A micro-grid cooperative optimization scheduling method and system

Abstract

This invention discloses a microgrid collaborative optimization scheduling method and system. The method includes: establishing mathematical models of various equipment units in the microgrid, including wind power generation systems, photovoltaic power generation systems, energy storage systems, diesel generators, electric vehicle clusters, and ice storage air conditioning; constructing an optimization objective function with the goal of minimizing the total operating cost of the system; setting system constraints, including power balance constraints, equipment power constraints, energy storage constraints, electric vehicle SOC constraints, and ice storage air conditioning capacity constraints; adjusting the operating parameters of the mathematical models of each equipment unit under the premise of satisfying the system constraints, so that the optimization objective function reaches its minimum value; and optimizing the scheduling of each equipment unit based on the solved operating parameters. The advantages of this invention are: fully leveraging the collaborative scheduling potential of emerging schedulable resources such as electric vehicle clusters and ice storage air conditioning systems, and effectively addressing the uncertainties of renewable energy.

Description

Technical Field

[0001] This invention relates to the field of microgrid energy management technology, specifically to a microgrid collaborative optimization scheduling method and system. Background Technology

[0002] With the accelerated transformation of the global energy structure and the in-depth implementation of the "dual carbon" target, new power systems are rapidly developing towards a high proportion of renewable energy. Renewable energy sources such as wind power and solar photovoltaic power are gradually becoming the main direction of future energy development, providing important support for achieving a clean and low-carbon energy transformation.

[0003] Microgrids, as small-scale regional power systems capable of efficiently utilizing distributed energy resources, possess technological advantages such as autonomous operation, plug-and-play functionality, and flexible dispatch, playing a crucial role in promoting the large-scale integration of new energy sources and the low-carbon development of power systems. By integrating various distributed resources such as wind power, photovoltaic power, energy storage systems, and dispatchable loads, microgrids can meet regional electricity demand while achieving friendly interaction with the main grid, effectively alleviating grid peak-shaving pressure.

[0004] However, with the continuous expansion of microgrid scale and the increasing complexity of application scenarios, existing technologies have revealed the following problems in operation optimization and scheduling:

[0005] (1) Renewable energy sources such as wind and solar power have significant volatility, intermittency and unpredictability. Traditional microgrid dispatch strategies mostly adopt optimization methods based on deterministic models, which are difficult to effectively cope with the uncertainty of renewable energy output, resulting in problems such as high system operating costs and low renewable energy absorption rate.

[0006] (2) Existing research focuses on the modeling and optimization of single-type dispatchable load resources, such as considering only energy storage systems or analyzing demand response loads separately, neglecting the scheduling optimization potential brought about by the coordinated operation of multiple dispatchable load resources. Different types of dispatchable loads have different response characteristics and regulation capacities. Through multi-resource coordinated optimization, complementary advantages can be achieved, significantly improving the overall scheduling flexibility of the system.

[0007] (3) With the rapid growth of electric vehicle ownership and the development of building energy efficiency technologies, emerging dispatchable resources such as electric vehicle clusters and ice storage air conditioning systems are increasingly widely used in microgrids. Electric vehicle clusters are characterized by strong mobility and great potential for aggregated scheduling; ice storage air conditioning systems can effectively smooth peak loads and fill valleys by shifting cooling load time. However, existing research lacks microgrid optimization scheduling methods that consider these emerging dispatchable resources in a coordinated manner, and has failed to fully explore their collaborative scheduling value. For example, a multi-objective joint optimization scheduling method for microgrids disclosed in Chinese Patent Publication No. CN117039867A considers the emerging resource of ice storage air conditioning, but does not consider electric vehicle clusters, thus failing to fully explore the collaborative scheduling value of emerging dispatchable resources.

[0008] Therefore, there is an urgent need to develop a microgrid optimization scheduling method that can synergistically utilize multiple dispatchable load resources, so as to fully leverage the synergistic scheduling potential of emerging dispatchable resources such as electric vehicle clusters and ice storage air conditioning systems, effectively address the uncertainties of renewable energy, improve the economic efficiency and flexibility of system operation, enhance the absorption capacity of renewable energy, and provide important technical support for building a more intelligent, efficient, and clean new power system. Summary of the Invention

[0009] The technical problem to be solved by this invention is how to provide a microgrid optimization scheduling method that can synergistically utilize multiple dispatchable load resources, so as to fully leverage the synergistic scheduling potential of emerging dispatchable resources such as electric vehicle clusters and ice storage air conditioning systems, and effectively cope with the uncertainty of renewable energy.

[0010] This invention solves the above-mentioned technical problems through the following technical means: a microgrid collaborative optimization scheduling method, comprising: S1. Establish mathematical models for each equipment unit in the microgrid, including wind power generation system, photovoltaic power generation system, energy storage system, diesel generator, electric vehicle cluster, and ice storage air conditioning. S2. Construct an optimization objective function with the goal of minimizing the total operating cost of the system; S3. Set system constraints, including power balance constraints, equipment power constraints, energy storage constraints, electric vehicle SOC constraints, and ice storage air conditioning capacity constraints. S4. Under the premise of satisfying the system constraints, adjust the operating parameters of the mathematical model of each equipment unit so that the optimization objective function reaches the minimum value, and optimize the scheduling of each equipment unit according to the solved operating parameters.

[0011] This invention incorporates air conditioning and electric vehicles as flexible loads into a microgrid dispatch model, creating a synergistic optimization effect by combining the load transfer characteristics of ice storage air conditioning with the mobile energy storage characteristics of electric vehicle clusters. Under the joint optimization and dispatching effect of both, the peak load and overall cost of microgrid operation are effectively reduced, and the stability of the system is enhanced. This fully leverages the synergistic dispatching potential of emerging dispatchable resources such as electric vehicle clusters and ice storage air conditioning systems, effectively addressing the uncertainties of renewable energy.

[0012] Further, S1 includes: Establish a mathematical model for the wind power generation system In the formula, WT represents the wind power generation system, and the formula is as follows: This indicates that WT is at a wind speed of Power generation output at that time; This indicates the rated output power of WT; , and These represent the cut-in wind speed, rated wind speed, and cut-out wind speed, respectively.

[0013] Establish a mathematical model for the photovoltaic power generation system In the formula, PV represents a photovoltaic power generation system. This indicates the actual operating output power of the PV; This indicates the output power under standard test conditions; This represents the actual intensity of solar radiation. This indicates the intensity of solar radiation under standard test conditions; The temperature coefficient of a photovoltaic module; This indicates the actual operating temperature of the photovoltaic module; This indicates the temperature under standard test conditions.

[0014] Establish a mathematical model for the energy storage system In the formula, ESS represents an energy storage system. and The ESS values ​​represent respectively in Time and The state of charge at any given moment; and These represent the charging and discharging efficiencies of the ESS, respectively. and They respectively represent ESS in The charging and discharging power at any given time; For time intervals; This indicates the maximum capacity of the ESS.

[0015] Furthermore, S1 also includes: Establish a mathematical model for the diesel generator. In the formula, This indicates the fuel cost of DG, where DG stands for diesel generator. This indicates that DG is in Output power during the time period; , and The coefficients represent the fuel cost function.

[0016] Establishing a mathematical model for electric vehicle clusters In the formula, and They represent the first EVs in Time period and State of charge over a period of time; and They represent the first The charging and discharging power of an EV during time period t; and Indicates charge / discharge efficiency; Indicates the charging status. This indicates the discharge state. This indicates the maximum battery capacity.

[0017] Establish a mathematical model for ice storage air conditioning. In the formula, This represents the amount of ice stored in the ISAC during time period t. ISAC stands for Ice Storage Air Conditioning. Indicates the self-loss coefficient; and These represent the ice accumulation and melting rates during time period t, respectively.

[0018] The electrical power of the ISAC refrigeration unit is expressed as follows:

[0019]

[0020] In the formula: This indicates the output cooling capacity of the ISAC during time period t; This indicates the maximum output cooling capacity of the ISAC; Rated energy efficiency ratio; , and Represents the fitting coefficient; This indicates the output cooling capacity of the unit during the cooling storage period; This indicates the output cooling capacity of the unit during the cooling period.

[0021] The total load of the ISAC for each time period is obtained by summing the electricity consumption of the ISAC in each time period, expressed as:

[0022] In the formula: This indicates the total number of ISACs.

[0023] Furthermore, S2 includes: The optimization objective function is expressed as follows:

[0024] In the formula: Indicates system operation and maintenance costs; Indicates the cost of system pollution control. Represented as

[0025] In the formula: This indicates the operation and maintenance cost of a wind power generation system; This indicates the operation and maintenance cost of a photovoltaic power generation system; This indicates the operation and maintenance cost of the diesel generator; This indicates the operation and maintenance cost of the energy storage system; This indicates the cost of exchanging electricity with the main grid; This represents the compensation cost to electric vehicle users; This represents the compensation cost for air conditioner users. Among them,

[0026]

[0027]

[0028]

[0029]

[0030]

[0031]

[0032] Where: Where: , , and These represent the operation and maintenance cost coefficients for WT, PV, DG, and ESS, respectively. and They respectively represent ESS in The charging and discharging power during a given period; This indicates the electricity exchange price between the microgrid and the distribution network; and They are respectively The purchase price and sales price of electricity from the microgrid for EVs during the specified time period; and They represent Electricity purchased from the distribution network and electricity sold to the distribution network during a given period. and They represent the first EVs in The charging and discharging power during a given period; and They represent the first The charging and discharging efficiency of an EV; This indicates the battery maintenance cost of an EV; Indicates the first EVs in The charging and discharging power during a given period; This indicates the power compensation cost of ISAC; Pollution control costs Represented as

[0033] In the formula: and These represent the costs of DG (Distributed Generation) and the pollution control costs of the power distribution network, respectively. Indicating governance Cost coefficients for pollutants of this type; and These represent the pollutant emissions per unit of electricity generated during the operation of the DG and distribution networks, respectively.

[0034] Furthermore, the power balance constraint includes:

[0035] In the formula: express Interaction power between microgrids and distribution networks during specific time periods; express Basic load demand for a given period; Indicates EVs in The charging and discharging power during a given period, with charging being positive and discharging being negative; Indicates that ISAC is in Power consumption during a given time period; Indicates ESS in The charging and discharging power during a given period, with charging being positive and discharging being negative.

[0036] Furthermore, the device power constraint includes:

[0037] In the formula: and Representing device units Upper and lower limits of output power;

[0038] in: and These represent the upper and lower limits of DG's ramp power, respectively.

[0039] Furthermore, the energy storage constraints include:

[0040]

[0041] In the formula: and These represent the minimum and maximum states of charge allowed by the ESS, respectively; Tie line constraints are represented as

[0042] In the formula: and These represent the upper and lower limits of the power exchange with the power distribution network per unit time.

[0043] Furthermore, the electric vehicle SOC constraint includes:

[0044]

[0045]

[0046]

[0047] In the formula: and These represent the upper and lower limits of the EV's state of charge, respectively. and These represent the upper and lower limits of power during EV charging; and These represent the upper and lower limits of power during EV discharge, respectively.

[0048] Furthermore, the ice storage air conditioning capacity constraint includes:

[0049]

[0050]

[0051]

[0052]

[0053] In the formula: and Indicates the upper and lower limits of the ice storage capacity of the ISAC (Ice Isolation and Control) system; Indicates the upper limit of the ice accumulation rate; Indicates that ISAC is in Cooling load demand during different time periods; This indicates the upper limit of the ice melting rate.

[0054] This invention also provides a system for implementing the above-described microgrid collaborative optimization scheduling method, comprising: The mathematical model building module is used to establish mathematical models of various equipment units in the microgrid, including wind power generation systems, photovoltaic power generation systems, energy storage systems, diesel generators, electric vehicle clusters, and ice storage air conditioning. The objective function building module is used to construct an optimization objective function that aims to minimize the total operating cost of the system. The constraint setting module is used to set system constraints, including power balance constraints, equipment power constraints, energy storage constraints, electric vehicle SOC constraints, and ice storage air conditioning capacity constraints. The optimization solution module is used to adjust the operating parameters of the mathematical models of each equipment unit under the premise of satisfying the system constraints, so that the optimization objective function reaches the minimum value, and to optimize the scheduling of each equipment unit based on the solved operating parameters.

[0055] The advantages of this invention are: (1) This invention incorporates air conditioning and electric vehicles as flexible loads into the microgrid dispatch model, forming a good synergistic optimization effect by combining the load transfer characteristics of ice storage air conditioning and the mobile energy storage characteristics of electric vehicle clusters. Under the joint optimization and dispatching effect of the two, the peak load and overall cost of microgrid operation are effectively reduced, and the stability of the system is enhanced. This fully leverages the synergistic dispatch potential of emerging dispatchable resources such as electric vehicle clusters and ice storage air conditioning systems, and effectively addresses the uncertainty of renewable energy.

[0056] (2) This invention, for the first time, incorporates electric vehicle clusters and ice storage air conditioning systems as collaboratively dispatchable resources into the microgrid optimization scheduling framework, fully leveraging the complementary characteristics of the two types of resources. Electric vehicle clusters provide bidirectional power regulation capabilities, while ice storage air conditioning systems achieve time-based transfer of cooling loads. Their synergistic effect significantly enhances the scheduling flexibility of the microgrid, resulting in a marked improvement in the system's peak-shaving capacity compared to single-resource scheduling schemes. Through collaborative optimization scheduling, the time-of-use pricing mechanism and the output characteristics of renewable energy are effectively utilized, achieving a significant reduction in operating costs. Electric vehicles charge during low-price periods and discharge during high-price periods, while ice storage air conditioning systems produce ice at night and provide cooling during the day, significantly reducing the system's electricity costs. Compared to traditional scheduling methods, the total system operating cost is significantly reduced.

[0057] (3) This invention effectively alleviates the volatility and intermittency of wind and solar power generation through multi-resource coordinated scheduling. The mobile energy storage characteristics of electric vehicle clusters and the load transfer characteristics of ice storage air conditioning provide flexible means for renewable energy consumption, increasing the utilization rate of renewable energy by 10-20% and significantly reducing the curtailment rate of wind and solar power. This enhances the renewable energy consumption capacity.

[0058] (4) This invention enhances the microgrid's resistance to disturbances through the coordinated configuration of multiple schedulable resources. When renewable energy output is insufficient or the load suddenly increases, electric vehicles can quickly discharge to support the system, and ice storage air conditioning can adjust the cooling strategy, effectively maintaining the system power balance and improving power supply reliability.

[0059] (5) This invention reduces the demand for fossil fuel power generation and lowers carbon emissions by increasing the proportion of renewable energy consumption. At the same time, optimized scheduling reduces the number of start-ups and shutdowns and operating time of diesel generator sets, further reducing pollutant emissions and achieving a dual improvement in economic and environmental benefits.

[0060] (6) This invention incorporates the bidirectional charging and discharging characteristics of electric vehicle clusters and the load transfer characteristics of ice storage air conditioning, which collaboratively participate in the optimized scheduling of the microgrid, improving the economic efficiency and environmental friendliness of system operation, while also increasing the renewable energy absorption rate. At the optimized scheduling level, by coordinating and optimizing multiple types of schedulable resources under the premise of satisfying power balance and equipment constraints, the output and load response of each device are dynamically adjusted, thereby smoothing out fluctuations in renewable energy output. Thus, the overall solution can cope with the uncertainty of renewable energy. Attached Figure Description

[0061] Figure 1 This is a diagram illustrating the internal structure of a microgrid in a microgrid collaborative optimization scheduling method disclosed in an embodiment of the present invention. Figure 2 This is a schematic diagram of time-of-use pricing in a microgrid collaborative optimization scheduling method disclosed in an embodiment of the present invention; Figure 3 This is a wind and solar power output and load demand prediction diagram in a microgrid collaborative optimization scheduling method disclosed in an embodiment of the present invention; Figure 4 This is a power balance diagram for scenario 1 (disorderly charging of EVs + ordinary air conditioning) in a microgrid collaborative optimization scheduling method disclosed in an embodiment of the present invention; Figure 5 This is a power balance diagram for scenario 2 (orderly charging of EVs + ordinary air conditioning) in a microgrid collaborative optimization scheduling method disclosed in an embodiment of the present invention; Figure 6 This is a power balance diagram for scenario 3 (orderly charging of EVs + ISAC) in a microgrid collaborative optimization scheduling method disclosed in an embodiment of the present invention; Figure 7 This is a comparison chart of EVs scheduling results in three scenarios of a microgrid collaborative optimization scheduling method disclosed in this embodiment of the invention; Figure 8 This is a comparison chart of air conditioning scheduling results in three scenarios of a microgrid collaborative optimization scheduling method disclosed in an embodiment of the present invention. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] Example 1 Embodiment 1 of this invention provides a microgrid collaborative optimization scheduling method. By collaboratively utilizing the mobile energy storage characteristics of electric vehicles and the load transfer characteristics of ice storage air conditioning, it minimizes the operating cost of the microgrid, improves the renewable energy absorption rate, and enhances the system's economic efficiency and environmental friendliness. This invention mainly addresses the following key technical issues: (1) Collaborative modeling of diverse heterogeneous schedulable resources. Electric vehicle clusters have the characteristics of bidirectional charging and discharging regulation capability, strong mobility, and large aggregation scale, which can provide flexible mobile energy storage services for microgrids; ice storage air conditioning systems can realize the transfer of cooling load in the time dimension through the operation mode of making ice at night and melting ice during the day, which has a significant peak shaving and valley filling effect. However, these two types of resources have significant differences in response speed, regulation capacity, constraints, and cost structure. How to establish a unified collaborative optimization model framework and accurately characterize their operating characteristics and interaction relationships is the premise for achieving effective collaborative scheduling.

[0064] (2) The charging and discharging behavior of electric vehicles has obvious spatiotemporal distribution characteristics, and their scheduling needs to consider multiple factors such as user travel demand, battery capacity constraints, and the distribution of charging facilities; the operation of ice storage air conditioning systems needs to take into account constraints such as building cooling load demand, ice storage tank capacity limitations, and changes in cooling efficiency. How to achieve coordinated and optimized scheduling of multiple resources at different time scales and spatial ranges under the premise of satisfying various constraints, and maximize the exploitation of their regulation potential, is the key technical problem that this invention needs to solve.

[0065] (3) Robust optimization problem under uncertain environment. Uncertain factors such as renewable energy output prediction error, randomness of electric vehicle user behavior, and building cooling load fluctuation are coupled with each other, which pose a serious challenge to the optimal scheduling of microgrids. How to formulate a robust collaborative scheduling strategy under multiple uncertain environments to ensure that the system can maintain safe and stable operation under various operating scenarios, while achieving optimal economic benefits, is the core technical objective of this invention.

[0066] To solve the above technical problems, such as Figure 1 As shown, the method provided by this invention includes the following steps: Step S1: Establish mathematical models for each device unit in the microgrid, including a complete microgrid architecture encompassing wind power (WT), photovoltaic (PV), energy storage (ESS), diesel generators (DG), electric vehicle clusters (EVs), ice storage air conditioning (ISAC), and base load. Each device unit is coordinated and dispatched through a unified energy management and control center, forming a system structure of centralized control and distributed execution. The specific model construction process is as follows: 1) Wind turbine model: A piecewise linear model is established based on wind speed changes. The model increases linearly between the cut-in wind speed and the rated wind speed, and maintains the rated power output between the rated wind speed and the cut-out wind speed.

[0067] The output power of the WT varies with wind speed, and the nonlinear relationship between its power and wind speed can be expressed by a mathematical model. (1) In the formula: This indicates that WT is at a wind speed of Power generation output at that time; This indicates the rated output power of WT; , and These represent the cut-in wind speed, rated wind speed, and cut-out wind speed, respectively.

[0068] 2) Photovoltaic generator model: An output power model is established based on solar radiation intensity and ambient temperature, taking into account the impact of temperature coefficient on power generation efficiency.

[0069] The output power of a PV is affected by various environmental factors, primarily depending on light intensity and ambient temperature. Its output power can be mathematically expressed as: (2) In the formula: This indicates the actual operating output power of the PV; This indicates the output power under standard test conditions; This represents the actual intensity of solar radiation. This indicates the intensity of solar radiation under standard test conditions; The temperature coefficient of a photovoltaic module; This indicates the actual operating temperature of the photovoltaic module; This indicates the temperature under standard test conditions.

[0070] 3) Energy storage system model: The state of charge (SOC) is used to characterize the charging and discharging process of the energy storage system, taking into account charging and discharging efficiency and capacity constraints.

[0071] An ESS (Energy Saving System) is primarily composed of batteries, storing excess energy when there is a surplus of electricity and releasing energy when load demand increases or renewable energy output is insufficient. Its charging and discharging process is typically characterized by its state of charge, denoted as _____. (3) In the formula: and The ESS values ​​represent respectively in Time and The state of charge at any given moment; and These represent the charging and discharging efficiencies of the ESS, respectively. and They respectively represent ESS in The charging and discharging power at any given time; For time intervals; This indicates the maximum capacity of the ESS.

[0072] 4) Diesel generator model: DG (Distributed Generator) is a crucial controllable generation unit in microgrid systems. When renewable energy output and ESS (Energy Storage System) energy storage are insufficient, DG serves as a vital supplementary power source, providing power support to ensure the continuity and reliability of the system's power operation. The fuel cost of DG primarily depends on its output power, expressed as... (4) In the formula: This indicates the fuel cost of DG; This indicates that DG is in Output power during the time period; , and The coefficients represent the fuel cost function.

[0073] 5) Electric vehicle cluster model: The charging behavior adopts a probability distribution model, including the log-normal distribution of driving mileage and the normal distribution of charging start and end times; V2G technology is considered in the orderly charging and discharging mode to realize bidirectional power interaction.

[0074] The charging and discharging behavior of EVs is mainly related to their driving range and charging start and end times. The daily driving distance of EVs can be approximately followed by a log-normal distribution, and the corresponding probability density function expression is as follows: (5) In the formula: This indicates the daily driving distance of EVs. This represents the expected driving range of the EV. Let be the standard deviation of the EV's driving range. The charging start time of EVs can be approximated by a normal distribution, and its probability density function is expressed as follows: (6) In the formula: Indicates the start time of charging for the EV. Expected value at the start of charging EVs Let be the standard deviation of the charging start time. The charging end times of EVs follow a normal distribution, and the probability density function is expressed as follows: (7) In the formula: Indicates the time when EVs finish charging. Expected value at the end of charging for EVs. The standard deviation of the charging completion time for EVs. The charging modes for EVs participating in optimized scheduling are divided into disordered charging and ordered charging. Disordered charging occurs when the microgrid operator does not adopt a special charging strategy; EV owners determine their charging behavior based on their travel routes. This typically manifests as owners starting charging immediately upon the end of their trip and stopping charging once the desired State of Charge (SOC) is reached. Disordered charging EVs can act as impact loads, affecting the stable operation of the microgrid. The daily battery charge loss of an EV is expressed as (8) In the formula: This indicates the average daily mileage of the EV; This indicates the power consumption of an EV for every 100 kilometers it travels. Indicates the first The rated capacity of the EV battery. Summing the charging power of EVs at different times yields the total charging load of EVs at each time period, expressed as: (9) In the formula: Indicates the first The charging power of an EV during time period t; I represents the number of EVs.

[0075] Orderly charging refers to the process by which EVs participating in V2G (Vehicle-to-Grid) act as mobile energy storage during the grid connection and disconnection periods, participating in microgrid dispatch. While meeting the charging needs of EVs, the charging and discharging times of EVs are adjusted, transforming them from traditional electricity loads into flexible adjustment resources, thus helping the grid to peak-shaving and valley-filling and improve resource utilization. The dynamic change of SOC during the charging and discharging process of EVs characterizes their charging and discharging characteristics, expressed as... (10) In the formula: and They represent the first EVs in Time period and State of charge over a period of time; and They represent the first The charging and discharging power of an EV during time period t; and Indicates charge / discharge efficiency; Indicates the charging status. This indicates the discharge state. This indicates the maximum battery capacity.

[0076] 6) Ice storage air conditioning model: Establish a model for the cold storage and cooling process, taking into account the self-loss coefficient, ice storage and melting rate constraints.

[0077] The operation of an ISAC (Ice Storage and Cooling Controller) consists of two phases: cold storage and cooling supply. During nighttime hours when electricity load is low, the ISAC can utilize refrigeration units to store ice for cooling. During daytime hours when electricity load is high, the ISAC can release cold energy through ice melting to meet cooling demand. The ISAC operates in three modes: independent cooling by the refrigeration unit, ice melting cooling, and combined cooling by the refrigeration unit and ice melting. The mathematical model for this is as follows: (11) In the formula: This represents the amount of ice stored in the ISAC during time period t; Indicates the self-loss coefficient; and These represent the ice accumulation and melting rates during time period t, respectively.

[0078] The electrical power of the ISAC refrigeration unit is expressed as follows: (12) (13) In the formula: This indicates the output cooling capacity of the ISAC during time period t; This indicates the maximum output cooling capacity of the ISAC; Rated energy efficiency ratio; , and Represents the fitting coefficient; This indicates the output cooling capacity of the unit during the cooling storage period; This indicates the output cooling capacity of the unit during the cooling period.

[0079] The total load of the ISAC for each time period is obtained by summing the electricity consumption of the ISAC in each time period, expressed as: (14) In the formula: This indicates the total number of ISACs.

[0080] The operating principle of step S1 above can be summarized as follows: First, a data acquisition and preprocessing system is built, establishing a real-time data acquisition system to obtain meteorological data such as wind speed, light intensity, and ambient temperature; second, behavioral data such as the travel patterns and charging needs of electric vehicle users are collected; third, changes in the cooling load demand of buildings are monitored; and so on. Figure 2 As shown, the time-of-use electricity price information and pollutant treatment cost parameters of the power grid are obtained. Then, the renewable energy output model is constructed. Specifically, a piecewise linear mathematical model of the wind turbine is established according to formula (1), and the parameters of cut-in wind speed, rated wind speed and cut-out wind speed are set. A photovoltaic power generation system model is established according to formula (2), considering the influence of solar radiation intensity and temperature coefficient on power generation efficiency. Next, the dynamic modeling of the energy storage system is carried out. Specifically, the concept of state of charge (SOC) is adopted, and a dynamic mathematical model of charging and discharging of the energy storage system is established according to formula (3), and the charging and discharging efficiency, power limit and capacity constraint are set. Then, the electric vehicle cluster modeling is carried out. Specifically, based on the probability and statistics method, a log-normal distribution model of the daily driving mileage of electric vehicles and a normal distribution model of charging start and end time are established according to formulas (5)-(7), respectively. A dynamic scheduling model of electric vehicle cluster under orderly charging and discharging mode is established according to formulas (8)-(10) to realize V2G bidirectional power interaction. Finally, the ice storage air conditioning system is modeled. Based on formulas (11)-(14), a two-stage operation model for ice storage air conditioning is established, including three operation modes: independent cooling by the chiller, ice melting cooling, and combined cooling by the chiller and ice melting. The self-loss coefficient and ice storage / melting rate constraints are considered.

[0081] Step S2: Construct the optimization objective function.

[0082] The optimization objective of the power grid is to minimize the total operating cost of the system, and the objective function is expressed as follows: (15) In the formula: Indicates system operation and maintenance costs; This indicates the cost of system pollution control. Represented as (16) In the formula: This indicates the operation and maintenance cost of a wind power generation system; This indicates the operation and maintenance cost of a photovoltaic power generation system; This indicates the operation and maintenance cost of the diesel generator; This indicates the operation and maintenance cost of the energy storage system; This indicates the cost of exchanging electricity with the main grid; This represents the compensation cost to electric vehicle users; This represents the compensation cost for air conditioning users. The operation and maintenance costs for each piece of equipment are calculated as follows: (17) (18) (19) (20) (twenty one) (twenty two) (twenty three) In the formula: , , and These represent the operation and maintenance cost coefficients for WT, PV, DG, and ESS, respectively. and They respectively represent ESS in The charging and discharging power during a given period; This indicates the electricity exchange price between the microgrid and the distribution network; and They are respectively The purchase price and sales price of electricity from the microgrid for EVs during the specified time period; and They represent Electricity purchased from the distribution network and electricity sold to the distribution network during a given period. and They represent the first EVs in The charging and discharging power during a given period; and They represent the first The charging and discharging efficiency of an EV; This indicates the battery maintenance cost of an EV; Indicates the first EVs in The charging and discharging power during a given period; This represents the power compensation cost of ISAC.

[0083] Pollution control costs This mainly includes the cost of treating pollutants generated by diesel generators and power distribution networks, expressed as... (twenty four) In the formula: and These represent the costs of DG (Distributed Generation) and the pollution control costs of the power distribution network, respectively. Indicating governance Cost coefficients for pollutants of this type; and These represent the pollutant emissions per unit of electricity generated during the operation of the DG and distribution networks, respectively.

[0084] The operating principle of step S2 above can be summarized as follows: Based on formulas (15)-(24), a comprehensive objective function is established with the goal of minimizing system operation and maintenance costs and environmental governance costs. Operation and maintenance costs include the maintenance costs of each power generation device, the interaction costs with the main grid, and the user compensation costs; environmental governance costs include the pollution control costs of diesel generators and distribution network.

[0085] Step S3: Set constraints. These include power balance constraints, equipment power constraints, SOC constraints for energy storage and electric vehicles, and capacity constraints for ice storage and air conditioning.

[0086] At any given time, the total power generation of the microgrid system remains in balance with its total power consumption, denoted as: (25) In the formula: express Interaction power between microgrids and distribution networks during specific time periods; express Basic load demand for a given period; Indicates EVs in The charging and discharging power during a given period, with charging being positive and discharging being negative; Indicates that ISAC is in Power consumption during a given time period; Indicates ESS in The charging and discharging power during a given period, with charging being positive and discharging being negative.

[0087] The output power of each device must be maintained within its rated range, expressed as follows: (26) In the formula: and Representing device units The upper and lower limits of output power. The rate of change of power of the DG must be within the specified range, expressed as... (27) In the formula: and These represent the upper and lower limits of DG's ramp power, respectively.

[0088] During the charging and discharging process of an ESS, the state of charge must be maintained within the battery capacity range, which is expressed as... (28) (29) In the formula: and These represent the minimum and maximum states of charge allowed by the ESS, respectively; Tie line constraints are represented as (30) In the formula: and These represent the upper and lower limits of the power exchange with the power distribution network per unit time.

[0089] EV constraints are expressed as (31) (32) (33) (34) In the formula: and These represent the upper and lower limits of the EV's state of charge, respectively. and These represent the upper and lower limits of power during EV charging; and These represent the upper and lower limits of power during EV discharge, respectively.

[0090] ISAC constraints are expressed as follows: (35) (36) (37) (38) (39) In the formula: and Indicates the upper and lower limits of the ice storage capacity of the ISAC (Ice Isolation and Control) system; Indicates the upper limit of the ice accumulation rate; Indicates that ISAC is in Cooling load demand during different time periods; This indicates the upper limit of the ice melting rate.

[0091] The operating principle of step S3 above can be summarized as follows: Constructing a complete system of constraints, including: power balance constraints (Formula 25): ensuring that power generation and power consumption are balanced at any time; equipment power constraints (Formulas 26-27): ensuring that the output power of each device is within the rated range; energy storage constraints (Formulas 28-29): ensuring that the ESS state of charge is kept within a safe range; tie line constraints (Formula 30): limiting the power interaction with the distribution network; electric vehicle constraints (Formulas 31-34): limiting the EV state of charge and charging / discharging power; ice storage air conditioning constraints (Formulas 35-39): constraints on ISAC ice storage capacity and operating rate.

[0092] Step S4: Solve the optimization model. The Gurobi solver is used to solve the constructed mixed-integer linear programming model to obtain the optimal scheduling strategy for each device within a 24-hour scheduling cycle. The solution process involves minimizing the objective function by adjusting the controllable decision variables corresponding to each device model in Step S1, while satisfying constraints such as power balance, device operation, and SOC. Parameters adjusted during optimization include: the output power of the diesel generator, the charging and discharging power of the electric vehicle cluster at different times, the charging and discharging power of the energy storage system, and the ice storage and melting rates of the ice storage air conditioning system at different times. These parameters are used as optimization variables in solving the objective function. Simultaneously, the probability density function model established in S1 is used to characterize the uncertainty of electric vehicle travel behavior and load demand. Its role is reflected in the data generation and parameter setting stage before scheduling, providing reasonable input boundaries and initial conditions for subsequent deterministic optimization scheduling. It is not directly used as an optimization variable in the solution, therefore it is not repeated in the solution stage. Based on Steps S3 and S2, the economic and environmental benefits of microgrid operation are comprehensively considered, and the scheduling results are analyzed. The specific execution logic for step S4 above is as follows: First, the scheduling cycle and time are discretized. Specifically, a complete scheduling cycle is set at 24 hours, and the scheduling cycle is discretized into 24 scheduling time periods at 1-hour intervals. Then, a multi-resource collaborative scheduling strategy is formulated, specifically a collaborative scheduling strategy for electric vehicle clusters and ice storage air conditioning systems. Electric vehicles are charged in an orderly manner during off-peak electricity hours (10:00 PM - 6:00 AM) and discharged during peak electricity hours (10:00 AM - 3:00 PM and 7:00 PM - 9:00 PM). Ice storage air conditioning produces ice for cooling during low-price periods at night and melts ice for cooling during high-price periods during the day. The energy storage system works in conjunction with these adjustments to smooth out system power fluctuations.

[0093] Next, the mathematical optimization model was solved. Specifically, the established mathematical model was transformed into a mixed-integer linear programming problem, which was solved using the Gurobi commercial solver to obtain the optimal scheduling strategy for each device over 24 time periods. The wind and solar power output and load demand forecast results are as follows: Figure 3 As shown.

[0094] Based on the aforementioned method and process, this invention establishes a hierarchical control architecture. The upper layer is the Energy Management System (EMS), responsible for global optimization and scheduling decisions; the middle layer is the equipment controller, executing specific equipment scheduling instructions; and the lower layer is the equipment execution layer, realizing the actual equipment action control. After the hierarchical control architecture is established, real-time scheduling execution is performed. Specifically, the Energy Management System issues scheduling instructions in real time based on the optimization results: sending charging and discharging time and power instructions to the Electric Vehicle Charging Management System; sending ice-making / ice-melting operation mode instructions to the Ice Storage Air Conditioning Control System; sending charging and discharging power instructions to the Energy Storage System; and sending start / stop and output instructions to the Diesel Generator. Subsequently, a closed-loop control mechanism is established to monitor the operating status of each device and the system power balance in real time. When the actual operation deviates from the optimization plan, re-optimization calculation is triggered, and the scheduling strategy is dynamically adjusted.

[0095] To verify the effectiveness of the method proposed in this invention, this embodiment conducts a multi-scenario comparative analysis, setting up different scheduling scenarios for comparative verification: Scenario 1: Unordered charging of electric vehicles + ordinary air conditioning; Scenario 2: Ordered charging of electric vehicles + ordinary air conditioning; Scenario 3: Ordered charging of electric vehicles + ice storage air conditioning. The scheduling effect is evaluated from three dimensions: economy, environmental protection, and system stability. Among them, the economic indicators include operation and maintenance costs and total operating costs; the environmental protection indicators include environmental governance costs and renewable energy absorption rate; and the stability indicators include power balance accuracy and peak-valley difference. Figure 4 A power balance diagram for scenario 1 (disorderly charging of EVs + ordinary air conditioning) is given. Figure 5 A power balance diagram for scenario 2 (orderly charging of EVs + ordinary air conditioning) is provided. Figure 6 A power balance diagram for scenario 3 (orderly charging of EVs + ISAC) is provided. Figure 7 The following charts compare the EVs scheduling results in three scenarios. Figure 8 A comparison chart of air conditioning scheduling results in three scenarios is provided. Figure 4This diagram illustrates the power balance of a microgrid in Scenario 1, involving EVs employing a disordered charging strategy and regular air conditioners. As shown in the figure, the positive and negative power outputs of the microgrid remain balanced during operation, but the load side exhibits a clear peak-valley characteristic. WT (Power Transmission) and PV (Power Generation) are the main power sources for the microgrid. WT generates more power at night when wind speeds are higher, while PV power output is mainly concentrated during the daytime. When the output of either is insufficient, the microgrid supplements its power demand through DG (Distributed Generation) or interaction with the grid. Figure 5 This demonstrates the power balance of microgrid dispatching using EVs employing an ordered charging and discharging strategy and ordinary air conditioners in Scenario 2. Compared to Scenario 1, the ordered charging mode in Scenario 2 shifts the EV charging load from peak to off-peak periods, with some EVs discharging during peak hours to participate in peak shaving and valley filling of the microgrid. Figure 6 This section demonstrates the power balance of microgrid dispatch using EVs and ISACs with an ordered charging / discharging strategy in Scenario 3. Compared to the previous two scenarios, Scenario 3 exhibits the optimal power balance effect. ISACs operate at high power during off-peak hours at night for ice storage and cooling, and melt ice for cooling during peak hours in the daytime, thereby reducing power consumption during peak periods. This synergizes with the ordered charging / discharging mode of EVs, effectively achieving peak shaving and valley filling. The power balance diagram shows a more balanced load distribution, a further reduction in peak-valley differences, and a significant improvement in renewable energy absorption. Figure 7 The data illustrates the charging and discharging scheduling results of EVs throughout the day in three scenarios. In Scenario 1, EVs employ a disordered charging strategy. As shown in the figure, the charging power of EVs exhibits typical peak-valley periods, which exacerbates the peak-valley difference in the microgrid. In Scenarios 2 and 3, EVs adopt an ordered charging strategy, allowing them to effectively participate in the power dispatching of the microgrid through charging and discharging. As shown in the figure, EVs mainly charge during the off-peak hours at night, while some EVs discharge during peak hours, feeding power back to the microgrid. This plays a crucial role in peak shaving and valley filling. Figure 8 The data shows the air conditioning scheduling results in three scenarios. In scenarios 1 and 2, the air conditioners are ordinary air conditioners, and as shown in the figure, their power consumption characteristics exhibit typical peak-valley features. Compared with scenarios 1 and 2, the power consumption characteristics of the ISAC change significantly in scenario 3. During the nighttime off-peak hours, the ISAC operates at high power for ice storage and cooling; during the daytime peak hours, the ISAC melts ice for cooling. This effectively shifts the cooling load from peak to off-peak periods, effectively smoothing out the peak-valley difference in microgrid operation. Through comparative analysis, the coordinated optimization scheduling of EVs and ISAC provides significant flexibility resources for the microgrid, improving the microgrid's economy, environmental friendliness, and renewable energy integration rate while ensuring user comfort and power demand.

[0096] Through the above technical solutions, this invention achieves coordinated and optimized scheduling of electric vehicle clusters and ice storage air conditioning in a microgrid, significantly improving the system's economy, environmental friendliness, and renewable energy absorption capacity. This implementation method has good engineering practicality and promotional value.

[0097] Example 2 Embodiment 2 of the present invention also provides a system for implementing the microgrid cooperative optimization scheduling method of Embodiment 1 described above, comprising: The mathematical model building module is used to establish mathematical models of various equipment units in the microgrid, including wind power generation systems, photovoltaic power generation systems, energy storage systems, diesel generators, electric vehicle clusters, and ice storage air conditioning. The objective function building module is used to construct an optimization objective function that aims to minimize the total operating cost of the system. The constraint setting module is used to set system constraints, including power balance constraints, equipment power constraints, energy storage constraints, electric vehicle SOC constraints, and ice storage air conditioning capacity constraints. The optimization solution module is used to adjust the operating parameters of the mathematical models of each device unit to achieve optimization, while satisfying the system constraints.

[0098] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A microgrid collaborative optimization scheduling method, characterized in that, include: S1. Establish mathematical models for each equipment unit in the microgrid, including wind power generation system, photovoltaic power generation system, energy storage system, diesel generator, electric vehicle cluster, and ice storage air conditioning. S2. Construct an optimization objective function with the goal of minimizing the total operating cost of the system; S3. Set system constraints, including power balance constraints, equipment power constraints, energy storage constraints, electric vehicle SOC constraints, and ice storage air conditioning capacity constraints. S4. Under the premise of satisfying the system constraints, adjust the operating parameters of the mathematical model of each equipment unit so that the optimization objective function reaches the minimum value, and optimize the scheduling of each equipment unit according to the solved operating parameters.

2. The microgrid collaborative optimization scheduling method according to claim 1, characterized in that, S1 includes: Establish a mathematical model for the wind power generation system In the formula, WT represents the wind power generation system. This indicates that WT is at a wind speed of Power generation output at that time; This indicates the rated output power of WT; , and These represent the cut-in wind speed, rated wind speed, and cut-out wind speed, respectively. Establish a mathematical model for the photovoltaic power generation system. In the formula, PV represents a photovoltaic power generation system. This indicates the actual operating output power of the PV; This indicates the output power under standard test conditions; This represents the actual intensity of solar radiation. This indicates the intensity of solar radiation under standard test conditions; The temperature coefficient of a photovoltaic module; This indicates the actual operating temperature of the photovoltaic module; Indicates the temperature under standard test conditions; Establish a mathematical model for the energy storage system In the formula, ESS represents an energy storage system. and The ESS values ​​represent respectively in Time and The state of charge at any given moment; and These represent the charging and discharging efficiencies of the ESS, respectively. and They respectively represent ESS in The charging and discharging power at any given time; For time intervals; This indicates the maximum capacity of the ESS.

3. The microgrid collaborative optimization scheduling method according to claim 2, characterized in that, S1 further includes: Establish a mathematical model for the diesel generator. In the formula, This indicates the fuel cost of DG, where DG stands for diesel generator. Indicating DG's Output power during the time period; , and The coefficients representing the fuel cost function; The mathematical model for establishing a photovoltaic electric vehicle cluster is as follows: In the formula, and They represent the first EVs in Time period and State of charge over a period of time; and They represent the first The charging and discharging power of an EV during time period t; and Indicates charge / discharge efficiency; Indicates charging status. Indicates the discharge state. Indicates the maximum battery capacity; Establish a mathematical model for ice storage air conditioning. In the formula, This represents the amount of ice stored in the ISAC during time period t. ISAC stands for Ice Storage Air Conditioning. Indicates the self-loss coefficient; and These represent the ice accumulation and melting rates during time period t, respectively. The electrical power of the ISAC refrigeration unit is expressed as follows: In the formula: This indicates the output cooling capacity of the ISAC during time period t; This indicates the maximum output cooling capacity of the ISAC; Rated energy efficiency ratio; , and Represents the fitting coefficient; This indicates the output cooling capacity of the unit during the cooling storage period; This indicates the output cooling capacity of the unit during the cooling period; The total load of the ISAC for each time period is obtained by summing the electricity consumption of the ISAC in each time period, expressed as: In the formula: This indicates the total number of ISACs.

4. The microgrid collaborative optimization scheduling method according to claim 3, characterized in that, S2 includes: The optimization objective function is expressed as follows: In the formula: Indicates system operation and maintenance costs; Indicates the cost of system pollution control. Represented as In the formula: This indicates the operation and maintenance cost of a wind power generation system; This indicates the operation and maintenance cost of a photovoltaic power generation system; This indicates the operation and maintenance cost of the diesel generator; This indicates the operation and maintenance cost of the energy storage system; This indicates the cost of exchanging electricity with the main grid; This represents the compensation cost to electric vehicle users; This represents the compensation cost for air conditioner users; among which, In the formula: , , and These represent the operation and maintenance cost coefficients for WT, PV, DG, and ESS, respectively. and They respectively represent ESS in The charging and discharging power during a given period; This indicates the electricity exchange price between the microgrid and the distribution network; and They are respectively The purchase price and sales price of electricity from the microgrid for EVs during the specified time period; and They represent Electricity purchased from the distribution network and electricity sold to the distribution network during a given period; and They represent the first EVs in The charging and discharging power during a given period; and They represent the first The charging and discharging efficiency of an EV; This indicates the battery maintenance cost of an EV; Indicates the first EVs in The charging and discharging power during a given period; This indicates the power compensation cost of ISAC; pollution control costs Represented as In the formula: and These represent the costs of DG (Distributed Generation) and the pollution control costs of the power distribution network, respectively. Indicating governance Cost coefficients for pollutants of this type; and These represent the pollutant emissions per unit of electricity generated during the operation of the DG and distribution networks, respectively.

5. The microgrid collaborative optimization scheduling method according to claim 4, characterized in that, The power balance constraints include: In the formula: express Interaction power between microgrids and distribution networks during specific time periods; express Basic load demand for a given period; Indicates EVs in The charging and discharging power during a given period, with charging being positive and discharging being negative; Indicates that ISAC is in Power consumption during a given time period; Indicates ESS in The charging and discharging power during a given period, with charging being positive and discharging being negative.

6. The microgrid collaborative optimization scheduling method according to claim 5, characterized in that, The device power constraints include: In the formula: and Representing device units Upper and lower limits of output power; in: and These represent the upper and lower limits of DG's ramp power, respectively.

7. The microgrid collaborative optimization scheduling method according to claim 6, characterized in that, The energy storage constraints include: In the formula: and These represent the minimum and maximum states of charge allowed by the ESS, respectively; Tie line constraints are represented as In the formula: and These represent the upper and lower limits of the power exchange with the power distribution network per unit time.

8. A microgrid collaborative optimization scheduling method according to claim 7, characterized in that, The electric vehicle SOC constraints include: In the formula: and These represent the upper and lower limits of the EV's state of charge, respectively. and These represent the upper and lower limits of power during EV charging; and These represent the upper and lower limits of power during EV discharge, respectively.

9. A microgrid collaborative optimization scheduling method according to claim 8, characterized in that, The ice storage air conditioning capacity constraints include: In the formula: and Indicates the upper and lower limits of the ice storage capacity of the ISAC (Ice Isolation and Control) system; Indicates the upper limit of the ice accumulation rate; Indicates that ISAC is in Cooling load demand during different time periods; This indicates the upper limit of the ice melting rate.

10. A system for implementing the microgrid collaborative optimization scheduling method according to any one of claims 1-9, characterized in that, include: The mathematical model building module is used to establish mathematical models of various equipment units in the microgrid, including wind power generation systems, photovoltaic power generation systems, energy storage systems, diesel generators, electric vehicle clusters, and ice storage air conditioning. The objective function building module is used to construct an optimization objective function that aims to minimize the total operating cost of the system. The constraint setting module is used to set system constraints, including power balance constraints, equipment power constraints, energy storage constraints, electric vehicle SOC constraints, and ice storage air conditioning capacity constraints. The optimization solution module is used to adjust the operating parameters of the mathematical models of each equipment unit under the premise of satisfying the system constraints, so that the optimization objective function reaches the minimum value, and to optimize the scheduling of each equipment unit based on the solved operating parameters.

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