacity planning model is solved based on the objective function and a preset set of constraints to obtain the target microgrid energy storage configuration result, including:

[0019] Obtain a set of preset constraints; based on the objective function and the set of preset constraints, solve the energy storage capacity planning model using an improved simulated annealing algorithm and the main objective method to obtain the energy storage configuration result of the target microgrid.

[0020] The improved simulated annealing algorithm provided by this invention solves the problem that traditional particle swarm optimization algorithms are prone to getting trapped in local extrema. Furthermore, by combining the main objective method to solve the energy storage capacity planning model, it does not mask the changing characteristics of microgrid energy storage in different time periods, thus improving the configuration accuracy of the target microgrid energy storage configuration results. In addition, it can improve the energy storage utilization rate of microgrids.

[0021] In one optional implementation, based on the objective function and a preset set of constraints, an improved simulated annealing algorithm and a principal objective method are used to solve the energy storage capacity planning model to obtain the target microgrid energy storage configuration result, including:

[0022] The first, second, and third objectives of the energy storage system are determined based on the objective function. Based on the preset constraint set, the energy storage capacity planning model is solved using an improved simulated annealing algorithm to obtain the initial microgrid energy storage configuration result that satisfies the first objective. Based on the second objective, the preset constraint set, and the initial microgrid energy storage configuration result, the energy storage capacity planning model is solved using the main objective method to obtain the target microgrid energy storage configuration result that satisfies the third objective.

[0023] This invention determines the final target microgrid energy storage configuration result by satisfying different objectives in the energy storage capacity planning model. It does not mask the changing characteristics of microgrid energy storage in different time periods, thus improving the configuration accuracy of the target microgrid energy storage configuration result. Furthermore, it can improve the energy storage utilization rate of the microgrid.

[0024] In one optional implementation, based on a preset set of constraints, an improved simulated annealing algorithm is used to solve the energy storage capacity planning model to obtain an initial microgrid energy storage configuration that satisfies the first objective, including:

[0025] An improved simulated annealing algorithm is used to determine the target energy storage output of the energy storage system that meets the preset set of constraints. Based on the target energy storage output, the energy storage capacity planning model is solved to obtain the initial microgrid energy storage configuration result that meets the first objective.

[0026] This invention utilizes an improved simulated annealing algorithm to solve the energy storage capacity planning model, thus solving the problem that traditional particle swarm optimization algorithms are prone to getting trapped in local extrema.

[0027] In one optional implementation, based on the second objective, a preset set of constraints, and the initial microgrid energy storage configuration results, the energy storage capacity planning model is solved using the primary objective method to obtain the microgrid energy storage configuration results that satisfy the third objective, including:

[0028] Obtain the target scaling factor; based on the target scaling factor, the second objective, and the preset constraint set, convert the initial microgrid energy storage configuration result into target constraints; solve the energy storage capacity planning model based on the target constraints to obtain the target microgrid energy storage configuration result that satisfies the third objective.

[0029] This invention uses the primary objective method to solve the energy storage capacity planning model, which does not mask the changing characteristics of microgrid energy storage over different time periods, thus improving the configuration accuracy of the target microgrid energy storage configuration results. Furthermore, it can improve the energy storage utilization rate of the microgrid.

[0030] In one optional implementation, the preset constraint set includes: generator set operation constraints, energy storage system constraints, and power system constraints.

[0031] This invention, by taking into account the constraints of generator set operation, energy storage system, and power system, can ensure the stable operation of the energy storage system.

[0032] In one alternative implementation, the generator set operating constraints include: gas turbine operating constraints and renewable energy operating constraints.

[0033] In one alternative implementation, the energy storage system constraints include: energy storage operation constraints, energy storage power constraints, and continuous discharge time constraints.

[0034] In one alternative implementation, power system constraints include: power balance constraints, reserve capacity constraints, and renewable energy penetration constraints.

[0035] In a second aspect, the present invention provides a microgrid energy storage configuration device, the microgrid energy storage configuration device comprising:

[0036] The module is used to acquire the total lifecycle cost of the energy storage system in the microgrid; the module is used to acquire the energy storage revenue and wind and solar utilization rate of the energy storage system within a preset time period, and to determine the objective function based on the energy storage revenue and wind and solar utilization rate; the module is used to establish an energy storage capacity planning model based on the total lifecycle cost; and the module is used to solve the energy storage capacity planning model based on the objective function and a preset set of constraints to obtain the energy storage configuration result of the target microgrid.

[0037] The microgrid energy storage configuration device provided by this invention considers the full life cycle cost of the energy storage system. With energy storage revenue and wind and solar utilization rate as objective functions, it optimizes the energy utilization rate of the energy storage system within a preset time period to obtain the target microgrid energy storage configuration result, ensuring the economic efficiency of energy storage. Furthermore, under the configuration of the target microgrid energy storage configuration result, the energy storage utilization rate of the microgrid can be improved.

[0038] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the microgrid energy storage configuration method of the first aspect or any corresponding embodiment described above.

[0039] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions, which are used to cause a computer to execute the microgrid energy storage configuration method of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0040] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0041] Figure 1 This is a schematic diagram of a DC bus-independent microgrid structure according to an embodiment of the present invention;

[0042] Figure 2 This is a flowchart illustrating a microgrid energy storage configuration method according to an embodiment of the present invention;

[0043] Figure 3 This is a flowchart illustrating another microgrid energy storage configuration method according to an embodiment of the present invention;

[0044] Figure 4 This is a flowchart illustrating another microgrid energy storage configuration method according to an embodiment of the present invention;

[0045] Figure 5 This is a structural block diagram of a microgrid energy storage configuration device according to an embodiment of the present invention;

[0046] Figure 6 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] Energy storage systems are an important component of the power system's "collection-generation-transmission-distribution-consumption-storage" structure and form the foundation for building new energy microgrids. Wind power and photovoltaic power generation are intermittent energy sources, exhibiting significant instability and suffering greatly from issues such as "wind curtailment" and "power restriction."

[0049] As a solution for integrating new energy sources, the concept of microgrids has emerged.

[0050] When a microgrid operates independently, local wind and solar power resources are converted into electricity and supplied to the loads within the microgrid in conjunction with an energy storage system. Microgrids are classified as DC, AC, or hybrid AC / DC microgrids based on the type of bus. In a DC bus microgrid, distributed power sources and energy storage are connected to the bus via a converter. This embodiment of the invention selects a DC bus independent microgrid for modeling and analysis, with the structure as follows: Figure 1 As shown.

[0051] Among them, P w (t) represents the total output power of the wind turbine generator set; P v (t) represents the total output power of photovoltaic power generation; P g (t) represents the total power output of the gas turbine generator; P l (t) represents the load demand power; P hess (t) represents the output power of the energy storage system; P bat (t) represents the battery output power.

[0052] Furthermore, based on the aforementioned microgrid structure, this embodiment of the invention provides a microgrid energy storage configuration method. By considering the full life-cycle cost of the energy storage system and using energy storage revenue and wind and solar utilization rates as objective functions, the energy utilization rate of the energy storage system within a preset time period is optimized to obtain the target microgrid energy storage configuration result, thereby ensuring the economic efficiency of energy storage and improving the energy storage utilization rate of the microgrid.

[0053] According to an embodiment of the present invention, a microgrid energy storage configuration method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0054] This embodiment provides a microgrid energy storage configuration method, which can be used for the above-mentioned... Figure 1 The DC bus-independent microgrid shown is Figure 2 This is a flowchart of a microgrid energy storage configuration method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps:

[0055] Step S201: Obtain the total lifecycle cost of the energy storage system in the microgrid.

[0056] The total lifecycle cost of an energy storage system refers to the cost incurred by the system during its entire lifespan.

[0057] Step S202: Obtain the energy storage revenue and wind and solar utilization rate of the energy storage system within a preset time period, and determine the objective function based on the energy storage revenue and wind and solar utilization rate.

[0058] In this embodiment, the preset time period represents the four seasons of spring, summer, autumn, and winter; the wind and solar utilization rate represents the wind and solar curtailment rate; and the objective function includes maximizing energy storage revenue and minimizing wind and solar utilization rate.

[0059] Specifically, when the grid's capacity to absorb renewable energy is insufficient, energy storage can store electricity and release it when the renewable energy absorption capacity is sufficient, thereby alleviating the problem of wind and solar curtailment. Therefore, in this embodiment, the increased wind and solar grid connection revenue after installing energy storage is taken as the energy storage revenue, and the maximum energy storage revenue is shown in the following relationship (1):

[0060] maxS ess =S new -C cost (1)

[0061] In the formula: S ess Indicates energy storage revenue; S new The revenue from newly connected renewable energy sources is represented by the following equation (2); C cost This indicates the annualized cost of energy storage.

[0062]

[0063] In the formula: i = 1, 2, 3, 4 represent the four seasons of spring, summer, autumn, and winter; t = 1, 2, 3, 4 represent the 24 time periods within a day; This represents the wind power output during time period t in season i; This represents the newly generated wind power during period t in season i; This represents the photovoltaic output during time period t in season i; p represents the newly generated photovoltaic power during period t in season i; w Indicates the on-grid tariff for wind power; p pvThis indicates the feed-in tariff for photovoltaic power.

[0064] Furthermore, the minimum utilization rate of wind and solar power is shown in the following equation (3):

[0065]

[0066] In the formula: L represents the wind and solar utilization rate; This represents the upper limit of wind power output during time period t in season i; This represents the upper limit of photovoltaic power output during the period t of season i.

[0067] Step S203: Establish an energy storage capacity planning model based on the full life cycle cost.

[0068] Specifically, according to the description of step S201, the total life cycle cost can represent the cost incurred by the energy storage system during its entire life cycle. Therefore, this embodiment establishes a corresponding energy storage capacity planning model based on the total life cycle cost, so that the constructed energy storage capacity planning model takes into account the total life cycle cost of the energy storage system and improves the accuracy of the model.

[0069] Step S204: Based on the objective function and the preset set of constraints, solve the energy storage capacity planning model to obtain the energy storage configuration result of the target microgrid.

[0070] The target microgrid energy storage configuration results include the optimal energy storage capacity and power.

[0071] Specifically, with the goal of maximizing energy storage revenue and minimizing wind and solar utilization, solving the energy storage capacity planning model can yield the target microgrid energy storage configuration result that satisfies the preset set of constraints.

[0072] The microgrid energy storage configuration method provided in this embodiment considers the full life cycle cost of the energy storage system. With energy storage revenue and wind and solar utilization rate as objective functions, the energy utilization rate of the energy storage system within a preset time period is optimized to obtain the target microgrid energy storage configuration result, which ensures the economic efficiency of energy storage. Furthermore, under the configuration of the target microgrid energy storage configuration result, the energy storage utilization rate of the microgrid can be improved.

[0073] This embodiment provides a microgrid energy storage configuration method, which can be used for the above-mentioned... Figure 1 The DC bus-independent microgrid shown is Figure 3 This is a flowchart of a microgrid energy storage configuration method according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps:

[0074] Step S301: Obtain the total lifecycle cost of the energy storage system in the microgrid.

[0075] Specifically, step S301 includes:

[0076] Step S3011: Obtain the target comprehensive cost and equivalent cycle number of the energy storage system in the microgrid.

[0077] The target comprehensive cost represents the annualized cost of energy storage in the energy storage system.

[0078] Specifically, the number of cycles of the battery in the energy storage system at different depths of discharge can be converted into the equivalent number of full cycles at 100% depth of discharge, which is the equivalent number of cycles of the energy storage system.

[0079] Step S3012: Establish an energy storage lifetime model based on the equivalent number of cycles.

[0080] Specifically, the energy storage lifetime model is an equivalent cycle lifetime model based on the depth of discharge. The principle is to convert the number of cycles at different depths of discharge into the equivalent number of cycles at 100% depth of discharge. Therefore, the corresponding energy storage lifetime model can be established by obtaining the equivalent number of cycles.

[0081] Step S3013: Based on the energy storage life model and the target comprehensive cost, determine the total life cycle cost of the energy storage system.

[0082] Specifically, according to the description of step S201, the total life cycle cost represents the cost incurred by the energy storage system during its entire life cycle. Therefore, based on the established energy storage life model and the target comprehensive cost, the total life cycle cost of the energy storage system can be determined.

[0083] This embodiment establishes an energy storage life model based on the equivalent number of cycles of the energy storage system, ignoring the impact of other factors on the energy storage life. Furthermore, by combining the target comprehensive cost of the energy storage system, the total life cycle cost of the energy storage system is determined, thereby improving the accuracy of the total life cycle cost of the energy storage system.

[0084] In some optional implementations, step S3011 above includes:

[0085] Step a1: Obtain the investment cost, replacement cost, operation and maintenance cost, processing cost, recycling cost, and energy storage discharge capacity of the energy storage system.

[0086] Step a2: Determine the target comprehensive cost based on investment cost, replacement cost, operation and maintenance cost, processing cost, and recovery cost.

[0087] Step a3: Determine the equivalent number of cycles based on the energy storage discharge amount.

[0088] Specifically, according to the IEC 60300-3-3 international standard, total life cycle cost typically includes six parts: product design cost, manufacturing cost, procurement cost, usage cost, maintenance cost, and disposal cost. This embodiment considers the total life cycle cost C. cost This includes investment cost C1, replacement cost C2, operation and maintenance cost C3, disposal cost C4, and recovery cost C5.

[0089] The energy storage system (ESS) consists of three parts: storage units, power conversion system (PCS), and auxiliary equipment. Therefore, the investment cost C1 is shown in the following equation (4):

[0090] C1 = c E E ess +c P P ess +c B E ess (4)

[0091] In the formula: c E This indicates the unit capacity price of the energy storage system, expressed in yuan / (kW·h); c P The price per unit power of the energy storage system is expressed in ¥ / kW; B This indicates the price of auxiliary equipment per kilowatt-hour, expressed in yuan / (kW·h); E ess Indicates the rated capacity of the energy storage system (ESS); P ess This indicates the rated power of the energy storage system (ESS).

[0092] Preferably, if the lifespan of the energy storage system (ESS) and the power conversion system (PCS) cannot meet the needs of the entire project cycle without replacement, the replacement cost C2 is as shown in the following equation (5):

[0093]

[0094] In the formula: k represents the total number of battery replacements in the energy storage system (rounded up, k = γ / n-1); ε represents the replacement order; β represents the average annual decline rate of the investment cost of the energy storage system ESS; σ represents the discount rate.

[0095] Preferably, the operation and maintenance cost C3 consists of labor costs and management costs, and is related to the initial investment, as shown in the following equation (6):

[0096] C3 = c f C1 (6)

[0097] In the formula: c f This represents the energy storage operation and maintenance cost coefficient.

[0098] Preferably, the energy storage system ESS is shut down at the end of its life cycle, therefore, the disposal cost C4 is as shown in the following relationship (7):

[0099]

[0100] In the formula: c d This represents a specific processing cost per unit power of an Energy Storage System (ESS), expressed in yuan / kW.

[0101] Preferably, the energy storage system ESS is recyclable; therefore, the recycling cost C5 is as shown in equation (8):

[0102] C5 = c res (C1+C2) (8)

[0103] In the formula: c res This indicates the recovery rate, typically between 3% and 5%.

[0104] Finally, the target comprehensive cost of the energy storage system is shown in the following equation (9):

[0105] cost = C1 + C2 + C3 + C4 - C5 (9)

[0106] Furthermore, by combining the energy storage lifespan model, the total lifecycle cost C of the energy storage system can be obtained. cost The following relation (10) is shown:

[0107] C cost =η(C1+C2+C3+C4-C5) (10)

[0108] In the formula: η represents the coefficient for the recovery of funds in the equal series.

[0109] Among them, considering the time value of money, the annualized investment cost is calculated based on the equivalent lifespan of energy storage, and the equivalent series of capital recovery coefficients η are shown in the following relationship (11):

[0110]

[0111] Where: n ess This represents the number of years when the battery energy storage reaches the end of its lifespan, i.e., the energy storage equivalent lifespan, which can be obtained from the energy storage lifespan model.

[0112] Preferably, the energy storage discharge quantity Q of the energy storage system cyc (t i The following relation (12) is shown:

[0113] Q cyc (t i )=(1-SOC(t i -1))S e (ti (12)

[0114] In the formula: Q cyc (t i ) represents the actual discharge amount of energy storage during period t in season i; SOC represents the state of charge of the battery in the energy storage system; S e S represents the 0-1 variable introduced by the occurrence of charge-discharge cycles. e =1 indicates that a charge-discharge cycle occurs at time t, and its discharge depth is calculated; otherwise, it indicates that no charge-discharge cycle has occurred, and the discharge depth is 0.

[0115] Furthermore, based on the energy storage discharge quantity Q cyc (t i Determine the equivalent number of iterations.

[0116] First, the number of cycles for each battery in the energy storage system at different depths is converted to the equivalent full cycle count at 100% discharge depth. The equivalent full cycle count n corresponding to each charge-discharge cycle is... eq We obtain the following relation (13):

[0117]

[0118] Where: n eq (t i ) represents the equivalent total number of cycles corresponding to each charge-discharge cycle in time period t of season i; k p This represents the fitting constant, which can be provided by the manufacturer of the battery in the energy storage system.

[0119] Furthermore, the equivalent number of cycles N of the energy storage system over its entire lifespan can be obtained. eq The following relation (14) is shown:

[0120]

[0121] The target comprehensive cost provided in this embodiment takes into account the investment cost, replacement cost, operation and maintenance cost, processing cost and recycling cost of the energy storage system, and meets the "IEC60300-3-3 International Standard".

[0122] In some optional implementations, step S3012 above includes:

[0123] Step b1: Obtain the first energy storage cycle number of the energy storage system.

[0124] Step b2: Determine the number of the second energy storage cycle based on the energy storage discharge amount and the number of the first energy storage cycle.

[0125] Step b3: Based on the second energy storage cycle number and the equivalent cycle number, establish an energy storage lifetime model.

[0126] Among them, the first energy storage cycle number N e This indicates the number of cycles a battery will have when it reaches the end of its lifespan, and can be provided by the manufacturer of the battery in the energy storage system.

[0127] Second energy storage cycle number N s This indicates the number of charge-discharge cycles a battery can complete at 100% depth of discharge until the end of its lifespan.

[0128] Specifically, the second energy storage cycle number N of the energy storage system can be obtained through the following relationship (15). s :

[0129]

[0130] Furthermore, combining the equivalent number of iterations N shown in the above relation (14) eq The energy storage lifetime model is established as shown in the following equation (16):

[0131]

[0132] Where: n ess This indicates the number of years a battery's energy storage reaches the end of its lifespan.

[0133] This embodiment converts the number of cycles at different depths of discharge into the number of cycles at 100% depth of discharge and establishes an energy storage life model. It only uses the depth of discharge as the core factor affecting battery life loss and ignores the influence of other factors on energy storage life.

[0134] Step S302: Obtain the energy storage revenue and wind / solar utilization rate of the energy storage system within a preset time period, and determine the objective function based on the energy storage revenue and wind / solar utilization rate. For details, please refer to [link to relevant documentation]. Figure 2 Step S202 of the illustrated embodiment will not be described again here.

[0135] Step S303: Establish an energy storage capacity planning model based on the total lifecycle cost. For details, please refer to [link / reference]. Figure 2 Step S203 of the illustrated embodiment will not be described again here.

[0136] Step S304: Based on the objective function and a preset set of constraints, solve the energy storage capacity planning model to obtain the target microgrid energy storage configuration result. For details, please refer to [link to relevant documentation]. Figure 2 Step S204 of the illustrated embodiment will not be described again here.

[0137] The microgrid energy storage configuration method provided by this invention converts the number of cycles at different discharge depths into the number of cycles at 100% discharge depth and establishes an energy storage lifetime model. It only uses the discharge depth as the core factor affecting battery life loss and ignores the impact of other factors on energy storage lifetime. It considers the total life cycle cost of the energy storage system and uses energy storage revenue and wind and solar utilization rates as objective functions to optimize the energy utilization rate of the energy storage system within a preset time period to obtain the target microgrid energy storage configuration result, thus ensuring the economic efficiency of energy storage. Furthermore, under the configuration result of the target microgrid energy storage configuration, the energy storage utilization rate of the microgrid can be improved.

[0138] This embodiment provides a microgrid energy storage configuration method, which can be used for the above-mentioned... Figure 1 The DC bus-independent microgrid shown is Figure 4 This is a flowchart of a microgrid energy storage configuration method according to an embodiment of the present invention, such as... Figure 4 As shown, the process includes the following steps:

[0139] Step S401: Obtain the total lifecycle cost of the energy storage system in the microgrid. For details, please refer to [link to relevant documentation]. Figure 3 Step S301 of the illustrated embodiment will not be described again here.

[0140] Step S402: Obtain the energy storage revenue and wind / solar utilization rate of the energy storage system within a preset time period, and determine the objective function based on the energy storage revenue and wind / solar utilization rate. For details, please refer to [link to relevant documentation]. Figure 2 Step S202 of the illustrated embodiment will not be described again here.

[0141] Step S403: Establish an energy storage capacity planning model based on the total lifecycle cost. For details, please refer to [link / reference]. Figure 2 Step S203 of the illustrated embodiment will not be described again here.

[0142] Step S404: Based on the objective function and the preset set of constraints, solve the energy storage capacity planning model to obtain the energy storage configuration result of the target microgrid.

[0143] Specifically, step S404 includes:

[0144] Step S4041: Obtain the preset constraint set.

[0145] The preset constraint set can include generator set operation constraints, energy storage system constraints, and power system constraints.

[0146] Preferably, the operating constraints of the generator set may include gas turbine operating constraints and renewable energy operating constraints:

[0147] (1) Gas turbine operation constraints: The output and start-up / shutdown status of a gas turbine cannot be changed arbitrarily during operation. Therefore, its upper and lower limits of output, start-up / shutdown time and ramp rate are constrained as shown in the following relationship (17):

[0148]

[0149] In the formula: Q represents the gas turbine output during time period t in season i; gas,min Indicates the lower limit of gas turbine output; Q gas ,max Indicates the upper limit of gas turbine output; This represents the continuous operating time of the gas turbine during season i, time period t. R represents the gas turbine downtime during period t in season i; on Indicates the minimum continuous operating time of the gas turbine; R off Indicates the minimum downtime of the gas turbine; u i,t The variable represents the gas turbine start-up and shutdown status (0-1) during the time period t of season i, where 0 and 1 represent shutdown and operation status, respectively. Q represents the gas turbine ramp rate during time period t in season i; th,v This indicates the upper limit of the gas turbine's ramp rate.

[0150] (2) Renewable energy operation constraints: Due to the influence of the natural environment, wind power and photovoltaic power have upper and lower limits in actual operation, as shown in the following relationship (18):

[0151]

[0152] In the formula: This represents the wind turbine output during time period t in season i; This represents the lower limit of the wind turbine output during time period t in season i; This represents the upper limit of wind turbine output during time period t in season i; This represents the photovoltaic output during time period t in season i; This represents the lower limit of photovoltaic power output during time period t in season i; This represents the upper limit of photovoltaic power output during the period t of season i.

[0153] Preferably, the constraints of the energy storage system may include energy storage operation constraints, energy storage power constraints, and continuous discharge time constraints:

[0154] (1) Energy storage operation constraints: The state of charge (SBC) is usually used to represent the operation of the energy storage system. In order to ensure the stable operation of the energy storage system, the SBC is limited to a certain range, and the SBC at the beginning and end of a scheduling cycle should be equal to ensure the regulation capability of the energy storage in the next cycle, as shown in the following relationship (19):

[0155]

[0156] In the formula: E represents the state of charge of energy storage during time period t in season i; soc,min Indicates the upper limit of the energy storage state of charge; E soc ,max Indicates the upper limit of the energy storage state of charge; ζ c Indicates energy storage charging efficiency; ζ d Indicates the energy storage discharge efficiency; This indicates the state of charge at the start of the seasonal i scheduling cycle; This represents the state of charge at the end of the seasonal i scheduling cycle.

[0157] (2) Energy storage power constraint: The energy storage system cannot be in a charging and discharging state at the same time, and the maximum charging and discharging power cannot exceed the rated power at the time of planning, as shown in the following relationship (20):

[0158]

[0159] In the formula: This represents the actual charging amount of energy storage during time period t in season i; This represents the actual discharge of energy storage during time period t in season i.

[0160] (3) Continuous discharge time constraint: When configuring energy storage capacity, if the capacity selection is too small, the adjustment effect on the dispatch system is limited; if the capacity selection is too large, the investment and operation and maintenance costs will be significantly increased. The continuous discharge time of energy storage refers to the time during which the energy storage operates continuously at rated power. It is directly related to the capacity and power of the energy storage. Therefore, the desired continuous discharge time is constrained to further control the energy storage cost, as shown in the following relationship (21):

[0161]

[0162] In the formula: H represents the expected continuous discharge time of the energy storage, which can be reasonably selected according to different energy storage configuration requirements; H min Indicates the minimum continuous discharge time of energy storage; H max This indicates the maximum continuous discharge time of the stored energy.

[0163] Preferably, power system constraints may include power balance constraints, reserve capacity constraints, and renewable energy penetration constraints:

[0164] (1) Power balance constraints, as shown in the following relationship (22):

[0165]

[0166] Where: G i,t This represents the load demand during time period t in season i.

[0167] (2) Reserve capacity constraint, as shown in the following relationship (23):

[0168]

[0169] In the formula: α represents the gas turbine reserve capacity coefficient.

[0170] (3) Renewable energy penetration rate constraint: The power fluctuation and randomness of renewable energy will impact the power grid. Under the premise of ensuring continuous and stable power supply, the maximum grid connection ratio of renewable energy should be constrained, as shown in the following relationship (24):

[0171]

[0172] In the formula: β represents the maximum penetration rate of renewable energy.

[0173] Step S4042: Based on the objective function and the preset constraint set, the energy storage capacity planning model is solved using the improved simulated annealing algorithm and the main objective method to obtain the energy storage configuration result of the target microgrid.

[0174] Traditional particle swarm optimization (PSO) algorithms are prone to getting trapped in local optima. Therefore, the idea of ​​simulated annealing (SA)

[13] and the Metropolis criterion are introduced into the particle swarm optimization algorithm to obtain an improved simulated annealing algorithm.

[0175] Furthermore, commonly used multi-objective solution methods mainly include intelligent algorithms and linear weighted methods. These methods require normalization of the objectives before transformation, but normalization can mask the changing characteristics of renewable energy in different seasons. Therefore, this embodiment uses the primary objective method to solve the model.

[0176] Specifically, by using the improved simulated annealing algorithm and the principal objective method to solve the energy storage capacity planning model, the target microgrid energy storage configuration result that satisfies the objective function and the preset constraint set can be obtained.

[0177] In some optional implementations, step S4042 above includes:

[0178] Step c1: Determine the first, second, and third objectives of the energy storage system based on the objective function.

[0179] Step c2: Based on the preset set of constraints, the improved simulated annealing algorithm is used to solve the energy storage capacity planning model to obtain the initial microgrid energy storage configuration result that satisfies the first objective.

[0180] Step c3: Based on the second objective, the preset constraint set, and the initial microgrid energy storage configuration results, the energy storage capacity planning model is solved using the main objective method to obtain the target microgrid energy storage configuration results that satisfy the third objective.

[0181] The first objective represents maximizing energy storage revenue; the second objective represents maximizing wind and solar power utilization; and the third objective represents minimizing wind and solar power utilization.

[0182] First, with the goal of maximizing energy storage benefits, and based on a set of preset constraints, an improved simulated annealing algorithm is used to solve the constraints of the energy storage capacity planning model, and the global optimal solution when the energy storage benefits are maximized is obtained, which is the initial microgrid energy storage configuration result.

[0183] Secondly, with the goal of minimizing wind and solar utilization, and based on the second objective, the preset constraint set, and the initial microgrid energy storage configuration results, the main objective method is used to solve the constraint problem of the energy storage capacity planning model to obtain the optimal energy storage capacity and power corresponding to the minimum wind and solar utilization, which is the target microgrid energy storage configuration result.

[0184] In some alternative implementations, step c2 above includes:

[0185] Step c21: Use the improved simulated annealing algorithm to determine the target energy storage output of the energy storage system that satisfies the preset set of constraints.

[0186] Step c22: Based on the target energy storage output, solve the energy storage capacity planning model to obtain the initial microgrid energy storage configuration result that satisfies the first objective.

[0187] First, by using an improved simulated annealing algorithm, the most economical energy storage output that satisfies the preset set of constraints is obtained.

[0188] Specifically, when the new particle generated by the perturbation is x′, the current best particle in the particle swarm is p. best The fitness difference between the two can be expressed as the following equation (25):

[0189] Δf = fitness(x′) - fitness(p) best (25)

[0190] If Δf < 0, meaning the fitness value of the new particle is smaller than that of the current best particle, then the new particle is accepted as the best particle; otherwise, e is used as the optimal particle. -ΔfT The probability of receiving this particle is [not specified]. This is the Metropolis criterion, where T is the current temperature during the simulated annealing process.

[0191] Furthermore, based on the target energy storage output, the energy storage capacity planning model is solved to obtain the global optimal solution when the energy storage benefit is maximized, which is the initial microgrid energy storage configuration result.

[0192] In some alternative implementations, step c3 above includes:

[0193] Step c31: Obtain the target scaling factor.

[0194] Step c32: Based on the target scaling factor, the second target, and the preset constraint set, the initial microgrid energy storage configuration result is converted into target constraints.

[0195] Step c33: Solve the energy storage capacity planning model based on the target constraints to obtain the target microgrid energy storage configuration result that satisfies the third objective.

[0196] Specifically, the second objective, namely maximizing wind and solar utilization, is set as the primary objective. Then, a target scaling factor is introduced to convert the initial microgrid energy storage configuration result when the total energy storage revenue is maximized into target constraints, as shown in the following relationship (26):

[0197]

[0198] In the formula: This represents the optimal value obtained by solving the problem with the single objective of maximizing the total energy storage revenue, which is the initial microgrid energy storage configuration result. Represents the target scaling factor, where, A value of 0 indicates that it is acceptable as long as the energy storage system does not incur losses; A value of 1 indicates that the maximum benefit can be achieved.

[0199] Furthermore, by taking the third objective, namely minimizing wind and solar utilization, and solving the energy storage capacity planning model, we can obtain the optimal energy storage capacity and power, which is the target microgrid energy storage configuration result.

[0200] The microgrid energy storage configuration method provided in this embodiment utilizes an improved simulated annealing algorithm to solve the problem that traditional particle swarm optimization algorithms are prone to getting trapped in local extrema. Furthermore, by combining the main objective method to solve the energy storage capacity planning model, the changing characteristics of microgrid energy storage in different time periods are not masked, thus improving the configuration accuracy of the target microgrid energy storage configuration results. In addition, it can improve the energy storage utilization rate of the microgrid.

[0201] In one example, a microgrid with an 800kW wind turbine, a 500kW photovoltaic power unit, and a 1000kW gas turbine is used as a case study for simulation analysis. The microgrid energy storage configuration method provided in the above embodiment is verified based on the simulation analysis results.

[0202] Specifically, based on meteorological classification, January and July are selected as typical months for winter and summer, respectively. Using historical data on wind speed, temperature, and solar radiation intensity for each time period of each typical month in a specific year, the output and load of wind power and photovoltaic power on typical days are predicted.

[0203] The on-grid tariffs for wind power and solar power are RMB 0.54 / (kW·h) and RMB 0.6 / (kW·h), respectively.

[0204] Energy storage achieves peak shaving and valley filling of electricity load and efficient utilization of renewable energy through energy time shifting, thus requiring a relatively long discharge time. This example uses lithium batteries configured on the generation side to power the energy storage system.

[0205] Set H to 3-6h; c P It is 1000 yuan / kW; c E The price is 1600 yuan / (kW·h); c f It is 5%; σ is 4.9%; n0 is 1500; ζ c , ζ d Both are 90%; It is 0.5; E soc,max E soc,min The values ​​are 0.9 and 0.1, respectively. Due to the regulating effect of energy storage, α decreases to 10% and β increases to 80%.

[0206] Furthermore, the deployment scale of energy storage and the operational effectiveness of the power system are analyzed under three scenarios:

[0207] Scenario 1: Energy storage configuration is based on maximizing the total revenue of the energy storage system as the single objective;

[0208] Scenario 2: Based on Scenario 1, the primary objective method is adopted to configure energy storage with the goal of minimizing wind and solar curtailment rates;

[0209] Scenario 3: Select a typical day to illustrate the energy storage configuration for a typical day throughout the year, and analyze the impact of the energy storage scale on the power system operation in other seasons.

[0210] Furthermore, the energy storage configuration results and power system operation effects under the above three scenarios are shown in Table 1 below:

[0211] Table 1. Energy storage configuration and power system operation performance under three scenarios.

[0212] parameter Scenario 1 Scenario 2 Scenario 3 <![CDATA[E ess / (kW·h)]]> 121.42 193.07 307.95 <![CDATA[P ess / kW]]> 36.16 46.13 58.38 H / h 3.36 4.18 5.27 <![CDATA[n ess / a]]> 6.79 6.25 5.13 <![CDATA[C ess / yuan]]> 52234.07 85070.83 151646.52 Annual revenue from newly connected wind power / yuan 53925.54 80599.98 118037.57 Annual revenue from newly connected photovoltaic power plants / yuan 11702.60 16880.53 30329.62 Annual revenue from energy storage / yuan 13394.07 12409.68 -3279.33

[0213] As shown in Table 1, current energy storage costs are relatively high. When optimizing configuration with the single objective of maximizing total energy storage revenue, the capacity and power of energy storage can be reduced as much as possible while ensuring the safe operation of the power system, and the discharge of energy storage can be minimized, thus extending the lifespan of energy storage and reducing energy storage costs. After installing energy storage, the minimum renewable energy utilization rate on typical days increased from 94.22% to 96.29% in two seasons, and the average wind and solar curtailment rate decreased by 2.05% in both seasons. To cope with the impact of high renewable energy penetration on the power system, the scale of energy storage can be significantly increased. When renewable energy generation is high, the previously curtailed electricity can be stored, and the energy time-shifting function of the energy storage system can be better utilized by extending the continuous discharge time. When sacrificing some energy storage revenue to pursue a higher renewable energy utilization rate, the minimum renewable energy utilization rate further increases to 97.11%, and the average wind and solar curtailment rate decreases by 2.995% in both seasons. However, the large-scale consumption of renewable energy means that energy storage can only maintain the stability of the power system through frequent charging and discharging, which has a huge impact on the lifespan of energy storage. However, the grid-connected revenue from newly generated wind and solar power can offset some of the costs of energy storage construction and operation, and its impact on the total revenue of energy storage is not significant. This is even more true in Scenario 3. When energy storage is configured only on typical summer days, a large storage capacity and power are required to achieve efficient absorption of renewable energy, resulting in an oversupply of energy storage in other seasons. Although this scenario increases the minimum wind and solar integrated utilization rate to 98.79%, reduces the average wind and solar curtailment rate by 4.57%, and achieves higher revenue from newly connected renewable energy, the annualized total cost of energy storage increases by nearly two times compared to Scenario 1 due to the combined effects of increased energy storage configuration scale and reduced equivalent lifespan, resulting in a negative return.

[0214] Furthermore, the renewable energy utilization rates for each season under the three scenarios are shown in Table 2 below:

[0215] Table 2 shows the utilization rate of renewable energy in winter and summer under three scenarios.

[0216]

[0217] As shown in Table 2, the utilization rates of both wind and solar power increase with the increase in energy storage scale, with Scenario 3 achieving 100% solar power consumption. Besides energy storage scale, actual wind and solar power output and load demand are also two important factors affecting the utilization rate of renewable energy. Seasonally, load demand is significantly higher in summer and winter. Wind power output is mainly concentrated in winter, hence in all three scenarios, winter exceeds 97%, but this requires significant costs. From the perspective of power source type, energy storage configuration is more effective than wind power in improving solar power consumption. This is because solar power has a higher grid-connected price, and prioritizing solar power consumption increases the overall revenue of energy storage.

[0218] In summary, based on the energy storage configuration and power system operation under the above three scenarios, the following conclusions can be drawn:

[0219] (1) When simply pursuing maximum returns, the energy storage discharge depth is small, which is more friendly to the lifespan, but reduces the utilization rate of renewable energy.

[0220] (2) The utilization rate of renewable energy can be improved by increasing the scale of energy storage and extending the continuous discharge time. Although the energy storage revenue is reduced, the reduction is not significant.

[0221] (3) When the wind and solar power utilization rate is low on a single typical day, the energy storage capacity may be excessive. Conversely, if energy storage is configured only according to the season with a high wind and solar power utilization rate, its capacity may not be sufficient to adjust according to wind and solar power output in other seasons, resulting in a large waste of renewable energy.

[0222] Therefore, when planning energy storage, the annual changes in renewable energy should be considered holistically, and the scale of energy storage and continuous discharge time should be appropriately increased to balance energy storage revenue and renewable energy utilization. Furthermore, hybrid energy storage configurations or the introduction of energy storage systems into the ancillary services market can be selected to leverage the "low storage, high generation" arbitrage opportunity, providing services such as frequency regulation, voltage regulation, and black start, thereby further improving the economic benefits of energy storage.

[0223] This embodiment also provides a microgrid energy storage configuration device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0224] This embodiment provides a microgrid energy storage configuration device, such as Figure 5 As shown, it includes:

[0225] The acquisition module 501 is used to acquire the full life cycle cost of the energy storage system in the microgrid.

[0226] The acquisition and determination module 502 is used to acquire the energy storage revenue and wind and solar utilization rate of the energy storage system within a preset time period, and to determine the objective function based on the energy storage revenue and wind and solar utilization rate.

[0227] Module 503 is established to create an energy storage capacity planning model based on the full life cycle cost.

[0228] The solver module 504 is used to solve the energy storage capacity planning model based on the objective function and the preset set of constraints to obtain the energy storage configuration result of the target microgrid.

[0229] In some optional implementations, the acquisition module 501 includes:

[0230] The first acquisition submodule is used to acquire the target comprehensive cost and equivalent cycle number of the energy storage system in the microgrid.

[0231] A submodule is established to build an energy storage lifetime model based on the equivalent number of cycles.

[0232] The determination submodule is used to determine the total life cycle cost of an energy storage system based on the energy storage life model and the target comprehensive cost.

[0233] In some optional implementations, the first acquisition submodule includes:

[0234] The first acquisition unit is used to acquire the investment cost, replacement cost, operation and maintenance cost, processing cost, recycling cost and energy storage discharge amount of the energy storage system.

[0235] The first determining unit is used to determine the target comprehensive cost based on investment cost, replacement cost, operation and maintenance cost, processing cost, and recovery cost.

[0236] The second determining unit is used to determine the equivalent number of cycles based on the energy storage discharge amount.

[0237] In some alternative implementations, establishing a submodule includes:

[0238] The second acquisition unit is used to acquire the first energy storage cycle number of the energy storage system.

[0239] The third determining unit is used to determine the number of the second energy storage cycle based on the energy storage discharge amount and the number of the first energy storage cycle.

[0240] Establish a unit to build an energy storage lifetime model based on the second energy storage cycle number and the equivalent cycle number.

[0241] In some alternative implementations, the solver module 504 includes:

[0242] The second acquisition submodule is used to acquire a set of preset constraint conditions.

[0243] The solver submodule is used to solve the energy storage capacity planning model based on the objective function and a set of preset constraints, using an improved simulated annealing algorithm and the main objective method, to obtain the energy storage configuration result of the target microgrid.

[0244] In some alternative implementations, the solver submodule includes:

[0245] The fourth determining unit is used to determine the first, second, and third objectives of the energy storage system based on the objective function.

[0246] The first solution unit is used to solve the energy storage capacity planning model based on a preset set of constraints using an improved simulated annealing algorithm, so as to obtain the initial microgrid energy storage configuration result that satisfies the first objective.

[0247] The second solution unit is used to solve the energy storage capacity planning model based on the second objective, the preset constraint set, and the initial microgrid energy storage configuration results, using the main objective method to obtain the target microgrid energy storage configuration results that satisfy the third objective.

[0248] In some alternative implementations, the first solving unit includes:

[0249] The sub-unit is determined to use an improved simulated annealing algorithm to determine the target energy storage output of the energy storage system that satisfies a preset set of constraints.

[0250] The first solution sub-unit is used to solve the energy storage capacity planning model based on the target energy storage output, and obtain the initial microgrid energy storage configuration result that satisfies the first objective.

[0251] In some alternative implementations, the second solving unit includes:

[0252] Get the sub-unit, used to obtain the target scaling factor.

[0253] The conversion subunit is used to convert the initial microgrid energy storage configuration result into target constraints based on the target scaling factor, the second target, and the preset constraint set.

[0254] The second solution sub-unit is used to solve the energy storage capacity planning model based on the target constraints, and obtain the target microgrid energy storage configuration result that satisfies the third objective.

[0255] In some optional implementations, the preset constraint set includes: generator set operation constraints, energy storage system constraints, and power system constraints.

[0256] In some alternative implementations, generator set operating constraints include gas turbine operating constraints and renewable energy operating constraints.

[0257] In some alternative implementations, power system constraints include: power balance constraints, reserve capacity constraints, and renewable energy penetration constraints.

[0258] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0259] In this embodiment, the microgrid energy storage configuration device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0260] This invention also provides a computer device having the above-described features. Figure 5 The microgrid energy storage configuration device shown.

[0261] Please see Figure 6 , Figure 6 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 6 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 6 Take a processor 10 as an example.

[0262] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0263] The memory 20 stores instructions executable by at least one processor 10 to cause at least one processor 10 to perform the method shown in the above embodiments.

[0264] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0265] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0266] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0267] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0268] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A microgrid energy storage configuration method, characterized in that, The method comprises: obtaining the full life cycle cost of the energy storage system in the micro-grid; obtaining the energy storage benefit and the wind-solar utilization rate of the energy storage system within a preset time period, and determining a target function based on the energy storage benefit and the wind-solar utilization rate; establishing an energy storage capacity planning model based on the full life cycle cost; solving the energy storage capacity planning model based on the target function and a preset constraint condition set to obtain a target micro-grid energy storage configuration result; wherein solving the energy storage capacity planning model based on the target function and the preset constraint condition set to obtain the target micro-grid energy storage configuration result comprises: obtaining the preset constraint condition set; determining a first target, a second target and a third target of the energy storage system based on the target function, wherein the first target represents the maximum energy storage benefit, the second target represents the maximum wind-solar utilization rate, and the third target represents the minimum wind-solar utilization rate; determining a target energy storage output of the energy storage system that satisfies the preset constraint condition set by using an improved simulated annealing algorithm; solving the energy storage capacity planning model based on the target energy storage output to obtain an initial micro-grid energy storage configuration result that satisfies the first target; obtaining a target scaling coefficient; converting the initial micro-grid energy storage configuration result into a target constraint condition based on the target scaling coefficient, the second target and the preset constraint condition set; solving the energy storage capacity planning model based on the target constraint condition to obtain the target micro-grid energy storage configuration result that satisfies the third target.

2. The method of claim 1, wherein, Obtaining the full life cycle cost of the energy storage system in the micro-grid comprises: obtaining the target comprehensive cost and the equivalent cycle number of the energy storage system in the micro-grid; establishing an energy storage life model based on the equivalent cycle number; determining the full life cycle cost of the energy storage system based on the energy storage life model and the target comprehensive cost.

3. The method of claim 2, wherein, Obtaining the target comprehensive cost and the equivalent cycle number of the energy storage system in the micro-grid comprises: obtaining the investment cost, replacement cost, operation and maintenance cost, disposal cost, recycling cost and energy storage discharge capacity of the energy storage system; determining the target comprehensive cost based on the investment cost, the replacement cost, the operation and maintenance cost, the disposal cost and the recycling cost; determining the equivalent cycle number based on the energy storage discharge capacity.

4. The method of claim 3, wherein, Establishing an energy storage life model based on the equivalent cycle number comprises: obtaining the first energy storage cycle number of the energy storage system; determining the second energy storage cycle number based on the energy storage discharge capacity and the first energy storage cycle number; establishing the energy storage life model based on the second energy storage cycle number and the equivalent cycle number.

5. The method of claim 1, wherein, The preset constraint condition set comprises: generator set operation constraint, energy storage system constraint and power system constraint.

6. The method of claim 5, wherein, The generator set operation constraint comprises: gas turbine operation constraint and renewable energy operation constraint.

7. The method of claim 5, wherein, The energy storage system constraint comprises: energy storage operation constraint, energy storage power constraint and continuous discharge time constraint.

8. The method of claim 5, wherein, The power system constraint comprises: power balance constraint, reserve capacity constraint and renewable energy penetration rate constraint.

9. A microgrid energy storage configuration device, comprising: The device comprises: an obtaining module for obtaining the full life cycle cost of the energy storage system in the micro-grid; An acquisition and determination module is configured to acquire energy storage benefits and wind-solar utilization rates of the energy storage system within a preset time period, and determine a target function based on the energy storage benefits and the wind-solar utilization rates; A building module is configured to build an energy storage capacity planning model based on the full life cycle cost; A solving module is configured to solve the energy storage capacity planning model based on the target function and a preset constraint condition set, and obtain a target micro-grid energy storage configuration result; The solving module includes: A second acquisition sub-module is configured to acquire the preset constraint condition set; A fourth determination unit is configured to determine a first target, a second target and a third target of the energy storage system based on the target function, wherein the first target represents maximum energy storage benefits, the second target represents maximum wind-solar utilization rates, and the third target represents minimum wind-solar utilization rates; A determination sub-unit is configured to determine a target energy storage output of the energy storage system that meets the preset constraint condition set by using an improved simulated annealing algorithm; A first solving sub-unit is configured to solve the energy storage capacity planning model based on the target energy storage output, and obtain an initial micro-grid energy storage configuration result that meets the first target; An acquisition sub-unit is configured to acquire a target scaling coefficient; A conversion sub-unit is configured to convert the initial micro-grid energy storage configuration result into a target constraint condition based on the target scaling coefficient, the second target and the preset constraint condition set; A second solving sub-unit is configured to solve the energy storage capacity planning model based on the target constraint condition, and obtain a target micro-grid energy storage configuration result that meets the third target.

10. A computer device, comprising: The memory and the processor are communicatively connected, the memory stores computer instructions, and the processor executes the computer instructions to perform the micro-grid energy storage configuration method in any one of claims 1 to 8. The computer readable storage medium stores computer instructions, and the computer instructions are used to make the computer execute the micro-grid energy storage configuration method in any one of claims 1 to 8.

11. A computer readable storage medium, characterized in that, ​

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