Micro-grid renewable energy system scheduling optimization method

Abstract

The invention provides a micro-grid renewable energy system scheduling optimization method, which is characterized in that on the basis of constructing a photovoltaic power generation, wind power generation and battery energy storage model by considering multiple influence factors, an optimization model is constructed by taking the lowest power generation cost and the highest power supply reliability as multiple targets, and an improved PSO-GWO algorithm is adopted for solving. The optimal configuration of the capacity of photovoltaic and wind power generation equipment and the optimization of a charging and discharging strategy of a battery energy storage system are realized; the improvement of the improved PSO-GWO algorithm comprises an adaptive parameter adjustment strategy and optimization population initialization. The comprehensive performance of the system is improved, the operation cost is reduced, and the power supply reliability is enhanced.

Description

Technical Field

[0001] The present invention belongs to the technical fields of microgrids, renewable energy, energy system scheduling optimization, etc., and specifically relates to a method for scheduling and optimizing a microgrid renewable energy system. Background Art

[0002] With the continuous growth of the global demand for clean energy, photovoltaic-wind-battery energy storage hybrid energy systems have been widely used in the fields of distributed generation and microgrids because they can comprehensively utilize renewable energies such as solar energy and wind energy, and suppress power fluctuations through battery energy storage to improve power supply stability.

[0003] During the operation of traditional photovoltaic-wind-battery energy storage hybrid energy systems, there are many problems, and the modeling of photovoltaic and wind power generation modules is often not accurate enough. In actual situations, light intensity and wind speed are dynamically changing and have strong randomness and intermittency. However, many existing models only simply consider the influence of average light intensity and wind speed on power generation, ignoring their instantaneous changes and the influence of complex climate conditions on power generation efficiency. For example, on a cloudy day, the light intensity will change drastically in a short period of time, and existing photovoltaic models cannot accurately reflect the immediate impact of this change on power generation, resulting in a large deviation in the prediction of photovoltaic power generation during system planning and operation.

[0004] For battery energy storage systems, most existing charge and discharge strategies are relatively simple and do not fully consider battery life loss. During the charge and discharge process of the battery, factors such as charge and discharge rate and depth of discharge will have a significant impact on battery life. Unreasonable charge and discharge strategies may cause the battery to age prematurely and increase the operation and maintenance costs of the system. For example, frequent deep discharges will accelerate the loss of chemical substances inside the battery and shorten the battery life, but existing strategies often only focus on power balance and ignore the protection of battery life.

[0005] In terms of the optimization algorithms of the system, traditional optimization algorithms have problems with insufficient optimization ability when facing complex multi-objective optimization problems such as hybrid energy systems. The optimization of hybrid energy systems needs to consider multiple objectives such as power generation cost and power supply reliability at the same time, and its search space has high nonlinearity and complexity. Traditional single optimization algorithms, such as genetic algorithms, particle swarm optimization (PSO), etc., are prone to falling into local optimal solutions and cannot find the globally optimal system configuration and operation strategy. Moreover, these algorithms often use fixed values for parameter settings and cannot be adaptively adjusted according to the running process of the algorithm and the characteristics of the problem, resulting in slow convergence speed of the algorithm and poor optimization effect.

[0006] Therefore, the existing photovoltaic-wind-battery energy storage hybrid energy system has obvious deficiencies in aspects such as model accuracy, battery charge and discharge strategies, and optimization algorithms. There is an urgent need for an optimization method that can improve the comprehensive performance of the system, reduce operating costs, and enhance power supply reliability. Summary of the Invention

[0007] Aiming at the defects and deficiencies of the existing technology, the present invention proposes a scheduling optimization method for a microgrid renewable energy system. By accurately constructing models of photovoltaic, wind power generation, and battery energy storage modules, and comprehensively considering various influencing factors such as light intensity, wind speed, temperature, and battery electrochemical characteristics, the operation state of the hybrid energy system is accurately simulated. An optimization model is constructed with the lowest power generation cost and the highest power supply reliability as multiple objectives, and an improved algorithm is used to solve it, realizing the optimal configuration of the capacities of photovoltaic and wind power generation equipment and the optimization of the charge and discharge strategies of the battery energy storage system.

[0008] Among them, the accurate modeling method for each module of the proposed hybrid energy system includes a power calculation model for the photovoltaic module considering multiple factors, a power model for the wind energy module combining factors such as wind shear, and a model for the battery energy storage module considering comprehensive electrical characteristics and life loss.

[0009] An improvement strategy is proposed for the PSO-GWO algorithm, including specific implementation methods for adaptive parameter adjustment and optimized population initialization, achieving a balance between global search and local search and accelerating convergence.

[0010] And a multi-objective optimization model based on power generation cost and power supply reliability, and using an improved algorithm to solve this model to determine the system operation parameters, including the capacity configuration of power generation equipment and the battery charge and discharge strategy.

[0011] The technical solution specifically adopted by the present invention to solve its technical problems is:

[0012] A scheduling optimization method for a microgrid renewable energy system: On the basis of considering multiple influencing factors to construct models of photovoltaic power generation, wind power generation, and battery energy storage, an optimization model is constructed with the lowest power generation cost and the highest power supply reliability as multiple objectives, and an improved PSO-GWO algorithm is used to solve it, realizing the optimal configuration of the capacities of photovoltaic and wind power generation equipment and the optimization of the charge and discharge strategies of the battery energy storage system; the improvement of the improved PSO-GWO algorithm includes an adaptive parameter adjustment strategy and optimized population initialization.

[0013] Furthermore, the specific method for constructing the photovoltaic power generation, wind power generation and battery energy storage models considering multiple influencing factors is as follows: For the modeling of photovoltaic power generation, the influence of temperature and dust accumulation on the conversion efficiency of photovoltaic panels is incorporated; for the modeling of wind power generation, the influence of the turbulent characteristics of wind speed, wind direction change and wind shear on the output power of wind turbines is incorporated; for the modeling of battery energy storage, based on the electrochemical characteristics of the battery, the influence reflecting the internal chemical reaction, temperature change and capacity attenuation during the charge and discharge process of the battery is incorporated.

[0014] Furthermore, the photovoltaic power generation model is specifically as follows:

[0015] Photovoltaic output power: P final-pv = P total-pv ·(1-β)

[0016] where P total-pv = N p ·P pv , Np is the number of parallel-connected batteries in the photovoltaic array, P pv = P max ·[1+α(T-T ref )]·G ref G, P max is the maximum power under standard test conditions, α is the power temperature coefficient, T ref and G ref are the standard test temperature and light intensity respectively, G is the light intensity, T is the battery temperature, β is the dust attenuation coefficient, and is related to the time t and the environmental dust concentration C;

[0017] The wind power generation model is specifically as follows:

[0018] Calculate the captured power of the wind turbine according to the wind speed v, air density ρ, wind turbine swept area A and the power coefficient Cp of the wind turbine

[0019] The power coefficient Cp is a function of the tip speed ratio λ and the pitch angle θ, and a look-up table of Cp(λ,θ) is established through experimental data;

[0020] The tip speed ratio λ = vωR, where ω is the rotational angular velocity of the wind turbine and R is the radius of the wind turbine;

[0021] The wind speed v(h) at different heights h is calculated by the power law formula , where v(h0) is the wind speed at the reference height h0 and αs is the wind shear exponent;

[0022] The battery energy storage model includes a battery life loss model. The battery life loss L is related to the depth of discharge DOD and the charge and discharge rate C-rate, and is calculated by the empirical formula L = g(DOD, C-rate) determined through experiments.

[0023] Furthermore, the improved PSO-GWO algorithm dynamically changes the inertia weight of PSO and the convergence factor of GWO according to the iterative process of the algorithm; at the initial stage of the algorithm, by increasing the inertia weight of PSO, the particles can explore in a broader search space; by adjusting the convergence factor of GWO, the gray wolf population can search more dispersedly; as the number of iterations increases, the inertia weight of PSO is gradually decreased to enhance the local search ability of the particles; at the same time, the convergence factor of GWO is adjusted to make the gray wolf population gradually converge to the optimal solution.

[0024] Furthermore, in the PSO part of the improved PSO-GWO algorithm, the inertia weight w adopts a linear decreasing strategy:

[0025] where w max and w min are the maximum and minimum values of the inertia weight respectively, T max is the maximum number of iterations, t is the current number of iterations; in the GWO part, the convergence factor where t is the current number of iterations, T max is the maximum number of iterations, k is a parameter controlling the decreasing speed, and the convergence factor a can finally be decreased to 0.

[0026] Furthermore, the population initialization method of the improved PSO-GWO algorithm is as follows: collect the historical operation data of the system, including at least the light intensity, wind speed, and load demand under different seasons and weather conditions; preprocess the data and extract key features; use the clustering analysis method to divide the historical data into several typical working conditions; randomly select a certain number of data points from each typical working condition, and combine with expert experience to generate the initial population.

[0027] Furthermore, in the multi-objective optimization model, the multi-objective function F = {f1, f2}, where f1 is the power generation cost function, where C pv 、C wind 、C batt are the unit capacity construction costs of the photovoltaic, wind power generation equipment, and battery energy storage system respectively, P rated-pv 、P rated-wind 、Q batt are the rated powers of the photovoltaic and wind power generation equipment and the capacity of the battery energy storage system respectively, C op-pv 、C op-wind are the unit operation and maintenance costs of the photovoltaic and wind power generation equipment, C ch-batt 、C dis-batt are the charge and discharge costs of the battery energy storage system, P ch-batt 、P dis-battis the charging and discharging power of the battery; f2 is the power supply reliability function, where U unserved (t) is the amount of power not supplied at time t, and P load (t) is the load demand at time t; solved by the improved PSO-GWO algorithm, through iterative search, the capacity configuration of the photovoltaic and wind power generation equipment and the charging and discharging strategy of the battery energy storage system that minimize the power generation cost and maximize the power supply reliability are found.

[0029] And, a microgrid renewable energy system, including: a photovoltaic power generation module, a wind power generation module and a battery energy storage module, characterized in that the above method is used for scheduling optimization.

[0030] An electronic device, including a memory, a processor and a computer program stored on the memory and executable on the processor, the steps of the above method are implemented when the processor executes the program.

[0031] A non-transitory computer-readable storage medium, on which a computer program is stored, and the steps of the above method are implemented when the computer program is executed by the processor.

[0032] Compared with the prior art, the photovoltaic-wind-battery energy storage hybrid energy system optimization method based on the PSO-GWO algorithm of the present invention and its preferred solutions bring many significant beneficial effects:

[0033] Improve the comprehensive performance of the system: By accurately constructing the models of each module of the hybrid energy system, the operating state of the system under different environmental conditions can be more accurately simulated and predicted. Taking the photovoltaic module as an example, fully considering the influence of multiple factors such as light intensity, temperature, and dust accumulation, the prediction accuracy of the photovoltaic power generation is greatly improved. This helps the system better match the power generation and load demand, reduce energy waste or power supply shortage caused by power prediction deviation, and thus comprehensively improve the comprehensive performance of the hybrid energy system. Compared with the traditional simple model, the prediction error of the power generation of the model of the present invention can be reduced to within [3-5]%, effectively ensuring the stable operation of the system.

[0034] Reducing operating costs: The reduction of operating costs is achieved from multiple perspectives. On the one hand, in terms of the capacity configuration of power generation equipment, by using the improved PSO-GWO algorithm to solve the multi-objective optimization problem, the optimal capacities of photovoltaic and wind power generation equipment can be determined, avoiding cost increases caused by over-configuration or under-configuration. For example, accurate capacity configuration can reduce the construction cost of photovoltaic and wind power generation equipment by

[10] % and the life cycle cost by more than

[20] % on the premise of meeting the power supply demand. On the other hand, for the battery energy storage system, an optimization strategy that comprehensively considers its charge and discharge costs and life loss extends the battery life, reduces the battery replacement frequency, and thus reduces the operation and maintenance costs. It is estimated that by adopting the charge and discharge strategy of the present invention, the battery life can be extended by [15 - 25]%, the corresponding operation and maintenance costs can be reduced by more than

[25] %, and the corresponding energy costs can be reduced by more than

[20] %. In addition, the optimized system can make more effective use of renewable energy, reduce the dependence on traditional energy, and further reduce the energy procurement cost.

[0035] Enhancing power supply reliability: The present invention takes power supply reliability as one of the important optimization objectives. Through multi-objective optimization, it ensures that the system can supply power stably and reliably under various complex working conditions. When facing drastic changes in light intensity and wind speed, the system can respond quickly. By reasonably adjusting the charge and discharge strategy of the battery energy storage system, it effectively suppresses power fluctuations and guarantees the stable power supply of the load. Compared with the traditional system, after adopting the method of the present invention, the power supply reliability is improved to more than

[99] %, providing a more stable and reliable power supply for users. For some application scenarios with high requirements for power reliability, such as hospitals and data centers, it has important practical significance.

[0036] Improving the algorithm optimization efficiency: The improvement of the PSO-GWO algorithm, including adaptive parameter adjustment and optimized population initialization method, significantly enhances the optimization ability of the algorithm in the complex search space of the hybrid energy system. Adaptive parameter adjustment enables the algorithm to achieve a good balance between global search and local search, and optimized population initialization accelerates the convergence speed of the algorithm. Compared with the traditional PSO-GWO algorithm, the improved algorithm of the present invention has a convergence speed increased by more than

[25] % and an optimization accuracy improved by more than

[15] %. It can find a better system operation plan in a shorter time, improve the optimization efficiency of the system, and save a large amount of computing time and resources for practical engineering applications. Description of the Drawings

[0037] The present invention will be further described in detail below in conjunction with the drawings and specific embodiments:

[0038] Figure 1 It is a schematic diagram of the hybrid energy system structure;

[0039] Figure 2It is a relational graph of factors affecting the power of a photovoltaic module;

[0040] Figure 3 It is a flow chart of the improved PSO-GWO algorithm according to an embodiment of the present invention;

[0041] Figure 4 It is a flow chart for optimizing the hybrid energy management system according to an embodiment of the present invention;

[0042] Figure 5 It is the project effect diagram optimized according to the solution of the embodiment of the present invention, corresponding to the power generated by the wind turbine generator every day. Detailed implementation manners

[0043] To make the features and advantages of this patent more obvious and understandable, specific embodiments are given below for detailed description as follows:

[0044] It should be noted that the following detailed descriptions are all illustrative and are intended to provide further explanations for the present application. Unless otherwise specified, all technical and scientific terms used in this specification have the same meaning as commonly understood by those of ordinary skill in the technical field to which this application belongs.

[0045] It should be noted that the terms used here are only for describing specific implementation manners and are not intended to limit the exemplary implementation manners according to the present application. As used here, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0046] As Figure 1 shown, the hybrid energy system includes the connection relationships among a photovoltaic array, a wind turbine generator, a battery energy storage system, and a load.

[0047] The photovoltaic array is connected to the DC bus through a DC / DC boost converter and then connected to the AC bus through an inverter. The wind turbine generator is connected to the DC bus through an AC / DC converter and then connected to the AC bus through an inverter. The battery energy storage system is connected to the DC bus through a DC / DC boost converter and then connected to the AC bus through a bidirectional DC-AC inverter. The load is connected to the AC bus.

[0048] As Figure 2 shown in the relational graph of factors affecting the power of the photovoltaic module, the abscissa is the light intensity, and the ordinate is the photovoltaic output power. Different curves represent the relationship between power and light intensity under different temperatures or dust accumulation levels (temperature 0 - 70 °C and irradiance 1000 W / m 2 ), which can intuitively present the influence of various factors on the photovoltaic power.

[0049] To address the shortcomings of the existing technology, such as insufficient model accuracy, suboptimal battery charge and discharge strategies, and weak optimization algorithm optimization capabilities, the present invention has made the following improvements:

[0050] Construct a more accurate hybrid energy system model: To more accurately reflect the characteristics of photovoltaic, wind power generation, and battery energy storage modules during actual operation, the present invention has deeply modeled each module. For the photovoltaic power generation module, not only the direct impact of light intensity on the power generation is considered, but also the effects of factors such as temperature and dust accumulation on the conversion efficiency of the photovoltaic panel are incorporated. By establishing a more complete mathematical model, the photovoltaic power generation under different environmental conditions can be calculated in real time and accurately. For example, according to the climate characteristics and environmental conditions of different regions, the model parameters are locally calibrated to make the model more in line with the actual situation. For the wind power generation module, the effects of factors such as the turbulence characteristics of wind speed, wind direction changes, and wind shear on the output power of wind turbines are fully considered. Advanced wind resource assessment methods and fluid mechanics principles are used to construct a high-precision wind power generation model to achieve accurate prediction of wind power generation. In terms of modeling the battery energy storage module, by deeply studying the electrochemical characteristics of the battery, a detailed model that can reflect the internal chemical reactions, temperature changes, and capacity attenuation during the charge and discharge process of the battery is established. Through this accurate modeling, the charge and discharge capabilities and life losses of the battery can be more accurately evaluated, providing a solid foundation for formulating reasonable charge and discharge strategies in the future.

[0051] Improve the PSO-GWO algorithm:

[0052] Adaptive parameter adjustment strategy: The traditional PSO-GWO algorithm lacks flexibility in parameter settings and is difficult to perform optimally in complex hybrid energy system optimization problems. The present invention proposes an adaptive parameter adjustment strategy that dynamically changes the inertia weight of PSO and the convergence factor of GWO according to the iteration process of the algorithm. At the initial stage of the algorithm, to expand the search range and improve the global search ability, the inertia weight of PSO is increased, enabling the particles to explore in a broader search space; at the same time, the convergence factor of GWO is appropriately adjusted to make the gray wolf population search more dispersedly. As the number of iterations increases, the inertia weight of PSO is gradually decreased to enhance the local search ability of the particles, enabling them to search more finely in the region near the optimal solution; at the same time, the convergence factor of GWO is adjusted to make the gray wolf population gradually converge to the optimal solution. Through this adaptive adjustment, the algorithm can achieve a balance between global search and local search, effectively enhancing the optimization ability in the complex search space of the hybrid energy system.

[0053] Optimized population initialization method: The quality of the initial solution population has an important impact on the convergence speed and optimization effect of the algorithm. Based on the system historical data and operation experience, the present invention proposes an optimized population initialization method. Through in-depth analysis of the historical data, characteristic parameters closely related to the operation state of the hybrid energy system are extracted, such as the light intensity, wind speed distribution law, and load demand pattern in different seasons and different time periods. Using these characteristic parameters and combining with expert experience, a more representative initial solution population is generated. The population initialized in this way can approach the optimal solution region more quickly, accelerate the convergence process of the algorithm, reduce the number of algorithm iterations, and improve the optimization efficiency.

[0054] Multi-objective optimization of system operation: With the lowest power generation cost and the highest power supply reliability of the system as multi-objectives, an improved PSO-GWO algorithm is used to solve. Traditional optimization methods often only focus on a single objective, such as only considering the power generation cost or only paying attention to the power supply reliability, resulting in poor performance of the system in other aspects. The present invention constructs a comprehensive multi-objective optimization model, comprehensively considering the mutual relationship and trade-off between multiple objectives. In terms of power generation cost, not only the construction cost, operation and maintenance cost of photovoltaic and wind power generation equipment, and the charge and discharge cost of the battery energy storage system are considered, but also the additional standby power cost caused by energy fluctuations is considered. In terms of power supply reliability, reliability indexes, such as power outage time, power outage frequency, etc., are introduced and incorporated into the optimization objective function. By using the improved PSO-GWO algorithm to solve the multi-objective function, the optimal capacity configuration of photovoltaic and wind power generation equipment and the charge and discharge strategy of the battery energy storage system can be optimized simultaneously, realizing the efficient coordinated operation of the hybrid energy system and meeting the dual requirements of users for economy and reliability.

[0055] The following specifically introduces the main improvements in each part of the present invention:

[0056] 1. Construction of the hybrid energy system model:

[0057] Modeling of the photovoltaic module: First, establish the basic physical model of the photovoltaic cell. Based on the principle of the photovoltaic effect, its output power P pv is related to the light intensity G and the cell temperature T. Through fitting experimental data, the empirical formula P pv =P max ·[1 + α(T - T ref )]·G ref G is obtained, where P max is the maximum power under standard test conditions, α is the power temperature coefficient, and T ref and G ref are the standard test temperature and light intensity respectively. At the same time, considering the series and parallel characteristics of the photovoltaic array, if there are Ns cells in series and Np cells in parallel, then the total output power P total-pv =Np ·P pv In addition, to simulate the impact of dust accumulation on the efficiency of photovoltaic panels, a dust attenuation coefficient β is introduced. β is related to time t and ambient dust concentration C, and its functional relationship β = f(t, C) is determined through experiments. Finally, the corrected photovoltaic output power P final-pv = P total-pv ·(1 - β).

[0058] Modeling of the wind energy module: According to the Betz theory, the captured power P of a wind turbine wind is related to the wind speed v, air density ρ, rotor swept area A, and power coefficient Cp of the wind turbine, that is The power coefficient Cp is a function of the tip speed ratio λ and pitch angle θ. A lookup table of Cp(λ, θ) is established through experimental data. The tip speed ratio λ = vωR, where ω is the angular velocity of the rotor rotation and R is the radius of the rotor. At the same time, considering the influence of wind shear, the wind speed v(h) at different heights h can be calculated by the power law formula where v(h0) is the wind speed at the reference height h0, and αs is the wind shear exponent.

[0059] Modeling of the battery energy storage module: An equivalent circuit model is used to describe the electrical characteristics of the battery, mainly including parameters such as open circuit voltage U oc , internal resistance R, state of charge SOC, etc. The update equation of SOC is (during charging) and (during discharging), where SOC0 is the initial state of charge, η c and η d are the charging and discharging efficiencies respectively, I is the charging and discharging current, and Q n is the rated capacity of the battery. At the same time, a battery life loss model is established. The battery life loss L is related to the depth of discharge DOD and charge-discharge rate C-rate, and the empirical formula L = g(DOD, C-rate) is determined through experiments.

[0060] 2. Improvement of the PSO-GWO algorithm:

[0061] Adaptive parameter adjustment: In the PSO part, the inertia weight w adopts a linear decreasing strategy:

[0062] where w max and w min are the maximum and minimum values of the inertia weight respectively, T max is the maximum number of iterations, and t is the current number of iterations. In the GWO part, the convergence factor where t is the current number of iterations, T maxis the maximum number of iterations, k is a parameter controlling the decreasing speed, and the convergence factor a can eventually decrease to 0. This adaptive adjustment enables the algorithm to have a strong global search ability in the initial stage of iteration and a fine local search ability in the later stage.

[0063] Population initialization: Collect the historical operation data of the system, including data such as light intensity, wind speed, and load demand under different seasons and weather conditions. Preprocess the data to extract key features, such as the daily change pattern of light intensity and the monthly average distribution of wind speed. Use the clustering analysis method to divide the historical data into several typical working conditions. Randomly select a certain number of data points from each typical working condition, and combine the experience of experts on the reasonable configuration of the hybrid energy system to generate the initial population. For example, for the working condition with high light intensity and low wind speed, the individuals in the initial population have a relatively large photovoltaic capacity configuration and a relatively small wind energy capacity configuration.

[0064] Such as Figure 3 As shown, it is the flow chart of the improved PSO-GWO algorithm in the embodiment of the present invention; it details a series of steps of the algorithm from initializing the population to calculating the fitness, updating the particle and gray wolf positions, and judging the convergence condition: After initialization, first judge the behavior strategy of the exploring wolf wandering. If not, then execute the behavior strategy of the wolves besieging, including updating the position of the wolf king and updating the wolf pack according to the prey distribution rule, and then select the PSO method to optimize and calculate the fitness function and update the global and local optimal solutions; if it is the behavior strategy of the exploring wolf wandering, then judge the prey odor concentration Ylead or the number of wandering times. If so, then execute the behavior strategy of the wolf king summoning a raid. Then, judge the prey odor concentration Ylead again. If so, then continue to judge whether the distance dis between the wolf king and the fierce wolf is greater than dnear. If not, then output the best solution. If so, then continue to select the PSO method to optimize and calculate the fitness function and update the global and local optimal solutions. When the maximum number of iterations is reached, output the best solution.

[0065] System operation optimization solution:

[0066] Objective function construction: Define the multi-objective function F = {f1, f2}, where f1 is the power generation cost function, are the unit capacity construction costs of the photovoltaic, wind power generation equipment, and battery energy storage system respectively, P rated-pv 、P rated-wind 、Q batt are the rated powers of the photovoltaic and wind power generation equipment and the capacity of the battery energy storage system respectively, C op-pv 、C op-wind are the unit operation and maintenance costs of the photovoltaic and wind power generation equipment, C ch-batt 、C dis-batt are the charge and discharge costs of the battery energy storage system, Pch-batt , P dis-batt is the charging and discharging power of the battery. f2 is the power supply reliability function, where U unserved (t) is the amount of power not supplied at time t, and P load (t) is the load demand at time t.

[0068] Algorithm solution: Apply the improved PSO-GWO algorithm to solve the above multi-objective function. First, initialize the population, and then calculate the fitness value (i.e., the objective function value) of each individual. In each iteration, update the positions of the particles (PSO part) and gray wolves (GWO part) according to the adaptively adjusted parameters. Through continuous iterative search, find the capacity configuration of the photovoltaic and wind power generation equipment and the charging and discharging strategy of the battery energy storage system that minimize the power generation cost and maximize the power supply reliability. For example, when the algorithm converges to an optimal solution, determine the rated power of the photovoltaic as the rated power of the wind energy as the capacity of the battery as and the charging and discharging power at each moment and

[0069] such as Figure 4 shown, a preferred hybrid energy management system optimization process finally obtained includes the following steps:

[0070] First, read the input data, including solar radiation, ambient temperature, wind speed, load demand, and the economic and technical specifications of the components.

[0071] Select the hybrid energy system components according to the read data.

[0072] Develop different system architectures.

[0073] In the developed system architectures, select the parameters of the particle swarm and gray wolf PSO-GWO hybrid optimization algorithm.

[0074] Check the feasible individual system settings.

[0075] For t = 1:8760, perform the following operations:

[0076] Evaluate the output of each renewable energy source and the load requirements.

[0077] Perform calculations:

[0078] Judge whether the power demand is met.

[0079] Determine the excess energy.

[0080] If the power demand is met and the excess energy is determined, the remaining energy from the renewable energy sources is stored during the battery charging process.

[0081] If the power demand is not met, the remaining power demand not covered by the energy is determined, and when the renewable energy is insufficient, the battery pack supplements the insufficient load.

[0082] Predict and optimize the matching between renewable energy output and load demand.

[0083] Judge whether the iteration number is reached:

[0084] If not, return to steps such as continuing to evaluate the output and load requirements of each renewable energy source.

[0085] If reached, determine the optimal capacity size of the RE system.

[0086] Finally, calculate the results, including the life cycle cost, energy cost, and performance of each energy source.

[0087] Its core process includes:

[0088] 1. Data acquisition: Obtain real-time photovoltaic and wind power generation data and the status of the energy storage system.

[0089] 2. Demand prediction: Predict energy demand based on historical data and real-time information.

[0090] 3. Optimal scheduling: Adopt the hybrid optimization algorithm of particle swarm and grey wolf (GWO-PSO) to optimize energy allocation and energy storage charge and discharge strategies.

[0091] 4. Execution control: Apply the optimization results to the system and adjust the operation of power generation and energy storage equipment.

[0092] 5. Feedback adjustment: Dynamically adjust the optimization strategy according to the actual operation effect to ensure the efficient and stable operation of the system. This process realizes the efficient utilization of energy and system optimization through intelligent algorithms.

[0093] As shown in Table 1, for the system power generation cost, annual operation cost, and power supply reliability optimization effect provided in the embodiments of the present invention, it can be seen that with the algorithm iteration, the power generation cost and annual operation cost of the solution of the present invention show a trend of decreasing, and the power supply reliability shows a trend of increasing.

[0094] Table 1 Optimization comparison table of system technical parameters 1. Life cycle cost (LCC, ten thousand yuan)

[0095]

[0096] 2. Power supply reliability

[0097]

[0098] 3. Reduction of energy cost

[0099]

[0100]

[0101] As Figure 5 shown, the following figure presents the project effect after optimizing the above solution according to the embodiments of the present invention, corresponding to the daily power generated by the wind turbine generator.

[0102] Figure 5 In the graph of the daily power generated by the wind turbine generator shown in (a): the abscissa is time (seconds), the ordinate is the output power of the wind turbine generator, and the power of the wind turbine is proportional to the cube of the wind speed, following the Betz limit theory. When the wind speed increases from 8 m / s to 16 m / s, the theoretical power increases by 8 times. In actual operation, the unit only generates electricity in the wind speed range of 4 - 25 m / s. The wind speed stability directly affects the power generation efficiency: the unit of this project uses lidar to predict the wind speed trend and combines the model predictive control algorithm to optimize the power curve, effectively alleviating the impact of wind speed fluctuations on the power grid. Figure 5 The analysis of the expanded output power of the wind turbine from 5280 to 5380 seconds in (b) shows that the output fluctuates between 6.02 - 47 kW, and the peak corresponds to a wind speed of 16 m / s, which conforms to the dynamic response of the pitch control system. Due to the sudden drop in wind speed to 4.95 m / s, the tip speed ratio suddenly changes, and the Cp value drops from 0.48 to 0.41, and the power suddenly drops to 6.02 kW at 5364 seconds. The overall stability meets the IEC61400 - 24 standard, and the direct - drive permanent - magnet technology effectively controls mechanical losses.

[0103] Based on the same inventive concept, the present invention also provides a computer device, which includes: one or more processors, and a memory for storing one or more computer programs; the program includes program instructions, and the processor is used to execute the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or may also be other general - purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field - Programmable Gate Arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is used to implement one or more instructions, specifically used to load and execute one or more instructions in the computer storage medium to implement the above - mentioned method.

[0104] It should be further noted that, based on the same inventive concept, the present invention also provides a computer storage medium, on which a computer program is stored. When the computer program is run by a processor, it executes the above-mentioned method. The storage medium can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electrical, magnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (a non-exhaustive list) of the computer-readable storage medium include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in combination with an instruction execution system, apparatus, or device.

[0105] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those with ordinary skills in the field to which the present invention belongs. The "first", "second", and similar terms used in the present invention do not indicate any order, quantity, or importance, but are only used to distinguish different components. Words such as "including" or "comprising" mean that the elements or objects appearing before this word cover the elements or objects listed after this word and their equivalents, without excluding other elements or objects. Words such as "connected" or "coupled" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Upper", "lower", "left", "right", etc. are only used to represent relative position relationships. When the absolute position of the object being described changes, the relative position relationship may also change accordingly.

[0106] As described above, these are only the preferred embodiments of the present invention, and the present invention is not limited to other forms. Any person skilled in the art may use the disclosed technical content to make changes or modifications into equivalent embodiments with equivalent changes. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

[0107] This patent is not limited to the above-mentioned optimal implementation mode. Anyone inspired by this patent can come up with various other forms of ship tracking control methods with predefined time-regulated performance. All equivalent changes and modifications made according to the scope of the patent application of this invention shall fall within the scope covered by this patent.

Claims

1. A scheduling optimization method for a microgrid renewable energy system, characterized in that: Based on the construction of photovoltaic power generation, wind power generation and battery energy storage models considering multiple influencing factors, an optimization model is constructed with the lowest power generation cost and the highest power supply reliability as multiple objectives, and an improved PSO-GWO algorithm is used to solve it, so as to realize the optimal configuration of the capacities of photovoltaic and wind power generation equipment and the optimization of the charge and discharge strategies of the battery energy storage system; the improvement of the improved PSO-GWO algorithm includes an adaptive parameter adjustment strategy and an optimized population initialization.

2. The scheduling optimization method for a microgrid renewable energy system according to claim 1, wherein: The construction of the photovoltaic power generation, wind power generation and battery energy storage models considering multiple influencing factors is specifically as follows: for the modeling of photovoltaic power generation, the influence of temperature and dust accumulation on the conversion efficiency of photovoltaic panels is incorporated; for the modeling of wind power generation, the influence of the turbulent characteristics of wind speed, wind direction change and wind shear on the output power of wind turbines is incorporated; for the modeling of battery energy storage, based on the electrochemical characteristics of the battery, the influence reflecting the internal chemical reactions, temperature changes and capacity attenuation during the charge and discharge process of the battery is incorporated.

3. A method for scheduling and optimizing a microgrid renewable energy system according to claim 2, characterized in that: The photovoltaic power generation model is specifically: Photovoltaic output power: P final-pv = P total-pv ·(1 - β) Among them, P total-pv = N p ·P pv , where Np is the number of cells connected in parallel in the photovoltaic array, and P pv = P max ·[1 + α(T - T ref )]·G ref G, P max is the maximum power under standard test conditions, α is the power temperature coefficient, T ref and G ref are the standard test temperature and light intensity respectively, G is the light intensity, T is the cell temperature, β is the dust attenuation coefficient, and is related to the time t and the environmental dust concentration C; The wind power generation model is specifically: Calculate the captured power of a wind turbine based on the wind speed v, air density ρ, the swept area A of the wind turbine rotor, and the power coefficient Cp of the wind turbine The power coefficient Cp is a function of the tip speed ratio λ and the pitch angle θ, and a look-up table of Cp(λ,θ) is established through experimental data; The tip speed ratio λ = vωR, where ω is the rotational angular velocity of the wind turbine and R is the radius of the wind turbine; The wind speed v(h) at different heights h is calculated by the power-law formula where v(h0) is the wind speed at the reference height h0 and αs is the wind shear exponent; The battery energy storage model includes a battery life loss model. The battery life loss L is related to the depth of discharge DOD and the charge and discharge rate C-rate, and is calculated by the empirical formula L = g(DOD, C-rate) determined through experiments.

4. A method for optimizing the scheduling of a microgrid renewable energy system according to claim 1, characterized in that: The improved PSO-GWO algorithm dynamically changes the inertia weight of PSO and the convergence factor of GWO according to the iterative process of the algorithm; in the initial stage of the algorithm, by increasing the inertia weight of PSO, the particles can explore in a wider search space; by adjusting the convergence factor of GWO, the gray wolf population can search more dispersedly; as the number of iterations increases, the inertia weight of PSO is gradually reduced to enhance the local search ability of the particles; at the same time, the convergence factor of GWO is adjusted to make the gray wolf population gradually converge to the optimal solution.

5. A method for scheduling and optimizing a microgrid renewable energy system according to claim 4, characterized in that: In the PSO part of the improved PSO-GWO algorithm, the inertia weight w adopts a linear decreasing strategy: where w max and w min are the maximum and minimum values of the inertia weight respectively, T max is the maximum number of iterations, and t is the current iteration number; In the GWO part, the convergence factor where t is the current iteration number, T max is the maximum iteration number, and k is a parameter that controls the decreasing speed.

6. The scheduling optimization method for a microgrid renewable energy system according to claim 1, characterized in that: The population initialization method of the improved PSO-GWO algorithm is: collect the historical operation data of the system, including at least the light intensity, wind speed and load demand under different seasons and different weather conditions; preprocess the data and extract key features; Use the clustering analysis method to divide the historical data into several typical working conditions; randomly select a certain number of data points from each typical working condition, and combine with expert experience to generate the initial population.

7. A scheduling optimization method for a microgrid renewable energy system according to claim 1, characterized in that: In the multi-objective optimization model, the multi-objective function F = {f1, f2}, where f1 is the power generation cost function, where C pv , C wind , C batt are the unit capacity construction costs of the photovoltaic, wind power generation equipment and the battery energy storage system respectively, P rated-pv , P rated-wind , Q batt are the rated powers of the photovoltaic, wind power generation equipment and the capacity of the battery energy storage system respectively, C op-pv , C op-wind are the unit operation and maintenance costs of the photovoltaic, wind power generation equipment, C ch-batt , C dis-batt are the charge and discharge costs of the battery energy storage system, P ch-batt , P dis-batt are the charge and discharge powers of the battery; f2 is the power supply reliability function, where U unserved (t) is the un-supplied power at time t, P load (t) is the load demand at time t; Solved by the improved PSO-GWO algorithm, through iterative search, find the capacity configuration of the photovoltaic and wind power generation equipment and the charge and discharge strategy of the battery energy storage system that minimize the power generation cost and maximize the power supply reliability.

8. A microgrid renewable energy system, comprising: A photovoltaic power generation module, a wind power generation module and a battery energy storage module, characterized in that the scheduling and optimization are carried out by using the method according to any one of claims 1-7.

9. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, the steps of the method according to any one of claims 1-7 are realized.

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

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