A multi-energy microgrid optimal scheduling method considering power grid voltage support

Abstract

The application discloses a kind of multi-energy microgrid optimization scheduling method considering power grid voltage support, belong to power generation system optimization scheduling technical field, including: S10: initialization multi-energy microgrid basic model, analysis microgrid system operation data characteristics;S20: according to typical daily load data and typical daily light data, predict load curve and the output power of photovoltaic generator unit;S30: for the voltage fluctuation of multi-energy microgrid grid-connected point, introduce voltage fluctuation penalty factor, the interactive power of microgrid-grid is constrained management;S40: establish double optimization objective function for multi-energy microgrid optimization scheduling model;S50: the objective function is solved using grey wolf algorithm, determine the dispatching target of photovoltaic generator unit, battery, micro gas turbine and public grid interactive power;S60: output scheduling result;The application can significantly improve energy utilization efficiency, reduce system operation cost, improve grid-connected voltage quality, and guarantee the stability and adaptability of system in dynamic environment.

Description

Technical Field

[0001] This invention relates to the field of power generation system optimization scheduling technology, and more specifically, to a multi-energy microgrid optimization scheduling method that takes into account grid voltage support. Background Technology

[0002] Over the past few decades, the rapid growth of global electricity demand has far exceeded the carrying capacity of traditional power supply systems, leading to an increasingly prominent contradiction between power supply and demand. In response, microgrids, as an emerging power distribution and management system, have gradually attracted widespread attention due to their flexibility, reliability, and ability to integrate renewable energy. By combining distributed energy sources (such as solar, wind, and energy storage systems) with local loads, microgrids can achieve more efficient energy management, meet local needs, and improve energy utilization efficiency. Furthermore, microgrids possess self-sufficiency capabilities in emergency situations, increasing the resilience of the power system. With the transformation of the global energy structure, the use of clean energy sources such as natural gas is gradually being combined with electricity, forming a new form of multi-energy microgrids. Multi-energy microgrids can not only handle multiple energy carriers such as electricity, natural gas, and heat, but also have the ability to coordinate and control various energy sources, thus demonstrating superior integration and dispatch capabilities in the face of increasingly complex energy management challenges. This advantage of multi-energy microgrids not only improves the overall efficiency of the energy system but also provides strong support for the optimization of the energy structure and the higher proportion of renewable energy applications.

[0003] However, with the rapid development of multi-energy microgrids, the voltage control problem of the power grid has become increasingly prominent. Voltage, as one of the core parameters in an energy system, directly affects the safety and economy of equipment operation. Microgrids aim to meet load demands through efficient resource utilization, but as the number of grid connection points in multi-energy microgrids continues to increase, voltage fluctuations and stability issues are also intensifying. Under different load and generation conditions, the voltage levels at these grid connection points may fluctuate significantly, potentially leading to voltage violations and threatening the stability of the public power grid. Traditional energy management optimization methods often focus on maximizing economic benefits while neglecting voltage fluctuations at grid connection points, making it difficult for microgrids to maintain good voltage stability during grid connection. In this situation, microgrids not only affect their own operational safety but may also negatively impact the public power grid, leading to greater financial and technological risks. Therefore, innovative dispatching methods are urgently needed to balance economic efficiency with voltage fluctuation control to ensure the safety and stability of the power grid. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, a multi-energy microgrid optimization scheduling method that considers grid voltage support can significantly improve energy utilization efficiency, reduce system operating costs, improve grid voltage quality, and ensure system stability and adaptability in dynamic environments.

[0005] The technical solution adopted by the invention to solve its technical problem is: a multi-energy microgrid optimal scheduling method considering grid voltage support, the improvement of which includes:

[0006] S10: Initialize the basic model of the multi-energy microgrid and analyze the operational data characteristics of the microgrid system, including electricity price, power generation price, load characteristics, photovoltaic power generation characteristics, and equipment operation constraints;

[0007] S20: Based on typical daily load data and typical solar irradiance data, predict the load curve and the output power of the photovoltaic generator set;

[0008] S30: To address the voltage fluctuation problem at the grid connection point of multi-energy microgrids, a voltage fluctuation penalty factor is introduced to constrain and manage the power interaction between the microgrid and the grid.

[0009] S40: Combining the operational constraints of the power generation units within the microgrid, a dual optimization objective function with operational economy and voltage quality as the goals is established for the multi-energy microgrid optimal scheduling model;

[0010] S50: The objective function is solved using the Grey Wolf algorithm to determine the scheduling target of the interactive power of photovoltaic generator sets, batteries, micro gas turbines and public power grid;

[0011] S60: Outputs scheduling results to achieve optimized allocation of power from photovoltaic power generation, battery charging and discharging, micro gas turbine power generation, and public grid interaction.

[0012] Furthermore, in step S20, the photovoltaic generator set operates in maximum power point tracking mode, and the formula for calculating the output power of the photovoltaic generator set is:

[0013] P PV =f PV P RPV I g [1+0.005(T cell -T STC )] / I STC ;

[0014] T cell =T a +0.0138(1+0.031T a (1-0.042v)I g ;

[0015] Among them, PPV f represents the output power of the photovoltaic panel. PV P is the derating factor, which describes the power reduction of photovoltaic panels caused by external factors such as aging and losses. RPV For the rated installation capacity of photovoltaic panels, I g This represents the actual light intensity, and its unit is generally W / m². 2 I STC T represents the light intensity under standard test conditions. STC For standard test temperature, T cell The temperature is the surface temperature of the photovoltaic panel.

[0016] Furthermore, the voltage fluctuation penalty factor is used to measure the impact of grid connection point interactive power on voltage fluctuation, and its expression is as follows:

[0017]

[0018] Among them, V S The voltage near the public power grid terminal, V R R is the voltage on the microgrid side, R+jX is the line impedance, and P is the voltage on the microgrid side. R Net active power absorbed by the bus and Q R The reactive power absorbed by the busbar.

[0019] Furthermore, in step S40, the expression for the dual optimization objective function is:

[0020]

[0021] Among them, C grid (k) represents the grid purchase cost at time k, C MT (k) represents the gas turbine operating cost at time k, P MT (k) represents the electrical power generated by the micro gas turbine at time k, P PV (k) represents the electrical power generated by the photovoltaic generator at time k, P grid (k) represents the interaction power with the grid at time k, when P grid When (k) is positive, it indicates that the power grid is supplying electricity to the multi-energy microgrid. grid When (k) is negative, it indicates that the multi-energy microgrid is selling electricity to the grid.

[0022] Furthermore, in step S50, the specific steps are as follows:

[0023] S501: Select a set of output data and load data of each device from the historical operation data of the multi-energy microgrid as candidate solutions to form a gray wolf population. Set the population size and the initial position of each gray wolf individual. These positions represent different scheduling schemes.

[0024] S502: Calculate the fitness value of each individual in the gray wolf population based on the dual optimization objective function;

[0025] S503: Select the top three gray wolves with the highest fitness values ​​as leaders. They represent the current optimal scheduling scheme. The other gray wolves are followers, and their positions will be updated based on their distance from and direction from the leaders.

[0026] S504: Followers update their positions by performing non-linear transformations based on their distance from the leader, direction, and time factors.

[0027] S505: Repeat steps S502-S504, iteratively updating the position and fitness of the gray wolf population until the termination condition is met. The final converged solution is the power scheduling target of each device in the multi-energy microgrid, i.e., the optimal scheduling scheme.

[0028] Reference Figures 3-4 The present invention also proposes a multi-energy microgrid optimized scheduling system considering grid voltage support, characterized in that the system includes:

[0029] A multi-energy microgrid, which is connected to the public power grid through a transformer, includes a busbar, photovoltaic generator sets, batteries, and micro gas turbines;

[0030] The photovoltaic, energy storage, and gas turbine dispatch control module is used to coordinate and optimize the dispatch of photovoltaic generator sets, batteries, micro gas turbines, and the public power grid.

[0031] The voltage fluctuation management module introduces a penalty factor to constrain the power interaction between the microgrid and the public grid, thereby controlling voltage fluctuations at the grid connection point.

[0032] The optimized scheduling algorithm module uses the Grey Wolf algorithm to optimize the allocation of scheduling targets for each power generation unit in the multi-energy microgrid, thereby achieving the dual objectives of operational economy and voltage fluctuation reliability.

[0033] In the above structure, the photovoltaic generator operates in maximum power point tracking mode, and realizes the conversion of light energy into electrical energy through the photoelectric effect of the solar panels, so as to ensure the maximum energy output of the photovoltaic power generation unit.

[0034] In the above structure, the state of charge of the battery is:

[0035]

[0036] Where E(k) represents the storage capacity of the battery at time k, which is determined by the capacity at time k-1 and the charge / discharge energy at time k-1, η ch η disThese represent the charge and discharge efficiencies of the battery, respectively, where SOC(k) is the state of charge at time k, and E... batt This represents the total energy storage capacity.

[0037] In the above structure, the photovoltaic-storage-gas turbine scheduling and control module, based on the economic objectives and voltage fluctuation constraints within the scheduling cycle, first utilizes the photovoltaic generator set for power supply. When the photovoltaic generator set's power supply is insufficient, it prioritizes the discharge of the storage battery. When the storage battery's discharge is insufficient to meet the load demand, it starts the micro gas turbine to supplement the power supply. When the micro gas turbine's power supply is still insufficient, it purchases electricity from the public grid to supplement the power supply. When photovoltaic power generation is excessive, it prioritizes charging the storage battery. If the storage battery is fully charged, it then sells electricity to the public grid to obtain economic benefits.

[0038] The beneficial effects of this invention are: it can significantly improve energy utilization efficiency, reduce system operating costs, improve grid voltage quality, and ensure system stability and adaptability in dynamic environments. Attached Figure Description

[0039] Figure 1 This is a flowchart of a multi-energy microgrid optimization scheduling method considering grid voltage support according to the present invention;

[0040] Figure 2 This is a multi-energy microgrid topology diagram for a multi-energy microgrid optimization scheduling method considering grid voltage support according to the present invention.

[0041] Figure 3 This invention presents a structural diagram of a multi-energy microgrid optimized dispatching system that considers grid voltage support.

[0042] Figure 4 This is a scheduling logic diagram of a multi-energy microgrid optimization scheduling method considering grid voltage support according to the present invention;

[0043] Figure 5 This invention provides a method for optimizing the scheduling of multi-energy microgrids while considering grid voltage support. The output diagram of each device in the multi-energy microgrid is shown.

[0044] Figure 6 This is a comparison diagram of the grid connection point voltage under the present invention and two other scheduling methods;

[0045] Figure 7 This is a comparison chart of the daily costs of the present invention with those of two other scheduling methods. Detailed Implementation

[0046] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0047] The following will clearly and completely describe the concept, specific structure, and technical effects of the present invention in conjunction with embodiments and accompanying drawings, so as to fully understand the purpose, features, and effects of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, not all of them. Other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are all within the scope of protection of the present invention. Furthermore, all connections / linkages involved in the patent do not simply refer to direct contact between components, but rather to the ability to form a better connection structure by adding or reducing connecting accessories according to specific implementation conditions. The various technical features in this invention can be combined interactively without contradicting each other.

[0048] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0049] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Secondly, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0050] See Figures 1 to 2 As shown, this invention provides a multi-energy microgrid optimal scheduling method considering grid voltage support, comprising:

[0051] S10: Initialize the basic model of the multi-energy microgrid and analyze the operational data characteristics of the microgrid system, including electricity price, power generation price, load characteristics, photovoltaic power generation characteristics, and equipment operation constraints;

[0052] S20: Based on typical daily load data and typical solar irradiance data, predict the load curve and the output power of the photovoltaic generator set;

[0053] S30: To address the voltage fluctuation problem at the grid connection point of multi-energy microgrids, a voltage fluctuation penalty factor is introduced to constrain and manage the power interaction between the microgrid and the grid.

[0054] S40: Combining the operational constraints of the power generation units within the microgrid, a dual optimization objective function with operational economy and voltage quality as the goals is established for the multi-energy microgrid optimal scheduling model;

[0055] S50: The objective function is solved using the Grey Wolf algorithm to determine the scheduling target of the interactive power of photovoltaic generator sets, batteries, micro gas turbines and public power grid;

[0056] S60: Outputs scheduling results to achieve optimized allocation of power from photovoltaic power generation, battery charging and discharging, micro gas turbine power generation, and public grid interaction.

[0057] The multi-energy microgrid basic model is a microgrid system that integrates multiple energy forms (such as electricity, heat, and gas). By coordinating the supply and demand of various energy sources such as electricity, heat, and gas, it achieves comprehensive demand response and optimizes operating costs and energy utilization efficiency.

[0058] In step S20, typical daily load data refers to a representative daily load variation curve over a certain period (such as a year or a quarter). This curve is typically used to analyze the load characteristics of the power grid and to make load forecasts. The typical daily load curve can reflect the 24-hour load variation of the highest load day or the average load day within that period. Typical solar sunshine data refers to representative sunshine hours and solar intensity data over a certain period.

[0059] In this example, typical daily load data is collected from historical summer and winter seasons, showing representative values ​​of load demand at 15-minute intervals throughout the day. The resulting average daily load data exhibits diurnal fluctuations and a clear seasonality. Typical solar sunshine data is obtained from publicly available online data, reflecting the solar radiation intensity and diurnal variation in a specific region. Similarly, representative values ​​of solar sunshine intensity at 15-minute intervals throughout the day are displayed, resulting in average sunshine intensity data that also shows diurnal fluctuations and a clear seasonality.

[0060] In step S30, the voltage fluctuation penalty factor is a parameter used to measure the impact of voltage fluctuations on the power system. In voltage fluctuation management at the grid connection point of a multi-energy microgrid, introducing the voltage fluctuation penalty factor means adding a term related to voltage fluctuations to the objective function or optimization model. The magnitude of this term is proportional to the amplitude of the voltage fluctuation; that is, the greater the voltage fluctuation, the higher the value of the penalty term. In this way, economic benefits and voltage stability can be automatically balanced during the optimization process, prompting the system to choose a more stable operating state.

[0061] The voltage fluctuation value at the multi-energy microgrid connection point is:

[0062] V S -VR =I R (R+jX);

[0063] In the above formula, V S The voltage near the public power grid terminal, V R R is the voltage on the microgrid side, R+jX is the line impedance, and I is the voltage on the microgrid side. R The current flowing through the line is represented by P. L and Q L These represent the active power and reactive power flowing into the line, respectively, and are represented by P. G and Q G Let P represent the generated active power and reactive power, respectively. Then the net active power P absorbed by the bus is... R and reactive power Q R The voltage fluctuation at the grid connection point of a multi-energy microgrid can be expressed as follows, and the voltage fluctuation value can be expressed by the following formula:

[0064] P R =|P L |-|P G |;

[0065] Q R =|Q L |-|Q G |;

[0066]

[0067] In the above equation, the imaginary part can be ignored, and it can be transformed into the following equation:

[0068]

[0069] Among them, V S The voltage near the public power grid terminal, V R R is the voltage on the microgrid side, R+jX is the line impedance, and P is the voltage on the microgrid side. R Net active power absorbed by the bus and Q R The reactive power absorbed by the busbar.

[0070] In low- and medium-voltage networks and distribution systems, active power has a more significant impact on voltage issues. The designed method is for lower-level multi-energy microgrids, therefore the influence of distribution line reactance can be ignored, and the above equation can be simplified to the following:

[0071]

[0072] Grid voltage control is a core issue in energy system control and operation, with voltage fluctuation constraints being particularly critical. Microgrids aim to meet local electricity demand and transfer surplus electricity to the grid, achieving efficient energy utilization. In microgrids with high renewable energy penetration, traditional energy management optimization methods focus only on internal power supply and demand balance, neglecting grid connection voltage fluctuations. This leads to significant voltage fluctuations at the grid connection point during actual operation, affecting the stability of both the microgrid and the public grid. Regarding the grid connection voltage quality issue, according to the above formula, the voltage change at the grid connection point will depend on the net active power P absorbed by the bus. R Line resistance R and microgrid side voltage V R Compared to P R In comparison, the latter two can be considered to be less influential items. When designing an energy management system, the constraint on grid interaction power is regarded as the management of the voltage change at the grid connection point. Introducing a penalty factor μ can effectively balance the dual objectives of economy and reliability, and effectively offset the ratio between the grid interaction power and the voltage change at the grid connection point.

[0073] This invention initializes a microgrid basic model and analyzes electricity prices, generation prices, load characteristics, photovoltaic characteristics, and equipment operating constraints, fully considering the operating characteristics and practical constraints of multi-energy microgrids to ensure the model's comprehensiveness and practical applicability. Based on typical daily data, it predicts load curves and photovoltaic power output, providing high-precision input data for the scheduling model, which helps to cope with the randomness and volatility of load and photovoltaic power generation. By introducing a voltage fluctuation penalty factor, it constrains and manages the interactive power between the microgrid and the grid, fundamentally solving the voltage fluctuation problem at the grid connection point and improving power quality. The Grey Wolf optimization algorithm is used to solve the objective function. The Grey Wolf algorithm has the characteristics of strong global search capability and fast convergence speed, which can quickly determine the optimal scheduling scheme for photovoltaic, battery, gas turbine, and public grid, improving energy utilization efficiency, reducing operating costs and carbon emissions, and promoting green development and energy structure optimization.

[0074] In this embodiment, the operational data characteristics of the microgrid system, including electricity price, power generation price, load characteristics, photovoltaic power generation characteristics, and equipment operation constraints, are analyzed. Specifically, these include: the cost of purchasing electricity from the grid by the microgrid system, the operating cost of the gas turbines within the microgrid system, the area of ​​photovoltaic panels within the microgrid system, the output threshold of the photovoltaic generator sets within the microgrid system, the power conversion efficiency of the photovoltaic generator sets within the microgrid system, the output threshold of the batteries within the microgrid system, the total capacity of the batteries within the microgrid system, the charging and discharging power threshold of the batteries within the microgrid system, the SOC threshold of the batteries within the microgrid system, the power conversion efficiency of the batteries within the microgrid system, the active power output threshold of the micro gas turbine units within the microgrid system, and the power conversion efficiency of the micro gas turbine units within the microgrid system.

[0075] A load curve is a graphical representation of how electrical load changes over time. It is typically displayed on a rectangular coordinate system, where the vertical axis represents the load value (active or reactive power), and the horizontal axis represents time (usually in hours). By collecting load data for a typical day, the load curve for that day can be plotted. This curve reflects the load changes of the power system at different time periods and is an important basis for power system dispatching and planning. The output power of photovoltaic (PV) generators is affected by various factors, with solar irradiance being one of the most significant. Therefore, when predicting the output power of PV generators, it is necessary to refer to solar irradiance data from a typical day.

[0076] Furthermore, in step S20, the photovoltaic generator set operates in maximum power point tracking mode, and the formula for calculating the output power of the photovoltaic generator set is:

[0077] P PV =f PV P RPV I g [1+0.005(T cell -T STC )] / I STC ;

[0078] T cell =T a +0.0138(1+0.031T a (1-0.042v)I g ;

[0079] Among them, P PV f represents the output power of the photovoltaic panel. PV P is the derating factor, which describes the power reduction of photovoltaic panels caused by external factors such as aging and losses. RPV For the rated installation capacity of photovoltaic panels, I g This represents the actual light intensity, and its unit is generally W / m². 2 I STC T represents the light intensity under standard test conditions. STC For standard test temperature, T cell The temperature is the surface temperature of the photovoltaic panel.

[0080] Furthermore, in step S40, the expression for the dual optimization objective function is:

[0081]

[0082] Among them, C grid (k) represents the grid purchase cost at time k, C MT (k) represents the gas turbine operating cost at time k, P MT(k) represents the electrical power generated by the micro gas turbine at time k, P PV (k) represents the electrical power generated by the photovoltaic generator at time k, P grid (k) represents the interaction power with the grid at time k, when P grid When (k) is positive, it indicates that the power grid is supplying electricity to the multi-energy microgrid. grid When (k) is negative, it indicates that the multi-energy microgrid is selling electricity to the grid.

[0083] Introducing μ as a penalty factor, the constraint on grid interaction power is viewed as management of voltage fluctuations at the grid connection point. This effectively offsets the ratio between grid interaction power and grid connection voltage fluctuations, achieving the dual goals of economy and voltage reliability. The voltage fluctuation penalty factor μ can be determined through repeated adjustments to its optimal parameters for practical multi-energy microgrid system scheduling. This factor can be adaptively adjusted according to different application scenarios of multi-energy microgrid systems, thereby optimizing the management of interaction power between the microgrid and the public grid and rationally allocating power scheduling targets for micro-gas turbines and energy storage power generation units. This aims to ensure that photovoltaic power generation units can maximize their energy output and improve voltage quality at the public grid connection point and the microgrid. The proposed method effectively limits voltage fluctuations at the multi-energy microgrid grid connection point while pursuing optimal electricity purchase costs, making it particularly suitable for microgrid scenarios with higher voltage quality requirements.

[0084] Furthermore, the Grey Wolf Optimizer (GWO) is a swarm intelligence optimization algorithm that simulates the hunting behavior of grey wolves. Based on the social hierarchy and hunting strategies of grey wolves in nature, this algorithm solves various optimization problems by mimicking the leadership hierarchy and hunting mechanisms of grey wolves. In a grey wolf population, wolves are divided into four ranks from highest to lowest: α wolves (leaders), β wolves (secondary leaders), γ wolves (ordinary members), and ω wolves (lowest-ranking wolves). During a hunt, these wolves act in a coordinated manner according to their rank, typically with higher-ranking wolves leading lower-ranking wolves to surround and attack the prey. The main phases of a grey wolf hunt include tracking, chasing, and approaching the prey; pursuing, surrounding, and harassing the prey until it stops moving; and launching an attack. In the algorithm, these phases are mathematically modeled to simulate the behavior of grey wolves in the search space.

[0085] In step S50, the specific steps are as follows:

[0086] S501: Select a set of output data and load data of each device from the historical operation data of the multi-energy microgrid as candidate solutions to form a gray wolf population. Set the population size and the initial position of each gray wolf individual. These positions represent different scheduling schemes.

[0087] S502: Calculate the fitness value of each individual in the gray wolf population based on the dual optimization objective function;

[0088] S503: Select the top three gray wolves with the highest fitness values ​​as leaders. They represent the current optimal scheduling scheme. The other gray wolves are followers, and their positions will be updated based on their distance from and direction from the leaders.

[0089] S504: Followers update their positions by performing non-linear transformations based on their distance from the leader, direction, and time factors.

[0090] S505: Repeat steps S502-S504, iteratively updating the position and fitness of the gray wolf population until the termination condition is met. The final converged solution is the power scheduling target of each device in the multi-energy microgrid, i.e., the optimal scheduling scheme.

[0091] This invention also provides a multi-energy microgrid optimized scheduling system that considers grid voltage support, the system comprising:

[0092] A multi-energy microgrid, which is connected to the public power grid through a transformer, includes a busbar, photovoltaic generator sets, batteries, and micro gas turbines;

[0093] The photovoltaic, energy storage, and gas turbine dispatch control module is used to coordinate and optimize the dispatch of photovoltaic generator sets, batteries, micro gas turbines, and the public power grid.

[0094] The voltage fluctuation management module introduces a penalty factor to constrain the power interaction between the microgrid and the public grid, thereby controlling voltage fluctuations at the grid connection point.

[0095] The optimized scheduling algorithm module uses the Grey Wolf algorithm to optimize the allocation of scheduling targets for each power generation unit in the multi-energy microgrid, thereby achieving the dual objectives of operational economy and voltage fluctuation reliability.

[0096] Coordinated and optimized scheduling of photovoltaic (PV) generators, batteries, micro gas turbines, and the public power grid can dynamically adjust output power based on different energy characteristics (such as the intermittency of PV and the stability of gas turbines), fully utilizing the power generation capacity of renewable energy and reducing energy waste. The introduction of batteries enables energy storage and reuse, especially during periods of low or high PV power generation, effectively balancing supply and demand and further improving energy efficiency. The Grey Wolf optimization algorithm optimizes the scheduling of power generation units in multi-energy microgrids, comprehensively considering the power generation costs of PV, gas turbines, batteries, and the public power grid. It can rationally allocate the operating tasks of each energy unit, reducing power generation and operating costs and optimizing economic benefits. The algorithm achieves optimal power allocation among energy units, avoiding unnecessary fuel consumption and power loss, thereby further improving economic efficiency.

[0097] Photovoltaic solar systems may be connected to inverters, external power grids, battery packs, or other electronic loads. Regardless of the connected load, maximum power point tracking (MPPT) addresses a similar problem: the efficiency of solar cell power transfer is related to the amount of sunlight reaching the solar panels and the electronic characteristics of the load. As sunlight conditions change, the load profile that provides the highest power transfer efficiency also changes. If the load can be adjusted to match the load profile with the highest power transfer efficiency, the system will achieve optimal efficiency. The load characteristic with the highest power transfer efficiency is called the maximum power point. MPPT aims to find this maximum power point and maintain the load characteristics at that power point. Circuits can be designed to represent any load connected to the solar cells, and then the voltage, current, or frequency can be converted to match other systems. MPPT finds the optimal load needed to obtain the maximum available power. MPPT is an optimization algorithm and control technique used to adjust the operating point of a photovoltaic system in real time, ensuring it always operates at the maximum power point of the solar panels, thereby maximizing the output power of the photovoltaic array.

[0098] Furthermore, the photovoltaic generator operates in maximum power point tracking mode, converting light energy into electrical energy through the photoelectric effect of the solar panels to ensure maximum energy output of the photovoltaic power generation unit. This configuration not only dynamically adapts to environmental changes and maximizes the energy output of the photovoltaic unit, but also provides stable, economical, and environmentally friendly power support for multi-energy microgrids.

[0099] Furthermore, the storage capacity of the battery at time k is:

[0100]

[0101] Where E(k) represents the storage capacity of the battery at time k, which is determined by the capacity at time k-1 and the charge / discharge energy at time k-1; τ represents the self-loss rate of the battery; and η ch η dis P represents the charge and discharge efficiency of the battery, respectively. ch (k-1), P dis (k-1) represent the charging and discharging power of the battery at time k-1, S ch (k-1), S dis (k-1) represent the charging and discharging states of the battery at time k-1.

[0102] The state of charge of the battery is:

[0103]

[0104] Where E(k) represents the storage capacity of the battery at time k, which is determined by the capacity at time k-1 and the charge / discharge energy at time k-1, η ch η dis Let SOC(k) represent the charge and discharge efficiency of the battery, SOC(k) be the state of charge of the energy storage at time k, and Ebatt be the total energy storage capacity.

[0105] The photovoltaic-storage-gas turbine dispatch control module, based on the economic objectives and voltage fluctuation constraints within the dispatch cycle, first utilizes photovoltaic generators for power supply. When the photovoltaic generators' power supply is insufficient, it prioritizes the discharge of batteries. When the battery discharge is insufficient to meet load demand, it starts micro gas turbines to supplement power supply. When the micro gas turbines' power supply is still insufficient, it purchases electricity from the public grid to supplement it. When photovoltaic power generation is excessive, it prioritizes charging the batteries. If the batteries are fully charged, it then sells electricity to the public grid to obtain economic benefits.

[0106] Reference Figure 5 As shown, the scheduling achieved by this invention results in a time of approximately 2 × 10⁻⁶. 4 Within seconds, the load curve shows a significant rise, indicating an increase in electricity demand. The photovoltaic generator's power output is approximately 2 × 10⁻⁶. 4 The micro gas turbine provides a relatively large power output per second, which aligns with the characteristics of photovoltaic power generation. The power output of the micro gas turbine is relatively stable throughout the time period, but at 5×10... 4 The power consumption fluctuates slightly over a few seconds. The battery power is negative during certain periods, indicating that the battery is discharging; it is positive during other periods, indicating that the battery is charging. The power of the public power grid is continuously adjusted throughout the time period to balance changes in the total load, especially during peak load periods (2×10). 4 The power output of the public power grid has increased significantly.

[0107] like Figure 6 As shown in the figure, this figure is a comparison of the grid connection point voltage under the present invention and two other scheduling methods. In the heuristic scheduling, the micro gas turbine is in full-load operation and the battery is under energy management, with the grid connection point voltage fluctuation range being 389-404V. Under the economically optimal scheduling method, the grid connection point voltage fluctuation range is 385-405V. Under the optimized scheduling method of the present invention, the grid connection point voltage fluctuation range is 392-400V, which has a significant improvement effect over the other two scheduling methods.

[0108] Figure 7The graph shows a comparison of daily costs under this invention and two other scheduling methods. The three curves represent the daily cost changes under different scheduling strategies. As can be seen from the graph, the cost curve for "heuristic scheduling" is generally higher than the other two curves, indicating that the daily cost of heuristic scheduling is higher. The cost curve for "economic optimization scheduling" is next; although it is lower than heuristic scheduling, it is still higher than "schedule considering voltage limit optimization." The cost curve for "schedule considering voltage limit optimization" is the lowest, indicating that the daily cost is lowest when voltage limit optimization is considered. The daily cost of the scheduling method with voltage penalty is only 6.4382% higher than that of the method considering only economic optimization. Furthermore, it significantly improves the ability to constrain voltage fluctuations at the grid connection point, achieving the dual objectives of economic efficiency and voltage fluctuation constraint.

[0109] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0110] Finally, it should be noted that the above is a detailed description of the preferred embodiments of the present invention, but the present invention is not limited to the embodiments described. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of the present invention, and these equivalent modifications or substitutions are all included within the scope defined by the claims of this application.

Claims

1. A method for optimal scheduling of multi-energy microgrids considering grid voltage support, characterized in that, include: S10: Initialize the basic model of the multi-energy microgrid and analyze the operational data characteristics of the microgrid system, including electricity price, power generation price, load characteristics, photovoltaic power generation characteristics, and equipment operation constraints; S20: Based on typical daily load data and typical solar irradiance data, predict the load curve and the output power of the photovoltaic generator set; S30: To address the voltage fluctuation problem at the grid connection point of multi-energy microgrids, a voltage fluctuation penalty factor is introduced to constrain and manage the microgrid-grid interaction power. The voltage fluctuation penalty factor measures the impact of the interaction power at the grid connection point on voltage fluctuations, and its expression is as follows: ； Among them, V S The voltage near the public power grid terminal, V R R is the voltage on the microgrid side, R+jX is the line impedance, and P is the voltage on the microgrid side. R Net active power absorbed by the bus and Q R The reactive power absorbed by the busbar; S40: Combining the operational constraints of the power generation units within the microgrid, a dual optimization objective function is established for the multi-energy microgrid optimal scheduling model, with operational economy and voltage quality as its objectives; the expression of the dual optimization objective function is as follows: ； Among them, C grid (k) represents the grid purchase cost, C MT (k) represents the operating cost of the gas turbine, P MT (k) represents the electrical power generated by the micro gas turbine at time k, P grid (k) represents the interaction power with the grid at time k, when P grid When (k) is positive, it indicates that the power grid is supplying electricity to the multi-energy microgrid. grid When (k) is negative, it means that the multi-energy microgrid sells electricity to the grid, N is the total number of dispatch periods, and μ is the voltage fluctuation penalty factor; S50: The objective function is solved using the Grey Wolf algorithm to determine the scheduling target of the interactive power of photovoltaic generator sets, batteries, micro gas turbines and public power grid; S60: Outputs scheduling results to achieve optimized allocation of power from photovoltaic power generation, battery charging and discharging, micro gas turbine power generation, and public grid interaction.

2. The multi-energy microgrid optimal scheduling method considering grid voltage support according to claim 1, characterized in that, In step S20, the photovoltaic generator set operates in maximum power point tracking mode, and the formula for calculating the output power of the photovoltaic generator set is: ； ； Among them, P PV f represents the output power of the photovoltaic panel. PV P is the derating factor, which describes the power reduction of photovoltaic panels caused by external factors such as aging and losses. RPV For the rated installation capacity of photovoltaic panels, I g This represents the actual light intensity, and its unit is generally W / m². 2 I STC T represents the light intensity under standard test conditions. STC For standard test temperature, T cell The temperature is the surface temperature of the photovoltaic panel.

3. The multi-energy microgrid optimal scheduling method considering grid voltage support according to claim 1, characterized in that, In step S50, the specific steps are as follows: S501: Select a set of output data and load data of each device from the historical operation data of the multi-energy microgrid as candidate solutions to form a gray wolf population. Set the population size and the initial position of each gray wolf individual. These positions represent different scheduling schemes. S502: Calculate the fitness value of each individual in the gray wolf population based on the dual optimization objective function; S503: Select the top three gray wolves with the highest fitness values ​​as leaders. They represent the current optimal scheduling scheme. The other gray wolves are followers, and their positions will be updated based on their distance from and direction from the leaders. S504: Followers update their positions by performing non-linear transformations based on their distance from the leader, direction, and time factors. S505: Repeat steps S502-S504, iteratively updating the position and fitness of the gray wolf population until the termination condition is met. The final converged solution is the power scheduling target of each device in the multi-energy microgrid, i.e., the optimal scheduling scheme.

4. A multi-energy microgrid optimized dispatching system considering grid voltage support, characterized in that, The system includes: A multi-energy microgrid, which is connected to the public power grid through a transformer, includes a busbar, photovoltaic generator sets, batteries, and micro gas turbines; The photovoltaic, energy storage, and gas turbine dispatch control module is used to coordinate and optimize the dispatch of photovoltaic generator sets, batteries, micro gas turbines, and the public power grid. The voltage fluctuation management module uses a penalty factor to constrain the interaction power between the microgrid and the public grid, thereby controlling voltage fluctuations at the grid connection point. The voltage fluctuation penalty factor measures the impact of the interaction power at the grid connection point on voltage fluctuations, and its expression is as follows: ； Among them, V S The voltage near the public power grid terminal, V R R is the voltage on the microgrid side, R+jX is the line impedance, and P is the voltage on the microgrid side. R Net active power absorbed by the bus and Q R The reactive power absorbed by the busbar; The optimized scheduling algorithm module employs the Grey Wolf algorithm to optimize the allocation of scheduling objectives for each power generation unit in the multi-energy microgrid, thereby achieving the dual optimization objectives of operational economy and voltage fluctuation reliability. The expression for the dual optimization objective function is as follows: ； Among them, C grid (k) represents the grid purchase cost, C MT (k) represents the operating cost of the gas turbine, P MT (k) represents the electrical power generated by the micro gas turbine at time k, P grid (k) represents the interaction power with the grid at time k, when P grid When (k) is positive, it indicates that the power grid is supplying electricity to the multi-energy microgrid. grid When (k) is negative, it indicates that the multi-energy microgrid sells electricity to the grid, N is the total number of dispatch periods, and μ is the voltage fluctuation penalty factor.

5. The multi-energy microgrid optimized dispatching system considering grid voltage support according to claim 4, characterized in that, The photovoltaic generator set operates in maximum power point tracking mode, converting light energy into electrical energy through the photoelectric effect of the solar panels to ensure maximum energy output of the photovoltaic power generation unit.

6. The multi-energy microgrid optimized dispatching system considering grid voltage support according to claim 4, characterized in that, The state of charge of the battery is: ； Where E(k) represents the storage capacity of the battery at time k, which is determined by the capacity at time k-1 and the charge / discharge energy at time k-1, η ch η dis Representing the charge and discharge efficiency of the battery, SOC(k) is the state of charge at time k, E batt This represents the total energy storage capacity.

7. The multi-energy microgrid optimized dispatching system considering grid voltage support according to claim 4, characterized in that, The photovoltaic-storage-gas turbine dispatch control module, based on the economic objectives and voltage fluctuation constraints within the dispatch cycle, first utilizes photovoltaic generators for power supply. When the photovoltaic generators' power supply is insufficient, it prioritizes the discharge of batteries. When the battery discharge is insufficient to meet load demand, it starts micro gas turbines to supplement power supply. When the micro gas turbines' power supply is still insufficient, it purchases electricity from the public grid to supplement it. When photovoltaic power generation is excessive, it prioritizes charging the batteries. If the batteries are fully charged, it then sells electricity to the public grid to obtain economic benefits.

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