Park micro-grid distributed power supply and energy storage coordinated optimization configuration method

Abstract

The invention relates to a park micro-grid distributed power supply and energy storage coordinated optimization configuration method, and belongs to the field of power system computer software application. The method comprises the steps that original data collection and import design is carried out, and a user interaction visual interface is created and used for importing and visualizing original data needed by micro-grid planning; establishing and designing a distributed power generation model, namely establishing a wind power generation model and a photovoltaic power generation model; establishing and designing an energy storage model, and adopting a KiBaM quasi-steady-state battery simulation model; time-of-use electricity price setting design: designing a time-of-use electricity price setting interface; model control and target optimization design, including micro-grid power balance and storage battery charging and discharging power control design; designing a micro-grid operation program, and optimizing system configuration and a scheduling strategy; and carrying out simulation calculation and result data analysis, verifying the effectiveness of a system optimization configuration scheme, and realizing park micro-grid distributed power supply and energy storage coordinated optimal configuration.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of computer software application of power system, and particularly relates to a method for coordinating and optimizing configuration of distributed power supply and energy storage of park micro-grid. BACKGROUND

[0002] With the rapid development of distributed power generation technology and energy storage technology and the adjustment of on-grid electricity price, micro-grid based on wind, light and storage will not be limited to island, remote area and other scenes, and industrial park micro-grid and regional micro-grid will be widely applied. Due to the difference of regional load, wind resources and light resources, the experience design method cannot be used for micro-grid planning and design. In addition, the performance parameters and investment costs of wind turbine, photovoltaic, converter and energy storage device are different, so that the micro-grid planning and design combination is various. Therefore, it is of important practical significance and engineering value to solve the optimal combination and proportion of each distributed power supply and energy storage in the park micro-grid.

[0003] At present, some mature technologies have been developed abroad for micro-grid planning and design, among which Hybrid2 and HOMER developed by the United States Renewable Energy Laboratory are widely used. HOMER can optimize the capacity configuration of energy storage system and intermittent power supply, but it uses traversal algorithm to calculate each combination, the capacity configuration combination needs to be input manually in advance, and the combination optimization has certain limitations. Hybrid2 has strong simulation function, but it does not have capacity configuration optimization function. The PDMG software developed by Tianjin University mainly has the functions of intermittent data analysis, distributed power supply and energy storage capacity optimization, energy storage system design and technical and economic comparison combined with expert intervention, which is a relatively complete micro-grid planning and design function, but its development cost is high and the scheme data analysis is not comprehensive. In addition, in the paper Micro-grid planning and simulation system development and application, scholar Guo Baoning introduces the idea of developing micro-grid planning and simulation system based on MATLAB platform, but it does not consider the influence of time-of-use price when interacting with the power grid, and the research is not comprehensive. Based on MATLAB platform, the application considers various factors, studies the method for coordinating and optimizing configuration of distributed power supply and energy storage of park micro-grid, and can realize the functions of wind, light, temperature data import, device parameter input, time-of-use price setting, scheme combination display and specific data and operation curve display, so as to effectively solve the engineering practical problems. SUMMARY

[0004] The application aims to solve the problems in the background art and provide a method for coordinating and optimizing configuration of distributed power supply and energy storage of park micro-grid.

[0005] To achieve the above purpose, the technical scheme of the application is as follows: a method for coordinating and optimizing configuration of distributed power supply and energy storage of park micro-grid, comprising:

[0006] Step S1: original data collection and import design, create a user interactive visualization interface in MATLAB APP Designer for importing and visualizing the original data required for microgrid planning;

[0007] Step S2: distributed power generation model establishment design, including the establishment of wind power generation model and photovoltaic power generation model;

[0008] Step S3: energy storage model establishment design, KiBaM quasi-steady-state battery simulation model is adopted;

[0009] Step S4: time-of-use electricity price setting design, based on the time-of-use electricity price setting interface, users can set the electricity price strategy of different months and different time periods;

[0010] Step S5: model control and target optimization design, including microgrid power balance and battery charging and discharging power control design, based on the economy of microgrid independent operation, considering the influence of time-of-use electricity price, the income generated by interaction with the large power grid is included in the cost, taking the minimum investment and operation cost in the system life cycle as the optimization target, increasing the construction of carbon emission reduction constraints and target system for the "zero carbon consumption" target;

[0011] Step S6: microgrid operation program design, based on the original natural data including wind speed, light intensity and temperature, equipment investment and operation data and time-of-use electricity price data, 8760 hours of operation data in a year is calculated, considering the energy storage charging and discharging constraints and time-of-use electricity price constraints, the maximum total power consumption of the system in all time periods is calculated, particle swarm algorithm, simulated annealing algorithm and genetic algorithm are used for numerical optimization solution, and the investment and operation cost in the system life cycle is obtained;

[0012] Step S7: simulation calculation and result data analysis, collect natural resources and electricity load data including wind speed, light intensity and temperature in the microgrid area, refer to the actual photovoltaic panel, fan, battery and exchanger type, set according to the time-of-use electricity price published in the corresponding area for simulation calculation; based on the 8760 hours of simulation data in a year, analyze the wind-light-storage collaborative operation characteristics, economy and reliability, take the capacity of photovoltaic, fan and battery as variables, take the minimum investment and operation cost in the whole life cycle as the target, carry out numerical optimization solution, verify the effectiveness of the system optimization configuration scheme, realize the optimal configuration of microgrid distributed power and energy storage in the park.

[0013] Further, in step S1, taking the minimum investment and operation cost in the whole life cycle and "zero carbon consumption" as the target, the wind, light, storage and load unit model is established, and a user interactive visualization interface is formed.

[0014] Furthermore, in step S1, the user-interactive visualization interface supports the import of four key data types, including: annual load data, wind speed data, light intensity data, and temperature data.

[0015] Furthermore, in step S1, in order to realize the custom import and display of user data, an original data import interface is designed in APPDesigner. This interface can import annual load data, wind speed data, light intensity data and temperature data. The imported data is in hourly units, that is, each imported data should contain 8760 points. The imported data is displayed on the coordinate axis by calling a function.

[0016] Furthermore, in step S2, with the goal of solving the problem of optimal combination and allocation of distributed power sources and energy storage in microgrids in actual engineering, based on the collection of natural resources such as wind speed and solar radiation and electricity load data in the microgrid area, and combined with existing reasonable and mature research schemes, wind power generation models and photovoltaic power generation models are built, and energy storage charging and discharging power and SOC state constraints are established in accordance with the actual working principle of energy storage.

[0017] In establishing the wind power generation model, the magnitude of wind speed *v* determines the amount of wind energy that the wind turbine can utilize. Based on annual wind speed data, the output power of the wind turbine can be represented by the following function:

[0018]

[0019] In equation (1): P is the actual output power of the fan; v c0 For the cut-in wind speed; v c1 To cut off the wind speed; v N P is the rated wind speed of the fan; N This is the rated output power of the fan.

[0020] The photovoltaic power generation model is established based on annual solar irradiance data, and the output power of the photovoltaic array can be expressed as:

[0021]

[0022] In equation (2): P1 is the actual output power of the photovoltaic array; P N f represents the rated power of the photovoltaic array, and f represents the output power of the photovoltaic array measured under standard conditions. PV This is the photovoltaic derating factor, used to calculate losses caused by surface contamination, shading, and aging of the photovoltaic panel itself; it is typically taken as 0.9. T Irradiance; α p I is the temperature correction factor; s T is a scalar measure of irradiance (1 kW / m²). C.STC Standard test condition temperature (25℃); T CThis refers to the temperature of the photovoltaic panel.

[0023] Furthermore, in step S1, through the establishment of a distributed power generation model, users can easily input parameters, import data, calculate power generation, and visualize the results, providing a reliable renewable energy power generation model for microgrid planning.

[0024] Furthermore, in step S3, the KiBaM quasi-steady-state battery simulation model can be effectively integrated into the microgrid simulation system, accurately simulating the charging and discharging behavior of the battery, considering the energy conversion process between different states, and providing a reliable energy storage model basis for the optimal scheduling of the microgrid.

[0025] In the KiBaM quasi-steady-state battery simulation model, the available energy and bound energy after charging and discharging can be expressed as:

[0026]

[0027] Where: Q is the total energy of the battery pack; Q1, Q2, Q... 1,end Q 2,end The available energy and bound energy at the beginning and end of the time step; P b This represents the actual charge / discharge power; k b is the rate constant; c is the ratio of available energy capacity to total capacity.

[0028] Furthermore, in step S4, during operation, the grid-connected microgrid both absorbs power from the grid and transmits power back to it. The differences between the grid-connected electricity price and the electricity consumption price, as well as the different time periods for electricity pricing, inevitably affect the operating costs of the microgrid. Therefore, to make the calculation results of the microgrid planning and design software more accurate, a time-of-use (TOU) pricing interface is designed. The TOU pricing interface uses two Table controls to implement the functions of defining and setting TOU pricing and setting TOU time periods, respectively. In the first Table control, each row represents a price, and each price corresponds to a color, containing data on the time period, the electricity consumption price, and the grid-connected electricity price. The second Table control has 12*24 cells, corresponding to the 12-month and 24-hour price periods. The TOU pricing interface can intuitively define and manage complex price period structures and supports economic analysis of microgrid operation.

[0029] Furthermore, the time-of-use pricing interface allows for the setting of monthly and time-of-use pricing periods by coloring different cells; the time-of-use pricing interface can intuitively define and manage complex pricing period structures and supports economic analysis of microgrid operation.

[0030] Furthermore, in step S5, model control and target optimization design, regarding system power balance, in this design, the microgrid power balance satisfies formula (5):

[0031] P grid (i)+P ES (i)=P PV (i)+P W (i)-P L (i) (5)

[0032] In the formula: P PV (i) represents the photovoltaic power at time step i (i = 1, 2, 3…8760); P W (i) represents the power of the wind turbine at time step i; P L (i) represents the load power at time step i; P grid (i) represents the grid interaction power at time step i, where a positive value indicates power transmitted to the main grid and a negative value indicates power absorbed from the external grid; P ES (i) represents the battery charging and discharging power at time step i, with positive values ​​indicating charging power and negative values ​​indicating discharging power.

[0033] Regarding the constraints on the state of charge and discharge of the battery, in this design, the battery's charging and discharging power must comply with the constraint of formula (6):

[0034] P ES.min ≤P ES (i)≤P ES.max (6)

[0035] In the formula: P(i) is the battery charging and discharging power at time step i; P ES.min P ES.max These are the minimum and maximum charging and discharging power allowed for the battery.

[0036] The State of Charge (SOC) is a crucial indicator for measuring the charge / discharge capacity and state of charge (SOC) of an energy storage system. When operating a battery energy storage system, upper and lower limits for SOC must be set to ensure the system operates within acceptable ranges. To extend battery life, overcharging and over-discharging should be avoided during battery use. The energy storage SOC should meet the following conditions:

[0037] S min ≤SOC(i)≤S max (7)

[0038] In the formula: SOC(i) is the state of charge of the battery at time step i; S min S max The minimum and maximum states of charge (SOC) allowed for the battery as set by the user.

[0039] Regarding the optimization objective, this design selects N as the number of photovoltaic array panels in the photovoltaic power generation system. PV The number of wind turbines N in a wind power generation system WThe number of battery packs N in the battery unit B As variables to be optimized, based on the economics of microgrids operating independently, and considering the impact of time-of-use pricing, the revenue generated from interaction with the main grid is incorporated into the cost, with the optimization objective being to minimize the investment and operating costs over the system's lifespan. To achieve the goal of "zero carbon consumption," a carbon emission reduction constraint and target system is added, with the objective function being:

[0040]

[0041] C(k)=C W,O&M (k)+C PV,O&M (k)+C B,O&M (k)+C C,O&M (k) (9)

[0042] B(k)=S(k)+W(k) (10)

[0043]

[0044] In the formula: C sum The total net present value (NPV) over the system's lifespan is denoted by n; the system's lifespan is denoted by r; and C is the discount rate. I C represents the installation cost of various power supply equipment; C(k) and B(k) represent other costs and revenues in year k; C W,O&M (k), C PV,O&M (k), C B,O&M (k), C C,O&M S(k) represents the operation and maintenance costs of the photovoltaic array, wind turbine, battery, and converter in year k; S(k) represents the discounted value of various power supply equipment, which only occurs in the last year of the system's lifespan; W(k) represents the interaction cost with the main grid in year k; C W,I C PV,I C B,I C C,I These represent the installation costs of the wind turbine, photovoltaic array, battery, and converter, respectively. Ccarbon(k) is the carbon trading cost item, where Pcarbon is the carbon price (currently approximately 60 yuan / ton CO2 in the national carbon market), P... grid (i) < 0, i.e., the amount of electricity purchased by the power grid, λ grid The carbon emission factor of the power grid (0.583 kg CO2 / kWh for this region); P grid (i)>0, i.e., clean electricity connected to the grid, λ offset The carbon offset factor is taken as 0.2 kg CO2 / kWh, reflecting the environmental value of green electricity.

[0045] Furthermore, in step S6, the microgrid operation program design realizes coordinated control of wind, solar and energy storage, power balance optimization and economic analysis, takes into account time-of-use pricing and equipment constraints, calculates 8760 hours of operation data throughout the year, and optimizes system configuration and scheduling strategies.

[0046] Furthermore, in step S6, the microgrid operation program design, based on the above theoretical analysis and the MATLAB platform, mainly involves the following specific ideas: Calculating the annual photovoltaic output using raw natural data such as wind speed and solar intensity, equipment investment and operation data, and time-of-use electricity price data; calculating the wind turbine output based on the relationship between real-time wind speed and the magnitudes of cut-in, cut-out, and rated wind speeds; and calculating the difference between the total output of distributed power sources and the electricity load (P...). d (i)=P PV (i)+P w (i)-P L Based on (i), considering the energy storage charging and discharging constraints, the energy storage charging and discharging power is determined; based on the total output of distributed power sources, the energy storage charging and discharging power, and the load, the interaction power P between each data point and the main grid is calculated. grid (i)=P d (i)-P ES (i) Calculate the grid interaction cost at the corresponding time, in conjunction with time-of-use pricing (if P...). grid (i)>0, meaning electricity is sold to the grid, and the electricity price at time i is f1(i) (yuan / kWh); if P grid (i)<0, meaning electricity is purchased from the grid, and the electricity price at time i is f2(i) (yuan / kWh); calculate the maximum total power consumption P of the system throughout the entire time period. c (i)=max(P grid (i)+P L (i) Calculate the investment and operating costs over the system's lifespan.

[0047] Furthermore, in step S7, the simulation calculation and result data analysis, using the capacity of photovoltaic, wind turbine, and battery as variables, and aiming at minimizing the investment and operating costs throughout the entire life cycle, involves numerical optimization. The essence of numerical optimization methods is to use iterative algorithms to find approximate optimal solutions and optimal function values ​​for the entire function or some practical problems. Commonly used optimization algorithms include simulated annealing, particle swarm optimization, and genetic algorithms. To ensure optimality is found, this design calls simulated annealing and genetic algorithms after the particle swarm optimization is completed. The fusion of multiple algorithms is achieved through a multi-stage collaborative optimization mechanism. This hybrid strategy fully leverages the fast convergence of the particle swarm optimization algorithm, the local refinement capability of the simulated annealing algorithm, and the population diversity preservation characteristics of the genetic algorithm. It is particularly suitable for solving microgrid capacity optimization problems with high-dimensional nonlinear constraints. In practical applications, the parameters of each algorithm are adjusted according to the system scale, and the optimal weight allocation is determined through sensitivity analysis. Practical applications show that this hybrid strategy improves the solution accuracy by more than 15% compared to a single algorithm.

[0048] Furthermore, taking an industrial park as an example, we collected and analyzed data on its natural resource distribution and load, including annual load data, annual wind speed data, annual solar intensity data, and annual temperature data. We then used actual photovoltaic panels, wind turbines, batteries, and exchanger types, and set them according to the time-of-use electricity price published in the region, and performed simulation calculations.

[0049] Data on the distribution of natural resources and their load were collected and analyzed. The load curve shows obvious diurnal fluctuations and seasonal characteristics, with the highest load reaching 1050kW in summer and the lowest at approximately 260kW in winter. Wind speed data indicates that the average annual wind speed in the region is 5.2m / s, with the richest wind resources occurring from March to May. Sunlight data shows an average annual solar irradiance of 0.38 W / m². 2 The annual temperature data shows that the summer temperature is nearly 40℃.

[0050] Based on the actual types of photovoltaic panels, wind turbines, batteries, and converters, the photovoltaic modules are selected as monocrystalline silicon models with a rated power of 350W and a derating factor of 0.92; the wind turbines are 2kW industrial wind turbines with cut-in, rated, and cut-out wind speeds of 3m / s, 11m / s, and 25m / s, respectively; the energy storage system is equipped with lithium iron phosphate batteries with a single-cell capacity of 4kWh, a charge / discharge efficiency of 92%, and a SOC operating range of 20%-90%; the converter efficiency is set at 96% to meet the bidirectional energy conversion requirements.

[0051] The electricity price is set according to the time-of-use pricing published in the region: the purchase price is 1.02 yuan / kWh during peak hours (8:00-12:00, 18:00-22:00), 1.12 yuan / kWh during peak hours, 0.65 yuan / kWh during normal hours, and 0.33 yuan / kWh during off-peak hours; the retail price is uniformly 0.32 yuan / kWh.

[0052] The selected region's total annual electricity consumption is approximately 4.39 million kilowatt-hours. Based on the set time-of-use pricing, assuming the load is entirely supplied by the main power grid and without considering load growth rate, the total electricity purchase cost over 10 years would reach 32.21 million yuan. Among the 14 calculated options, Option 5 has the lowest total investment and operating cost of 22.2366 million yuan, saving approximately 10 million yuan.

[0053] Simulation results show that, over the entire lifecycle, the initial investment for photovoltaic power generation is 1.6128 million yuan; considering the discount rate, the total operation and maintenance cost is 2.5966 million yuan, and the total cost is 4.2094 million yuan. The initial investment for wind turbines is 139,100 yuan; considering the discount rate, the total operation and maintenance cost is 236,300 yuan, and the total cost is 375,400 yuan. The initial investment for batteries is 645,700 yuan; considering the discount rate, the total operation and maintenance cost is 648,100 yuan, and the total cost is 1.2938 million yuan. The initial investment for converters is 1.0212 million yuan; considering the discount rate, the total operation and maintenance cost is 1.1274 million yuan, and the total cost is 2.1486 million yuan. Over the entire lifecycle, considering the discount rate, the grid connection cost is 12,900 yuan, and the electricity consumption cost is 13.5617 million yuan. The microgrid operates as follows annually: photovoltaic power generation is 1,342,162 kWh, with an annual operating time of 4,564 hours; wind power generation is 177,593 kWh, with an annual operating time of 5,666 hours; the total annual charging and discharging of energy storage is 214,330 kWh, with 1,173 charging times and 827 discharging times; the annual grid-connected electricity is 5,467 kWh, with 31 hours of grid connection; and the main grid provides 2,875,794 kWh of electricity, with 6,997 hours of grid connection.

[0054] Taking Option 5 as an example, by selecting the monthly curve and the daily curve respectively, you can view the output of photovoltaic and wind power, the charging and discharging of batteries, and the interaction with the main power grid.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] 1. With the goal of minimizing investment and operating costs and achieving zero carbon consumption throughout the entire life cycle, establish wind, solar, storage, and load unit models to create a user-interactive visual interface;

[0057] 2. This invention aims to solve the problem of optimal combination and allocation of distributed power sources and energy storage in microgrids in practical engineering. Based on the collection of natural resources such as wind speed and solar radiation and electricity load data in the microgrid area, and combined with existing reasonable and mature research schemes, a mathematical model of wind turbine and photovoltaic power output is built. Following the actual working principle of energy storage, the charging and discharging power and SOC state constraints of energy storage are established.

[0058] 3. This invention takes power generation and supply balance and energy conservation as the premise, and comprehensively considers the impact of equipment investment costs, operation and maintenance costs and time-of-use electricity prices of the power grid. It adopts a combination of multiple optimization algorithms such as particle swarm optimization and genetic algorithm to achieve the optimal configuration of distributed power sources and energy storage in the park microgrid. Attached Figure Description

[0059] Figure 1 This is a schematic diagram of the method flow in one embodiment of the present invention;

[0060] Figure 2 This is a schematic diagram of the microgrid operation program design process in one embodiment of the present invention;

[0061] Figure 3 This is a schematic diagram of the annual load data parameter settings for a certain park in one embodiment of the present invention;

[0062] Figure 4 This is a schematic diagram of the annual wind speed data parameter settings in a certain park according to an embodiment of the present invention;

[0063] Figure 5 This is a schematic diagram of the annual light intensity data parameter settings for a certain park in one embodiment of the present invention;

[0064] Figure 6 This is a schematic diagram of the annual temperature data parameter settings for a certain park in one embodiment of the present invention;

[0065] Figure 7 This is a schematic diagram of equipment parameter settings in a certain park according to an embodiment of the present invention;

[0066] Figure 8 This is a schematic diagram of time-of-use electricity pricing in a certain industrial park according to an embodiment of the present invention;

[0067] Figure 9 This is a schematic diagram of a simulation result list of a certain park in one embodiment of the present invention;

[0068] Figure 10 This is a data analysis interface diagram of Scheme 5 in a certain park according to an embodiment of the present invention;

[0069] Figure 11 This is a curve result graph of a certain park scheme in one embodiment of the present invention in May;

[0070] Figure 12 This is a 5-day curve result graph of a certain park scheme in one embodiment of the present invention. Detailed Implementation

[0071] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings.

[0072] This invention provides a method for coordinated and optimized configuration of distributed power sources and energy storage in a park microgrid, including:

[0073] Step S1: Raw data collection and import design. Create a user-interactive visualization interface in MATLAB APP Designer to import and visualize the raw data required for microgrid planning;

[0074] Step S2: Design and establish distributed power generation models, including the establishment of wind power generation models and photovoltaic power generation models;

[0075] Step S3: Energy storage model design, using the KiBaM quasi-steady-state battery simulation model;

[0076] Step S4: Time-of-use pricing setting design. Based on the time-of-use pricing setting interface, users can set electricity pricing strategies for different months and different time periods.

[0077] Step S5: Model control and target optimization design, including microgrid power balance and battery charging and discharging power control design. Based on the economics of microgrid independent operation, and considering the impact of time-of-use pricing, the revenue generated from interaction with the large power grid is included in the cost. The optimization objective is to minimize the investment and operating costs over the system's life cycle. In response to the "zero carbon consumption" target, a carbon emission reduction constraint and target system is added.

[0078] Step S6: Microgrid operation program design. Based on raw natural data including wind speed, light intensity, and temperature, equipment investment and operation data, and time-of-use electricity price data, calculate the annual 8760-hour operation data. Considering energy storage charging and discharging constraints and time-of-use electricity price constraints, calculate the maximum total power consumption of the system in all time periods. Use particle swarm optimization algorithm, simulated annealing algorithm, and genetic algorithm for numerical optimization solution to obtain the investment and operation cost within the system's life cycle.

[0079] Step S7: Simulation calculation and result data analysis. Collect natural resource and electricity load data, including wind speed, solar irradiance, and temperature, within the microgrid area. Refer to the actual types of photovoltaic panels, wind turbines, batteries, and switches, and perform simulation calculations according to the time-of-use electricity prices published in the corresponding regions. Based on 8760 hours of simulation data throughout the year, analyze the characteristics, economy, and reliability of wind-solar-storage coordinated operation. Using the capacity of photovoltaics, wind turbines, and batteries as variables, and aiming at the lowest investment and operating cost over the entire life cycle, perform numerical optimization to verify the effectiveness of the system optimization configuration scheme and achieve the optimal coordinated configuration of distributed power sources and energy storage in the park's microgrid.

[0080] The following is a detailed implementation process of the present invention.

[0081] Please refer to Figure 1 The present invention provides a method for coordinated and optimized configuration of distributed power sources and energy storage in a park microgrid, comprising the following steps:

[0082] Step S1: Raw Data Collection and Import Design. Create a user-friendly interface in the MATLAB App Designer to import and visualize the raw data required for microgrid planning. This interface will support the import of four key data types: annual load data, wind speed data, solar irradiance data, and temperature data.

[0083] Step S2: Design and establish a distributed power generation model, including a wind power generation model and a photovoltaic power generation model. Through the establishment of the distributed power generation model, users can easily input parameters, import data, calculate power generation and visualize the results, providing a reliable renewable energy power generation model for microgrid planning.

[0084] Step S3: Energy storage model design. The KiBaM quasi-steady-state battery simulation model is adopted in the design. The KiBaM battery model can be effectively integrated into the microgrid simulation system to accurately simulate the charging and discharging behavior of the battery, consider the energy conversion process between different states, and provide a reliable energy storage model basis for the optimal scheduling of the microgrid.

[0085] Step S4: Time-of-use pricing design. The time-of-use pricing interface can intuitively define and manage complex time-of-use pricing structures and support economic analysis of microgrid operation. Users can easily set pricing strategies for different months and time periods through tables and color coding, providing accurate pricing inputs for optimized microgrid operation;

[0086] Step S5: Model control and target optimization design, including microgrid power balance and battery charging and discharging power control design. Based on the economics of microgrid independent operation, and taking into account the impact of time-of-use pricing, the revenue generated from interaction with the large power grid is included in the cost, with the minimum investment and operating cost over the system life cycle as the optimization objective.

[0087] Step S6: Microgrid operation program design. The microgrid operation program realizes coordinated control of wind, solar and energy storage, power balance optimization and economic analysis, takes into account time-of-use pricing and equipment constraints, calculates 8760 hours of operation data throughout the year, and optimizes system configuration and scheduling strategies.

[0088] Step S7: Simulation calculation and result data analysis. Based on 8760 hours of simulation data throughout the year, analyze the characteristics, economic indicators and reliability of wind, solar and energy storage coordinated operation. With the capacity of photovoltaic, wind turbine and battery as variables, and with the goal of minimizing investment and operating costs throughout the entire life cycle, perform numerical optimization to verify the effectiveness of the system optimization configuration scheme and achieve the optimal coordinated configuration of distributed power sources and energy storage in the park's microgrid.

[0089] Reference in this embodiment Figure 2Based on the theoretical analysis of the above-mentioned original data collection and import design, equipment parameter import and distributed power generation model design, energy storage model design, time-of-use pricing interface design, model control and target optimization, the main specific idea of ​​the program design is as follows: Calculate the annual photovoltaic output using original natural data such as wind speed and solar intensity, equipment investment and operation data, and time-of-use pricing data; calculate the wind turbine output based on the relationship between real-time wind speed and the magnitude of cut-in, cut-out, and rated wind speed; and calculate the difference between the total output of the distributed power source and the electricity load (P...). d (i)=P PV (i)+P w (i)-P L Based on (i), considering the energy storage charging and discharging constraints, the energy storage charging and discharging power is determined; based on the total output of distributed power sources, the energy storage charging and discharging power, and the load, the interaction power P between each data point and the main grid is calculated. grid (i)=P d (i)-P ES (i) Calculate the grid interaction cost at the corresponding time, in conjunction with time-of-use pricing (if P...). grid (i)>0, meaning electricity is sold to the grid, and the electricity price at time i is f1(i) (yuan / kWh); if P grid (i)<0, meaning electricity is purchased from the grid, and the electricity price at time i is f2(i) (yuan / kWh); calculate the maximum total power consumption P of the system throughout the entire time period. c (i)=max(P grid (i)+P L (i) Calculate the investment and operating costs over the system's lifespan.

[0090] Preferred, Reference Figure 3 , Figure 4 , Figure 5 and Figure 6 Taking an industrial park in Anxi, Fujian Province as an example, this study collected and analyzed data on its natural resource distribution and load. Annual load, wind speed, solar irradiance, and temperature data were collected and analyzed according to equipment parameters. The highest load in summer reached 1050kW, while the lowest in winter was approximately 260kW. Wind speed data showed an average annual wind speed of 5.2m / s, with the most abundant wind resources occurring from March to May. Solar irradiance data showed an average annual solar irradiance of 0.38 W / m². 2 The annual temperature data shows that the summer temperature is nearly 40℃.

[0091] Preferred, Reference Figure 7Based on the actual types of photovoltaic panels, wind turbines, batteries, and converters, and according to the equipment parameters, the photovoltaic modules are selected as monocrystalline silicon models with a rated power of 350W and a derating factor of 0.92; the wind turbine is a 2kW industrial wind turbine generator with cut-in, rated, and cut-out wind speeds of 3m / s, 11m / s, and 25m / s, respectively; the energy storage system is equipped with lithium iron phosphate batteries, with a single-cell capacity of 4kWh, a charge / discharge efficiency of 92%, and a SOC operating range of 20%-90%; the converter efficiency is set to 96% to meet the bidirectional energy conversion requirements.

[0092] Preferred, Reference Figure 8 The electricity price will be set according to the time-of-use pricing published for that region. The purchase price will be RMB 1.02 / kWh during peak hours (8:00-12:00, 18:00-22:00), RMB 1.12 / kWh during peak hours, RMB 0.65 / kWh during normal hours, and RMB 0.33 / kWh during off-peak hours; the retail price will be a uniform RMB 0.32 / kWh.

[0093] Preferred, Reference Figure 9 Simulation calculations were performed based on the above settings. The total annual electricity consumption of the selected region is approximately 4.39 million kWh. According to the set time-of-use pricing, assuming the load is entirely supplied by the main power grid and without considering the load growth rate, the total electricity purchase cost over 10 years reaches 32.21 million yuan. Fourteen scenarios were calculated.

[0094] Preferred, Reference Figure 10 Among the 14 calculated options, option 5 has the lowest total investment and operating cost of 22.2366 million yuan, saving approximately 10 million yuan.

[0095] Over the entire life cycle, the initial investment for photovoltaic power generation is RMB 1.6128 million; considering the discount rate, the total operation and maintenance cost is RMB 2.5966 million, and the total cost is RMB 4.2094 million. The initial investment for wind turbines is RMB 139,100; considering the discount rate, the total operation and maintenance cost is RMB 236,300, and the total cost is RMB 375,400. The initial investment for batteries is RMB 645,700; considering the discount rate, the total operation and maintenance cost is RMB 648,100, and the total cost is RMB 1.2938 million. The initial investment for converters is RMB 1.0212 million; considering the discount rate, the total operation and maintenance cost is RMB 1.1274 million, and the total cost is RMB 2.1486 million. Over the entire life cycle, considering the discount rate, the grid connection cost is RMB 12,900, and the electricity consumption cost is RMB 13.5617 million. The microgrid operates as follows annually: photovoltaic power generation is 1,342,162 kWh, with an annual operating time of 4,564 hours; wind power generation is 177,593 kWh, with an annual operating time of 5,666 hours; the total annual charging and discharging of energy storage is 214,330 kWh, with 1,173 charging times and 827 discharging times; the annual grid-connected electricity is 5,467 kWh, with 31 hours of grid connection; and the main grid provides 2,875,794 kWh of electricity, with 6,997 hours of grid connection.

[0096] Preferred, Reference Figure 11 , Figure 12 Taking Scheme 5 as an example, by selecting the monthly curve and the daily curve respectively, you can view the output of photovoltaic and wind power, the charging and discharging of batteries, and the interaction with the main power grid.

[0097] The above are preferred embodiments of the present invention. Any changes made to the technical solution of the present invention that do not exceed the scope of the technical solution of the present invention shall fall within the protection scope of the present invention.

Claims

1. A method for coordinated and optimized configuration of distributed power sources and energy storage in a park microgrid, characterized in that, include: Step S1: Raw data collection and import design. Create a user-interactive visualization interface in MATLAB APP Designer to import and visualize the raw data required for microgrid planning; Step S2: Design and establish distributed power generation models, including the establishment of wind power generation models and photovoltaic power generation models; Step S3: Energy storage model design, using the KiBaM quasi-steady-state battery simulation model; Step S4: Time-of-use pricing setting design. Based on the time-of-use pricing setting interface, users can set electricity pricing strategies for different months and different time periods. Step S5: Model control and target optimization design, including microgrid power balance and battery charging and discharging power control design. Based on the economics of microgrid independent operation, and considering the impact of time-of-use pricing, the revenue generated from interaction with the large power grid is included in the cost. The optimization objective is to minimize the investment and operating costs over the system's life cycle. In response to the "zero carbon consumption" target, a carbon emission reduction constraint and target system is added. Step S6: Microgrid operation program design. Based on raw natural data including wind speed, light intensity, and temperature, equipment investment and operation data, and time-of-use electricity price data, calculate the annual 8760-hour operation data. Considering energy storage charging and discharging constraints and time-of-use electricity price constraints, calculate the maximum total power consumption of the system in all time periods. Use particle swarm optimization algorithm, simulated annealing algorithm, and genetic algorithm for numerical optimization solution to obtain the investment and operation cost within the system's life cycle. Step S7: Simulation calculation and result data analysis. Collect natural resource and electricity load data, including wind speed, solar irradiance, and temperature, within the microgrid area. Refer to the actual types of photovoltaic panels, wind turbines, batteries, and switches, and perform simulation calculations according to the time-of-use electricity prices published in the corresponding regions. Based on 8760 hours of simulation data throughout the year, analyze the characteristics, economy, and reliability of wind-solar-storage coordinated operation. Using the capacity of photovoltaics, wind turbines, and batteries as variables, and aiming at the lowest investment and operating cost over the entire life cycle, perform numerical optimization to verify the effectiveness of the system optimization configuration scheme and achieve the optimal coordinated configuration of distributed power sources and energy storage in the park's microgrid.

2. The method for coordinated and optimized configuration of distributed power sources and energy storage in a park microgrid according to claim 1, characterized in that, In step S1, with the goal of minimizing investment and operating costs throughout the entire life cycle and achieving "zero carbon consumption," wind, solar, storage, and load unit models are established to form a user-interactive visual interface.

3. A method for coordinated and optimized configuration of distributed power sources and energy storage in a park microgrid according to claim 1 or 2, characterized in that, In step S1, the user-interactive visual interface supports the import of four key data types, including: annual load data, wind speed data, light intensity data, and temperature data.

4. The method for coordinated and optimized configuration of distributed power sources and energy storage in a park microgrid according to claim 1, characterized in that, In step S1, to enable user data to be imported and displayed in a custom manner, an original data import interface is designed in APPDesigner. This interface can import annual load data, wind speed data, light intensity data and temperature data. The imported data is in hourly units, that is, each imported data should contain 8760 points. The imported data is displayed on the coordinate axis by calling a function.

5. The method for coordinated and optimized configuration of distributed power sources and energy storage in a park microgrid according to claim 1, characterized in that, In step S2, with the goal of solving the optimal combination and allocation problem of distributed power sources and energy storage in microgrids in actual engineering, based on the collection of natural resources such as wind speed and solar radiation and electricity load data in the microgrid area, and combined with existing reasonable and mature research schemes, wind power generation models and photovoltaic power generation models are built. Following the actual working principle of energy storage, energy storage charging and discharging power and SOC state constraints are established.

6. A method for coordinated and optimized configuration of distributed power sources and energy storage in a park microgrid according to claim 1 or 5, characterized in that, In step S1, through the establishment of a distributed power generation model, users can easily input parameters, import data, calculate power generation, and visualize the results, providing a reliable renewable energy power generation model for microgrid planning.

7. The method for coordinated and optimized configuration of distributed power sources and energy storage in a park microgrid according to claim 1, characterized in that, In step S3, the KiBaM quasi-steady-state battery simulation model can be effectively integrated into the microgrid simulation system, accurately simulating the charging and discharging behavior of the battery, considering the energy conversion process between different states, and providing a reliable energy storage model basis for the optimal scheduling of the microgrid.

8. The method for coordinated and optimized configuration of distributed power sources and energy storage in a park microgrid according to claim 1, characterized in that, In step S4, the time-of-use pricing setting interface uses two Table controls to implement the functions of defining and setting time-of-use pricing and setting time-of-use pricing periods. In the first Table control, each row represents a price, and each price corresponds to a color, containing data on the pricing period, electricity consumption price, and grid connection price. In the second Table control, there are 12*24 cells, corresponding to the 12-month and 24-hour pricing periods. The time-of-use pricing setting interface can intuitively define and manage complex pricing period structures and supports economic analysis of microgrid operation.

9. A method for coordinated and optimized configuration of distributed power sources and energy storage in a park microgrid according to claim 1 or 8, characterized in that, The time-of-use pricing interface allows users to set monthly and time-of-use pricing periods by coloring different cells. It also enables users to intuitively define and manage complex pricing period structures and supports economic analysis of microgrid operation.

10. The method for coordinated and optimized configuration of distributed power sources and energy storage in a park microgrid according to claim 1, characterized in that, In step S6, the microgrid operation program design realizes coordinated control of wind, solar and energy storage, power balance optimization and economic analysis, takes into account time-of-use pricing and equipment constraints, calculates 8760 hours of operation data throughout the year, and optimizes system configuration and scheduling strategies.

Images