A wind-solar-diesel-storage microgrid operation method for coping with multi-source coupling uncertainty

Abstract

The wind-solar-diesel storage microgrid operation method for coping with multi-source coupling uncertainty of the application introduces a polyhedral uncertainty set to describe the load demand and renewable energy output in the microgrid system. Unlike the traditional box uncertainty set, the polyhedral uncertainty set limits the number or amplitude of the bias by introducing an uncertainty budget parameter, making the modeling more realistic and more flexible. The wind power output, photovoltaic output, basic load and electric vehicle charging load are respectively described by the polyhedral uncertainty set, effectively integrating the random characteristics of the source- load on both sides.

Description

Technical Field

[0001] This invention belongs to the field of wind-solar-diesel-storage microgrid optimization and scheduling technology, specifically involving a wind-solar-diesel-storage microgrid operation method to address the uncertainty of multi-source coupling. Background Technology

[0002] As a power generation and distribution system that integrates wind power, photovoltaic renewable energy units, diesel generators and other components, as well as multi-source loads such as electric vehicles, the wind-solar-diesel-storage microgrid needs to achieve dynamic balance between power supply and demand in the region through internal regulation.

[0003] In the actual operation of wind-solar-diesel-storage microgrids, multiple uncertainties significantly impact their safe and stable operation. The inherent volatility, intermittency, and randomness of wind and solar power output lead to strong uncontrollability in power output on the power source side. Simultaneously, electric vehicle charging loads also exhibit significant randomness in connection time, charging power, and duration, resulting in more complex fluctuation characteristics on the load side compared to traditional loads. The superposition of these uncertainties on both the source and load sides, coupled with the inter-temporal coupling characteristics of these uncertainties, exacerbates net load fluctuations and increases the difficulty of power balancing in wind-solar-diesel-storage microgrids. The output power of diesel generators and energy storage devices must not only meet power boundary constraints and output ramp-up rate limits but also adhere to the dynamic balance equation of the energy storage's state of charge (SOC). During the process of smoothing rapid net load fluctuations, the inherent physical boundaries of the two types of equipment significantly increase the difficulty and complexity of operation and control of the wind-solar-diesel-storage microgrid. Summary of the Invention

[0004] The purpose of this invention is to provide a method for operating a wind-solar-diesel-storage microgrid to address the uncertainty of multi-source coupling, thereby solving the problems of high difficulty and complexity in the operation and regulation of existing wind-solar-diesel-storage microgrids.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0006] A method for operating a wind-solar-diesel-storage microgrid to address uncertainties arising from multi-source coupling, wherein the wind-solar-diesel-storage microgrid is connected to the main grid and includes renewable energy generation equipment, power consumption equipment, and energy storage equipment; the renewable energy generation equipment includes photovoltaic power generation equipment, wind power generation equipment, and diesel power generation equipment, and includes the following steps: Step 1: Obtain the following data for the wind-solar-diesel-storage microgrid: Key parameters of wind-solar-diesel-storage microgrids; Historical information on random factors in multiple scheduling periods of the wind-solar-diesel-storage microgrid, including: In each scheduling period, the charging load (MW) of all electric vehicles connected to the power equipment of the wind-solar-diesel-storage microgrid; The base load of the wind-solar-diesel-storage microgrid in each dispatch period is MW; The output load of renewable energy power generation equipment in each dispatch period, in MW; Based on the historical information of random factors in multiple scheduling periods of the wind-solar-diesel-storage microgrid, the upper and lower limits of the values ​​of each random factor, and the budget constraint parameters of each scheduling period, a budget uncertainty set of the net load of the wind-solar-diesel-storage microgrid is constructed. Step 2, establish a collaborative operation model for the wind-solar-diesel-storage microgrid, as shown in the following formula:

[0007] in, This indicates the operating cost of the diesel generator set; express and The sum of; This represents the transaction costs between the wind-solar-diesel-storage microgrid and the main power grid; and These represent the costs incurred by the wind-solar-diesel-storage microgrid in purchasing electricity from the main grid and the revenue generated from selling electricity, respectively, during any given dispatch period. Indicates taking Larger values ​​between 0 and 0; Indicates taking The smaller value between 0 and 0; The duration of a scheduling period is expressed in hours (h). Indicates the first One scheduling period, h; This represents the transmission power (MW) between the wind-solar-diesel-storage microgrid and the main grid during any given scheduling period. Step 3: Based on the key parameters obtained in Step 1 and the budget uncertainty set of the net load of the wind-solar-diesel-storage microgrid, determine the constraints of the wind-solar-diesel-storage microgrid collaborative operation model. The constraints include: Constraints on power exchange between the wind-solar-diesel-storage microgrid and the main power grid; The output constraint of diesel generator set and the coupled constraint of output rate in adjacent time periods; Balance constraints between the power generation capacity of the power generation equipment and the power consumption capacity of the power consumption equipment in the wind-solar-diesel-storage microgrid; The charging and discharging power constraints of energy storage devices in wind-solar-diesel-storage microgrids; Dynamic equation of energy state for energy storage devices in a wind-solar-diesel-storage microgrid; Step 4: Using the particle swarm optimization algorithm, the output constraints of the diesel generator and the coupled constraints of the output rate in adjacent time periods are transformed into upper and lower limits of the equivalent output for each scheduling period. All other constraints remain unchanged; Step 5: Based on the constraints of Steps 3 and 4, establish the feasible range of energy storage capacity of energy storage devices in each scheduling period of the wind-solar-diesel-storage microgrid system and the preconditions for meeting the feasible range. Step 6: Based on the feasible range of energy storage capacity of the energy storage equipment and the preconditions for meeting the feasible range, use the wind-solar-diesel-storage microgrid collaborative operation model to realize the dispatch of the wind-solar-diesel-storage microgrid.

[0008] The present invention also has the following features: Furthermore, step 1 includes the following sub-steps: Step 11: Obtain key parameters of the wind-solar-diesel-storage microgrid, including: The upper and lower limits of energy storage capacity of energy storage devices in a wind-solar-diesel-storage microgrid, in MWh; The charge / discharge efficiency coefficient of the energy storage equipment in the wind-solar-diesel-storage microgrid is dimensionless. The maximum charging and discharging power of the energy storage equipment in a wind-solar-diesel-storage microgrid, in MW; Upper and lower limits, in MW, of the power output of diesel generators in a wind-solar-diesel-storage microgrid; Upper and lower limits of the ramp-up power of diesel generators in a wind-solar-diesel-storage microgrid, in MW; Total number of dispatch periods for the wind-solar-diesel-storage microgrid; duration of each dispatch period, in hours; Upper and lower limits of transmission power capacity (MW) for wind-solar-diesel-storage microgrids; Step 12: Obtain historical information on random factors and corresponding budget constraint parameters for each scheduling period of the wind-solar-diesel-storage microgrid, and establish the corresponding uncertainty set, as shown in the following formula:

[0009]

[0010] in, express The charging load of electric vehicles during the dispatch period, in MW; express The lower limit of electric vehicle charging load during the dispatch period, in MW; express The upper limit of electric vehicle charging load during the scheduling period, in MW; Indicates the first One scheduling period; Indicates the total scheduling period; express Constraint parameters for electric vehicle charging load during scheduling periods; The first represents the charging load of electric vehicles i Budget constraints; This represents the total budget constraint for electric vehicle charging load. Budgetary parameters representing the charging load of electric vehicles;

[0011]

[0012] in, express Base load of the wind-solar-diesel-storage microgrid during dispatch periods, in MW; express The lower limit of the base load of the wind-solar-diesel-storage microgrid during the dispatch period, in MW; express The upper limit of the base load of the wind-solar-diesel-storage microgrid during the dispatch period, in MW; express Constraint parameters of the base load of the wind-solar-diesel-storage microgrid during the dispatch period; Indicates the base load number Budget constraints; This represents the total budget constraint for the base load; Budgetary parameters representing the base load of the wind-solar-diesel-storage microgrid;

[0013]

[0014] in, express Output of renewable energy generation equipment during the dispatch period, in MW; express Lower limit of renewable energy power generation equipment output during dispatch period, in MW; express Maximum output of renewable energy power generation equipment during dispatch periods, in MW; express Constraint parameters for the output of renewable energy power generation equipment during the dispatch period; Budget parameters representing the output of renewable energy power generation equipment; The first indicator of the output of renewable energy power generation equipment k Budget constraints; This represents the total budget constraint for the output of renewable energy power generation equipment; Step 13: Based on the above uncertainty set, establish the uncertainty set of the net load of the wind-solar-diesel-storage microgrid, as shown in the following equation:

[0015]

[0016]

[0017]

[0018] in, This indicates that the wind-solar-diesel-storage microgrid is in Net load demand during the scheduling period, in MW; This indicates that the wind-solar-diesel-storage microgrid is in Lower limit of net load demand during the scheduling period, in MW; This indicates that the wind-solar-diesel-storage microgrid is in Net load demand ceiling for the scheduling period, in MW; This indicates that the wind-solar-diesel-storage microgrid is in Budget parameters for the scheduling period; This indicates that the wind-solar-diesel-storage microgrid is in Constraint parameters for the scheduling period; The first indicates the net load m Budget constraints; This represents the total budget constraint for net load.

[0019] Furthermore, the constraints for step 3 are as follows: The coupling constraint between the output constraint of the diesel generator and the output rate in adjacent time periods is as follows:

[0020] =1, 2, ..., T, ∈Ω in, Represents net load demand, in MW; Indicates the first to the last Net load demand during the scheduling period, in MW; and These represent the downhill and uphill limits, respectively, in MW / h; express Output of diesel generators during the dispatch period, in MW; express -1 Output of diesel generators during the dispatch period, in MW; Ω represents the range of uncertainties in the net load; The power exchange constraints between the wind-solar-diesel-storage microgrid and the main grid are as follows:

[0021] =1, 2, ..., T, ∈Ω in, express The exchange power (MW) between the wind-solar-diesel-storage microgrid and the main grid during the dispatch period; express Lower limit of the exchange power between the wind-solar-diesel-storage microgrid and the main grid during the dispatch period, in MW; express The upper limit of the exchange power between the wind-solar-diesel-storage microgrid and the main grid during the dispatch period, in MW; The power generation capacity of the power generation equipment and the power consumption capacity of the power consumption equipment in the wind-solar-diesel-storage microgrid are balanced by the following formula:

[0022] =1, 2, ..., T, ∈Ω in, Indicates that energy storage devices are in Charging and discharging power during the scheduling period, in MW; The charging and discharging power constraints of energy storage devices in a wind-solar-diesel-storage microgrid are as follows:

[0023] =1, 2, ..., T, ∈Ω in, express The energy stored in the energy storage device during the dispatch period, in MWh; and These respectively represent the energy storage devices in Lower and upper limits of energy storage capacity during the scheduling period, in MWh; Indicates that energy storage devices are in Charging and discharging power during the scheduling period, in MW; and These respectively represent the energy storage devices in Maximum charging power and maximum discharging power during the scheduling period, in MW; The dynamic equation of energy state for the energy storage devices in a wind-solar-diesel-storage microgrid is as follows:

[0024] =1, 2, ..., T, ∈Ω in, express 1. Energy storage capacity of the energy storage device during the scheduling period, in MWh.

[0025] Furthermore, the auxiliary equation is as follows:

[0026] in, and All are auxiliary equations; This indicates the change in stored energy calculated based on charging and discharging power. for The inverse function of represents the calculation of charging and discharging power based on changes in stored energy; As the independent variable; The length of a scheduling period is represented by h; These represent discharge efficiency and charging efficiency, respectively, and are dimensionless. Indicates taking The larger of the two, 0 and 0; Indicates taking The smaller of 0 and 0.

[0027] Furthermore, step 4 includes the following sub-steps: Step 41, Initialize the particle swarm: Randomly generated Each particle represents a possible sequence of diesel generator outputs and is randomly initialized. The position and velocity of each particle; Step 42: Record the current position of each particle as its individual optimal position. Using the climbing constraint and the upper and lower limits of output as constraints, calculate the scheduling cost corresponding to the individual optimal position of each particle, select the particle with the lowest cost, and record it as the global optimal position. Among them, the global optimal position corresponds to the upper and lower limits of the equivalent output; Step 43: Use the update formula of the particle swarm algorithm to generate a new generation of particle swarms; Step 44: Repeat the method of steps 42-43 to perform multiple iterations until the preset maximum number of iterations is reached or the global optimal position remains unchanged in multiple consecutive iterations, and obtain the upper and lower limits of the equivalent output.

[0028] Furthermore, in step 5, the feasible range of energy storage capacity of the energy storage devices for each dispatch period of the wind-solar-diesel-storage microgrid system is as follows:

[0029]

[0030] in, express During the dispatch period, the upper limit of safe energy storage capacity of energy storage equipment, in MWh; express During the dispatch period, the lower limit of the safe energy storage capacity of the energy storage device, in MWh; express During the dispatch period, the upper limit of safe energy storage capacity of energy storage equipment, in MWh; express During the dispatch period, the lower limit of the safe energy storage capacity of the energy storage device, in MWh; express During the dispatch period, the upper limit of the physical energy storage capacity of the energy storage device, in MWh; express During the dispatch period, the lower limit of the physical energy storage capacity of the energy storage device, in MWh; express During the dispatch period, the maximum charging power of the energy storage device, in MW; express During the dispatch period, the maximum discharge power of the energy storage device, in MW; and They represent During the dispatch period, the output of photovoltaic power generation equipment and wind power generation equipment; This indicates that the diesel generator obtained in step 3 is in The upper limit of equivalent output during the scheduling period; This indicates that the diesel generator obtained in step 3 is in The scheduling period is the lower limit of the equivalent output.

[0031] Furthermore, the prerequisite for the feasible range of energy storage capacity of the energy storage device in each dispatch period of the wind-solar-diesel-storage microgrid system in step 5 is as follows:

[0032] in, express , The sum of the two.

[0033] A wind-solar-diesel-storage microgrid dispatching system includes a data module, a construction module, a range module, and a dispatching module; The data module is used to acquire key parameters of the wind-solar-diesel-storage microgrid and historical information on random factors in multiple scheduling periods; The aforementioned building module is used for: Based on the historical information of random factors in multiple scheduling periods of the wind-solar-diesel-storage microgrid, the upper and lower limits of the values ​​of each random factor and the corresponding budget constraint parameters are determined, and the budget uncertainty set of the net load of the wind-solar-diesel-storage microgrid is constructed. Establish a collaborative operation model for wind, solar, diesel, and energy storage microgrids; Based on the key parameters and the budget uncertainty set of the net load of the wind-solar-diesel-storage microgrid, the constraints of the wind-solar-diesel-storage microgrid collaborative operation model are determined. Using the particle swarm optimization algorithm, the coupled constraints of the output of diesel generators and the output rate of adjacent time periods are transformed into upper and lower limits of the equivalent output for each scheduling period. The range module is used to determine the feasible range of the energy storage level of the energy storage device, as well as the preconditions for meeting the feasible range. The aforementioned operation module establishes a hybrid integer programming model for the coordinated operation of wind, solar, diesel, and energy storage microgrids based on the collaborative operation model and feasibility range of the wind, solar, diesel, and energy storage microgrids, and optimizes the collaborative operation model using a rolling optimization algorithm, thereby realizing the scheduling of the wind, solar, diesel, and energy storage microgrids.

[0034] A terminal device, comprising a processor and a memory, is used to run the aforementioned method for operating a wind-solar-diesel-storage microgrid to address uncertainties in multi-source coupling.

[0035] Compared with the prior art, the present invention has the following technical effects: (I) The wind-solar-diesel-storage microgrid operation method of the present invention for addressing multi-source coupling uncertainties introduces a polyhedral uncertainty set to describe the load demand and renewable energy output in the microgrid system. Unlike the traditional box-type uncertainty set, the polyhedral uncertainty set introduces an uncertainty budget parameter to limit the number or magnitude of deviations, making the modeling more realistic and flexible. Wind power output, photovoltaic power output, base load, and electric vehicle charging load are each characterized using a polyhedral uncertainty set, effectively integrating the stochastic characteristics of both the source and load sides.

[0036] (II) This invention establishes a collaborative operation model for wind-solar-diesel-storage microgrids. The model aims to minimize the sum of the operating cost of diesel generators and the grid transaction cost of the exchanged power at the gateway. It comprehensively considers power balance constraints, upper and lower bound constraints on the exchanged power at the gateway, upper and lower bound constraints on the output of diesel generators and ramping constraints, charging and discharging power constraints of energy storage devices, and energy storage constraints. For the constraints on diesel generators, a particle swarm optimization algorithm is introduced to decouple the different time periods, thus transforming them into feasible upper and lower bound constraints. For the multi-time-period nonlinear coupling constraints of energy storage devices, feasible decoupling conditions are constructed to separate the decision spaces for each time period. Through this decoupling process, the model, which was originally difficult to solve directly, is transformed into a series of independent mixed-integer linear programming subproblems for each time period, providing a feasible solution path for the engineering application of the model.

[0037] By solving a set of mixed-integer linear programming problems, the feasible operating range of energy storage devices in each time period is obtained. This feasible range is the decoupling condition that ensures the existence of a feasible scheduling scheme for the microgrid system under any uncertainty. Secondly, within this feasible range, with the objective of minimizing operating cost, the optimal scheduling model for the current time period is solved to obtain the optimal decisions for gateway switching power, diesel generator output, and energy storage charging and discharging power. The rolling optimization mechanism can dynamically adjust the scheduling scheme for subsequent time periods based on real-time information on the realization of uncertainties, effectively balancing the optimality and robustness of the decision. Attached Figure Description

[0038] Figure 1 This is a flowchart of the wind-solar-diesel-storage microgrid operation method for addressing uncertainties in multi-source coupling according to the present invention; Figure 2 This is a schematic diagram of the wind-solar-diesel-storage microgrid in this invention; Figure 3 This is a schematic diagram of the upper and lower limits of basic load demand on a typical day in one embodiment of the present invention; Figure 4 This is a schematic diagram of the upper and lower limits of typical daily wind power output in one embodiment of the present invention; Figure 5 This is a schematic diagram of the upper and lower limits of typical solar photovoltaic power output in one embodiment of the present invention; Figure 6 This is a schematic diagram of the upper and lower limits of the output of a typical daily diesel generator in one embodiment of the present invention; Figure 7 This refers to the feasible range of energy levels of the energy storage device at different times, obtained based on the method of this invention. Figure 8 This is a block diagram of a chip provided in an embodiment of the present invention. Detailed Implementation

[0039] It should be noted that, unless otherwise specified, all components in this invention are components known in the prior art.

[0040] The following are specific embodiments of the present invention. It should be noted that the present invention is not limited to the following specific embodiments. All equivalent modifications made based on the technical solutions of this application fall within the protection scope of the present invention.

[0041] like Figure 2 As shown, the wind-solar-diesel-storage microgrid is connected to the main grid. The wind-solar-diesel-storage microgrid includes renewable energy power generation equipment, power consumption equipment, and energy storage equipment. The renewable energy power generation equipment includes photovoltaic power generation equipment, wind power generation equipment, and diesel power generation equipment.

[0042] The wind-solar-diesel-storage microgrid is connected to the main power grid and can exchange energy bidirectionally with the main power grid, meaning it can both purchase electricity from the main power grid and sell electricity to the main power grid.

[0043] The aforementioned photovoltaic power generation equipment, wind power generation equipment, diesel power generation equipment, electrical equipment, energy storage equipment, and other basic loads of the wind-solar-diesel-storage microgrid are all connected through the same bus, enabling direct energy exchange within the wind-solar-diesel-storage microgrid.

[0044] Among them, diesel power generation equipment is usually diesel generator. As a controllable distributed power source, diesel generator can flexibly adjust its output according to the system power demand and inject electrical energy into the system when the output of renewable energy is insufficient or the load demand is at its peak. Photovoltaic and wind power generation equipment, as renewable energy sources, are given priority to supply power to loads. Surplus electricity can be stored in energy storage devices or sent to the main grid through the point of common connection. As a flexible resource, energy storage devices can charge and store electricity when renewable energy is in surplus and discharge to support the system when there is a power shortage. Electric vehicle charging loads can directly obtain electricity from wind power, photovoltaic, diesel generators and energy storage devices.

[0045] This embodiment further illustrates the specific structural features of the wind-solar-diesel-storage microgrid: Wind power generation equipment consists of a wind turbine, an asynchronous or synchronous wind turbine generator, a tower supporting the generator set, a grid-connected controller, and other auxiliary equipment, which can convert the kinetic energy of wind into electrical energy.

[0046] Photovoltaic power generation equipment consists of solar cell modules (arrays), controllers, AC / DC inverters, and other auxiliary equipment, which can convert solar radiation energy into electrical energy.

[0047] Diesel generators consist of a fuel supply system, a cooling system, a lubrication system, etc. They can flexibly adjust their output according to system dispatch instructions and inject electrical energy into the system when renewable energy output is insufficient or when load demand is high.

[0048] Energy storage equipment consists of battery packs, battery management systems, power conversion systems, and collection lines. It is used for storing and releasing electrical energy, charging when renewable energy is abundant, and discharging when power is scarce, thus playing a role in peak shaving, valley filling, and smoothing out fluctuations.

[0049] The charging load of electric vehicles consists of charging piles, charging controllers and connecting lines, and its charging demand has significant uncertainty in time distribution.

[0050] The aforementioned units are interconnected via an AC bus to form a multi-energy complementary microgrid power generation, distribution, and consumption system. The total load of the microgrid system is the sum of the power consumed by all electrical devices in the system, mainly including electric vehicle charging load and basic power load. The wind-solar-diesel-storage microgrid collaborative operation model and its solution algorithm ensure the economy and safety of microgrid operation. Therefore, establishing the wind-solar-diesel-storage microgrid collaborative operation method proposed in this invention is of great significance. Based on the above-described wind-solar-diesel-storage microgrid, this embodiment presents a method for operating a wind-solar-diesel-storage microgrid to address the uncertainties of multi-source coupling, such as... Figure 1 As shown, it includes the following steps: Step 1: Obtain the following data for the wind-solar-diesel-storage microgrid: Key parameters of wind-solar-diesel-storage microgrids; Historical information on random factors in multiple scheduling periods of the wind-solar-diesel-storage microgrid, including: In each scheduling period, the charging load (MW) of all electric vehicles connected to the power equipment of the wind-solar-diesel-storage microgrid; The base load of the wind-solar-diesel-storage microgrid in each dispatch period is MW; The output load of renewable energy power generation equipment in each dispatch period, in MW; Based on the historical information of random factors in multiple scheduling periods of the wind-solar-diesel-storage microgrid, the upper and lower limits of the values ​​of each random factor, and the budget constraint parameters of each scheduling period, a budget uncertainty set of the net load of the wind-solar-diesel-storage microgrid is constructed. Step 1 includes the following sub-steps: Step 11: Obtain key parameters of the wind-solar-diesel-storage microgrid, including: The upper and lower limits of energy storage capacity of energy storage devices in a wind-solar-diesel-storage microgrid, in MWh; The charge / discharge efficiency coefficient of the energy storage equipment in the wind-solar-diesel-storage microgrid is dimensionless. The maximum charging and discharging power of the energy storage equipment in a wind-solar-diesel-storage microgrid, in MW; Upper and lower limits, in MW, of the power output of diesel generators in a wind-solar-diesel-storage microgrid; Upper and lower limits of the ramp-up power of diesel generators in a wind-solar-diesel-storage microgrid, in MW; The number of dispatch periods for the wind-solar-diesel-storage microgrid, and the duration of each dispatch period, in hours (h). Upper and lower limits of transmission power capacity (MW) for wind-solar-diesel-storage microgrids; Step 12: Obtain historical information on random factors and corresponding budget constraint parameters for each scheduling period of the wind-solar-diesel-storage microgrid, and establish the corresponding uncertainty set, as shown in the following formula:

[0051]

[0052] in, express The charging load of electric vehicles during the dispatch period, in MW; express The lower limit of electric vehicle charging load during the dispatch period, in MW; express The upper limit of electric vehicle charging load during the scheduling period, in MW; Indicates the first t One scheduling period; Indicates the total scheduling period; express Constraint parameters for electric vehicle charging load during scheduling periods; The first represents the charging load of electric vehicles Budget constraints; This represents the total budget constraint for electric vehicle charging load. Budgetary parameters representing the charging load of electric vehicles;

[0053]

[0054] in, express The base load of the wind-solar-diesel-storage microgrid during the dispatch period; express The lower limit of the base load of the wind-solar-diesel-storage microgrid during the dispatch period; express The upper limit of the base load of the wind-solar-diesel-storage microgrid during the dispatch period; express Constraint parameters of the base load of the wind-solar-diesel-storage microgrid during the dispatch period; Indicates the base load number Budget constraints; This represents the total budget constraint for the base load; Budgetary parameters representing the base load of a wind-solar-diesel-storage microgrid;

[0055]

[0056] in, express Output of renewable energy generation equipment during the dispatch period, in MW; express Lower limit of renewable energy power generation equipment output during dispatch period, in MW; express Maximum output of renewable energy power generation equipment during dispatch periods, in MW; express Constraint parameters for the output of renewable energy power generation equipment during the dispatch period; Budget parameters representing the output of renewable energy power generation equipment; The first indicator of the output of renewable energy power generation equipment Budget constraints; This represents the total budget constraint for the output of renewable energy power generation equipment; Step 13: Based on the above uncertainty set, establish the uncertainty set of the net load of the wind-solar-diesel-storage microgrid, as shown in the following equation:

[0057]

[0058]

[0059] in, This indicates that the wind-solar-diesel-storage microgrid is in Net load demand during the scheduling period; This indicates that the wind-solar-diesel-storage microgrid is in The lower limit of net load demand during the scheduling period; This indicates that the wind-solar-diesel-storage microgrid is in The upper limit of net load demand during the scheduling period; This indicates that the wind-solar-diesel-storage microgrid is in Budget parameters for the scheduling period; This indicates that the wind-solar-diesel-storage microgrid is in Constraint parameters for the scheduling period; The first indicates the net load Budget constraints; This represents the total budget constraint for net load.

[0060] Step 2: Establish a collaborative operation model for wind-solar-diesel-storage microgrids. The goal of this model is to minimize the total operating cost of the wind-solar-diesel-storage microgrids throughout the entire dispatch cycle, while meeting the system's operational requirements.

[0061] Total operating costs include the operating costs of diesel generators and the transaction costs between the microgrid and the main grid.

[0062] As shown in the following formula:

[0063] in, This indicates the operating cost of the diesel generator set; express and The sum of; This represents the transaction costs between the wind-solar-diesel-storage microgrid and the main power grid; and These represent the costs incurred by the wind-solar-diesel-storage microgrid in purchasing electricity from the main grid and the revenue generated from selling electricity, respectively, during any given dispatch period. Indicates taking Larger values ​​between 0 and 0; Indicates taking The smaller value between 0 and 0; The duration of a scheduling period is expressed in hours (h). Indicates the first One scheduling period, h; This represents the transmission power (MW) between the wind-solar-diesel-storage microgrid and the main grid during any given scheduling period. Step 3: Based on the key parameters obtained in Step 1 and the budget uncertainty set of the net load of the wind-solar-diesel-storage microgrid, determine the constraints of the wind-solar-diesel-storage microgrid collaborative operation model. The constraints include: Constraints on power exchange between the wind-solar-diesel-storage microgrid and the main power grid; The output constraint of diesel generator set and the coupled constraint of output rate in adjacent time periods; Balance constraints between the power generation capacity of the power generation equipment and the power consumption capacity of the power consumption equipment in the wind-solar-diesel-storage microgrid; The charging and discharging power constraints of energy storage devices in wind-solar-diesel-storage microgrids; Dynamic equation of energy state for energy storage devices in a wind-solar-diesel-storage microgrid; The above steps yield the collaborative operation model of wind-solar-diesel-storage microgrid. The operation of the diesel generator must simultaneously satisfy the upper and lower limits of output constraints and the ramping constraint. The latter causes the decision variables between adjacent time periods to be coupled with each other, increasing the difficulty of solving the model.

[0064] To address this issue, this invention introduces a particle swarm optimization algorithm to transform the aforementioned coupled constraints into independent equivalent upper and lower bound constraints for each time period, and defines the equivalent upper and lower bounds of the transformed diesel generator output as follows: and This ensures that for any output sequence that satisfies the equivalent constraints, there exists a way to simultaneously satisfy both the original ramp constraint and the output upper and lower bound constraints. Conversely, any output sequence in the original feasible region must also fall within the interval determined by the equivalent upper and lower bounds.

[0065] The constraints for step 3 are as follows: The coupling constraint between the output constraint of the diesel generator and the output rate in adjacent time periods is as follows:

[0066] =1, 2, ..., T, ∈Ω in, This represents the net load demand for all scheduling periods; and These represent the downhill and uphill limits, respectively, in MW / h; express Output of diesel generators during the dispatch period, in MW; express -1 Output of diesel generators during the dispatch period, in MW; Ω represents the range of uncertainties in the net load; The power exchange constraints between the wind-solar-diesel-storage microgrid and the main grid are as follows:

[0067] =1, 2, ..., T, ∈Ω in, express The exchange power (MW) between the wind-solar-diesel-storage microgrid and the main grid during the dispatch period; express Lower limit of the exchange power between the wind-solar-diesel-storage microgrid and the main grid during the dispatch period, in MW; express The upper limit of the exchange power between the wind-solar-diesel-storage microgrid and the main grid during the dispatch period, in MW; The power generation capacity of the power generation equipment and the power consumption capacity of the power consumption equipment in the wind-solar-diesel-storage microgrid are balanced by the following formula:

[0068] =1, 2, ..., T, ∈Ω The charging and discharging power constraints of energy storage devices in a wind-solar-diesel-storage microgrid are as follows:

[0069] =1, 2, ..., T, ∈Ω in, express The energy stored in the energy storage device during the dispatch period, in MWh; and These respectively represent the energy storage devices in The lower and upper limits of energy storage capacity of energy storage devices during the dispatch period, in MWh; Indicates that energy storage devices are in Charging and discharging power during the scheduling period, in MWh; and These respectively represent the energy storage devices in Maximum charging power and maximum discharging power during the scheduling period, in MWh; The dynamic equation of energy state for the energy storage devices in a wind-solar-diesel-storage microgrid is as follows:

[0070] =1, 2, ..., T, ∈Ω in, express 1. Energy storage capacity of the energy storage device during the scheduling period, in MWh.

[0071] The auxiliary equation is as follows:

[0072] in, and All are auxiliary equations; This indicates the change in stored energy calculated based on charging and discharging power. for The inverse function of represents the calculation of charging and discharging power based on changes in stored energy; As the independent variable; The length of a scheduling period is represented by h; These represent discharge efficiency and charging efficiency, respectively, and are dimensionless. Indicates taking The larger of the two, 0 and 0; Indicates taking The smaller of 0 and 0.

[0073] Step 4: Using the particle swarm optimization algorithm, the output constraints of the diesel generator and the coupled constraints of the output rate in adjacent time periods are transformed into upper and lower limits of the equivalent output for each scheduling period. All other constraints remain unchanged; Step 4 includes the following sub-steps: Step 41, Initialize the particle swarm: Randomly generated Each particle represents a possible sequence of diesel generator outputs and is randomly initialized. The position and velocity of each particle; Step 42: Record the current position of each particle as its individual optimal position. Using the climbing constraint and the upper and lower limits of output as constraints, calculate the scheduling cost corresponding to the individual optimal position of each particle, select the particle with the lowest cost, and record it as the global optimal position. Among them, the global optimal position corresponds to the upper and lower limits of the equivalent output; Step 43: Use the update formula of the particle swarm algorithm to generate a new generation of particle swarms; Step 44: Repeat the method of steps 42-43 to perform multiple iterations until the preset maximum number of iterations is reached or the global optimal position remains unchanged in multiple consecutive iterations, and obtain the upper and lower limits of the equivalent output.

[0074] Step 5: Based on the constraints of Steps 3 and 4, establish the feasible range of energy storage level of energy storage devices for each scheduling period of the wind-solar-diesel-storage microgrid system, as well as the preconditions for meeting the feasible range. In step 5, the feasible range of energy storage capacity for the energy storage devices during each dispatch period of the wind-solar-diesel-storage microgrid system is as follows:

[0075]

[0076] in, express During the dispatch period, the upper limit of safe energy storage capacity of energy storage equipment, in MWh; express During the dispatch period, the lower limit of the safe energy storage capacity of the energy storage device, in MWh; express During the dispatch period, the upper limit of safe energy storage capacity of energy storage equipment, in MWh; express During the dispatch period, the lower limit of the safe energy storage capacity of the energy storage device, in MWh; express During the dispatch period, the upper limit of the physical energy storage capacity of the energy storage device, in MWh; express During the dispatch period, the lower limit of the physical energy storage capacity of the energy storage device, in MWh; express During the dispatch period, the maximum charging power of the energy storage device, in MW; express During the dispatch period, the maximum discharge power of the energy storage device, in MW; and They represent During the dispatch period, the output of photovoltaic power generation equipment and wind power generation equipment; This indicates that the diesel generator obtained in step 3 is in The upper limit of equivalent output during the scheduling period; This indicates that the diesel generator obtained in step 3 is in The scheduling period is the lower limit of the equivalent output.

[0077] The following formula presupposes the feasible range of energy storage capacity of the energy storage device during each dispatch period of the wind-solar-diesel-storage microgrid system in step 5:

[0078] in, express , The sum of the two.

[0079] Step 6: Based on the feasible range of energy storage capacity of the energy storage equipment and the preconditions for meeting the feasible range, use the wind-solar-diesel-storage microgrid collaborative operation model to realize the dispatch of the wind-solar-diesel-storage microgrid.

[0080] A wind-solar-diesel-storage microgrid dispatching system corresponding to the above-mentioned wind-solar-diesel-storage microgrid operation method for dealing with multi-source coupling uncertainties includes a data module, a construction module, a range module, and a dispatching module; The data module is used to acquire key parameters of the wind-solar-diesel-storage microgrid and historical information on random factors in multiple scheduling periods; Build modules are used for: Based on the historical information of random factors in multiple scheduling periods of the wind-solar-diesel-storage microgrid, the upper and lower limits of the values ​​of each random factor and the corresponding budget parameters are determined, and the budget uncertainty set of the net load of the wind-solar-diesel-storage microgrid is constructed. Based on the key parameters and the budget uncertainty set of the net load of the wind-solar-diesel-storage microgrid, the constraints of the wind-solar-diesel-storage microgrid collaborative operation model are determined. Establish a collaborative operation model for wind, solar, diesel, and energy storage microgrids; Using the particle swarm optimization algorithm, the coupled constraints of the output of diesel generators and the output rate of adjacent time periods are transformed into upper and lower limits of the equivalent output for each scheduling period. The range module is used to determine the feasible range of the energy storage capacity of the energy storage device, as well as the preconditions for meeting the feasible range. The operation module, based on the wind-solar-diesel-storage microgrid collaborative operation model and feasibility range, and using a rolling optimization algorithm to optimize the wind-solar-diesel-storage microgrid collaborative operation model, establishes a rolling optimization-based mixed integer programming model for wind-solar-diesel-storage microgrid collaborative operation, and uses the rolling optimization-based mixed integer programming model for wind-solar-diesel-storage microgrid collaborative operation to realize the scheduling of the wind-solar-diesel-storage microgrid.

[0081] Historical information on random factors such as wind power output, photovoltaic power output, base load, and electric vehicle charging load, as well as key parameters in the wind-solar-diesel-storage microgrid, is obtained. A polyhedral uncertainty set for each random factor is established, and the difference between total load demand and renewable energy output is defined as net load, thus establishing a net load polyhedral uncertainty set. With the objective function of minimizing the sum of diesel generator operating costs and grid transaction costs for power exchange at the gateway, a collaborative operation model for the wind-solar-diesel-storage microgrid is established, comprehensively considering power balance constraints, gateway power exchange constraints, upper and lower bounds and ramping constraints of diesel generator output, charging and discharging power constraints of energy storage devices, and energy storage constraints. Utilizing the model characteristics, the ramping constraints and upper and lower bounds of diesel generator output are transformed into independent equivalent interval constraints for each time period using a particle swarm optimization algorithm. Based on the model characteristics, feasible operating range conditions for energy storage devices in each time period of the microgrid system are established. Based on the above decoupling conditions, the decoupled model is embedded into a rolling optimization framework to establish a mixed integer programming model for the wind-solar-diesel-storage microgrid. The minimum total operating cost and feasible scheduling solutions and ranges for each time period are obtained by directly solving this model.

[0082] Please see Figure 8 This embodiment provides a schematic diagram of a chip. The chip 600 includes a processor 622, the number of which can be configured as one or more according to actual needs, and a memory 632 for storing a computer program, which consists of several program instructions. The processor 622 is configured to run the computer program stored in the memory 632 to implement the aforementioned method for coordinating the operation of a wind-solar-diesel-storage microgrid to address uncertainties in multi-source coupling.

[0083] Specifically, the processor 622 executes program instructions in memory 632 to complete the following processes: It collects historical data on wind power, photovoltaic power, base load, and electric vehicle charging load, and combines this data with key parameters of the microgrid system to construct a polyhedral uncertainty set of various uncertain factors. The difference between the total load and renewable energy output is then defined as the net load, forming a net load polyhedral uncertainty set. With the optimization objective of minimizing the sum of diesel generator generation costs and grid interaction costs for power exchange at the gateway, a collaborative operation model for the wind-solar-diesel-storage microgrid is established, comprehensively considering constraints such as system power balance, gateway power exchange limits, diesel generator output range and ramp rate, energy storage charging and discharging capacity, and energy storage limitations. Based on the model characteristics, the ramp constraint and output upper and lower bound constraints of the diesel generator are transformed into independent equivalent interval constraints for each time period using a particle swarm optimization algorithm. Then, based on the model characteristics, feasible operating range conditions for energy storage devices in each time period of the microgrid system are established. On this basis, the decoupled model is incorporated into a rolling optimization framework to construct a directly solvable mixed integer programming model. The minimum system operating cost and the optimal scheduling scheme for each time period are obtained through solving this model.

[0084] In addition, chip 600 may be configured with a power supply component 626 and a communication component 650. The power supply component 626 manages the power supply status of the chip, and the communication component 650 enables wired or wireless communication interaction between the chip and external devices. Chip 600 may also be provided with an input / output interface 658 for connecting external devices. The operation of chip 600 can be managed based on an operating system stored in memory 632.

[0085] In this embodiment, a computer-readable storage medium is also provided. This storage medium serves as a data storage carrier for a terminal device, used to store program code and related data. It should be noted that the storage medium can be either the terminal device's built-in internal memory or an external extended memory supported by the terminal device. The storage medium contains a storage space that, in addition to storing the terminal operating system, also stores one or more instructions that can be loaded and executed by the processor. These instructions constitute one or more computer programs (including program code). Furthermore, the storage medium can be high-speed random access memory (RAM) or non-volatile memory, such as a disk drive.

[0086] The processor loads and runs the instructions stored in the storage medium to execute the wind-solar-diesel-storage microgrid collaborative operation method described in the above embodiments for addressing uncertainties in multi-source coupling. Specifically, after the instructions in the storage medium are loaded by the processor, the following operation flow is performed: Historical data on wind power output, photovoltaic power output, base load, and electric vehicle charging load are acquired. Combined with key parameters of each device in the microgrid system, a polyhedral uncertainty set of various stochastic factors is constructed. The difference between the total load and renewable energy output is defined as the net load, forming a polyhedral constrained uncertainty set of the net load. With the objective of minimizing the sum of diesel generator generation cost and grid interaction costs for power exchange at the gateway, a collaborative operation model for the wind-solar-diesel-storage microgrid is established, comprehensively considering power balance constraints, gateway power exchange limits, diesel generator output range and ramp rate limits, energy storage charging and discharging capacity, and energy storage constraints. Based on the model characteristics, the ramp constraint and output upper and lower bound constraints of the diesel generator are transformed into independent equivalent interval constraints for each time period using a particle swarm optimization algorithm. Then, based on the model characteristics, feasible operating range conditions for energy storage devices in each time period of the microgrid system are established. On this basis, the decoupled model is incorporated into a rolling optimization framework to construct a directly solvable mixed integer programming model. Solving this model yields the minimum system operating cost and the optimal scheduling scheme for each time period.

[0087] This embodiment aims to verify the collaborative operation method of a wind-solar-diesel-storage microgrid system and obtain relevant information from the microgrid system.

[0088] Table 1 shows the main parameters of the equipment in the microgrid system. Figure 3 , Figure 4 , Figure 5 and Figure 6 The system displays information related to load demand, wind power output, solar power output, and diesel generator output for a typical day, covering 24 time periods, each lasting one hour.

[0089] Table 1. Main Parameters of Microgrid System

Claims

1. A method for operating a wind-solar-diesel-storage microgrid to address uncertainties arising from multi-source coupling, wherein the wind-solar-diesel-storage microgrid is connected to the main grid, and the microgrid includes renewable energy power generation equipment, power consumption equipment, and energy storage equipment; wherein the renewable energy power generation equipment includes photovoltaic power generation equipment, wind power generation equipment, and diesel power generation equipment, characterized in that, Includes the following steps: Step 1: Obtain the following data for the wind-solar-diesel-storage microgrid: Key parameters of wind-solar-diesel-storage microgrids; Historical information on random factors in multiple scheduling periods of the wind-solar-diesel-storage microgrid, including: In each scheduling period, the charging load (MW) of all electric vehicles connected to the power equipment of the wind-solar-diesel-storage microgrid; The base load of the wind-solar-diesel-storage microgrid in each dispatch period is MW; The output load of renewable energy power generation equipment in each dispatch period, in MW; Based on the historical information of random factors in multiple scheduling periods of the wind-solar-diesel-storage microgrid, the upper and lower limits of the values ​​of each random factor, and the budget constraint parameters of each scheduling period, a budget uncertainty set of the net load of the wind-solar-diesel-storage microgrid is constructed. Step 2, establish a collaborative operation model for the wind-solar-diesel-storage microgrid, as shown in the following formula: in, This indicates the operating cost of the diesel generator set; express and The sum of; This represents the transaction costs between the wind-solar-diesel-storage microgrid and the main power grid; and These represent the costs incurred by the wind-solar-diesel-storage microgrid in purchasing electricity from the main grid and the revenue generated from selling electricity, respectively, during any given dispatch period. Indicates taking Larger values ​​between 0 and 0; Indicates taking The smaller value between 0 and 0; The duration of a scheduling period is expressed in hours (h). Indicates the first One scheduling period, h; This represents the transmission power (MW) between the wind-solar-diesel-storage microgrid and the main grid during any given scheduling period. Step 3: Based on the key parameters obtained in Step 1 and the budget uncertainty set of the net load of the wind-solar-diesel-storage microgrid, determine the constraints of the wind-solar-diesel-storage microgrid collaborative operation model. The constraints include: Constraints on power exchange between the wind-solar-diesel-storage microgrid and the main power grid; The output constraint of diesel generator set and the coupled constraint of output rate in adjacent time periods; Balance constraints between the power generation capacity of the power generation equipment and the power consumption capacity of the power consumption equipment in the wind-solar-diesel-storage microgrid; The charging and discharging power constraints of energy storage devices in wind-solar-diesel-storage microgrids; Dynamic equation of energy state for energy storage devices in a wind-solar-diesel-storage microgrid; Step 4: Using the particle swarm optimization algorithm, the output constraints of the diesel generator and the coupled constraints of the output rate in adjacent time periods are transformed into upper and lower limits of the equivalent output for each scheduling period. All other constraints remain unchanged; Step 5: Based on the constraints of Steps 3 and 4, establish the feasible range of energy storage capacity of energy storage devices in each scheduling period of the wind-solar-diesel-storage microgrid system and the preconditions for meeting the feasible range. Step 6: Based on the feasible range of energy storage capacity of the energy storage equipment and the preconditions for meeting the feasible range, use the wind-solar-diesel-storage microgrid collaborative operation model to realize the dispatch of the wind-solar-diesel-storage microgrid.

2. The method for operating a wind-solar-diesel-storage microgrid to address uncertainties in multi-source coupling as described in claim 1, characterized in that, Step 1 includes the following sub-steps: Step 11: Obtain key parameters of the wind-solar-diesel-storage microgrid, including: The upper and lower limits of energy storage capacity of energy storage devices in a wind-solar-diesel-storage microgrid, in MWh; The charge / discharge efficiency coefficient of the energy storage equipment in the wind-solar-diesel-storage microgrid is dimensionless. The maximum charging and discharging power of the energy storage equipment in a wind-solar-diesel-storage microgrid, in MW; Upper and lower limits, in MW, of the power output of diesel generators in a wind-solar-diesel-storage microgrid; Upper and lower limits of the ramp-up power of diesel generators in a wind-solar-diesel-storage microgrid, in MW; Total number of dispatch periods for the wind-solar-diesel-storage microgrid; duration of each dispatch period, in hours; Upper and lower limits of transmission power capacity (MW) for wind-solar-diesel-storage microgrids; Step 12: Obtain historical information on random factors and corresponding budget constraint parameters for each scheduling period of the wind-solar-diesel-storage microgrid, and establish the corresponding uncertainty set, as shown in the following formula: in, express The charging load of electric vehicles during the dispatch period, in MW; express The lower limit of electric vehicle charging load during the dispatch period, in MW; express The upper limit of electric vehicle charging load during the scheduling period, in MW; Indicates the first One scheduling period; Indicates the total scheduling period; express Constraint parameters for electric vehicle charging load during scheduling periods; The first represents the charging load of electric vehicles i Budget constraints; This represents the total budget constraint for electric vehicle charging load. Budgetary parameters representing the charging load of electric vehicles; in, express Base load of the wind-solar-diesel-storage microgrid during dispatch periods, in MW; express The lower limit of the base load of the wind-solar-diesel-storage microgrid during the dispatch period, in MW; express The upper limit of the base load of the wind-solar-diesel-storage microgrid during the dispatch period, in MW; express Constraint parameters of the base load of the wind-solar-diesel-storage microgrid during the dispatch period; Indicates the base load number Budget constraints; This represents the total budget constraint for the base load; Budgetary parameters representing the base load of the wind-solar-diesel-storage microgrid; in, express Output of renewable energy generation equipment during the dispatch period, in MW; express Lower limit of renewable energy power generation equipment output during dispatch period, in MW; express Maximum output of renewable energy power generation equipment during dispatch periods, in MW; express Constraint parameters for the output of renewable energy power generation equipment during the dispatch period; Budget parameters representing the output of renewable energy power generation equipment; The first indicator of the output of renewable energy power generation equipment k Budget constraints; This represents the total budget constraint for the output of renewable energy power generation equipment; Step 13: Based on the above uncertainty set, establish the uncertainty set of the net load of the wind-solar-diesel-storage microgrid, as shown in the following equation: in, This indicates that the wind-solar-diesel-storage microgrid is in Net load demand during the scheduling period, in MW; This indicates that the wind-solar-diesel-storage microgrid is in Lower limit of net load demand during the scheduling period, in MW; This indicates that the wind-solar-diesel-storage microgrid is in Net load demand ceiling for the scheduling period, in MW; This indicates that the wind-solar-diesel-storage microgrid is in Budget parameters for the scheduling period; This indicates that the wind-solar-diesel-storage microgrid is in Constraint parameters for the scheduling period; The first indicates the net load m Budget constraints; This represents the total budget constraint for net load.

3. The method for operating a wind-solar-diesel-storage microgrid to address uncertainties in multi-source coupling as described in claim 2, characterized in that, The constraints for step 3 are as follows: The coupling constraint between the output constraint of the diesel generator and the output rate in adjacent time periods is as follows: =1,2,…T, ∈Ω in, Represents net load demand, in MW; Indicates the first to the last Net load demand during the scheduling period, in MW; and These represent the downhill and uphill limits, respectively, in MW / h; express Output of diesel generators during the dispatch period, in MW; express -1 Output of diesel generators during the dispatch period, in MW; Ω represents the range of uncertainties in the net load; The power exchange constraints between the wind-solar-diesel-storage microgrid and the main grid are as follows: =1,2,…T, ∈Ω in, express The exchange power (MW) between the wind-solar-diesel-storage microgrid and the main grid during the dispatch period; express Lower limit of the exchange power between the wind-solar-diesel-storage microgrid and the main grid during the dispatch period, in MW; express The upper limit of the exchange power between the wind-solar-diesel-storage microgrid and the main grid during the dispatch period, in MW; The power generation capacity of the power generation equipment and the power consumption capacity of the power consumption equipment in the wind-solar-diesel-storage microgrid are balanced by the following formula: =1,2,…T, ∈Ω in, Indicates that energy storage devices are in Charging and discharging power during the scheduling period, in MW; The charging and discharging power constraints of energy storage devices in a wind-solar-diesel-storage microgrid are as follows: =1,2,…T, ∈Ω in, express The energy stored in the energy storage device during the dispatch period, in MWh; and These respectively represent the energy storage devices in Lower and upper limits of energy storage capacity during the scheduling period, in MWh; Indicates that energy storage devices are in Charging and discharging power during the scheduling period, in MW; and These respectively represent the energy storage devices in Maximum charging power and maximum discharging power during the scheduling period, in MW; The dynamic equation of energy state for the energy storage devices in a wind-solar-diesel-storage microgrid is as follows: =1,2,…T, ∈Ω in, express 1. Energy storage capacity of the energy storage device during the scheduling period, in MWh.

4. The method for operating a wind-solar-diesel-storage microgrid to address uncertainties in multi-source coupling as described in claim 3, characterized in that, The auxiliary equation is as follows: in, and All are auxiliary equations; This indicates the change in stored energy calculated based on charging and discharging power. for The inverse function of represents the calculation of charging and discharging power based on changes in stored energy; As the independent variable; The length of a scheduling period is represented by h; These represent discharge efficiency and charging efficiency, respectively, and are dimensionless. Indicates taking The larger of the two, 0 and 0; Indicates taking The smaller of 0 and 0.

5. The method for operating a wind-solar-diesel-storage microgrid to address uncertainties in multi-source coupling as described in claim 4, characterized in that, Step 4 includes the following sub-steps: Step 41, Initialize the particle swarm: Randomly generated Each particle represents a possible sequence of diesel generator outputs and is randomly initialized. The position and velocity of each particle; Step 42: Record the current position of each particle as its individual optimal position. Using the climbing constraint and the upper and lower limits of output as constraints, calculate the scheduling cost corresponding to the individual optimal position of each particle, select the particle with the lowest cost, and record it as the global optimal position. Among them, the global optimal position corresponds to the upper and lower limits of the equivalent output; Step 43: Use the update formula of the particle swarm algorithm to generate a new generation of particle swarms; Step 44: Repeat the method of steps 42-43 to perform multiple iterations until the preset maximum number of iterations is reached or the global optimal position remains unchanged in multiple consecutive iterations, and obtain the upper and lower limits of the equivalent output.

6. The method for operating a wind-solar-diesel-storage microgrid to address uncertainties in multi-source coupling as described in claim 5, characterized in that, In step 5, the feasible range of energy storage capacity for the energy storage devices during each dispatch period of the wind-solar-diesel-storage microgrid system is as follows: in, express During the dispatch period, the upper limit of safe energy storage capacity of energy storage equipment, in MWh; express During the dispatch period, the lower limit of the safe energy storage capacity of the energy storage device, in MWh; express During the dispatch period, the upper limit of safe energy storage capacity of energy storage equipment, in MWh; express During the dispatch period, the lower limit of the safe energy storage capacity of the energy storage device, in MWh; express During the dispatch period, the upper limit of the physical energy storage capacity of the energy storage device, in MWh; express During the dispatch period, the lower limit of the physical energy storage capacity of the energy storage device, in MWh; express During the dispatch period, the maximum charging power of the energy storage device, in MW; express During the dispatch period, the maximum discharge power of the energy storage device, in MW; and They represent During the dispatch period, the output of photovoltaic power generation equipment and wind power generation equipment; This indicates that the diesel generator obtained in step 3 is in The upper limit of equivalent output during the scheduling period; This indicates that the diesel generator obtained in step 3 is in The scheduling period is the lower limit of the equivalent output.

7. The method for operating a wind-solar-diesel-storage microgrid to address uncertainties in multi-source coupling as described in claim 6, characterized in that, The following formula presupposes the feasible range of energy storage capacity of the energy storage device during each dispatch period of the wind-solar-diesel-storage microgrid system in step 5: in, express , The sum of the two.

8. A wind-solar-diesel-storage microgrid dispatching system corresponding to the wind-solar-diesel-storage microgrid operation method for addressing multi-source coupling uncertainties as described in claim 7, characterized in that, It includes a data module, a construction module, a range module, and a scheduling module; The data module is used to acquire key parameters of the wind-solar-diesel-storage microgrid and historical information on random factors in multiple scheduling periods; The aforementioned building module is used for: Based on the historical information of random factors in multiple scheduling periods of the wind-solar-diesel-storage microgrid, the upper and lower limits of the values ​​of each random factor and the corresponding budget constraint parameters are determined, and the budget uncertainty set of the net load of the wind-solar-diesel-storage microgrid is constructed. Establish a collaborative operation model for wind, solar, diesel, and energy storage microgrids; Based on the key parameters and the budget uncertainty set of the net load of the wind-solar-diesel-storage microgrid, the constraints of the wind-solar-diesel-storage microgrid collaborative operation model are determined. Using the particle swarm optimization algorithm, the coupled constraints of the output of diesel generators and the output rate of adjacent time periods are transformed into upper and lower limits of the equivalent output for each scheduling period. The range module is used to determine the feasible range of the energy storage level of the energy storage device, as well as the preconditions for meeting the feasible range. The aforementioned operation module establishes a hybrid integer programming model for the coordinated operation of wind, solar, diesel, and energy storage microgrids based on the collaborative operation model and feasibility range of the wind, solar, diesel, and energy storage microgrids, and optimizes the collaborative operation model using a rolling optimization algorithm, thereby realizing the scheduling of the wind, solar, diesel, and energy storage microgrids.

9. A terminal device, characterized in that, The terminal device includes a processor and a memory, and is used to run the wind-solar-diesel-storage microgrid operation method for addressing uncertainties in multi-source coupling as described in claim 7.

Images