Multi-objective scheduling method and system for ac-dc hybrid microgrid based on digital twinning

Abstract

The application provides an AC / DC hybrid micro-grid multi-objective scheduling method and system based on digital twinning, relates to the technical field of micro-grid scheduling, and comprises the following steps: establishing an AC / DC hybrid micro-grid digital twinning model and dynamically integrating grid related data; and establishing a comprehensive total cost model of a small AC / DC hybrid micro-grid system; the application combines photovoltaic power generation, photo-thermal power generation and wind power generation, and an energy storage device, establishes a device dynamic efficiency model based on the influence of local load, external grid environment and other factors on the efficiency of the AC / DC hybrid micro-grid equipment, obtains various twinning data such as local load and external grid environment through the digital twinning model, takes economic benefits and the health degree of the energy storage device as the target, proposes a multi-objective particle swarm optimization algorithm based on an improved file maintenance strategy and a fuzzy decision method, and optimizes the output of the energy storage device in real time, thereby improving the multi-energy complementation and conversion capacity of the system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of micro-grid scheduling, in particular to a multi-objective scheduling method and system for AC-DC hybrid micro-grid based on digital twinning. BACKGROUND

[0002] With the rapid development of renewable energy, AC-DC hybrid micro-grid has become an important research direction of energy system due to its flexibility and efficiency. However, the existing technology has the following problems:

[0003] Device modeling simplification: traditional AC-DC hybrid micro-grid scheduling models usually ignore the real-time influence of device dynamic efficiency and environmental factors (such as wind speed and irradiance), resulting in insufficient scheduling accuracy;

[0004] Information interaction lag: existing methods rely on offline data and cannot respond to local weather conditions, load fluctuations and grid price changes in real time, affecting the timeliness of scheduling strategies;

[0005] Insufficient multi-objective optimization: existing algorithms focus on a single economic objective and ignore the health of energy storage devices (such as the impact of state of charge cycling on life), resulting in increased long-term operating costs of the system;

[0006] Algorithm performance limitations: traditional multi-objective optimization algorithms (such as NSGA-II and MOPSO) are prone to local optimization and cannot effectively balance the convergence and distribution of solutions;

[0007] Therefore, the present application proposes a multi-objective scheduling method and system for AC-DC hybrid micro-grid based on digital twinning to solve the problems existing in the prior art. SUMMARY

[0008] To solve the above problems, the present application proposes a multi-objective scheduling method and system for AC-DC hybrid micro-grid based on digital twinning, which optimizes the output of energy storage devices in real time, enhances the economy and environmental performance of the system, and improves the multi-energy complementation and conversion capacity of the system.

[0009] To achieve the purpose of the present application, the present application realizes the following technical solutions: a multi-objective scheduling method for AC-DC hybrid micro-grid based on digital twinning, comprising the following steps:

[0010] S1: Establish a digital twinning model of AC-DC hybrid micro-grid, dynamically integrate grid-related data;

[0011] S2: Establish a comprehensive total cost model for a small AC-DC hybrid micro-grid system, taking into account the impact of state of charge (SOC) cycling interval on life, and using an exponential function to quantify health;

[0012] S3: Establish the constraint conditions of the AC-DC hybrid micro-grid system;

[0013] S4: Based on the above, establish a mathematical model for the multi-objective optimization scheduling of the system and obtain the basic parameters of the system;

[0014] S5: Construct an improved multi-objective particle swarm optimization algorithm, and introduce a displacement-based density estimation index and a fuzzy decision-making method;

[0015] S6: Combine time-of-use pricing with renewable energy output to dynamically adjust energy storage charging and discharging strategies;

[0016] S7: Output the optimal scheduling scheme and update the parameters of the digital twin model synchronously.

[0017] Further improvements are made in the following aspects: In S1, the AC / DC hybrid microgrid digital twin model includes a wind power generation model, a photovoltaic power generation model, a solar thermal power generation model, and an energy storage device model; it dynamically integrates grid-related data, including wind speed, irradiance, load demand, and time-of-use electricity price data.

[0018] A further improvement is made in that the wind power generation model is as follows:

[0019]

[0020] Among them, v t For instantaneous wind speed, v in v r and v out These are the cut-in wind speed, rated wind speed, and cut-out wind speed, respectively, P r This refers to the rated power of the fan;

[0021] The photovoltaic power generation model is as follows:

[0022]

[0023] Among them, P PV G represents the output power of the photovoltaic panel. t G represents solar irradiance. STC Irradiance under standard test conditions. With a temperature coefficient of -0.0047, T STC For the photovoltaic panel surface temperature under standard test conditions of 25 ℃, P STC The standard is the output power of the photovoltaic panel.

[0024] A further improvement is made in that the solar thermal power generation model is as follows:

[0025] Heat collection section:

[0026]

[0027] in, The heat collection power of concentrated solar power (CSP) For the light and heat power station's heat collection efficiency, For the mirror field area, GI is the solar irradiance;

[0028] Power generation part:

[0029]

[0030] Wherein, For the output power of photo-thermal power generation, For the heat collection power of photo-thermal power generation, For the power generation efficiency of photo-thermal power generation;

[0031] The energy storage device model, including the model of the battery and the heat storage device, is:

[0032]

[0033] Wherein, SOC (t+1) and SOC (t) are the state of charge of the battery at t+1 and t, And Respectively, the charging and discharging power of the battery, And Respectively, the charging and discharging efficiency of the battery;

[0034]

[0035] Wherein, S HS (t+1) and S HS (t) are the capacity of the heat storage device at t+1 and t, And Respectively, the charging and discharging power of the heat storage device, And Respectively, the charging and discharging efficiency of the heat storage device.

[0036] Further improvement is that in the S2, the comprehensive total cost model of the small AC / DC hybrid micro-grid system contains the power generation cost, the energy storage operation and maintenance cost and the grid interaction cost, while taking into account the influence of the state of charge SOC cycle interval on the life, and adopting an exponential function to quantify the health degree:

[0037]

[0038] Wherein, Operating cost; Z - number of subnets; N - total number of time periods; s1 - operating cost of wind turbine generator per unit time, yuan / h; s2 - operating cost of photovoltaic generator per unit time, yuan / h; s3 - operating cost of solar-thermal generator per unit time, yuan / h; s4 - operating cost of energy storage device per unit time, yuan / h; δ - unit time interval, h; c(t) - electricity purchase and sale price of large power grid at time t, yuan / kWh; P load (t) - power grid electricity load at time t, kW; f2 - change range of state of charge of energy storage device, %; State of charge of energy storage device at time t Average state of charge of energy storage device, %

[0039] Further improvement lies in that in the S3, the constraint condition specifically comprises:

[0040] The operation of the AC-DC hybrid microgrid system needs to meet the power constraint in the AC-DC hybrid microgrid:

[0041]

[0042] Wherein, Power of the i-th subnet at time t, kW Photovoltaic power of the i-th subnet at time t, kW Wind power of the i-th subnet at time t, kW Solar-thermal power of the i-th subnet at time t, kW Power of the energy storage device in the i-th subnet at time t, kW Load power of the i-th subnet at time t, kW

[0043] The operation of the AC-DC hybrid microgrid system needs to meet the power constraint between the AC-DC hybrid microgrids:

[0044]

[0045] The operation of the AC-DC hybrid microgrid system needs to meet the state of charge constraint of the energy storage device and the charge and discharge power constraint of the energy storage device:

[0046]

[0047] Wherein, State of charge of the i-th subnet at the initial time, % State of charge of the i-th subnet at the terminal time, % Minimum charge amount of the energy storage device, % Maximum charge amount of the energy storage device, %

[0048] The AC-DC hybrid micro-grid system operation needs to meet the tie-line power constraint:

[0049]

[0050] In the formula: Lower limit value, kW; Upper limit value, kW; Power of sub-network i at time t, kW;

[0051] The AC-DC hybrid micro-grid system operation needs to meet the external power purchase and sale constraint:

[0052]

[0053] In the formula: Upper limit of external power sale, kW; Upper limit of external power purchase, kW; External grid power purchase at time t, kW.

[0054] Further improvement lies in that: in the S4, the mathematical model of multi-objective optimization scheduling of the system is established based on the system model, objective function and constraint condition proposed in the S1-S3, the initial load data of the micro-grid is acquired, the annual meteorological historical data, time-of-use price data are acquired, and the device operation efficiency, rated power and start-stop time basic parameters are acquired.

[0055] Further improvement lies in that: in the S5, an improved multi-objective particle swarm algorithm is designed, and the archive maintenance strategy is improved; the ISDE index based on displacement density estimation is introduced to screen the non-inferior solution with convergence and distribution; the fuzzy decision method is introduced to quantify the satisfaction degree of each target through the membership function, and the optimal scheduling scheme is selected.

[0056] Further improvement lies in that: in the S6, the energy storage charging and discharging strategy is:

[0057] When the electricity price is in the peak period, the power sale is preferred to earn the income, when the power generation of the AC-DC hybrid micro-grid is higher than the local load, the energy storage device is discharged as much as possible within the allowed range; when the power generation of the AC-DC hybrid micro-grid is lower than the local load, the energy storage device is discharged to meet the power demand, and when the energy storage device cannot meet the demand, the power purchase from the grid is considered;

[0058] When the electricity price is in the valley period, the electricity purchase strategy is given priority, the electricity is purchased from the large power grid within the state of charge range of the energy storage device, at this time, the electricity purchase power meets the charging demand of the energy storage device and the local load gap at the same time, when the state of charge of the energy storage device reaches the upper limit, the energy storage ends, at this time, the electricity purchase power only meets the local load gap, when the electricity price is in the flat stage, the state of charge of the energy storage device is given priority to maintain, when the power generation of the AC-DC hybrid microgrid is higher than the local load, the energy storage device is preferentially charged, when the local load exceeds the generator set capacity of the AC-DC hybrid microgrid, the energy storage device fills the gap;

[0059] The AC-DC hybrid microgrid multi-objective scheduling system based on digital twinning includes a power system and a digital twinning platform, the power system is used for providing power, and the power system is composed of three energy utilization forms of photovoltaic power generation, photo-thermal power generation and wind power generation and energy storage equipment;

[0060] The digital twinning platform is used for data acquisition and provides an interactive interface, and builds a digital twinning model, and based on the multi-objective particle swarm optimization algorithm of the improved archive maintenance strategy and the fuzzy decision method, the output of the power system is optimized in real time.

[0061] The beneficial effects of the present application are:

[0062] 1、The present application combines photovoltaic power generation, photo-thermal power generation and wind power generation in three power generation forms and energy storage devices, based on the influence of local load, external large power grid environment and other factors on the efficiency of AC-DC hybrid microgrid equipment, a dynamic efficiency model of the equipment is established, the local load, external large power grid environment and other twin data are obtained through the digital twinning model, the economic benefit and the health degree of the energy storage device are taken as the target, the multi-objective particle swarm optimization algorithm based on the improved archive maintenance strategy and the fuzzy decision method is proposed, the output of the energy storage device is optimized in real time, the economy and environmental performance of the system are enhanced, and the multi-energy complementary and conversion capacity of the system is improved. BRIEF DESCRIPTION OF DRAWINGS

[0063] Figure 1 It is a structure block diagram of the power system of the present application;

[0064] Figure 2 It is a structure diagram of the digital twinning platform of the present application;

[0065] Figure 3 It is a schematic diagram of the energy storage charging and discharging strategy of the present application;

[0066] Figure 4 It is a basic algorithm framework diagram of the MOPSO-IAFD of the present application. DETAILED DESCRIPTION

[0067] In order to deepen the understanding of the present application, the present application will be further described below in conjunction with examples, which are only used to explain the present application and do not constitute a limitation on the protection scope of the present application.

[0068] Example one

[0069] According to Figure 1 、 2 , 3, 4, the present embodiment proposes a multi-objective scheduling method for AC / DC hybrid microgrid based on digital twinning, including the following steps:

[0070] The device model in the AC / DC hybrid microgrid system is built, including wind power generation model, photovoltaic power generation model, solar-thermal power generation model and energy storage device model, specifically including:

[0071] (1) Wind power generation model:

[0072]

[0073] Where, v t is the instantaneous wind speed, v in , v r and v out are the cut-in wind speed, rated wind speed and cut-out wind speed respectively, P r is the rated power of the fan.

[0074] (2) Photovoltaic power generation model:

[0075]

[0076] Where, P PV is the output power of the photovoltaic panel, G t is the solar irradiance, G STC is the irradiance under standard test conditions, is the temperature coefficient -0.0047, T STC is the surface temperature of the photovoltaic panel under standard test conditions 25 ℃, P STC is the standard test photovoltaic panel output power.

[0077] (3) The solar-thermal power generation model is:

[0078] Heat collection part:

[0079]

[0080] Where, is the heat collection power of solar-thermal power generation, is the heat collection efficiency of the solar-thermal power station, is the mirror field area, and GI is the solar irradiance.

[0081] Power generation part:

[0082]

[0083] wherein, is the output power of the photo-thermal power generation, is the heat collection power of the photo-thermal power generation, is the power generation efficiency of the photo-thermal power generation.

[0084] (4) the energy storage device, including the model of the battery and the heat storage device is:

[0085]

[0086] wherein, SOC (t+1) and SOC (t) are the state of charge of the battery at t+1 and t, and are the charging and discharging power of the battery, respectively, and are the charging and discharging efficiency of the battery, respectively.

[0087]

[0088] wherein, S HS (t+1) and S HS (t) are the capacity of the heat storage device at t+1 and t, and are the charging and discharging power of the heat storage device, respectively, and are the charging and discharging efficiency of the heat storage device, respectively.

[0089] A comprehensive total cost model of a small AC / DC hybrid micro-grid system is established, which includes the generation cost, energy storage operation and maintenance cost, and grid interaction cost. The influence of the state of charge (SOC) cycle interval on the life is considered, and the health degree is quantified by using an exponential function.

[0090]

[0091] wherein, the operation cost, Z is the number of subnets, N is the total number of time periods, s1 is the operation cost of the wind turbine per unit time, yuan / h; s2 is the operation cost of the photovoltaic generator per unit time, yuan / h; s3 is the operation cost of the photo-thermal generator per unit time, yuan / h; s4 is the operation cost of the energy storage device per unit time, yuan / h; δ is the unit time interval, h; c(t) is the purchase and sale electricity price of the large power grid at t, yuan / kWh; P load (t) is the power grid load at time t, kW; f2 is the change amplitude of the state of charge of the energy storage device, %; — the state of charge of the energy storage device at time t; — the average state of charge of the energy storage device.

[0092] The constraint conditions for establishing the AC / DC hybrid micro-grid system are specifically as follows:

[0093] (1) The AC / DC hybrid micro-grid system operation needs to meet the power constraint in the AC / DC hybrid micro-grid:

[0094]

[0095] Wherein: — the power of the i-th sub-grid at time t, kW; — the photovoltaic power of the i-th sub-grid at time t, kW; — the wind power of the i-th sub-grid at time t, kW — the solar-thermal power of the i-th sub-grid at time t, kW; — the power of the energy storage device in the i-th sub-grid at time t, kW; — the load power of the i-th sub-grid at time t, kW.

[0096] (2) The AC / DC hybrid micro-grid system operation needs to meet the power constraint between the AC / DC hybrid micro-grids:

[0097]

[0098] (3) The AC / DC hybrid micro-grid system operation needs to meet the state of charge constraint of the energy storage device and the charge and discharge power constraint of the energy storage device:

[0099]

[0100] Wherein: — the state of charge of the i-th sub-grid at the initial time; — the state of charge of the i-th sub-grid at the terminal time; — the minimum charge amount of the energy storage device; — the maximum charge amount of the energy storage device.

[0101] (4) The AC / DC hybrid micro-grid system operation needs to meet the tie-line power constraint:

[0102]

[0103] Wherein: — the lower limit value, kW; — the upper limit value, kW; — the power of the i-th sub-grid at time t, kW.

[0104] (5) The operation of the AC-DC hybrid microgrid system needs to meet the external power purchase and sale constraints:

[0105]

[0106] In the formula: - upper limit of external power sale, kW; - upper limit of external power purchase, kW; - external grid power purchase at time t, kW.

[0107] Based on the above-mentioned system model, objective function and constraint conditions, a mathematical model of multi-objective optimization scheduling of the system is established; initial load data of the microgrid is obtained; annual meteorological historical data, time-of-use electricity price data are obtained; basic parameters such as equipment operation efficiency, rated power, start-up and shutdown time are obtained.

[0108] An improved multi-objective particle swarm optimization algorithm (MOPSO-IAFD) is designed: the archive maintenance strategy is improved: the ISDE index based on displacement is introduced, and the non-inferior solution with convergence and distribution is selected; the fuzzy decision method: the membership function is used to quantify the satisfaction degree of each target, and the optimal scheduling scheme is selected.

[0109] The real-time optimization of energy storage charging and discharging strategy is combined with time-of-use electricity price and renewable energy output to dynamically adjust the power purchase and sale plan and energy storage power;

[0110] Specifically, when the electricity price is in the peak period, the power sale is preferred to earn revenue. If the power generation of the AC-DC hybrid microgrid is higher than the local load, the energy storage device will discharge as much as possible within the allowed range; if the power generation of the AC-DC hybrid microgrid is lower than the local load, the energy storage device discharges to meet the power demand. When the energy storage device cannot meet the demand, power purchase from the grid is considered.

[0111] When the electricity price is in the valley period, the power purchase strategy should be considered first. Within the state of charge range of the energy storage device, try to purchase power from the grid, and the power purchase power meets the charging demand of the energy storage device and the local load gap at the same time. When the state of charge of the energy storage device reaches the upper limit, the energy storage ends. At this time, the power purchase power only meets the local load gap. When the electricity price is in the flat stage, the state of charge of the energy storage device is preferred to be maintained. When the power generation of the AC-DC hybrid microgrid is higher than the local load, the energy storage device is preferred to be charged. If the local load exceeds the generator capacity of the AC-DC hybrid microgrid, the energy storage device will fill the gap.

[0112] The optimal scheduling scheme is output, and the digital twin model parameters are updated synchronously.

[0113] The scheduling of AC / DC hybrid microgrid needs to determine the charging and discharging power of energy storage devices in 24 time periods in i subnets, which needs to determine i x 24 decision variables, so the matrix coding method is adopted, that is:

[0114]

[0115] In the formula: The power of the energy storage device of the i-th subnet in the first time period, kW.

[0116] In the multi-objective particle swarm optimization algorithm, a significant feature is to use the archive to retain elite solutions. The results of such algorithms are often a set of multiple solutions rather than a single solution, so efficient archive management strategies are needed to store high-quality optimal solution sets. However, current archive management methods based on the Pareto dominance principle only focus on whether a solution is not dominated by other solutions. Notably, the Pareto dominance relationship belongs to the category of qualitative evaluation. Whether a solution is in a non-dominated state is completely based on whether its objective value is superior to that of other solutions in the archive, that is, at least one target performs better. For non-dominated solutions, they are considered to be at the same level in terms of convergence. However, the convergence efficiency of particles is also affected by the superiority of their objective values relative to other particles. Consider a two-dimensional target space scenario, assuming there are three mutually non-superior particles: X(1.0, 0.5), Y(1.2, 0.4), and Z(0.9, 0.6). In this situation, particle Z performs significantly better than X and Y in the first target, thus exhibiting more prominent convergence characteristics. However, according to the Pareto dominance theory, X, Y, and Z are considered to have equal convergence efficiency, which ignores the actual performance differences between them and fails to fully reflect their true strengths and weaknesses. In view of the above challenges, a strategy for maintaining the archive based on quantitative evaluation of solution quality is conceived. The first step is to introduce a quantitative evaluation method that can accurately judge the superiority of a solution's objective value compared to other solutions. Then, a step-by-step refinement strategy is designed, which gradually removes relatively weak solutions from the candidate solution set until the preset solution set capacity limit is reached. When the archive exceeds the limit, the solution with the smallest performance evaluation index is removed from the archive.

[0117] After obtaining a series of candidate scheduling schemes, this study uses a fuzzy comprehensive evaluation-based strategy to select the optimal scheduling scheme. This strategy differs from traditional decision-making methods in that it does not require explicit weight allocation between targets. First, define the membership function of the i-th target of the k-th scheduling scheme. The larger the membership function value of the scheduling scheme on the target, the higher the satisfaction of the target. Then, for the k-th candidate scheduling scheme, calculate the normalized membership function. The scheduling scheme with the maximum value is the optimal scheduling scheme.

[0118] Embodiment Two

[0119] According to Figure 1 , 2 , 3, 4, the embodiment proposes a multi-objective scheduling system for AC / DC hybrid microgrid based on digital twinning. In the AC / DC hybrid microgrid, each subgrid is composed of wind power generation devices, photovoltaic power generation equipment, energy storage devices, local loads, and interface converters connecting the bus and each power source. The public DC bus connects each subgrid through an interconnection converter. Through a static switch, the public DC bus is connected to the power grid to realize energy exchange.

[0120] The power system includes wind turbine generators for converting wind energy into electrical energy, photovoltaic and thermal generators for converting light energy into electrical energy, and an external power grid. The energy storage system includes batteries and the like.

[0121] As shown in Figure 2 , real-time monitoring, data analysis, and optimized scheduling of physical systems can be achieved through digital twinning models, providing strong support for the operation and management of microgrids and other physical systems. The data of the real model is transmitted to the digital twinning platform, and the digital twinning platform will establish a digital model. The digital model performs real-time calculation and analysis, and the results obtained are fed back to the digital twinning platform. The conceptual model of the AC / DC hybrid microgrid based on digital twinning is composed of a physical model and a digital model. The physical model (including all devices and sensors in the AC / DC hybrid microgrid) transmits real-time environmental, load data, and device information to the digital model. Real-time optimization of the microgrid is achieved in the digital model.

[0122] Embodiment Three

[0123] According to Figure 1 , 2 , 3, 4, in this embodiment, the improved charging and discharging strategy is as shown in Figure 3 . This strategy dynamically adjusts the energy storage operation mode based on time-of-use pricing mechanism, as follows:

[0124] (1) Peak period scheduling:

[0125] When the electricity price is in the peak interval, preferentially sell electricity to the grid to obtain revenue;

[0126] If the system generation exceeds the local load demand, the energy storage device maximizes discharging within the power limit;

[0127] If the generation is insufficient, the energy storage discharges to make up the load gap;

[0128] When the energy storage capacity is exhausted and still cannot meet the demand, start grid power purchase.

[0129] (2) Valley period scheduling:

[0130] In low electricity price period, prefer to buy electricity from the grid to charge the energy storage, while covering the load gap;

[0131] When the state of charge (SOC) of the energy storage reaches the upper limit, stop charging, and buy electricity only for load power supply.

[0132] (3) Normal period scheduling:

[0133] To maintain the stability of the energy storage SOC as the goal:

[0134] If the system power generation is surplus, prefer to charge the energy storage;

[0135] If the load exceeds the power generation capacity, the energy storage discharges to fill the power difference;

[0136] Through dynamic balance of charging and discharging behavior, reduce the depth of energy storage cycle, prolong the service life of the equipment.

[0137] Technical effects:

[0138] The strategy realizes the maximization of economic benefits and the collaborative optimization of energy storage health through real-time matching of electricity price signals and power generation-load, and improves the renewable energy consumption rate.

[0139] Embodiment four

[0140] According to Figure 1 , 2 , 3, 4, in this embodiment, for optimization solution proposed AC-DC hybrid microgrid multi-objective optimization model, a multi-objective particle swarm optimization based on improved archive and fuzzy decision method (MOPSO-IAFD) is proposed. First, the improved archive maintenance strategy based on the indicator based on shift for density estimation (ISDE) index is proposed. Not only the solutions with good convergence are maintained, but also the solutions in the archive have good distribution, so as to realize the complete record of the entire Pareto optimal front. On the other hand, the best scheduling scheme selection strategy based on fuzzy decision is introduced, which effectively solves the defect that the traditional multi-objective particle swarm optimization algorithm can only solve one non-inferior solution set. The basic algorithm framework of MOPSO-IAFD is shown in Figure 4 .

[0141] The dispatching of AC / DC hybrid microgrid needs to determine the charging and discharging power of energy storage devices in 24 time periods in i subnets, which needs to determine i x 24 decision variables, so the matrix coding method is adopted, that is:

[0142]

[0143] In the formula: The power of the energy storage device of the i-th subnet in the first time period, kW.

[0144] In the multi-objective particle swarm optimization algorithm, a notable feature is to use the archive to retain elite solutions. The results of such algorithms are often a set of multiple solutions rather than a single solution, so efficient archive management strategies are needed to store high-quality optimal solution sets. However, the current archive management method based on the Pareto dominance principle only focuses on whether a solution is not dominated by other solutions. It is worth noting that the Pareto dominance relationship only belongs to the category of qualitative evaluation. Whether a solution is in a non-dominated state is completely based on whether its target value is superior to that of other solutions in the archive, that is, at least one target performs better. For non-dominated solutions, they are considered to be at the same level in terms of convergence. However, the convergence efficiency of particles is also affected by the superiority of their target values relative to other particles. Consider a two-dimensional target space scenario, assuming there are 3 mutually non-optimal particles: X(1.0, 0.5), Y(1.2, 0.4) and Z(0.9, 0.6). In this situation, particle Z performs significantly better than X and Y in the first target, thus showing more prominent convergence characteristics. However, according to the Pareto dominance theory, X, Y and Z are considered to have equal convergence efficiency, which ignores the actual performance difference between them and fails to fully reflect their true advantages and disadvantages. In view of the above challenges, a kind of archive maintenance strategy based on quantitative evaluation of solution quality is conceived. The first step is to introduce a quantitative evaluation method that can accurately judge the superiority of the solution's target value compared to other solutions. Then, a step-by-step simplification strategy is designed, which gradually removes relatively weak solutions from the candidate solution set until the preset solution set capacity limit is reached. When the archive exceeds the limit, the solution with the smallest performance evaluation index is removed from the archive.

[0145] After obtaining a series of candidate scheduling schemes, this study uses a fuzzy comprehensive evaluation-based strategy to select the optimal scheduling scheme. This strategy is different from traditional decision-making methods, as it does not require prior determination of explicit weight allocation between targets. First, define the membership function of the i-th target of the k-th scheduling scheme. The larger the membership function value of the scheduling scheme on this target, the higher the satisfaction of this target. Then, for the k-th candidate scheduling scheme, calculate the normalized membership function, and the scheduling scheme with the maximum value is the optimal scheduling scheme.

[0146] Example Five

[0147] According to Figure 1 、 2 , 3, 4, in the embodiment, the physical model of the simulated AC / DC hybrid microgrid with multiple subnets is built on the Cloudpss platform, containing 2 AC subnets and 3 DC subnets. The parameters of each generator set and energy storage device in the subnet remain the same, and the time-of-use pricing mechanism is used in the experiment, and the electricity purchase and sale prices of each period are shown in Table 1. The digital twin technology is used for renewable energy unit output prediction. First, the predicted values of wind speed, irradiance and battery surface temperature at each time of the day are obtained through the physical model built on the Cloudpss platform; then, the power of wind, photovoltaic and photo-thermal generator sets is calculated according to the mathematical model of distributed power supply; finally, the daily prediction curve of the generator set is obtained. Regarding MOPSO-IAFD: the inertia weight ω is set to 3.2, the learning factor c1 is set to 1.8, and the learning factor c2 is 1.6, the population size is 100, and the execution is repeated five times. The electricity purchase and sale situation and the state of charge of the storage battery in each subnet can be obtained.

[0148] Table 1 Time-of-use pricing table

[0149]

[0150] The application combines photovoltaic power generation, photo-thermal power generation and wind power generation with energy storage devices, establishes a device dynamic efficiency model based on the influence of local load, external large grid environment and other factors on the efficiency of AC / DC hybrid microgrid equipment, obtains local load, external large grid environment and other twin data through a digital twin model, takes economic benefits and energy storage device health as the target, proposes a multi-objective particle swarm optimization algorithm based on improved file maintenance strategy and fuzzy decision method, optimizes the output of the energy storage device in real time, enhances the economy and environmental performance of the system, and improves the multi-energy complementary and conversion capacity of the system. And the application comprehensively considers local meteorological conditions, local load, external large grid environment and other factors, establishes a digital model of AC / DC hybrid microgrid, then proposes a multi-objective particle swarm optimization algorithm MOPSO-IAFD based on improved file maintenance strategy and fuzzy decision method, takes economic benefits and energy storage device health as optimization targets, optimizes the output of the energy storage device in real time, and finally uses the Cloudpss platform to generate massive data to build a digital twin.

[0151] The above shows and describes the basic principles, main features and advantages of the present application. Those skilled in the art should understand that the present application is not limited to the above-mentioned embodiments, and the above-mentioned embodiments and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the claimed present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A multi-objective scheduling method for AC / DC hybrid microgrid based on digital twinning, characterized in that, The method comprises the following steps: S1: a digital twin model of an AC-DC hybrid microgrid is established, and grid-related data is dynamically integrated; S2: a comprehensive total cost model of a small AC-DC hybrid microgrid system is established, taking into account the influence of the state of charge (SOC) cycle interval on the service life, and using an exponential function to quantify the health degree; S3: a constraint condition of the AC-DC hybrid microgrid system is established; S4: based on the above-mentioned mathematical model of the multi-objective optimization scheduling of the system, the basic parameters of the system are obtained; S5: an improved multi-objective particle swarm optimization algorithm is constructed, a density estimation index based on displacement is introduced, and a fuzzy decision method is introduced; S6: in combination with time-of-use electricity price and renewable energy output, the charging and discharging strategy of the energy storage device is dynamically adjusted; S7: an optimal scheduling scheme is output, and the parameters of the digital twin model are updated synchronously; In S2, the comprehensive total cost model of the small AC-DC hybrid microgrid system includes generation cost, energy storage operation and maintenance cost, and grid interaction cost, and takes into account the influence of the state of charge (SOC) cycle interval on the service life, and uses an exponential function to quantify the health degree: ， Wherein, Operating cost; Z—number of subnets; N—total number of time periods; s1—cost of operating a wind turbine generator per unit time, yuan / h; s2—cost of operating a photovoltaic generator per unit time, yuan / h; s3—cost of operating a photo-thermal generator per unit time, yuan / h; s4—cost of operating an energy storage device per unit time, yuan / h; δ—unit time interval, h; c(t)—purchasing and selling electricity price of the large power grid at time t, yuan / kWh; P load (t)—power grid electricity load at time t, kW; f2—change range of the state of charge of the energy storage device, %; State of charge of the energy storage device at time t; Average state of charge of the energy storage device, %.

2. The digital-twin-based multi-objective scheduling method for AC / DC hybrid microgrids according to claim 1, characterized in that: In S1, the digital twin model of the AC-DC hybrid microgrid includes a wind power generation model, a photovoltaic power generation model, a photo-thermal power generation model, and an energy storage device model; grid-related data is dynamically integrated, including wind speed, irradiance, load demand, and time-of-use electricity price data.

3. The digital-twin-based multi-objective scheduling method for AC / DC hybrid microgrids according to claim 2, characterized in that: The wind power generation model is: ， where v t is the instantaneous wind speed, v in , v r and v out are the cut-in, rated and cut-out wind speeds, respectively, and P r is the rated power of the wind turbine. The photovoltaic power generation model is: ， where P PV is the output power of the photovoltaic panel, G t is the solar irradiance, G STC is the irradiance under standard test conditions, is the temperature coefficient -0.0047, T STC is the photovoltaic panel surface temperature under standard test conditions 25 °C, P STC is the photovoltaic panel output power under standard test conditions.

4. The digital-twin-based multi-objective scheduling method for AC / DC hybrid microgrids according to claim 2, characterized in that: The photo-thermal power generation model is: The heat collection part is: ， wherein is the thermal power collected by the photovoltaic-thermal system, is the thermal efficiency of the photovoltaic-thermal system, is the mirror field area, and G1is the solar irradiance; The power generation part is: ， wherein, Pth is the output power of the photothermal power generation, Pth is the output power of the photothermal power generation, Pth is the output power of the photothermal power generation, The energy storage device model includes models of a battery and a heat storage device: ， wherein SOC (t+1) and SOC (t) are the state of charge of the battery at time t+1 and t, respectively, and Pbat,discharge(t) and Pbat,charge(t) are the discharging and charging power of the battery, respectively, and ηbat,discharge(t) and ηbat,charge(t) are the discharging and charging efficiency of the battery, respectively. ， wherein S HS S (t+1) and S HS (t) are the capacities of the thermal storage device at t+1 and t, and are the charging and discharging power of the thermal storage device, respectively, and are the charging and discharging efficiency of the thermal storage device, respectively.

5. The digital-twin-based multi-objective scheduling method for AC / DC hybrid microgrids according to claim 1, characterized in that: In S3, the constraint condition specifically includes: The AC-DC hybrid microgrid system operation needs to meet the power constraint within the AC-DC hybrid microgrid: ， wherein, — Power of the i-th subgrid at time t, kW; — Photovoltaic power of the i-th subgrid at time t, kW; — Wind power of the i-th subgrid at time t, kW — Solar-thermal power of the i-th subgrid at time t, kW; — Power of the energy storage device in the i-th subgrid at time t, kW; — Load power of the i-th subgrid at time t, kW; The AC-DC hybrid microgrid system operation needs to meet the power constraint between the AC-DC hybrid microgrids: ， The AC-DC hybrid microgrid system operation needs to meet the state of charge constraint of the energy storage device, the charging and discharging power constraint of the energy storage device: ， wherein: - the state of charge of the i-th subnetwork at the initial time instant, - the state of charge of the i-th subnetwork at the final time instant, - the minimum charge amount of the energy storage device, - the maximum charge amount of the energy storage device; The AC-DC hybrid microgrid system operation needs to meet the tie-line power constraint: ， In the formula: — lower limit value, kW; — upper limit value, kW; — power of the subnetwork i at time t, kW; The AC-DC hybrid microgrid system operation needs to meet the external power purchase and sale constraint: ， In the formula: — upper limit of external power selling, kW; — upper limit of external power purchasing, kW; — external grid power purchase amount at time t, kW.

6. The digital-twin-based multi-objective scheduling method for AC / DC hybrid microgrids according to claim 1, characterized in that: In S4, based on the system model, objective function, and constraint condition proposed in S1-S3, a mathematical model of the multi-objective optimization scheduling of the system is established, the initial load data of the microgrid is obtained, the annual meteorological historical data, time-of-use electricity price data, and basic parameters of equipment operation efficiency, rated power, and start-stop time are obtained.

7. The digital-twin-based multi-objective scheduling method for AC / DC hybrid microgrids according to claim 1, characterized in that: In S5, an improved multi-objective particle swarm optimization algorithm is designed, and the archive maintenance strategy is improved; an ISDE index based on displacement is introduced to screen non-inferior solutions with convergence and distribution; A fuzzy decision method is introduced to quantify the satisfaction degree of each target through a membership function, and an optimal scheduling scheme is selected.

8. The digital-twin-based multi-objective scheduling method for AC / DC hybrid microgrids according to claim 1, characterized in that: In S6, the energy storage charging and discharging strategy is: When the electricity price is in the peak period, electricity sales are preferred to earn revenue, and when the power generation of the AC-DC hybrid microgrid is higher than the local load, the energy storage device is discharged as much as possible within the allowed range; when the power generation of the AC-DC hybrid microgrid is lower than the local load, the energy storage device is discharged to meet the electricity demand, and when the energy storage device cannot meet the demand, power is purchased from the grid; When the electricity price is in the valley period, the electricity purchase strategy is given priority, within the state of charge range of the energy storage device, electricity is purchased from the large power grid first, at this time, the electricity purchase power meets the charging demand of the energy storage device and the local load gap at the same time, when the state of charge of the energy storage device reaches the upper limit, the energy storage ends, at this time, the electricity purchase power only meets the local load gap, when the electricity price is in the flat stage, the state of charge of the energy storage device is given priority to maintain, when the power generation of the AC-DC hybrid microgrid is higher than the local load, the energy storage device is given priority to charge, when the local load exceeds the generator set capacity of the AC-DC hybrid microgrid, the energy storage device fills the gap.

9. The multi-objective scheduling system for AC / DC hybrid microgrid based on digital twinning, applied to the multi-objective scheduling method for AC / DC hybrid microgrid based on digital twinning in any one of claims 1-8, characterized in that: The power system is used for providing power, and the power system is composed of three energy utilization forms of photovoltaic power generation, photo-thermal power generation and wind power generation and energy storage equipment; The digital twin platform is used for data acquisition and provides an interactive interface, and constructs a digital twin model, based on an improved archive maintenance strategy and a multi-objective particle swarm algorithm of a fuzzy decision method, the output of the power system is optimized in real time.

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