Multi-energy microgrid optimization scheduling method considering power grid voltage support

Abstract

The invention discloses a multi-energy microgrid optimization scheduling method considering grid voltage support, which belongs to the technical field of power generation system optimization scheduling, and comprises the following steps: S10: initializing a multi-energy microgrid basic model, and analyzing microgrid system operation data characteristics; s20, predicting a load curve and the output power of the photovoltaic generator set according to the typical daily load data and the typical daily illumination data; s30, introducing a voltage fluctuation penalty factor for the voltage fluctuation of the grid-connected point of the multi-energy micro-grid, and carrying out constraint management on micro-grid-power grid interaction power; s40, establishing a dual optimization objective function for a multi-energy micro-grid optimization scheduling model; s50, solving the target function by adopting a grey wolf algorithm, and determining a scheduling target of the interaction power of the photovoltaic generator set, the storage battery, the micro gas turbine and the public power grid; and S60, outputting a scheduling result. According to the method, the energy utilization efficiency can be remarkably improved, the system operation cost is reduced, the grid-connected voltage quality is improved, and the stability and adaptability of the system are guaranteed in a dynamic environment.

Description

Technical Field

[0001] The present invention relates to the technical field of optimal dispatching of power generation systems, and more specifically, to an optimal dispatching method for a multi - energy microgrid considering grid voltage support. Background Art

[0002] In 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. For this reason, as an emerging power distribution and management system, the microgrid has gradually attracted wide attention due to its flexibility, reliability, and renewable energy integration capabilities. By combining distributed energy sources (such as solar energy, wind energy, and energy storage systems) with local loads, the microgrid can achieve more efficient energy management, meet local demands, and improve energy utilization efficiency. In addition, the microgrid has the ability to be self - sufficient in emergency situations, adding guarantee to the resilience of the power system. With the transformation of the global energy structure, the use of clean energy such as natural gas has gradually been combined with electricity, forming a new form of multi - energy microgrid. The multi - energy microgrid can not only handle multiple energy carriers such as electricity, natural gas, and heat energy, but also has the ability to coordinate and control various types of energy, thus showing more excellent integration and dispatching capabilities when facing increasingly complex energy management challenges. This advantage of the multi - energy microgrid 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 application of renewable energy.

[0003] However, with the rapid development of multi - energy microgrids, the problem of grid voltage control has gradually become prominent. Voltage, as one of the core parameters in the energy system, directly affects the safety and economy of equipment operation. The microgrid aims to meet load demands by efficiently utilizing resources, but due to the increasing number of grid connection points in the multi - energy microgrid, voltage fluctuations and stability problems have also intensified. Under different load and power generation conditions, the voltage levels at these grid connection points may fluctuate significantly, and may even lead to voltage violations, thus posing a threat to the stability of the public grid. Traditional optimal energy management methods often focus on maximizing economic benefits and insufficiently consider the voltage fluctuation problems at grid connection points, resulting in the difficulty of maintaining good voltage stability when the microgrid is connected to the grid. In this case, the microgrid will not only affect its own operation safety, but may also have a negative impact on the public grid, bringing greater financial and technical risks. Therefore, there is an urgent need for innovative dispatching methods that take into account both economy and voltage fluctuation control to ensure the safety and stability of the grid. Summary of the Invention

[0004] To overcome the deficiencies of the prior art, a multi - energy micro - grid optimal scheduling method considering grid voltage support can significantly improve energy utilization efficiency, reduce system operation costs, improve grid - connected voltage quality, and ensure the stability and adaptability of the system in a dynamic environment.

[0005] The technical solution adopted by the invention to solve its technical problems is: a multi - energy micro - grid optimal scheduling method considering grid voltage support, the improvement lies in that it includes:

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

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

[0008] S30: For the voltage fluctuation problem at the multi - energy micro - grid grid - connection point, introduce a voltage fluctuation penalty factor to manage the constraint of the micro - grid - grid interaction power;

[0009] S40: Combining the operation constraints of the power generation units in the micro - grid, establish a dual - optimization objective function with operation economy and voltage quality as the objectives for the multi - energy micro - grid optimal scheduling model;

[0010] S50: Use the grey wolf algorithm to solve the objective function and determine the scheduling objectives of the photovoltaic generator set, battery, micro - gas turbine, and the interaction power with the public grid;

[0011] S60: Output the scheduling result to achieve the optimal distribution of photovoltaic power generation, battery charge - discharge, micro - gas turbine power generation, and the interaction power with the public grid.

[0012] Furthermore, in step S20, the photovoltaic generator set operates in the maximum power point tracking mode, and the calculation formula for 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 represents the output power of the photovoltaic power generation panel, f PV is the derating factor, which is used to describe the power reduction of the photovoltaic power generation panel caused by external reasons such as aging and loss, P RPV is the rated installation capacity of the photovoltaic power generation panel, I g represents the actual light intensity, and its unit is generally W / m 2 ,I STC is the light intensity under standard test conditions, T STC is the standard test temperature, T cell is the temperature of the photovoltaic panel surface.

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

[0017]

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

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

[0020]

[0021] Among them, C grid (k) represents the grid power purchase cost at time k, C MT (k) represents the gas turbine operation cost at time k, P MT (k) represents the electric power generated by the micro gas turbine at time k, P PV (k) represents the electric power generated by the photovoltaic power generation unit at time k, P grid (k) represents the interactive power with the grid at time k. When P grid (k) is positive, it means that the grid sends power to the multi - energy microgrid. When P grid (k) is negative, it means that the multi - energy microgrid sells power to the grid.

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

[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, and set the population size and the initial positions 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 according to the dual optimization objective function;

[0025] S503: Select the top three gray wolves with the highest fitness values as leaders, which represent the current optimal scheduling scheme, and the other gray wolves are followers. They will update their positions according to the distance and direction from the leaders;

[0026] S504: Followers perform a non-linear transformation based on their distance, direction from the leaders, and time factor to update their positions;

[0027] S505: Repeat steps S502 - S504, continuously iterate to update the positions and fitness values of the gray wolf population until the termination condition is met. The finally converged solution is the power scheduling target for each device in the multi-energy microgrid, that is, the optimal scheduling scheme.

[0028] Refer to Figures 3 - 4 As shown, the present invention also proposes an optimized scheduling system for a multi-energy microgrid considering grid voltage support, characterized in that the system includes:

[0029] A multi-energy microgrid, which is connected to the public grid through a transformer. The multi-energy microgrid includes a bus, a photovoltaic power generation unit, a storage battery, and a micro gas turbine;

[0030] A photovoltaic-storage-gas scheduling control module for coordinating and optimizing the scheduling of the photovoltaic power generation unit, the storage battery, the micro gas turbine, and the public grid;

[0031] A voltage fluctuation management module that constrains and manages the microgrid-public grid interaction power by introducing a penalty factor to achieve control of the voltage fluctuation at the grid connection point;

[0032] An optimized scheduling algorithm module that uses the gray wolf algorithm to optimize the allocation of the scheduling targets of each power generation unit in the multi-energy microgrid, thereby achieving the dual goals of operation economy and voltage fluctuation reliability.

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

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

[0035]

[0036] Among them, E(k) represents the storage capacity of the storage battery at time k, which is determined by the capacity at time k - 1 and the charge and discharge energy at time k - 1, η ch 、η disrespectively represent the charge and discharge energy efficiency of the storage battery, SOC(k) is the state of charge of the energy storage at time k, and E batt is the total energy storage capacity.

[0037] In the above structure, the photovoltaic-storage-combined heat and power dispatching control module, according to the economic objective and voltage fluctuation constraint within the dispatching period, first uses the photovoltaic power generation unit for power supply. When the power supply of the photovoltaic power generation unit is insufficient, it preferentially uses the storage battery for discharging; when the discharge of the storage battery is insufficient to meet the load demand, it starts the micro gas turbine for supplementary power supply; when the power supply of the micro gas turbine is still insufficient, it purchases power from the public power grid for supplementation; when the photovoltaic power generation is excessive, it preferentially charges the storage battery, and if the storage battery is already full, it then sells power to the public power grid to obtain economic benefits.

[0038] The beneficial effects of the present invention are: it can significantly improve the energy utilization efficiency, reduce the system operation cost, improve the grid-connected voltage quality, and ensure the stability and adaptability of the system in a dynamic environment. Brief Description of the Drawings

[0039] Figure 1 is a flowchart of an optimized dispatching method for a multi-energy microgrid considering grid voltage support according to the present invention;

[0040] Figure 2 is a topological structure diagram of a multi-energy microgrid of an optimized dispatching method for a multi-energy microgrid considering grid voltage support according to the present invention;

[0041] Figure 3 is a structure diagram of an optimized dispatching system for a multi-energy microgrid considering grid voltage support according to the present invention;

[0042] Figure 4 is a dispatching logic diagram of an optimized dispatching method for a multi-energy microgrid considering grid voltage support according to the present invention;

[0043] Figure 5 is an output diagram of each device of a multi-energy microgrid of an optimized dispatching method for a multi-energy microgrid considering grid voltage support according to the present invention;

[0044] Figure 6 is a comparison diagram of the grid-connected point voltage under the present invention and two other dispatching methods;

[0045] Figure 7 is a comparison diagram of the daily cost under the present invention and two other dispatching methods. Detailed Embodiments

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

[0047] The concept, specific structure, and technical effects of the present invention will be clearly and completely described below in conjunction with the embodiments and the accompanying drawings 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, rather than all embodiments. Based on the embodiments of the present invention, other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention. In addition, all connection / connection relationships involved in the patent do not simply refer to the direct connection of components, but refer to the formation of a more optimal connection structure by adding or reducing connection accessories according to specific implementation situations. Each technical feature in the present invention can be interactively combined without conflicting with each other.

[0048] It should be noted that if there are directional indications (such as up, down, left, right, front, back...) involved in the embodiments of the present invention, then such directional indications are only used to explain the relative positional relationship and movement conditions between components in a specific posture (as shown in the accompanying drawings). If this specific posture changes, then the directional indications will also change accordingly.

[0049] In addition, if there are descriptions such as "first", "second", etc. involved in the embodiments of the present invention, then such descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one such feature. Secondly, the technical solutions between various embodiments can be combined with each other, but it must be based on the ability of those skilled in the art to implement. When the combination of technical solutions conflicts with each other or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0050] See Figures 1 to 2 As shown, the present invention provides a multi - energy micro - grid optimal scheduling method considering grid voltage support, including:

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

[0052] S20: Predict the load curve and the output power of the photovoltaic generator set based on the typical - day load data and the typical - day sunlight data;

[0053] S30: For the voltage fluctuation problem at the connection point of the multi - energy micro - grid and the grid, introduce a voltage - fluctuation penalty factor to constraint - manage the micro - grid - grid interaction power;

[0054] S40: Combine the operation constraints of the power generation units in the microgrid to establish a dual optimization objective function aiming at operation economy and voltage quality for the multi - energy microgrid optimization scheduling model;

[0055] S50: Use the Grey Wolf Algorithm to solve the objective function and determine the scheduling objectives of the interactive power of the photovoltaic power generation unit, battery, micro - gas turbine and public grid;

[0056] S60: Output the scheduling results to realize the optimal allocation of photovoltaic power generation, battery charge - discharge, micro - gas turbine power generation and the interactive power of the public grid.

[0057] The multi - energy microgrid basic model is a microgrid system integrating various energy forms (such as electricity, heat, gas, etc.). By coordinating the supply and demand of various energies such as electricity, heat and gas, it realizes comprehensive demand response and optimizes the operation cost and energy utilization efficiency.

[0058] In step S20, the typical daily load data refers to the representative daily load change curve within a certain period (such as a whole year or a quarter). This curve is usually used to analyze the load characteristics of the power grid and conduct load forecasting. The typical daily load curve can reflect the 24 - hour load change situation of the highest - load day or the average - load day within this period. The typical daily sunlight data refers to the representative sunshine hours and light intensity data within a certain period.

[0059] In this actual example, the typical daily load data is the collected historical daily electricity demands in different seasons of summer / winter. The representative values of the load demands at different time intervals within a day are shown at 15 - minute intervals, and the average - load - day data shows obvious day - night fluctuations and seasonality. The typical daily sunlight data is the historical sunlight values obtained through publicly available network data, which reflects the solar radiation intensity and daily variation in a certain area. Similarly, the representative values of the sunlight intensity at different time intervals within a day are shown at 15 - minute intervals, and the average sunlight intensity data shows obvious day - night fluctuations and seasonality.

[0060] In step S30, the voltage fluctuation penalty factor is a parameter used to measure the impact degree of voltage fluctuation on the power system. In the voltage fluctuation management of the multi - energy microgrid connection point, introducing the voltage fluctuation penalty factor means adding a term related to voltage fluctuation in 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, the economic benefit and voltage stability can be automatically weighed during the optimization process, prompting the system to select 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 is the voltage at the near public grid end, V R is the voltage on the microgrid side, R + jX is the line impedance, and I R is the current flowing through the line. Using P L and Q L to represent the active power and reactive power flowing into the line respectively, and using P G and Q G to represent the generated active power and reactive power respectively, then the net active power P R absorbed by the bus and the reactive power Q R can be expressed as follows, and the voltage fluctuation value at the multi - energy microgrid connection point 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 formula, the imaginary part can be ignored and can be converted into the following formula:

[0068]

[0069] where V S is the voltage at the near public grid end, V R is the voltage on the microgrid side, R + jX is the line impedance, P R is the net active power absorbed by the bus, and Q R is the reactive power absorbed by the bus.

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

[0071]

[0072] The voltage control of the public grid is a core issue in the control and operation of the energy system, and the voltage fluctuation constraint is particularly crucial. The microgrid aims to meet the local electricity demand and deliver the surplus electricity to the public grid to achieve efficient energy utilization. In a microgrid with a high penetration rate of new energy, traditional optimal energy management methods only focus on the internal power supply and demand balance, ignoring the voltage fluctuation problem at the grid connection point, resulting in significant voltage fluctuations at the grid connection point during actual operation, which affects the stability of the microgrid and the public grid. Regarding the voltage quality problem at the grid connection point, according to the above formula, it can be known that the voltage change at the grid connection point will depend on the net active power P absorbed by the busbar R , the line resistance R, and the microgrid-side voltage V R . Compared with P R , the latter two can be considered as items with less influence. When designing the energy management system, regarding the constraint on the grid interaction power as the management of the voltage change amount at the grid connection point, introducing the penalty factor μ can well balance the dual goals of economy and reliability, and effectively offset the magnification between the grid interaction power amount and the voltage change amount at the grid connection point.

[0073] In the present invention, by initializing the microgrid basic model and analyzing electricity prices, generation prices, load characteristics, photovoltaic characteristics, and equipment operation constraints, the operation characteristics and actual constraints of the multi-energy microgrid are fully considered to ensure the comprehensiveness and practical applicability of the model; based on typical day data, the load curve and photovoltaic power generation output are predicted to provide high-precision input data for the scheduling model, which helps to cope with the randomness and volatility of the load and photovoltaic power generation; by introducing the voltage fluctuation penalty factor, the grid interaction power between the microgrid and the grid is constrained and managed, fundamentally solving the voltage fluctuation problem at the grid connection point and improving the power quality; the gray wolf optimization algorithm is used to solve the objective function. The gray wolf algorithm has the characteristics of strong global search ability and fast convergence speed, and can quickly determine the optimal scheduling schemes of photovoltaic, battery, gas turbine, and public grid, improving the energy utilization efficiency, reducing the operation cost and carbon emission, and promoting green development and energy structure optimization.

[0074] In this embodiment, the operation data characteristics including electricity prices, generation prices, load characteristics, photovoltaic power generation characteristics, and equipment operation constraints in the microgrid system are analyzed, specifically including: the power purchase cost of the microgrid system from the grid, the operation cost of the gas turbine in the microgrid system, the photovoltaic panel area in the microgrid system, the output threshold of the photovoltaic generator set in the microgrid system, the power conversion efficiency of the photovoltaic generator set in the microgrid system, the output threshold of the battery in the microgrid system, the total capacity of the battery in the microgrid system, the charge and discharge power threshold of the battery in the microgrid system, the threshold of the SOC of the battery in the microgrid system, the power conversion efficiency of the battery in the microgrid system, the active power output threshold of the micro gas turbine unit in the microgrid system, and the power conversion efficiency of the micro gas turbine unit in the microgrid system.

[0075] The load curve is a graph that characterizes the variation of electric load over time. It is usually presented in the form of a rectangular coordinate system, where the vertical axis represents the load value (active power or reactive power), and the horizontal axis represents time (usually in hours). By collecting the load data of a typical day, the load curve of that day can be plotted. This curve reflects the load variation of the power system at different time periods and is an important basis for power system dispatching and planning. The output power of a photovoltaic generator set is affected by various factors, among which the light intensity is one of the most important factors. Therefore, when predicting the output power of a photovoltaic generator set, the light data of a typical day needs to be referred to.

[0076] Furthermore, in step S20, the photovoltaic generator set operates in the maximum power point tracking mode, and the calculation formula for 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 represents the output power of the photovoltaic panel, f PV is the derating factor, which is used to describe the power reduction of the photovoltaic panel due to external reasons such as aging and loss, P RPV is the rated installation capacity of the photovoltaic panel, I g represents the actual light intensity, and its unit is generally W / m 2 , I STC is the light intensity under standard test conditions, T STC is the standard test temperature, and T cell is the temperature of the photovoltaic panel surface.

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

[0081]

[0082] Among them, C grid (k) represents the grid power purchase cost at time k, C MT (k) represents the operating cost of the gas turbine at time k, P MT(k) represents the electric power output by the micro gas turbine at time k, P PV (k) represents the electric power output by the photovoltaic power generation unit at time k, P grid (k) represents the interaction power with the power grid at time k. When P grid (k) is positive, it means the power grid sends electricity to the multi - energy microgrid. When P grid (k) is negative, it means the multi - energy microgrid sells electricity to the power grid.

[0083] Introduce μ as the penalty factor. Consider the constraint on the interaction power with the power grid as the management of the voltage change at the grid connection point, effectively offsetting the magnification between the interaction power quantity and the voltage change at the grid connection point, and achieving the dual goals of economy and voltage reliability. The voltage fluctuation penalty factor μ can be determined by repeated debugging to obtain the optimal parameters for application to the actual multi - energy microgrid system scheduling. This factor can be adaptively adjusted according to the application scenarios of different multi - energy microgrid systems, so as to more optimally manage the interaction power between the microgrid and the utility grid, and reasonably allocate the power scheduling objectives of the micro gas turbine and the energy storage power generation unit. This move aims to ensure that the photovoltaic power generation unit can maximize its energy output and improve the voltage quality of the common grid connection point and the microgrid. The proposed method effectively limits the voltage fluctuation at the grid connection point of the multi - energy microgrid while pursuing the optimization of the power purchase cost, and is especially suitable for microgrid scenarios with higher requirements for voltage quality.

[0084] Furthermore, the Grey Wolf Optimizer (GWO) is a swarm intelligence optimization algorithm that simulates the hunting behavior of grey wolves. This algorithm is based on the social hierarchy and hunting strategies of grey wolves in nature, and solves various optimization problems by imitating the leadership hierarchy and hunting mechanism of grey wolves. In the grey wolf population, grey wolves are divided into α wolves (leaders), β wolves (secondary leaders), γ wolves (ordinary members), and ω wolves (wolves with the lowest status) from high to low in status. When hunting, these wolves act cooperatively in order of rank, usually with the higher - status wolves leading the lower - status wolves to surround and attack the prey together. The main stages of grey wolf hunting include tracking, chasing and approaching the prey, pursuing, surrounding and harassing the prey until it stops moving, and launching an attack on the prey. In the algorithm, these stages are mathematized to simulate the behavior of grey wolves in the search space.

[0085] In the 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 grey wolf population, set the population size and the initial positions of each grey wolf individual, and these positions represent different scheduling schemes;

[0087] S502: Calculate the fitness value of each individual in the grey wolf population according to the dual - optimization objective function;

[0088] S503: Select the top three gray wolves with the fitness values as the leaders, which represent the current optimal scheduling scheme, and the other gray wolves are followers. They will update their positions according to the distance and direction from the leaders.

[0089] S504: The followers perform a non-linear transformation based on the distance, direction from the leaders and time factor to update their positions.

[0090] S505: Repeat steps S502 - S504, continuously iterate to update the positions and fitness of the gray wolf population until the termination condition is met. The finally converged solution is the power scheduling target of each device in the multi-energy microgrid, that is, the optimal scheduling scheme.

[0091] The present invention also provides an optimized scheduling system for a multi-energy microgrid considering grid voltage support. The system includes:

[0092] A multi-energy microgrid, which is connected to the public grid through a transformer. The multi-energy microgrid includes a bus, a photovoltaic power generation unit, a storage battery and a micro gas turbine.

[0093] A photovoltaic-storage-gas turbine scheduling and control module for coordinately optimizing the scheduling of the photovoltaic power generation unit, the storage battery, the micro gas turbine and the public grid.

[0094] A voltage fluctuation management module, which introduces a penalty factor to constrain and manage the microgrid-public grid interactive power to achieve the control of the voltage fluctuation at the connection point.

[0095] An optimized scheduling algorithm module, which uses the gray wolf algorithm to optimize the allocation of the scheduling targets of each power generation unit in the multi-energy microgrid, so as to achieve the dual goals of operation economy and voltage fluctuation reliability.

[0096] The coordinated and optimized scheduling of the photovoltaic power generation unit, the storage battery, the micro gas turbine and the public grid can dynamically adjust the output power according to different energy characteristics (such as the intermittency of photovoltaic and the stability of gas turbines), make full use of the power generation capacity of renewable energy, and reduce energy waste. The introduction of the storage battery realizes the energy storage and reuse. Especially during the low valley period of photovoltaic power generation or the peak electricity consumption period, it can effectively balance the supply and demand and further improve the energy utilization efficiency. The gray wolf optimization algorithm optimizes the scheduling of the power generation units in the multi-energy microgrid, comprehensively considers the power generation costs of photovoltaic, gas turbine, storage battery and public grid, can reasonably allocate the operation tasks of each energy unit, reduce the power generation and operation costs, and optimize the economic benefits. Through the algorithm, the optimal power distribution among energy units is realized, avoiding unnecessary fuel consumption and power loss, thus further improving the economy.

[0097] A photovoltaic solar system may be connected to an inverter, an external power grid, a battery pack, or other electrical loads. Regardless of the load it is connected to, the issues to be addressed by maximum power point tracking are similar. That is, the power transfer efficiency of the solar cells is related to the amount of sunlight incident on the solar panels and also to the electrical characteristics of the load. When the sunlight conditions change, the load curve that can provide the maximum power transfer efficiency also changes. If the load can be adjusted to match the load curve with the highest power transfer efficiency, the system will have the best efficiency. The load characteristic with the highest power transfer efficiency is called the maximum power point. Maximum power point tracking is to find the maximum power point and maintain the load characteristic at this power point. A circuit 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. Maximum power point tracking can find the optimal load required to obtain the maximum available power. The maximum power point tracking (MPPT) technology is an optimization algorithm and control technology used to adjust the operating point of a photovoltaic system in real time so that 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 power generation unit operates in the maximum power point tracking mode, and realizes the conversion of light energy to electrical energy through the photovoltaic effect of the solar panels to ensure the maximum energy output of the photovoltaic power generation unit. Such a setting can not only dynamically adapt to environmental changes, achieve the maximum energy output of the photovoltaic unit, but also provide stable, economical, and environmentally friendly power support for the multi - energy microgrid.

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

[0100]

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

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

[0103]

[0104] Among them, 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 and discharge energy at time k - 1, η ch , η dis respectively represent the charge and discharge energy efficiency of the battery, SOC(k) is the state of charge of the energy storage at time k, and Ebatt is the total energy storage capacity.

[0105] The photovoltaic - energy - storage - gas - turbine dispatching control module, according to the economic objective and voltage fluctuation constraint within the dispatching period, first uses the photovoltaic power generation unit for power supply. When the power supply of the photovoltaic power generation unit is insufficient, it preferentially uses the battery for discharging; when the battery discharge is insufficient to meet the load demand, it starts the micro - gas turbine for supplementary power supply; when the power supply of the micro - gas turbine is still insufficient, it purchases power from the public grid for supplementation; when the photovoltaic power generation is excessive, it preferentially charges the battery, and if the battery is already full, it then sells power to the public grid to obtain economic benefits.

[0106] Referring to Figure 5 as shown, under the dispatching of the present invention, between about 2×10 4 seconds, there is an obvious upward trend in the load curve, indicating that the power demand increases at this time. The power of the photovoltaic power generation unit provides a relatively large power at about 2×10 4 seconds, which conforms to the characteristics of photovoltaic power generation. The power of the micro - gas turbine is relatively stable throughout the time period, but fluctuates around 5×10 4 seconds. The power of the battery is negative in some time periods, indicating that the battery is discharging; and it is positive in the remaining time periods, indicating that the battery is charging. The power of the public grid is continuously adjusted throughout the time period to balance the change of the total load, especially at the peak load of 2×10 4 seconds, the power of the public grid increases significantly.

[0107] As Figure 6 shown, this figure is a comparison chart of the grid - connection point voltage under the present invention and two other dispatching methods. In the heuristic dispatching, the micro - gas turbine is in a full - load state, and the grid - connection point voltage fluctuates in the range of 389 - 404V under the energy management of the battery. The grid - connection point voltage fluctuates in the range of 385 - 405V under the economic optimal dispatching method. The grid - connection point voltage fluctuates in the range of 392 - 400V under the optimized dispatching method of the present invention, showing an obvious improvement effect compared with the other two dispatching methods.

[0108] Figure 7This is a comparison graph of daily costs under the present invention and two other scheduling methods. The three curves respectively represent the changes in daily costs under different scheduling strategies. It can be seen from the graph that the cost curve of "heuristic scheduling" is generally higher than the other two curves, indicating that the daily cost of heuristic scheduling is relatively high. The cost curve of "economic optimization scheduling" is the second. Although it is lower than the cost of heuristic scheduling, it is still higher than that of "optimized scheduling considering voltage violation". The cost curve of "optimized scheduling considering voltage violation" is the lowest, indicating that the daily cost is the lowest when voltage violation is considered in the optimization. The daily cost of the optimized scheduling method with voltage penalty only increases by 6.4382% compared with the economic optimization scheduling method only. In addition, it has a significant effect on the ability to constrain the voltage fluctuation at the grid connection point, achieving the dual goals of economy and voltage fluctuation constraint.

[0109] The above-described embodiments merely represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the appended claims.

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

Claims

1. A multi - energy microgrid optimal scheduling method considering grid voltage support, characterized in that, Including: S10: Initialize the basic model of the multi - energy microgrid, and analyze the operating data characteristics including electricity price, power generation price, load characteristics, photovoltaic power generation characteristics, and equipment operation constraints in the microgrid system; S20: Based on the typical - day load data and typical - day sunlight data, predict the load curve and the output power of the photovoltaic power generation unit; S30: For the voltage fluctuation problem at the connection point of the multi - energy microgrid, introduce a voltage fluctuation penalty factor to constraint - manage the microgrid - grid interaction power; S40: Combine the operation constraints of the power generation units in the microgrid to establish a dual - optimization objective function with operation economy and voltage quality as the objectives for the multi - energy microgrid optimal scheduling model; S50: Use the grey wolf algorithm to solve the objective function, and determine the scheduling objectives of the photovoltaic power generation unit, battery, micro - gas turbine, and the interaction power with the public grid; S60: Output the scheduling result to realize the optimal allocation of photovoltaic power generation, battery charge - discharge, micro - gas turbine power generation, and the interaction power with the public grid.

2. The multi - energy microgrid optimal scheduling method considering grid voltage support according to claim 1, characterized in that, In the step S20, the photovoltaic power generation unit operates in the maximum power point tracking mode, and the calculation formula for the output power of the photovoltaic power generation unit is: P PV = f PV P RPV I g [1 + 0.005(T cell - T STC )] / I STC ; T cell = T a + 0.0138(1 + 0.031T a )(1 - 0.042v)I g ; Among them, P PV represents the output power of the photovoltaic panel, and f PV is the derating factor, which is used to describe the power reduction of the photovoltaic panel caused by external reasons such as aging and loss. P RPV is the rated installation capacity of the photovoltaic panel, and I g represents the actual light intensity, and its unit is generally W / m 2 , and I STC is the light intensity under standard test conditions. T STC is the standard test temperature, and T cell is the temperature on the surface of the photovoltaic panel.

3. A multi - energy microgrid optimal scheduling method considering grid voltage support according to claim 1, characterized in that, In the step S30, the voltage fluctuation penalty factor is used to measure the impact of the interaction power at the connection point on voltage fluctuation, and its expression is as follows: Among them, V S is the voltage at the near common power grid side, V R is the voltage on the microgrid side, R + jX is the line impedance, P R is the net active power absorbed by the bus and Q R is the reactive power absorbed by the bus.

4. A multi - energy microgrid optimal scheduling method considering grid voltage support according to claim 1, characterized in that, In the step S40, the expression of the dual - optimization objective function is: Among them, C grid (k) represents the electricity purchase cost of the power grid, C MT (k) represents the operating cost of the gas turbine, P MT (k) represents the electric power output by the micro gas turbine at time k, P PV (k) represents the electric power output by the photovoltaic power generation unit at time k, P grid (k) represents the interaction power with the power grid at time k. When P grid (k) is positive, it means the power grid sends electricity to the multi - energy microgrid. When P grid (k) is negative, it means the multi - energy microgrid sells electricity to the power grid.

5. A multi - energy microgrid optimal scheduling method considering grid voltage support according to the claim, characterized in that, In the step S50, the specific steps are: 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 grey wolf population, set the population size and the initial positions of each grey wolf individual, and these positions represent different scheduling schemes; S502: According to the dual - optimization objective function, calculate the fitness value of each individual in the grey wolf population; S503: Select the top three grey wolves with the fitness value as the leaders, which represent the current optimal scheduling schemes, and the other grey wolves are followers, and they will update their positions according to the distance and direction from the leaders; S504: The followers perform a non - linear transformation according to their own distance, direction from the leaders, and time factors to update their positions; S505: Repeat steps S502 - S504, continuously iterate and update the positions and fitness of the grey wolf population until the termination condition is met, and the finally converged solution is the power scheduling objective of each device in the multi - energy microgrid, that is, the optimal scheduling scheme.

6. A multi - energy microgrid optimal scheduling system considering grid voltage support, characterized in that, The system includes: A multi - energy microgrid, which is connected to the public grid through a transformer, and the multi - energy microgrid includes a bus, a photovoltaic power generation unit, a battery, and a micro - gas turbine; A photovoltaic - storage - gas scheduling control module for coordinating and optimizing the scheduling of the photovoltaic power generation unit, battery, micro - gas turbine, and the public grid; A voltage fluctuation management module, which constraint - manages the microgrid - public grid interaction power by introducing a penalty factor to control the voltage fluctuation at the connection point; An optimal scheduling algorithm module, which uses the grey wolf algorithm to optimize the allocation of the scheduling objectives of each power generation unit in the multi - energy microgrid, so as to achieve the dual objectives of operation economy and voltage fluctuation reliability.

7. The multi - energy microgrid optimal scheduling system considering grid voltage support according to claim 6, characterized in that, The photovoltaic power generation unit operates in the maximum power point tracking mode, and realizes the conversion of light energy to electrical energy through the photovoltaic effect of the solar panels to ensure the maximum energy output of the photovoltaic power generation unit.

8. The multi - energy microgrid optimal scheduling system according to claim 6, characterized in that, The state of charge of the storage battery is: Among them, 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 and discharge energy at time k-1, η ch , η dis respectively represent the charge and discharge energy efficiency of the battery, SOC(k) is the state of charge of the energy storage at time k, and E batt is the total energy storage capacity.

9. The multi - energy microgrid optimal scheduling system considering grid voltage support according to claim 6, characterized in that, The photovoltaic-battery-gas turbine dispatching and control module, according to the economic objectives and voltage fluctuation constraints within the dispatching period, first uses the photovoltaic power generation unit for power supply. When the power supply of the photovoltaic power generation unit is insufficient, it preferentially uses the storage battery for discharging; when the discharge of the storage battery is insufficient to meet the load demand, it starts the micro gas turbine for supplementary power supply; when the power supply of the micro gas turbine is still insufficient, it purchases power from the public grid for supplementation; when the photovoltaic power generation is excessive, it preferentially charges the storage battery, and if the storage battery is already full, it sells the power to the public grid to obtain economic benefits.

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