Capacity optimization configuration model method and system for off-grid micro-grid energy storage system

Abstract

The invention relates to the technical field of off-grid micro-grid energy management and optimization, in particular to an off-grid micro-grid energy storage system capacity optimization configuration model method and system. Setting fixed parameters required by the model; according to the fixed parameters, establishing a mathematical model and defining a target function; defining constraint conditions, and meeting the requirements of power balance, output limitation and energy storage state in combination with the established objective function; inputting basic parameters required by model operation; according to the established mathematical model and basic parameters, the model is operated for solving, and a solving result is analyzed; and performing comparison between simulation test and actual operation data, and checking the performance of a solving result in different scenes. According to the invention, the utilization rate of the energy storage system can be effectively improved, wind and light abandoning phenomena are reduced, and cost minimization is realized, so that the overall performance and economical efficiency of the micro-grid energy storage system are improved, and the reliability and environmental protection property of the system are enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of off-grid microgrid energy management and optimization, and in particular to a capacity optimization configuration model method and system for an off-grid microgrid energy storage system. Background Art

[0002] The current microgrid energy storage system has many deficiencies in terms of capacity configuration and scheduling schemes. First, the traditional energy storage optimization model usually focuses on a single type of energy management and fails to fully consider the coordinated operation of multiple energy sources such as wind power, photovoltaics, and gas turbines. This single type of optimization leads to low utilization of the energy storage system, which cannot respond in time during peak loads, resulting in insufficient power supply, and excess energy storage during low loads.

[0003] In addition, the existing technology has made certain progress in dealing with the phenomenon of wind and solar power abandonment and load abandonment, but there are still some areas that need improvement. Due to the volatility and uncontrollability of wind power and photovoltaic power generation, sometimes the power generation exceeds the system load demand, resulting in some renewable energy not being fully utilized, thereby increasing the operating cost of the system. Traditional models are also insufficient in dealing with fluctuations in electricity demand in different time periods. The power load characteristics on weekdays and holidays are significantly different. Traditional models sometimes fail to dynamically adjust according to the load characteristics of different time periods, resulting in the energy storage system being unable to meet the load demand in some time periods, and there is an over-configuration problem in other time periods. This patent has made further improvements in these aspects, and has improved the economy and environmental protection of the system by optimizing the capacity configuration and scheduling scheme of the energy storage system.

[0004] In summary, the existing technologies have the following main shortcomings in optimizing the configuration of energy storage system capacity: 1) Lack of coordinated optimization of multiple energy sources (such as wind power, photovoltaics, and gas turbines), and failure to fully consider their comprehensive utilization and synergy effects; 2) Insufficient in dealing with wind and solar power abandonment and load abandonment, and failure to maximize the use of renewable energy; 3) Failure to dynamically optimize according to changes in power load characteristics (such as the load difference between weekdays and holidays), resulting in inconsistencies in the configuration and scheduling of energy storage systems in different time periods. Summary of the invention

[0005] In view of the above problems existing in the prior art, the present invention is proposed.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions, a capacity optimization configuration model method for an off-grid microgrid energy storage system, comprising:

[0007] 1) Set the fixed parameters required by the model. These parameters include the basic characteristics of wind power, photovoltaic, energy storage and gas turbine equipment, such as installed capacity, power generation efficiency, output limit and climbing ability;

[0008] 2) Based on the fixed parameters in step 1), a mathematical model is established and the objective function is defined. The objective function is to minimize the cost of the system within a time period. The model needs to comprehensively consider multiple factors such as installation cost, operation and maintenance cost, startup cost, wind and solar power abandonment penalty cost, and load abandonment penalty cost;

[0009] 3) Define constraints, including the system's installed capacity, gas turbine output, start-stop and ramp-up, wind and solar output, the charge and discharge status of energy storage equipment, and the amount of wind and solar power abandoned. Combined with the objective function established in step 2), ensure the feasibility and effectiveness of the model during operation, and that the system can meet the requirements of power balance, output limitation, and energy storage status in actual operation;

[0010] 4) Input the basic parameters required for the model to run. These parameters include initial investment cost, startup cost, operation and maintenance cost, discount rate and operating life;

[0011] 5) Based on the models in steps 2) and 3) and the basic parameters in step 4), run the model to solve and analyze the results. These results include power balance, energy output fluctuations, and energy storage applications. Compare the solution results with the actual data to check whether the power balance output by the model is reasonable, whether the energy output is within the expected fluctuation range, and whether the energy storage application effectively meets the demand;

[0012] 6) Compare the simulation test data with the actual operation data to check whether the solution results can stably meet the system requirements in different scenarios. If the solution results do not meet expectations, it is necessary to adjust the model parameters and constraints according to steps 2) and 3) and solve again. After verification, the optimal configuration plan for the off-grid microgrid energy storage capacity is finally output.

[0013] The off-grid microgrid energy storage system optimization model provided by the present invention has the following beneficial effects: it can effectively improve the utilization rate of the energy storage system, reduce the phenomenon of wind and solar power abandonment, and minimize costs, thereby improving the overall performance and economy of the microgrid energy storage system, and enhancing the reliability and environmental protection of the system.

[0014] As a preferred solution of the off-grid microgrid energy storage system capacity optimization configuration model method described in the present invention, wherein: according to the fixed parameters in step 1), a mathematical model is established and an objective function is defined. The purpose of the objective function is to minimize the cost of the system within a time period. The model needs to comprehensively consider multiple factors such as installation cost, operation and maintenance cost, startup cost, wind and solar power abandonment penalty cost, and load abandonment penalty cost.

[0015] As a preferred solution of the off-grid microgrid energy storage system capacity optimization configuration model method described in the present invention, the objective function involved in step 2) is the minimum cost within the time period T, and the optimal energy storage configuration capacity and its scheduling plan are solved based on the existing capacity and cost parameters of wind power, photovoltaics, and gas turbines.

[0016] As a preferred solution of the off-grid microgrid energy storage system capacity optimization configuration model method described in the present invention, wherein: 3) define constraints, which include the system's installed capacity, gas turbine output, start-stop and ramp-up, wind and solar output, the charge and discharge status of the energy storage equipment, and the amount of abandoned load and abandoned wind and solar. Combined with the objective function established in step 2), ensure the feasibility and effectiveness of the model during operation, and ensure that the system can meet the requirements of power balance, output limitation and energy storage status in actual operation;

[0017] According to the variables involved in step 2), set the following detailed constraints:

[0018] Real-time power balance constraints; wind and solar power output constraints; thermal power output constraints; energy storage status constraints; load abandonment and wind and solar power abandonment constraints; gas turbine start and stop constraints; gas unit ramping constraints.

[0019] As a preferred solution of the off-grid microgrid energy storage system capacity optimization configuration model method described in the present invention, the basic parameters required for the model operation are input, including initial investment cost, startup cost, operation and maintenance cost, discount rate and operating life.

[0020] As a preferred embodiment of the off-grid microgrid energy storage system capacity optimization configuration model method described in the present invention, the model is run to solve and analyze the solution results according to the model in steps 2) and 3) and the basic parameters in step 4). These results include power balance, energy output fluctuations and energy storage application. Compare the solution results with the actual data to check whether the power balance output by the model is reasonable, whether the energy output is within the expected fluctuation range, and whether the energy storage application effectively meets the demand.

[0021] As a preferred solution of the off-grid microgrid energy storage system capacity optimization configuration model method described in the present invention, wherein: a comparison of simulation test and actual operation data is performed to check whether the performance of the solution results in different scenarios can stably meet the system requirements. If the solution results do not meet expectations, it is necessary to adjust the model parameters and constraints according to step 2) and step 3), and re-solve. After verification, the optimal configuration plan for the off-grid microgrid energy storage capacity is finally output.

[0022] Another object of the present invention is to provide a capacity optimization configuration model system for an off-grid microgrid energy storage system. The system includes multiple modules: a parameter setting module ensures the consistency and stability of the model operation, improves the system reliability, and provides standardized input for model construction and solution; a model construction module abstracts the actual problem into a mathematical model, improves the accuracy and efficiency of problem solving, and provides a theoretical basis for objective function optimization; a constraint definition module ensures that the solution results meet actual requirements such as power balance and output limitation, avoids invalid solutions and infeasible solutions, and provides boundary conditions for model solution; a parameter input module realizes effective data transmission, improves system interactivity, and supports model solution; a model solution analysis module obtains the optimized objective function value, provides a basis for decision-making, and indicates the model optimization direction by analyzing the solution process; a result verification and comparison module verifies the accuracy and reliability of the solution results, evaluates the adaptability and robustness of the model in different scenarios, and provides a reference for practical engineering applications. The synergy of these modules significantly improves the operating efficiency and resource utilization of the system.

[0023] As a preferred solution of the off-grid microgrid energy storage system capacity optimization configuration model system described in the present invention, it includes: a parameter setting module, a model construction module, a constraint definition module, a parameter input module, a model solution analysis module, and a result verification and comparison module.

[0024] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of any one of the methods for optimizing the configuration model of the capacity of an off-grid microgrid energy storage system are implemented.

[0025] A computer-readable storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the steps of any one of the methods described in a method for optimizing the configuration model of an off-grid microgrid energy storage system capacity are implemented.

[0026] Beneficial effects of the present invention: The present invention solves the problem of mismatch of energy storage demand under different operating conditions in traditional methods by optimizing the configuration of the battery energy storage system. Specifically, when wind, solar and load abandonment are not considered, the high installed capacity of the system on weekdays indicates a strong ability to cope with electricity demand, while the significant increase in energy storage demand on holidays reflects the characteristics of load fluctuations. After the introduction of wind, solar and load abandonment, the installed capacity on weekdays is reduced and the storage capacity is increased, which not only reflects the effective response of the system to the instability of wind and solar resources, but also optimizes the economic benefits. In addition, by increasing the storage capacity, the system effectively reduces the economic losses caused by resource waste and load loss, ensures the reliability and stability of power supply, and thus improves the overall performance and environmental benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0028] Figure 1 A flowchart of a capacity optimization configuration model method for an off-grid microgrid energy storage system provided by an embodiment of the present invention.

[0029] Figure 2 A wind / solar output and load curve diagram of a capacity optimization configuration model method for an off-grid microgrid energy storage system provided by an embodiment of the present invention.

[0030] Figure 3 An embodiment of the present invention provides an off-grid microgrid energy storage system capacity optimization configuration model method and various power optimization output results.

[0031] Figure 4 An embodiment of the present invention provides a method for optimizing the capacity configuration of an off-grid microgrid energy storage system and provides the results of wind, solar and load abandonment in various situations. DETAILED DESCRIPTION

[0032] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0033] Example 1

[0034] Reference Figure 1 , which is the first embodiment of the present invention, and provides a capacity optimization configuration model method for an off-grid microgrid energy storage system, comprising:

[0035] The off-grid microgrid energy storage system optimization model provided by the present invention has the following beneficial effects: it can effectively improve the utilization rate of the energy storage system, reduce the phenomenon of wind and solar power abandonment, and minimize costs, thereby improving the overall performance and economy of the microgrid energy storage system, and enhancing the reliability and environmental protection of the system.

[0036] like Figure 1 As shown, the off-grid microgrid energy storage system capacity optimization configuration model provided by the present invention includes the following steps performed in sequence:

[0037] 1) Set the fixed parameters required by the model. These parameters include the basic characteristics of wind power, photovoltaic, energy storage and gas turbine equipment, such as installed capacity, power generation efficiency, output limit and climbing ability;

[0038] 2) Based on the fixed parameters in step 1), a mathematical model is established and the objective function is defined. The objective function is to minimize the cost of the system within a time period. The model needs to comprehensively consider multiple factors such as installation cost, operation and maintenance cost, startup cost, wind and solar power abandonment penalty cost, and load abandonment penalty cost;

[0039] The objective function involved in step 2) is the minimum cost within the time period T. Based on the existing capacity and cost parameters of wind power, photovoltaic power, and gas turbines, the optimal energy storage configuration capacity and its scheduling scheme are solved. The specific model is constructed as follows:

[0040]

[0041] In the formula, C ins(t) , C om(t) They are the installation cost and operation and maintenance cost of wind, solar, fuel and storage at time point t respectively; is the startup cost of the gas turbine at time t; C pl(t) , C ll(t) They represent the penalty cost of wind power abandonment and load abandonment at time t respectively. The components of the objective function are as follows:

[0042] Installation cost C ins(t)

[0043] The installed capacity cost at time t is the sum of a series of expenses involved in the purchase, construction, installation, design, and commissioning of various power generation equipment and energy storage equipment to achieve usable conditions. The expression is as follows:

[0044]

[0045] In the formula, C ins,i(t) is the cost (yuan / kW) of the i-th type of generator set (wind power, photovoltaic, diesel generator) at time t, C ins,j(t) I is the cost of energy storage j at time t (yuan / kW). i ,I j Represent the installed capacity (kW) of the i-th type of generator set and energy storage j respectively. inins,i , C inins,j is the initial investment cost of the i-th type of generator set and energy storage j (yuan / kW), r i 、r j is the discount rate of the i-th type of generator set and energy storage j, n i 、n j is the operating life of the i-th type of generating unit and energy storage j.

[0046] Operation and maintenance cost C om(t)

[0047] Operation and maintenance costs refer to the repair costs that are dynamically invested to ensure the normal operation of generators and energy storage systems during their life cycle. They are as follows:

[0048]

[0049] C om,i(t) =C yrom,i ÷8760

[0050] C om,j(t) =C yrom,j ÷8760

[0051] C om,i(t) , C om,j(t) , C yrom,i , C yrom,j I represents the operation and maintenance cost (yuan / kw.hour) and annual operation and maintenance cost (yuan / kW.year) of the i-th type of generator set and energy storage j at time point t. i ,I j It represents the rated power (kW) of the i-th type of generator set and energy storage j.

[0052] Startup costs

[0053] The startup cost refers to all costs incurred in starting the micro gas turbine from a shutdown state under different startup conditions. See the expression

[0054]

[0055] In the formula, represents the startup cost of the gas turbine at time point t (yuan / time), Represents the startup status of the gas turbine at time t. It is a 0,1 variable. The value is 1 when it is started and 0 when it is stopped.

[0056] Wind power abandonment penalty cost C pl(t)

[0057] The penalty cost for abandoned wind and solar power refers to the penalty cost incurred because part of the wind and solar power generation is abandoned because the wind and solar power generation exceeds the maximum capacity that the system can accept.

[0058]

[0059] C pl Refers to the penalty cost per unit for abandoning wind and solar power (yuan / kW), is the amount of wind and solar power abandoned at time t (kW).

[0060] Load abandonment penalty cost C ll(t)

[0061] In the renewable energy power system, due to prediction errors and the volatility of renewable energy itself, the system may be forced to abandon load at certain extreme moments. The cost caused by this phenomenon is the load abandonment penalty cost.

[0062] C ll(t) =C LL ×LL (t)

[0063] C LL is the unit load abandonment penalty cost (yuan / kW); LL (t) is the load abandonment at time t (kw).

[0064] 3) Define constraints, including the system's installed capacity, gas turbine output, start-stop and ramp-up, wind and solar output, the charge and discharge status of energy storage equipment, and the amount of wind and solar power abandoned. Combined with the objective function established in step 2), ensure the feasibility and effectiveness of the model during operation, and that the system can meet the requirements of power balance, output limitation, and energy storage status in actual operation;

[0065] According to the variables involved in step 2), set the following detailed constraints:

[0066] Real-time power balance constraints

[0067] In order to ensure the safe and stable operation of the system, it is required that the system processing and load be balanced within the operation scheduling time, while considering the losses of wind and solar power abandonment. The energy balance constraint at time t is expressed as:

[0068]

[0069] in, They represent the output power (kW) of various power generation equipment (photovoltaic, wind power, micro gas turbine) at time t, They represent the discharge and charging power (kW) of energy storage j at time t, respectively, P de (t) represents the real-time load demand at time t (kW).

[0070] Wind and solar power output constraints:

[0071] Output constraints are usually formulated by power dispatching agencies based on the system's operating conditions and safety requirements. They limit the output power or speed of generators, motors and other equipment to ensure the stable operation and safety of the system, avoid equipment overload or underload, and prevent fluctuations in system frequency and voltage.

[0072]

[0073] In the formula, They are the rated power (kW) of photovoltaic and wind power respectively. This constraint limits the fluctuation range of wind and solar power output to ensure the safe and stable operation of the power system.

[0074] Thermal power output constraints:

[0075]

[0076] In the formula, They represent the minimum and maximum output limits of thermal power at time t respectively.

[0077] Energy storage state constraints:

[0078] State of Charge SOC j(t) Describes the ratio of the remaining power of the energy storage device to the rated capacity at time point t, reflecting its remaining energy and available capacity. The state of charge constraint is used to prevent damage to the energy storage system caused by excessive or improper power consumption, and is used to guide the charging and discharging strategy of the energy storage system to maximize the utilization rate of the energy storage system and extend its service life. The state of charge of the energy storage is limited to the minimum value and maximum value to prevent over-charge and discharge and extend battery life.

[0079]

[0080] In the formula, u ch 、u dch are respectively the charging and discharging efficiency of energy storage (%), E j is the rated capacity of the energy storage (kWh).

[0081]

[0082] The above formula limits the maximum and minimum values ​​of the charging and discharging power of the energy storage device.

[0083]

[0084] The above formula ensures that the energy storage device is not charged and discharged at the same time.

[0085] Load abandonment and wind and solar abandonment constraints:

[0086]

[0087] 0≤LL (t) ≤P de (t

[0088]

[0089] On the one hand, the above formula limits the maximum and minimum values ​​of wind and solar power abandonment and load abandonment. On the other hand, it ensures that load abandonment, wind abandonment, and solar abandonment do not occur at the same time.

[0090] Gas turbine start and stop constraints:

[0091]

[0092] is the startup cost of the gas turbine. This constraint means that When the gas turbine is started at time t, there is a startup cost at time t In other combinations The value of is 0.

[0093] Gas unit climbing constraints:

[0094] When adjusting power generation, thermal power units are subject to mechanical, thermal and other limitations and cannot immediately reach the set target value. A gradual adjustment process is required, which is the ramp constraint of the thermal power system.

[0095]

[0096] The formula ensures that the gas turbine is limited by the up and down climbing ability during continuous startup. When the unit is started and stopped, it is not affected by the climbing ability and the minimum thermal power output is used. Start and stop.

[0097] 4) Input the basic parameters required for the model to run. These parameters include initial investment cost, startup cost, operation and maintenance cost, discount rate and operating life.

[0098] 5) Based on the models in steps 2) and 3) and the basic parameters in step 4), run the model to solve and analyze the results. These results include power balance, energy output fluctuations, and energy storage applications. Compare the solution results with the actual data to check whether the power balance output by the model is reasonable, whether the energy output is within the expected fluctuation range, and whether the energy storage application effectively meets the demand.

[0099] 6) Compare the simulation test data with the actual operation data to check whether the solution results can stably meet the system requirements in different scenarios. If the solution results do not meet expectations, it is necessary to adjust the model parameters and constraints according to steps 2) and 3) and solve again. After verification, the optimal configuration plan for the off-grid microgrid energy storage capacity is finally output.

[0100] Embodiment 2, the second embodiment of the present invention, is different from the previous embodiment in that:

[0101] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc., which can store program codes.

[0102] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0103] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0104] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0105] Embodiment 3 is the third embodiment of the present invention, which provides an off-grid microgrid energy storage system capacity optimization configuration model system, characterized by: comprising a parameter setting module, a model building module, a constraint definition module, a parameter input module, a model solution analysis module, and a result verification and comparison module;

[0106] Parameter setting module, which sets the fixed parameters required by the model;

[0107] Model building module, which builds mathematical models and defines objective functions based on fixed parameters;

[0108] Constraint definition module, which defines constraints and combines the established objective function to meet the requirements of power balance, output limitation and energy storage status;

[0109] Parameter input module, input the basic parameters required for model operation;

[0110] Model solution analysis module, which runs the model to solve and analyze the solution results according to the established mathematical model and basic parameters;

[0111] Result verification and comparison module: Compare the simulation test data with the actual operation data to check the performance of the solution results in different scenarios.

[0112] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0113] Example 4, reference Figure 2-Figure 4 , which is the fourth embodiment of the present invention, and provides a capacity optimization configuration model method for an off-grid microgrid energy storage system, specific data and wind and solar output curves, load curves Figure 2 As shown in Table 1:

[0114] According to the optimization results of the method of the present invention, the configuration of the battery energy storage system shows obvious differences under different conditions, indicating that the system has a high degree of adaptability and optimization capabilities in dealing with actual operation challenges.

[0115] Table 1. Basic data

[0116]

[0117]

[0118] Without considering wind and solar power abandonment and load abandonment, the optimal battery energy storage capacity of the system on weekdays is 42.61kW and the energy storage is 360.63kWh, while on holidays it is 58.52kW and 557.23kWh respectively. These data show that in the absence of additional losses, the system has selected a relatively high energy storage capacity and energy storage capacity to meet power demand and improve energy storage capacity. The energy storage demand during holidays increases significantly, reflecting the higher power demand and greater volatility during holidays, and the system responds to these demands by increasing energy storage capacity.

[0119] When wind and solar power curtailment and load abandonment are taken into account in the optimization model, the optimal battery energy storage installed capacity on weekdays is reduced to 27.75kW, and the energy storage capacity is increased to 429.58kWh, while on holidays it is 39.88kW and 557.72kWh respectively. The lower installed capacity and higher energy storage capacity indicate that in actual operation, the system needs to optimize the battery energy storage configuration to cope with the instability of wind and solar resources and the fluctuation of load. The adjustment here reflects the system's ability to respond to wind and solar resource fluctuations and load changes in actual applications. By increasing the energy storage capacity, the system can better handle wind and solar power curtailment and load abandonment, thereby optimizing energy utilization efficiency.

[0120] These results show that after considering wind and solar power abandonment and load abandonment, the system's energy storage configuration can more flexibly respond to challenges in actual operation. The optimized energy storage configuration responds to the fluctuations of wind and solar power resources and the uncertainty of load by increasing the amount of storage, reducing the economic losses caused by resource waste and load loss. Lower installed capacity and higher energy storage also show that the system can allocate resources more efficiently, reducing the need for additional power generation capacity during peak loads, while improving the efficiency of renewable energy utilization. Ultimately, these adjustments and optimizations improve the overall economic and environmental benefits of the system and ensure the stability and reliability of power supply.

[0121] like Figure 4As shown in the figure, the peak load of wind and solar curtailment during holidays is significantly higher than that on weekdays, and is concentrated in the same time period (about 10 am). Compared with weekdays, the curtailment phenomenon is more obvious on holidays, especially the curtailment of wind power and photovoltaic power during the low electricity consumption period, indicating that the decline in electricity demand during holidays is the main reason for the increase in curtailment. Figure 3 As shown in the figure, after considering the abandonment of wind and solar power and the abandonment of load, the energy storage configuration of the system can more flexibly cope with the challenges in actual operation. The optimized energy storage configuration responds to the fluctuation of wind and solar resources and the uncertainty of load by increasing the amount of storage energy, reducing the economic losses caused by resource waste and load loss. The lower installed capacity and higher amount of storage energy also show that the system can allocate resources more efficiently, reducing the need for additional power generation capacity during peak loads, while improving the efficiency of renewable energy utilization. Ultimately, these adjustments and optimizations improve the overall economy and environmental benefits of the system and ensure the stability and reliability of power supply.

Claims

1. A capacity optimization configuration model method for an off-grid microgrid energy storage system, characterized by: include, Set the fixed parameters required by the model; Based on fixed parameters, a mathematical model is established and the objective function is defined; Define constraints and combine them with the established objective function to meet the requirements of power balance, output limitation and energy storage status; Enter the basic parameters required to run the model; According to the established mathematical model and basic parameters, run the model to solve the problem and analyze the solution results; Compare the simulation test data with the actual operation data to check the performance of the solution results in different scenarios.

2. The off-grid microgrid energy storage system capacity optimization configuration model method according to claim 1, characterized in that: The fixed parameters include basic characteristics of wind power, photovoltaic, energy storage and gas turbine equipment; The basic characteristics of the gas turbine equipment include installed capacity, power generation efficiency, output limit and climbing ability.

3. The off-grid microgrid energy storage system capacity optimization configuration model method according to claim 2, characterized in that: The establishment of the mathematical model and definition of the objective function include comprehensively considering multiple factors such as installation cost, operation and maintenance cost, startup cost, penalty cost for abandoned wind and solar power, and penalty cost for abandoned load, so as to minimize the cost of the system within a time period.

4. The off-grid microgrid energy storage system capacity optimization configuration model method according to claim 3, characterized in that: The defined constraints include the installed capacity of the system, the output of the gas turbine, start-stop and ramp-up, wind and solar output, the charging and discharging status of the energy storage equipment, and the amount of load and wind power abandonment.

5. The off-grid microgrid energy storage system capacity optimization configuration model method according to claim 4, characterized in that: The basic parameters required for the operation of the input model include initial investment cost, startup cost, operation and maintenance cost, discount rate and operating life.

6. A capacity optimization configuration model method for an off-grid microgrid energy storage system as claimed in claim 5, characterized in that: The analysis and solution results include power balance, energy output fluctuations and energy storage application. The solution results are compared with actual data to check whether the power balance output by the model is reasonable, whether the energy output is within the expected fluctuation range, and whether the energy storage application effectively meets the demand.

7. The off-grid microgrid energy storage system capacity optimization configuration model method according to claim 6, characterized in that: The comparison between the simulation test and the actual operation data includes adjusting the model parameters and constraints and re-solving if the solution result does not meet expectations. After verification, the optimal configuration plan of the off-grid microgrid energy storage capacity is finally output.

8. A system based on the off-grid microgrid energy storage system capacity optimization configuration model method according to any one of claims 1 to 7, characterized in that: It includes parameter setting module, model building module, constraint definition module, parameter input module, model solution analysis module and result verification and comparison module.

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

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

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