A rural micro-energy network optimization configuration method and system

Abstract

The application discloses a rural micro-energy network optimization configuration method and system, and the method comprises the following steps: acquiring wind and light resource time sequence, multi-element load time sequence, equipment parameters and time-of-use electricity price data; constructing a three-dimensional decision vector with a photovoltaic capacity proportion factor, a wind power capacity proportion factor and a biomass rated power as decision variables; establishing a target function with the minimum total operation cost in the whole life cycle as the target, and physically anchoring the energy storage rated capacity through a source and storage proportion linkage mechanism to avoid the unlimited divergence of the energy storage capacity under extreme working conditions; setting instantaneous multi-energy flow topology balance constraints, core load hard binding constraints, seasonal power supply reliability adaptive control constraints and core load robust reserve margin constraints; solving the target function within the constraint boundary, and outputting an optimal configuration scheme; the energy storage capacity divergence is effectively avoided, the whole life cycle cost is reduced, the core load power supply reliability is ensured, and the method is suitable for rural multi-energy complementary micro-energy network planning scenes.

Description

Technical Field

[0001] This invention belongs to the field of integrated energy system planning and microgrid optimization scheduling technology, and particularly relates to a method and system for optimizing the configuration of rural microgrids. Background Technology

[0002] Constructing multi-source complementary microgrids based on photovoltaic, wind, and biomass energy has become an important way to improve the quality of electricity use and solve energy supply problems in rural areas. However, compared with conventional industrial microgrids, rural microgrids face extremely unique application scenarios.

[0003] Existing microgrid capacity configuration methods typically treat all loads equally, employing a uniform load loss penalty mechanism, severely neglecting the "core and non-core" differences in rural diversified electricity consumption. In reality, if core loads such as poultry greenhouse insulation and agricultural product cold storage experience power outages under extreme conditions such as continuous windless and sunless conditions, it will directly lead to serious secondary economic disasters such as damage to agricultural products or large-scale poultry deaths. Existing conventional configuration schemes generally lack underlying hard isolation and robust backup protection mechanisms for such core loads.

[0004] On the other hand, rural production and daily life are characterized by strong seasonal fluctuations, but traditional microgrid planning often sets a fixed and stringent probability of power shortage (LPSP) threshold throughout the year. This "one-size-fits-all" static indicator forces the system to maintain high equipment redundancy during most off-peak periods throughout the year in order to meet the demands of extreme peak periods, resulting in extremely poor economic efficiency over the entire life cycle. At the same time, in existing models that rely solely on economic efficiency or simple power balance, in order to compensate for the long-term intermittent nature of wind and solar power output, optimization algorithms are prone to getting trapped in local optima with unbounded expansion of energy storage capacity, leading to severe divergence in energy storage configuration of microgrids under extreme operating conditions.

[0005] Therefore, there is an urgent need for a new configuration method that can adapt to the seasonal characteristics of rural loads, provide robust protection for key agricultural production loads, and converge on the physical boundary. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the present invention aims to provide a method and system for optimizing the configuration of rural microgrids, taking into account robust reserve for core loads and seasonal adaptability. This method aims to minimize the system's total lifecycle operating costs while mitigating the risk of power outages for critical rural industrial loads under extreme conditions, and effectively avoiding the dispersion and redundant investment of energy storage capacity.

[0007] In a first aspect, the present invention provides a method for optimizing the configuration of rural microgrids, characterized in that it includes:

[0008] Acquire basic data of the target rural microgrid, including wind and solar resource time-series data, multi-source load time-series data, equipment parameters, and time-of-use electricity price information;

[0009] Based on the time-series data of wind and solar resources, the time-series data of multi-source loads, and the equipment parameters, the range of values ​​for the photovoltaic power generation capacity ratio factor, the range of values ​​for the wind power generation capacity ratio factor, and the range of values ​​for the rated configuration power of the biomass generator set are determined. A three-dimensional decision vector is constructed using the photovoltaic power generation capacity ratio factor, the wind power generation capacity ratio factor, and the rated configuration power of the biomass generator set as decision variables.

[0010] With the goal of minimizing the annualized total operating cost over the entire life cycle, an objective function is established that includes the annualized initial investment cost of equipment assets, the annual operation and maintenance cost of intermittent power sources and energy storage systems, the total operating cost of controllable biomass power sources, the cost of curtailment penalties, and the cost of power outage penalties due to load failure. The rated capacity of the energy storage system is physically anchored through a source-storage ratio linkage mechanism.

[0011] Set system constraint boundaries, solve the objective function under the constraints of the system constraint boundaries, obtain the optimal three-dimensional decision vector that minimizes the objective function, and output the optimal configuration scheme of the rural microgrid based on the optimal three-dimensional decision vector.

[0012] Secondly, the present invention provides a rural microgrid optimization configuration system, comprising:

[0013] The acquisition module is configured to acquire basic data of the target rural microgrid, including wind and solar resource time-series data, multi-source load time-series data, equipment parameters, and time-of-use electricity price information.

[0014] The determination module is configured to determine the value range of the photovoltaic power generation capacity ratio factor, the value range of the wind power generation capacity ratio factor, and the value range of the rated configuration power of the biomass generator set based on the time series data of the wind and solar resources, the time series data of the multi-source load, and the equipment parameters, and to construct a three-dimensional decision vector using the photovoltaic power generation capacity ratio factor, the wind power generation capacity ratio factor, and the rated configuration power of the biomass generator set as decision variables.

[0015] The module is configured to minimize the annualized total operating cost over the entire life cycle. It establishes an objective function that includes the annualized initial investment cost of equipment assets, the annual operation and maintenance cost of intermittent power sources and energy storage systems, the total operating cost of controllable biomass power sources, the cost of curtailment penalties, and the cost of power outage penalties due to load shedding. The rated capacity of the energy storage system is physically anchored through a source-storage ratio linkage mechanism.

[0016] The solution module is configured to set system constraint boundaries, solve the objective function under the constraints of the system constraint boundaries, obtain the optimal three-dimensional decision vector that minimizes the objective function, and output the optimal configuration scheme of the rural microgrid based on the optimal three-dimensional decision vector.

[0017] Thirdly, an electronic device is provided, comprising: at least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the steps of the rural microgrid optimization configuration method according to any embodiment of the present invention.

[0018] Fourthly, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the steps of the rural microgrid optimization configuration method according to any embodiment of the present invention.

[0019] The rural microgrid optimization configuration method and system of this application have the following beneficial effects:

[0020] By introducing robust reserve margin constraints for core loads and seasonal reliability adaptive control constraints, the system effectively avoids secondary disasters caused by power outages in key rural industries, while preventing power outages. The high-cost equipment redundancy caused by fixed annual targets is avoided. The allowable power outage probability thresholds in spring and autumn are higher than those in summer and winter, enabling the system to maintain high reliability during the busy farming season and moderately reduce requirements during the off-season, thus significantly improving the economic efficiency throughout the entire life cycle.

[0021] A full life cycle cost objective function was constructed, and a source-storage ratio linkage mechanism based on the penetration rate of new energy was introduced for physical anchoring, locking the rated capacity of energy storage with the installed capacity of wind and solar heterogeneous power sources, thus avoiding the risk of divergence in energy storage capacity configuration under extreme operating conditions from the underlying logic.

[0022] By using multi-period tiered hard-binding constraints on core loads, core loads such as poultry insulation and cold storage are hard isolated from non-core loads from the load deconstruction end. The baseline of uninterrupted core loads is embedded as an independent constraint condition into the optimization model, thus eliminating the risk of power outages of critical loads under extreme conditions from the planning level.

[0023] By constructing a three-dimensional decision vector using photovoltaic capacity scaling factor, wind power capacity scaling factor, and biomass rated power, four heterogeneous power sources—photovoltaic, wind power, energy storage, and biomass—are incorporated into a unified optimization framework. Under the premise of meeting the requirements of seasonal fluctuations in rural loads and the reliability of core loads, optimal capacity matching of multi-source complementary systems is achieved. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A flowchart illustrating a method for optimizing the configuration of a rural microgrid, as provided in an embodiment of the present invention;

[0026] Figure 2 This is a block diagram of a rural microgrid optimization configuration system provided in an embodiment of the present invention;

[0027] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0028] 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.

[0029] Please see Figure 1 The diagram shows a flowchart of a rural microgrid optimization configuration method according to this application.

[0030] like Figure 1 As shown, the rural microgrid optimization configuration method specifically includes the following steps:

[0031] Step S101: Obtain basic data of the target rural microgrid. The basic data includes wind and solar resource time-series data, multi-source load time-series data, equipment parameters, and time-of-use electricity price information.

[0032] In this step, hourly wind and solar resource time-series data, such as wind speed and solar irradiance, are obtained for the target rural microgrid area throughout the year. The wind and solar resource time-series data can be obtained from historical observation data of meteorological stations or NASA remote sensing databases and used as input for wind and solar power output simulation.

[0033] Obtain multi-dimensional load time-series data for rural areas, including agricultural production loads (such as poultry farming insulation, greenhouses, and cold storage for agricultural products), residential electricity loads, and public service loads. Load characteristics vary significantly across seasons—cold storage loads dominate in summer, insulation loads dominate in winter, and loads are relatively stable in spring and autumn.

[0034] Acquire equipment parameters, including unit capacity construction cost, operation and maintenance rate, conversion efficiency, and service life of photovoltaic modules, wind turbine generators, energy storage systems, and biomass generators. At the same time, obtain local time-of-use electricity price information to evaluate the economics of microgrid electricity purchase and sale.

[0035] Step S102: Based on the time-series data of wind and solar resources, the time-series data of multi-source loads, and the equipment parameters, determine the value range of the photovoltaic power generation capacity ratio factor, the value range of the wind power generation capacity ratio factor, and the value range of the rated configuration power of the biomass generator set, and construct a three-dimensional decision vector using the photovoltaic power generation capacity ratio factor, the wind power generation capacity ratio factor, and the rated configuration power of the biomass generator set as decision variables.

[0036] In this step, the photovoltaic resource endowment level and the wind power resource endowment level are determined based on the annual average sunshine hours and annual average wind speed in the time series data of the wind and solar resources, respectively.

[0037] The upper and lower limits of the photovoltaic power generation capacity ratio factor are determined based on the photovoltaic resource endowment level. When the photovoltaic resource endowment level is excellent, the upper limit of the photovoltaic power generation capacity ratio factor is greater than the upper limit when the photovoltaic resource endowment level is poor, and the lower limit of the photovoltaic power generation capacity ratio factor is less than the lower limit when the photovoltaic resource endowment level is poor.

[0038] The upper and lower limits of the wind power generation capacity ratio factor are determined based on the wind power resource endowment level.

[0039] Based on the annual maximum load power in the multi-source load time series data, the upper and lower limits of the rated configuration power of the biomass generator set are determined.

[0040] Furthermore, the photovoltaic power generation capacity scaling factor is used as the first dimension component of the three-dimensional decision vector to represent the ratio of the photovoltaic power generation unit capacity to the baseline load power.

[0041] The wind power generation capacity scaling factor is used as the second dimension component of the three-dimensional decision vector to represent the ratio of the wind power generation unit capacity to the benchmark load power.

[0042] The rated configuration power of the biomass generator set is used as the third dimension component of the three-dimensional decision vector;

[0043] The three-dimensional decision vector is composed of the first dimension component, the second dimension component, and the third dimension component, and each component in the three-dimensional decision vector is a variable to be optimized.

[0044] Step S103: With the goal of minimizing the annualized total operating cost over the entire life cycle, establish an objective function that includes the annualized initial investment cost of equipment assets, the annual operation and maintenance cost of intermittent power sources and energy storage systems, the total operating cost of controllable biomass power sources, the cost of curtailment penalties, and the cost of power outage penalties for load shedding. The rated capacity of the energy storage system is physically anchored through a source-storage ratio linkage mechanism.

[0045] In this step, the expression for the objective function is:

[0046] ,

[0047] In the formula, The total annualized operating cost over the entire lifecycle. This represents the annualized initial investment cost of the equipment assets. The annual operation and maintenance costs of intermittent power sources and energy storage systems, The total operating cost of controllable biomass power sources. To cover the cost of power curtailment penalties, Penalty costs for power outages due to load shedding, Cost of equipment replacement;

[0048] The annualized initial investment of the equipment assets The expression is:

[0049] ,

[0050] In the formula, To restore the operator for asset-based annuities, , , These are the unit capacity construction cost coefficients for photovoltaic equipment, wind power equipment, and energy storage systems, respectively. This is the photovoltaic power generation capacity ratio factor. This is the wind power generation capacity scaling factor. The annual composite average reference power of the time-series load, This refers to the rated capacity of the energy storage system.

[0051] The expression for the asset-based annuity recovery operator is:

[0052] ,

[0053] In the formula, The weighted discount rate for microgrid investments. The planning lifespan of the microgrid system;

[0054] The rated capacity of the energy storage system is physically anchored through a source-storage ratio linkage mechanism, expressed as follows:

[0055] ,

[0056] In the formula, Configure a power ratio factor for source and storage. This is the photovoltaic power generation capacity ratio factor. This is the baseline installed capacity for photovoltaic power generation. This is the wind power generation capacity scaling factor. This is the benchmark installed capacity for wind power generation. This is the parameter representing the equivalent energy support duration of the energy storage system.

[0057] The expression for the total operating cost of the controllable biomass power source is:

[0058] ,

[0059] In the formula, The annual depreciation cost of biomass generator sets, Annual maintenance costs for biomass generator sets, For biomass power generation units in The actual dispatch output power at any given time. For discrete time steps, Let the normalized unit quality acquisition cost be the cost of the j-th batch of fuel transactions. This refers to the total number of batches of fuel purchased centrally throughout the year. This represents the total number of simulation periods throughout the year.

[0060] The expression for the cost of the power curtailment penalty is:

[0061] ,

[0062] In the formula, The penalty coefficient per unit of abandoned electricity. for The amount of wind and solar power curtailed at any given moment;

[0063] The cost of power outage penalty due to load failure The expression is:

[0064] ,

[0065] In the formula, This is the unit load loss compensation coefficient for rural power grid areas. for The final power loss at any given moment.

[0066] Step S104: Set system constraint boundaries, solve the objective function under the constraints of the system constraint boundaries to obtain the optimal three-dimensional decision vector that minimizes the objective function, and output the optimal configuration scheme of the rural microgrid based on the optimal three-dimensional decision vector.

[0067] In this step, the instantaneous multi-energy flow topology equilibrium constraint is set, and the expression is:

[0068] ,

[0069] In the formula, This is the photovoltaic power generation capacity ratio factor. This is the wind power generation capacity scaling factor. for Normalized time-series per-unit output value of photovoltaic power at any given time. for Normalized time-series per-unit output value of wind power at any given moment for The actual response power of the biomass generator set at any given time. for The actual throughput power of energy storage at all times, with positive for discharging and negative for charging. for The amount of wind and solar power curtailed at any given time. for The instantaneous total power consumption of diverse loads in rural areas at any given time;

[0070] The planning search space boundary constraints are set as follows: the range of values ​​for the photovoltaic power generation capacity ratio factor, the range of values ​​for the wind power generation capacity ratio factor, and the range of values ​​for the rated configuration power of the biomass generator set are limited.

[0071] Set up a multi-time-period tiered core load hard binding constraint, specifically: extract the uninterrupted core power load baseline by deconstructing industry correlation, and constrain the system's power supply capacity to the uninterrupted core load at any time to be no less than the core load baseline;

[0072] Set energy storage operation constraints, specifically: constrain the charging and discharging power of the energy storage system at any time to not exceed the maximum allowable throughput power, and constrain the state of charge of the energy storage system to be between the preset minimum state of charge and the maximum state of charge.

[0073] Set seasonal power supply reliability adaptive control constraints, with the expression as follows:

[0074] ,

[0075] In the formula, For the first Expected probability of system power shortage in each season For the first A collection of time periods in each season. For the first The threshold for the expected probability of power shortage allowed in a given season The discrete time step;

[0076] Set a robust reserve margin constraint for core load, expressed as follows:

[0077] ,

[0078] In the formula, for The response power of the biomass generator set at all times. This represents the maximum permissible discharge power of the energy storage system. This is the reserve margin factor. The baseline for the maximum uninterrupted core power load deconstructed under different production seasons;

[0079] Using the range of values ​​for the three-dimensional decision vector as the search space, a heuristic optimization algorithm is employed to solve the objective function under the constraints of the system boundary, yielding the optimal three-dimensional decision vector that minimizes the objective function. The solution process is as follows:

[0080] Initialize the population: Randomly generate multiple three-dimensional decision vectors to form the initial population.

[0081] Time-series simulation: For each individual in the population, the system's operating status is simulated hourly, driven by annual wind and solar resource time-series data and multivariate load time-series data. During the simulation, the aforementioned time-series hierarchical compensation mechanism is invoked to calculate the energy storage charging and discharging power, biomass output, abandoned power, and load shedding power at each moment.

[0082] Fitness assessment: Based on the simulation results, the objective function value and the degree of violation of each constraint are calculated. The penalty function method is used to transform the constraint violation into a penalty term and add it to the objective function to obtain the fitness value of the individual.

[0083] Evolutionary iteration: Genetic operations such as selection, crossover and mutation are used to generate a new generation of population. The above simulation and evaluation process is repeated until the convergence condition is met (such as reaching the maximum number of iterations or the population fitness no longer increases).

[0084] Output the optimal solution: Take the decision variable value corresponding to the individual with the best fitness as the optimal configuration scheme, and calculate the corresponding photovoltaic installed capacity, wind power installed capacity, energy storage rated capacity and biomass generator rated power.

[0085] Furthermore, the photovoltaic power generation capacity scaling factor in the optimal three-dimensional decision vector is multiplied by the baseline load power to obtain the optimal configuration capacity of the photovoltaic power generation unit;

[0086] Multiply the wind power generation capacity scaling factor in the optimal three-dimensional decision vector by the base load power to obtain the optimal configuration capacity of the wind power generation unit.

[0087] The rated configuration power of the biomass generator set in the optimal three-dimensional decision vector is taken as the optimal configuration capacity of the biomass generator set.

[0088] Based on the optimal configuration capacity of the photovoltaic power generation unit and the optimal configuration capacity of the wind power generation unit, the optimal rated capacity of the energy storage system is calculated through the source-storage ratio linkage mechanism:

[0089] ,

[0090] In the formula, For the optimal rated capacity of the energy storage system, The optimal configuration capacity for photovoltaic power generation units. This represents the optimal configuration capacity for wind power generation units. Configure a power ratio factor for source and storage. This is the parameter representing the equivalent energy support duration of the energy storage system.

[0091] The optimal configuration scheme of the rural microgrid is output by combining the optimal configuration capacity of the photovoltaic power generation unit, the optimal configuration capacity of the wind power generation unit, the optimal configuration capacity of the biomass generator set, and the optimal rated capacity of the energy storage system.

[0092] In summary, the method of this application acquires time-series data of wind and solar resources, time-series data of multiple loads, equipment parameters, and time-of-use electricity prices; constructs a three-dimensional decision vector using photovoltaic capacity ratio factor, wind power capacity ratio factor, and biomass rated power as decision variables; establishes an objective function with the goal of minimizing the annualized total operating cost over the entire life cycle, and physically anchors the rated energy storage capacity through a source-storage ratio linkage mechanism to avoid boundless divergence of energy storage capacity under extreme operating conditions; sets instantaneous multi-energy flow topology balance constraints, core load hard binding constraints, seasonal power supply reliability adaptive control constraints (the expected probability threshold for power shortage is dynamically adjusted with the season, with the allowable threshold in spring and autumn being higher than that in summer and winter), and core load robust reserve margin constraints; solves the objective function within the constraint boundaries and outputs the optimal configuration scheme; effectively avoids energy storage capacity divergence, reduces the total life cycle cost, and ensures the power supply reliability of the core load, making it suitable for rural multi-energy complementary microgrid planning scenarios.

[0093] Please refer to Figure 2 The diagram shows a block diagram of a rural microgrid optimization configuration system according to an embodiment of the present invention.

[0094] like Figure 2As shown, the rural microgrid optimization configuration system 200 includes an acquisition module 210, a determination module 220, a construction module 230, and a solution module 240.

[0095] The acquisition module 210 is configured to acquire basic data of the target rural microgrid, including wind and solar resource time-series data, multi-source load time-series data, equipment parameters, and time-of-use electricity price information. The determination module 220 is configured to determine the value ranges of the photovoltaic power generation capacity ratio factor, the wind power generation capacity ratio factor, and the rated configuration power of the biomass generator set based on the wind and solar resource time-series data, the multi-source load time-series data, and the equipment parameters, and to construct a three-dimensional decision vector using the photovoltaic power generation capacity ratio factor, the wind power generation capacity ratio factor, and the rated configuration power of the biomass generator set as decision variables. Module 230 is configured to minimize the annualized total operating cost over the entire lifecycle. It establishes an objective function that includes the annualized initial investment cost of equipment assets, the annual operation and maintenance cost of intermittent power sources and energy storage systems, the total operating cost of controllable biomass power sources, the cost of curtailment penalties, and the cost of power outage penalties due to load shedding. The rated capacity of the energy storage system is physically anchored through a source-storage ratio linkage mechanism. Module 240 is configured to set system constraint boundaries. Under the constraints of these boundaries, it solves the objective function to obtain the optimal three-dimensional decision vector that minimizes the objective function. Based on this optimal three-dimensional decision vector, it outputs the optimal configuration scheme for the rural microgrid.

[0096] It should be understood that Figure 2 The modules and references described in the document Figure 1 The steps described in the text correspond to those in the method described above. Therefore, the operations, features, and corresponding technical effects described above also apply to the method described in the text. Figure 2 The various modules in the document will not be described in detail here.

[0097] In other embodiments, the present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein when the program instructions are executed by a processor, the processor performs the rural microgrid optimization configuration method in any of the above method embodiments.

[0098] In one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions, which are configured as follows:

[0099] Acquire basic data of the target rural microgrid, including wind and solar resource time-series data, multi-source load time-series data, equipment parameters, and time-of-use electricity price information;

[0100] Based on the time-series data of wind and solar resources, the time-series data of multi-source loads, and the equipment parameters, the range of values ​​for the photovoltaic power generation capacity ratio factor, the range of values ​​for the wind power generation capacity ratio factor, and the range of values ​​for the rated configuration power of the biomass generator set are determined. A three-dimensional decision vector is constructed using the photovoltaic power generation capacity ratio factor, the wind power generation capacity ratio factor, and the rated configuration power of the biomass generator set as decision variables.

[0101] With the goal of minimizing the annualized total operating cost over the entire life cycle, an objective function is established that includes the annualized initial investment cost of equipment assets, the annual operation and maintenance cost of intermittent power sources and energy storage systems, the total operating cost of controllable biomass power sources, the cost of curtailment penalties, and the cost of power outage penalties due to load failure. The rated capacity of the energy storage system is physically anchored through a source-storage ratio linkage mechanism.

[0102] Set system constraint boundaries, solve the objective function under the constraints of the system constraint boundaries, obtain the optimal three-dimensional decision vector that minimizes the objective function, and output the optimal configuration scheme of the rural microgrid based on the optimal three-dimensional decision vector.

[0103] Computer-readable storage media may include a stored program area and a stored data area, wherein the stored program area may store an operating system and application programs required for at least one function; the stored data area may store data created based on the use of the rural microgrid optimization configuration system, etc. Furthermore, the computer-readable storage medium may include high-speed random access memory, and may also include memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, the computer-readable storage medium may optionally include memory remotely disposed relative to a processor, and these remote memories may be connected to the rural microgrid optimization configuration system via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0104] Figure 3 This is a schematic diagram of the structure of the electronic device provided in the embodiment of the present invention, such as... Figure 3 As shown, the device includes a processor 310 and a memory 320. The electronic device may also include an input device 330 and an output device 340. The processor 310, memory 320, input device 330, and output device 340 can be connected via a bus or other means. Figure 3Taking a bus connection as an example, the memory 320 is the computer-readable storage medium described above. The processor 310 executes various server functions and data processing by running non-volatile software programs, instructions, and modules stored in the memory 320, thereby implementing the rural microgrid optimization configuration method described in the above embodiment. The input device 330 can receive input digital or character information and generate key signal inputs related to user settings and function control of the rural microgrid optimization configuration system. The output device 340 may include a display screen or other display device.

[0105] The aforementioned electronic device can execute the method provided in the embodiments of the present invention, and has the corresponding functional modules and beneficial effects for executing the method. Technical details not described in detail in this embodiment can be found in the method provided in the embodiments of the present invention.

[0106] In one implementation, the above-described electronic device is applied in a rural microgrid optimization configuration system for a client, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to:

[0107] Acquire basic data of the target rural microgrid, including wind and solar resource time-series data, multi-source load time-series data, equipment parameters, and time-of-use electricity price information;

[0108] Based on the time-series data of wind and solar resources, the time-series data of multi-source loads, and the equipment parameters, the range of values ​​for the photovoltaic power generation capacity ratio factor, the range of values ​​for the wind power generation capacity ratio factor, and the range of values ​​for the rated configuration power of the biomass generator set are determined. A three-dimensional decision vector is constructed using the photovoltaic power generation capacity ratio factor, the wind power generation capacity ratio factor, and the rated configuration power of the biomass generator set as decision variables.

[0109] With the goal of minimizing the annualized total operating cost over the entire life cycle, an objective function is established that includes the annualized initial investment cost of equipment assets, the annual operation and maintenance cost of intermittent power sources and energy storage systems, the total operating cost of controllable biomass power sources, the cost of curtailment penalties, and the cost of power outage penalties due to load failure. The rated capacity of the energy storage system is physically anchored through a source-storage ratio linkage mechanism.

[0110] Set system constraint boundaries, solve the objective function under the constraints of the system constraint boundaries, obtain the optimal three-dimensional decision vector that minimizes the objective function, and output the optimal configuration scheme of the rural microgrid based on the optimal three-dimensional decision vector.

[0111] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for optimizing the configuration of rural microgrids, characterized in that, include: Acquire basic data of the target rural microgrid, including wind and solar resource time-series data, multi-source load time-series data, equipment parameters, and time-of-use electricity price information; Based on the time-series data of wind and solar resources, the time-series data of multi-source loads, and the equipment parameters, the range of values ​​for the photovoltaic power generation capacity ratio factor, the range of values ​​for the wind power generation capacity ratio factor, and the range of values ​​for the rated configuration power of the biomass generator set are determined. A three-dimensional decision vector is constructed using the photovoltaic power generation capacity ratio factor, the wind power generation capacity ratio factor, and the rated configuration power of the biomass generator set as decision variables. With the goal of minimizing the annualized total operating cost over the entire life cycle, an objective function is established that includes the annualized initial investment cost of equipment assets, the annual operation and maintenance cost of intermittent power sources and energy storage systems, the total operating cost of controllable biomass power sources, the cost of curtailment penalties, and the cost of power outage penalties due to load failure. The rated capacity of the energy storage system is physically anchored through a source-storage ratio linkage mechanism. Set system constraint boundaries, solve the objective function under the constraints of the system constraint boundaries, obtain the optimal three-dimensional decision vector that minimizes the objective function, and output the optimal configuration scheme of the rural microgrid based on the optimal three-dimensional decision vector.

2. The method for optimizing the configuration of a rural microgrid according to claim 1, characterized in that, The step of determining the value range of the photovoltaic power generation capacity ratio factor, the value range of the wind power generation capacity ratio factor, and the value range of the rated configuration power of the biomass generator set based on the wind and solar resource time-series data, the multi-source load time-series data, and the equipment parameters includes: Based on the annual average sunshine hours and annual average wind speed in the aforementioned wind and solar resource time series data, the photovoltaic resource endowment level and wind power resource endowment level are determined respectively. The upper and lower limits of the photovoltaic power generation capacity ratio factor are determined based on the photovoltaic resource endowment level. When the photovoltaic resource endowment level is excellent, the upper limit of the photovoltaic power generation capacity ratio factor is greater than the upper limit when the photovoltaic resource endowment level is poor, and the lower limit of the photovoltaic power generation capacity ratio factor is less than the lower limit when the photovoltaic resource endowment level is poor. The upper and lower limits of the wind power generation capacity ratio factor are determined based on the wind power resource endowment level. Based on the annual maximum load power in the multi-source load time series data, the upper and lower limits of the rated configuration power of the biomass generator set are determined.

3. The method for optimizing the configuration of a rural microgrid according to claim 1, characterized in that, The construction of a three-dimensional decision vector using the photovoltaic power generation capacity ratio factor, the wind power generation capacity ratio factor, and the rated configuration power of the biomass generator set as decision variables includes: The photovoltaic power generation capacity scaling factor is used as the first dimension component of the three-dimensional decision vector to represent the ratio of the photovoltaic power generation unit capacity to the baseline load power. The wind power generation capacity scaling factor is used as the second dimension component of the three-dimensional decision vector to represent the ratio of the wind power generation unit capacity to the benchmark load power. The rated configuration power of the biomass generator set is used as the third dimension component of the three-dimensional decision vector; The three-dimensional decision vector is composed of the first dimension component, the second dimension component, and the third dimension component, and each component in the three-dimensional decision vector is a variable to be optimized.

4. The method for optimizing the configuration of a rural microgrid according to claim 1, characterized in that, The expression for the objective function is: ， In the formula, The total annualized operating cost over the entire lifecycle. This represents the annualized initial investment cost of the equipment assets. The annual operation and maintenance costs of intermittent power sources and energy storage systems, The total operating cost of controllable biomass power sources. To cover the cost of power curtailment penalties, Penalty costs for power outages due to load shedding, Cost of equipment replacement; The annualized initial investment of the equipment assets The expression is: ， In the formula, To restore the operator for asset-based annuities, , , These are the unit capacity construction cost coefficients for photovoltaic equipment, wind power equipment, and energy storage systems, respectively. This is the photovoltaic power generation capacity ratio factor. This is the wind power generation capacity scaling factor. The annual composite average reference power of the time-series load, This refers to the rated capacity of the energy storage system. The expression for the asset-based annuity recovery operator is: ， In the formula, The weighted discount rate for microgrid investments. The planning lifespan of the microgrid system; The rated capacity of the energy storage system is physically anchored through a source-storage ratio linkage mechanism, expressed as follows: ， In the formula, Configure a power ratio factor for source and storage. This is the photovoltaic power generation capacity ratio factor. This is the baseline installed capacity for photovoltaic power generation. This is the wind power generation capacity scaling factor. This is the benchmark installed capacity for wind power generation. This is the parameter representing the equivalent energy support duration of the energy storage system. The expression for the total operating cost of the controllable biomass power source is: ， In the formula, The annual depreciation cost of biomass generator sets, Annual maintenance costs for biomass generator sets, For biomass power generation units in The actual dispatch output power at any given time. For discrete time steps, Let the normalized unit quality acquisition cost be the cost of the j-th batch of fuel transactions. This refers to the total number of batches of fuel purchased centrally throughout the year. This represents the total number of simulation periods throughout the year. The expression for the cost of the power curtailment penalty is: ， In the formula, The penalty coefficient per unit of abandoned electricity. for The amount of wind and solar power curtailed at any given moment; The cost of power outage penalty due to load failure The expression is: ， In the formula, This is the unit load loss compensation coefficient for rural power grid areas. for The final power loss at any given moment.

5. The method for optimizing the configuration of a rural microgrid according to claim 1, characterized in that, The system constraint boundaries include instantaneous multi-energy flow topology balance constraints, planning search space boundary constraints, multi-period stepped core load hard binding constraints, energy storage operation constraints, seasonal power supply reliability adaptive control constraints, and core load robust reserve margin constraints. The process of setting system constraint boundaries and solving the objective function under these boundaries to obtain the optimal three-dimensional decision vector that minimizes the objective function includes: Set the instantaneous multi-energy flow topology equilibrium constraint, the expression is: ， In the formula, This is the photovoltaic power generation capacity ratio factor. This is the wind power generation capacity scaling factor. for Normalized time-series per-unit output value of photovoltaic power at any given time. for Normalized time-series per-unit output value of wind power at any given moment for The actual response power of the biomass generator set at any given time. for The actual throughput power of energy storage at all times, with positive for discharging and negative for charging. for The amount of wind and solar power curtailed at any given time. for The instantaneous total power consumption of diverse loads in rural areas at any given time; The planning search space boundary constraints are set as follows: the range of values ​​for the photovoltaic power generation capacity ratio factor, the range of values ​​for the wind power generation capacity ratio factor, and the range of values ​​for the rated configuration power of the biomass generator set are limited. Set up a multi-time-period tiered core load hard binding constraint, specifically: extract the uninterrupted core power load baseline by deconstructing industry correlation, and constrain the system's power supply capacity to the uninterrupted core load at any time to be no less than the core load baseline; Set energy storage operation constraints, specifically: constrain the charging and discharging power of the energy storage system at any time to not exceed the maximum allowable throughput power, and constrain the state of charge of the energy storage system to be between the preset minimum state of charge and the maximum state of charge. Set seasonal power supply reliability adaptive control constraints, with the expression as follows: ， In the formula, For the first Expected probability of system power shortage in each season For the first A collection of time periods in each season. For the first The threshold for the expected probability of power shortage allowed in a given season. The discrete time step; Set a robust reserve margin constraint for core load, expressed as follows: ， In the formula, for The response power of the biomass generator set at all times. This represents the maximum permissible discharge power of the energy storage system. This is the reserve margin factor. The baseline for the maximum uninterrupted core power load deconstructed under different production seasons; Using the range of values ​​of the three-dimensional decision vector as the search space, a heuristic optimization algorithm is used to solve the objective function under the constraints of the system constraint boundary to obtain the optimal three-dimensional decision vector that minimizes the objective function.

6. The method for optimizing the configuration of a rural microgrid according to claim 1, characterized in that, The optimal configuration scheme for the rural microgrid, output based on the optimal three-dimensional decision vector, includes: Multiply the photovoltaic power generation capacity scaling factor in the optimal three-dimensional decision vector by the baseline load power to obtain the optimal configuration capacity of the photovoltaic power generation unit. Multiply the wind power generation capacity scaling factor in the optimal three-dimensional decision vector by the base load power to obtain the optimal configuration capacity of the wind power generation unit. The rated configuration power of the biomass generator set in the optimal three-dimensional decision vector is taken as the optimal configuration capacity of the biomass generator set. Based on the optimal configuration capacity of the photovoltaic power generation unit and the optimal configuration capacity of the wind power generation unit, the optimal rated capacity of the energy storage system is calculated through the source-storage ratio linkage mechanism: , In the formula, For the optimal rated capacity of the energy storage system, The optimal configuration capacity for photovoltaic power generation units. This represents the optimal configuration capacity for wind power generation units. Configure a power ratio factor for source and storage. This is the parameter representing the equivalent energy support duration of the energy storage system. The optimal configuration scheme of the rural microgrid is output by combining the optimal configuration capacity of the photovoltaic power generation unit, the optimal configuration capacity of the wind power generation unit, the optimal configuration capacity of the biomass generator set, and the optimal rated capacity of the energy storage system.

7. A rural microgrid optimization configuration system, characterized in that, include: The acquisition module is configured to acquire basic data of the target rural microgrid, including wind and solar resource time-series data, multi-source load time-series data, equipment parameters, and time-of-use electricity price information. The determination module is configured to determine the value range of the photovoltaic power generation capacity ratio factor, the value range of the wind power generation capacity ratio factor, and the value range of the rated configuration power of the biomass generator set based on the time series data of the wind and solar resources, the time series data of the multi-source load, and the equipment parameters, and to construct a three-dimensional decision vector using the photovoltaic power generation capacity ratio factor, the wind power generation capacity ratio factor, and the rated configuration power of the biomass generator set as decision variables. The module is configured to minimize the annualized total operating cost over the entire life cycle. It establishes an objective function that includes the annualized initial investment cost of equipment assets, the annual operation and maintenance cost of intermittent power sources and energy storage systems, the total operating cost of controllable biomass power sources, the cost of curtailment penalties, and the cost of power outage penalties due to load shedding. The rated capacity of the energy storage system is physically anchored through a source-storage ratio linkage mechanism. The solution module is configured to set system constraint boundaries, solve the objective function under the constraints of the system constraint boundaries, obtain the optimal three-dimensional decision vector that minimizes the objective function, and output the optimal configuration scheme of the rural microgrid based on the optimal three-dimensional decision vector.

8. An electronic device, characterized in that, include: At least one processor, and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by a processor, it implements the method described in any one of claims 1 to 6.

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