A micro-grid virtual energy storage collaborative scheduling method based on temperature constraint

Abstract

The application discloses a kind of microgrid virtual energy storage collaborative scheduling methods based on temperature constraint, belong to microgrid energy management technical field, including: after obtaining microgrid operating parameter, respectively establish photovoltaic power generation power model, energy storage device model and air conditioner virtual energy storage model, and with microgrid operating cost minimum and human thermal comfort optimal as target energy management objective function is established, combined constraint condition constitutes collaborative scheduling model, after solving, each time period power purchase and sale power, energy storage device charging and discharging power and air conditioner set temperature are output.The application is equivalent to virtual energy storage device by air conditioner-building system and participates in microgrid optimization scheduling, under the premise of guaranteeing user thermal comfort, effectively reduce microgrid operating cost and improve energy utilization efficiency.

Description

Technical Field

[0001] This invention relates to the field of microgrid energy management technology, and in particular to a temperature-constrained microgrid virtual energy storage collaborative scheduling method. Background Technology

[0002] A microgrid is a small-scale power generation and distribution system comprised of distributed power sources, energy storage devices, energy conversion devices, loads, monitoring and protection devices. It is an autonomous system capable of self-control, protection, and management, and can operate either connected to the external power grid or in isolation. With the increasing shortage of traditional energy supplies and environmental pollution, as well as the gradual improvement of power reliability, efficient and clean distributed power sources have been widely used in power systems. Microgrids, as an effective way to solve the problem of interconnecting distributed generation with the power grid, have become a research hotspot in recent years.

[0003] The core advantage of microgrids lies in their ability to effectively integrate and coordinate various distributed power sources, achieving efficient energy utilization and stable power supply. However, microgrids also face numerous challenges in actual operation. Because microgrids include various types of distributed power sources such as photovoltaic (PV) power generation, which is greatly affected by factors such as sunlight intensity and ambient temperature, exhibiting significant randomness, intermittency, and volatility, its output characteristics can sometimes show abrupt changes. Directly connecting it to the grid without processing will adversely affect power quality, posing serious challenges to the microgrid's power supply and demand balance, voltage stability, and frequency maintenance.

[0004] To address the aforementioned issues, a common practice is to integrate energy storage devices (such as battery banks and supercapacitors) into the microgrid system. Energy storage devices can effectively mitigate power fluctuations from distributed generation sources, improving the power quality and operational stability of the microgrid. However, configuring dedicated energy storage devices requires high initial investment and operation and maintenance costs, and factors such as the charging and discharging efficiency and cycle life of these devices also limit the overall economic viability of the microgrid. How to improve the microgrid's ability to regulate distributed generation fluctuations and reduce operating costs without over-reliance on traditional energy storage devices is a pressing technical problem to be solved in the field of microgrid energy management.

[0005] In recent years, some studies have proposed utilizing the inherent thermal storage characteristics of buildings to treat air conditioning loads as virtual energy storage devices for microgrid energy management. Air conditioning provides comfortable indoor temperatures, and the building's insulation capabilities contribute to this effect, allowing the air conditioning load to function as virtual energy storage. By adjusting the air conditioning setpoint, flexible control of the load can be achieved while maintaining human thermal comfort, thus achieving peak shaving and valley filling, and reducing microgrid operating costs. However, most existing studies treat virtual energy storage as an independent component, failing to deeply integrate the dynamic characteristics of virtual energy storage (such as the impact of building equivalent heat capacity, thermal resistance, and outdoor temperature changes on virtual energy storage capacity) with the multi-timescale optimization scheduling of the microgrid. Furthermore, a systematic and comprehensive technical solution for the coordinated scheduling of virtual energy storage and the microgrid, with temperature constraints at its core, is lacking. Summary of the Invention

[0006] The purpose of this invention is to provide a temperature-constrained microgrid virtual energy storage collaborative scheduling method, aiming to solve the technical problems of strong volatility of distributed power sources and high energy storage configuration costs in existing microgrids. By equating the air conditioning-building system with virtual energy storage, a collaborative scheduling model is constructed with operating cost and thermal comfort as objectives, under the premise of meeting user thermal comfort constraints, to achieve coordinated optimization of microgrid economy and comfort.

[0007] To achieve the above objectives, this invention provides a microgrid virtual energy storage collaborative scheduling method based on temperature constraints, comprising the following steps: S1. Obtain microgrid operating parameters, including irradiance, outdoor temperature, indoor temperature, air conditioner rated power, air conditioner energy efficiency ratio, energy storage device capacity, energy storage device power limit, time-of-use electricity price, and load power. S2. Based on the light intensity and outdoor temperature, establish a photovoltaic power generation model and calculate the photovoltaic power generation. S3. Establish an energy storage device model, and calculate the charging and discharging power and state of charge of the energy storage device based on the charging and discharging characteristics of the battery and supercapacitor. S4. Establish a virtual energy storage model for air conditioning. Based on the room temperature thermodynamic equation, the air conditioning power model, the air conditioning energy efficiency ratio, and the comfort temperature range, calculate the virtual energy storage capacity and the virtual energy storage state of the air conditioning, and correct the virtual energy storage capacity of the air conditioning according to the outdoor temperature. S5. With the goals of minimizing microgrid operating costs and maximizing human thermal comfort, establish a microgrid energy management objective function, which includes an economic objective function and a thermal comfort objective function. S6. Set constraints, including power balance constraints, equipment power upper and lower limit constraints, state mutual exclusion constraints, energy storage device capacity constraints, and indoor temperature constraints, to limit the air conditioning set temperature to within the comfortable temperature range; construct a collaborative scheduling model based on the microgrid energy management objective function and constraints. S7. Solve the collaborative scheduling model, output the power purchased and sold, the charging and discharging power of the energy storage device, and the set temperature of the air conditioner for each time period, and complete the collaborative scheduling of virtual energy storage in the microgrid.

[0008] Preferably, in S2, the photovoltaic power generation model includes a light intensity probability model, a photovoltaic cell surface temperature model, and a photovoltaic output power model; The probability model for light intensity is as follows: ; The surface temperature model for photovoltaic cells is as follows: ; The photovoltaic output power model is as follows: ; In the formula, Light intensity, For maximum light intensity, , For the shape parameters of the Beta distribution, For gamma function, The surface temperature of the photovoltaic cell. For ambient temperature, For ambient wind speed, Photovoltaic power generation capacity, For photovoltaic power factor, Rated power of photovoltaic, For standard testing of light intensity, For power temperature coefficient, This is the standard test temperature.

[0009] Preferably, in S3, the energy storage device model includes a battery model and a supercapacitor model; The no-load voltage and terminal voltage of the battery model are: ; The battery capacity is: ; The state of charge of the battery is: ; The terminal voltage of the supercapacitor model is: ; The energy stored in a supercapacitor is: ; The effective capacitance of a supercapacitor is: ; The state of charge of a supercapacitor is: ; In the formula, This is the no-load voltage. Internal potential, This represents the total battery capacity. The amount of electricity already used. The polarization voltage coefficient is... This represents the pressure drop coefficient in the exponential region. The internal resistance of the battery. This refers to the battery terminal voltage. For battery power, The charging and discharging current of the battery, This refers to the battery's state of charge. For the remaining capacity of the battery, The rated capacity of the battery, This is the terminal voltage of the supercapacitor. This is the ideal voltage for a supercapacitor. This refers to the current in the supercapacitor. This is the equivalent series internal resistance of a supercapacitor. To store energy for supercapacitors This refers to the capacitance value of the supercapacitor. This refers to the effective capacitance of the supercapacitor. This is the initial capacitance of the supercapacitor. This is the maximum operating voltage of the supercapacitor. This is the minimum operating voltage for a supercapacitor.

[0010] Preferably, in S4, the thermodynamic equation for room temperature is: ; In the formula, Indoor temperature, Outdoor temperature For air conditioning cooling capacity, For the building's equivalent heat capacity, The equivalent thermal resistance of the building.

[0011] Preferably, in S4, the air conditioning power model includes the relationship between air conditioning power and cooling capacity, steady-state air conditioning power calculation, and air conditioning start-stop control rules; The relationship between air conditioner power consumption and cooling capacity is as follows: ; When the indoor temperature remains constant, the air conditioner's power consumption is calculated based on the difference between the outdoor and indoor temperatures: ; The air conditioner start / stop control rules are as follows: when hour, ; when hour, ; In the formula, For air conditioner power, For air conditioning cooling capacity, For air conditioner energy efficiency ratio, This refers to the rated power of the air conditioner. Set the temperature for the air conditioner. Indoor temperature, Outdoor temperature The equivalent thermal resistance of the building.

[0012] Preferably, in S4, calculating the virtual energy storage capacity and virtual energy storage state of charge of the air conditioner includes: Calculate heat change based on room temperature change: ; The virtual power storage capacity of the air conditioner is: ; The rated storage capacity of the air conditioner virtual energy storage is: ; The state of charge of the virtual energy storage of the air conditioner is: ; Adjust the virtual energy storage capacity of the air conditioner based on the outdoor temperature: ; In the formula, For heat changes, The room temperature after the change The initial room temperature. for Virtual power storage for air conditioning at all times This is the upper limit of the comfortable temperature range. This represents the lower limit of the comfortable temperature range. The rated storage capacity for virtual energy storage of air conditioners, This represents the virtual energy storage state of charge for the air conditioner. To correct the virtual energy storage capacity of the air conditioner, Outdoor temperature For the building's equivalent heat capacity, This refers to the air conditioner's energy efficiency ratio.

[0013] Preferably, in S5, the economic objective function is: ; in, Cost of purchasing and selling electricity: ; Cost of charging and discharging energy storage devices: ; For operation and maintenance costs: ; In the formula, For electricity purchase price, For electricity sales price, For electricity purchase markings, This is a sign for electricity sales. For the power purchase capacity, For electricity sales capacity, This is the charging cost coefficient. This is the discharge cost coefficient. This is a charging symbol. For discharge indication, For charging power, For discharge power, This is the photovoltaic maintenance cost coefficient. This is the air conditioning maintenance cost coefficient. Photovoltaic power generation capacity, For air conditioner power, This is the air conditioner start / stop indicator. The length of the scheduling period. t For scheduling period index, It is the set of all time periods within the scheduling period.

[0014] Preferably, in S5, the thermal comfort objective function is characterized by a human thermal sensation prediction index, which is: ; In the formula, for Real-time human thermal sensation prediction index Set the temperature for the air conditioner. Indoor temperature, The human body's energy metabolism rate. For the thermal resistance of clothing; The objective function for thermal comfort is: ; In the formula, Let thermal comfort be the objective function. As a penalty factor, It is the set of all time periods within the scheduling period; The objective function for microgrid energy management is: ; In the formula, The total objective function value, The objective function is the economic efficiency.

[0015] Preferably, in S6, the power balance constraint is: ; Equipment power upper and lower limit constraints include: Photovoltaic power generation constraints: ; Power purchase constraints: ; Electricity sales capacity constraints: ; Energy storage device charging power constraints: ; Energy storage device discharge power constraints: ; Air conditioning power constraints: ; In the formula, Photovoltaic power generation capacity, Power to charge energy storage devices, This refers to the discharge power of the energy storage device. This is a charging symbol. For discharge indication, For the power purchase capacity, For electricity sales capacity, For electricity purchase markings, This is a sign for electricity sales. For air conditioner power, This is the air conditioner start / stop indicator. For load power, This indicates the lower limit of the corresponding power. This indicates the corresponding power limit.

[0016] Preferably, in S6, the mutual exclusion constraint of states is: ; ; The capacity constraint of energy storage devices is: ; Indoor temperature constraints are: ; In the formula, , These are the charging indicator and the discharging indicator, respectively. , These are the electricity purchase sign and the electricity sales sign, respectively. The energy storage device is in its state of charge. This is the limit of the state of charge. This is the upper limit of the state of charge. For capacity adjustment parameters, Set the temperature for the air conditioner. This represents the lower limit of the comfortable temperature range. This represents the upper limit of the comfortable temperature range.

[0017] Therefore, the present invention employs the above-mentioned microgrid virtual energy storage collaborative scheduling method based on temperature constraints, achieving the following technical effects: (1) The air conditioning-building system is equivalent to virtual energy storage and participates in the microgrid optimization scheduling. The building heat storage characteristics are used for "charging and discharging" management, peak shaving and valley filling, which significantly reduces the cost of purchasing electricity from the main grid and the overall operating cost of the microgrid.

[0018] (2) The equivalent electricity released by virtual energy storage can effectively meet part of the demand-side load, smooth out power fluctuations, make the supply and demand matching between the microgrid and the main grid more stable, and improve the reliability of microgrid operation.

[0019] (3) The air conditioner setting temperature is strictly limited to the range of human comfort temperature. Energy saving and consumption reduction are achieved on the premise of ensuring the thermal comfort of users, avoiding the drawback of sacrificing user experience in the pursuit of economy.

[0020] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0021] Figure 1 This is a flowchart illustrating a temperature-constrained microgrid virtual energy storage collaborative scheduling method according to the present invention. Figure 2 This is a schematic diagram of the equivalent circuit model of the battery according to an embodiment of the present invention; Figure 3 This is a schematic diagram of the equivalent circuit model of the supercapacitor in an embodiment of the present invention; Figure 4 This is a schematic diagram of the first-order equivalent thermal parameter model of the air conditioning-building system according to an embodiment of the present invention; Figure 5 This is a schematic diagram of the time-of-use electricity pricing for grid sales according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the time-of-use electricity pricing for grid purchases according to an embodiment of the present invention; Figure 7 This is a schematic diagram of photovoltaic power fluctuations according to an embodiment of the present invention; Figure 8 This is a schematic diagram of outdoor temperature changes in summer according to an embodiment of the present invention; Figure 9 This is a schematic diagram of the power dispatching results in an embodiment of the present invention without considering virtual energy storage for air conditioning; Figure 10 This is a schematic diagram of the power dispatching results after introducing virtual energy storage for air conditioning in an embodiment of the present invention. Detailed Implementation

[0022] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0023] 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 with reference to the accompanying drawings. 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.

[0024] Example 1 The following section uses a specific residential microgrid system as an example to further illustrate this implementation method in detail.

[0025] like Figure 1 As shown in the figure, this embodiment provides a microgrid virtual energy storage collaborative scheduling method based on temperature constraints, which includes the following steps: S1. Obtain microgrid operating parameters.

[0026] This embodiment takes a summer cooling scenario in a region of Gansu Province as an example to carry out day-ahead economic optimization scheduling for a microgrid with virtual energy storage. For the next 24 hours, one hour is taken as a scheduling period, for a total of 24 scheduling periods (i.e., a set of time periods). (Including 24 time periods). First, obtain the microgrid operating parameters, including irradiance, outdoor temperature, indoor temperature, air conditioning rated power, air conditioning energy efficiency ratio, energy storage device capacity, energy storage device power limit, time-of-use electricity price, and load power.

[0027] In this embodiment, the microgrid system includes a 200kW photovoltaic power station, 20 air conditioners with a rated power of 5kW each (total air conditioning load 100kW), an energy storage device with a rated power of 140kW and a capacity of 280kWh, and 50 residential households. Time-of-use pricing data is shown in the attached figure. Figure 5 and Figure 6 The electricity price sold by the grid and the electricity price purchased by the grid are both set according to peak and off-peak periods. Figure 5 and Figure 6 The horizontal axis represents time; the vertical axis represents electricity price (unit: yuan / kWh). Typical peak hours are 8:00-22:00, and off-peak hours are 22:00-8:00 the next day. Specific electricity prices are determined according to the actual time-of-use pricing policy published by the local power authority. See the attached figure for photovoltaic power fluctuations and summer outdoor temperature data. Figure 7 and Figure 8 , Figure 7 The horizontal axis represents time in hours (h), and the vertical axis represents photovoltaic power in kilowatts (kW). Figure 8 The x-axis represents time, and the y-axis represents temperature (in °C). The parameters for the example are as follows: 200kW photovoltaic unit, 5kW air conditioning load (20 units), 140kW rated power of energy storage device, penalty factor γ is taken as 0.6, and energy storage device charge and discharge efficiency is taken as 0.9.

[0028] S2. Based on the light intensity and outdoor temperature, establish a photovoltaic power generation model and calculate the photovoltaic power generation.

[0029] A photovoltaic (PV) power generation system converts light energy into electrical energy through solar panels. Its output is non-linearly affected by light intensity and temperature. The PV power generation model in this step includes a light intensity probability model, a PV cell surface temperature model, and a PV output power model.

[0030] The probability model for light intensity is as follows: ; in, Light intensity, For maximum light intensity, , The shape parameter of the Beta distribution has a range of values. , The specific values ​​were obtained by fitting the data using the maximum likelihood estimation method based on local historical sunshine statistics. This is a gamma function.

[0031] The light intensity probabilistic model is used to describe the uncertainty distribution characteristics of light intensity, providing a foundation for subsequent extension to stochastic optimization scheduling; in actual day-ahead scheduling, Take the deterministic forecast value from the weather forecast and substitute it into the photovoltaic output power model to calculate the photovoltaic power.

[0032] The surface temperature model for photovoltaic cells is as follows: ; in, The surface temperature of the photovoltaic cell. For ambient temperature, The ambient wind speed is represented. The coefficients 0.0138, 0.031, and 0.042 are empirically fitted values ​​based on the heat transfer characteristics of typical photovoltaic modules. In practical applications, these coefficients are calibrated and corrected according to the encapsulation materials, installation tilt angle, and heat dissipation conditions of specific photovoltaic modules.

[0033] The photovoltaic output power model is as follows: ; in, Photovoltaic power generation capacity, The photovoltaic power factor is typically taken as 0.9. Rated power of photovoltaic, Standard test light intensity (1000W / m) 2 ), The power temperature coefficient (unit: % / ℃). The standard test temperature is 25℃.

[0034] S3. Establish an energy storage device model and calculate the charging and discharging power and state of charge of the energy storage device based on the charging and discharging characteristics of the battery and supercapacitor.

[0035] To mitigate photovoltaic power fluctuations, this embodiment employs a hybrid energy storage device consisting of a battery and a supercapacitor. The equivalent circuit model of the battery is as follows: Figure 2 As shown, the equivalent circuit model of a supercapacitor is as follows: Figure 3 As shown. The model is as follows: Battery model – open-circuit voltage and terminal voltage are: ; in, This is the no-load voltage. Internal potential, This represents the total battery capacity. This represents the amount of electricity that has flowed (i.e., the integral of current over time). The polarization voltage coefficient is... This represents the pressure drop coefficient in the exponential region. The internal resistance of the battery. This is the battery terminal voltage.

[0036] The battery capacity is: ; in, The charging and discharging current of the battery; The state of charge of the battery is: ; in, This refers to the battery's state of charge. For the remaining capacity of the battery, This represents the rated capacity of the battery. Both the charging and discharging efficiency of the battery are taken as 0.9, but this efficiency value will be adjusted according to the actual battery type and operating conditions.

[0037] The terminal voltage of the supercapacitor model is: ; in, This is the terminal voltage of the supercapacitor. This is the ideal voltage for a supercapacitor. This refers to the current in the supercapacitor. This is the equivalent series internal resistance of the supercapacitor.

[0038] The energy stored in a supercapacitor is: ; The effective capacitance of a supercapacitor is: ; The state of charge of a supercapacitor is: ; in, To store energy for supercapacitors This refers to the capacitance value of the supercapacitor. This refers to the effective capacitance of the supercapacitor. This is the initial capacitance of the supercapacitor. This is the maximum operating voltage of the supercapacitor. This is the minimum operating voltage for a supercapacitor.

[0039] S4. Establish a virtual energy storage model for air conditioning. Based on the room temperature thermodynamic equation, the air conditioning power model, the air conditioning energy efficiency ratio, and the comfort temperature range, calculate the virtual energy storage capacity and the virtual energy storage state of charge of the air conditioning. Then, correct the virtual energy storage capacity of the air conditioning according to the outdoor temperature.

[0040] Buildings possess thermal storage characteristics, and air conditioning systems can achieve virtual energy storage by adjusting set temperatures. The first-order equivalent thermal parameter (ETP) model of the air conditioning-building system is as follows: Figure 4 As shown.

[0041] The thermodynamic equation for room temperature is: ; In the formula, Indoor temperature, Outdoor temperature For air conditioning cooling capacity, For the building's equivalent heat capacity, This refers to the building's equivalent thermal resistance. Typical values ​​for the building's equivalent heat capacity range from 47 to 230 kJ / ℃, and typical values ​​for the building's equivalent thermal resistance range from 0.001 to 0.003 ℃ / kW. and equivalent thermal resistance The determination is based on factors such as building envelope materials, wall thickness, window area, shading coefficient, and insulation measures. Specifically, it can be obtained through the following methods: (1) Calculation based on building drawings and material thermal properties, referring to the "Code for Thermal Design of Civil Buildings" (GB 50176); (2) Estimation using parameter identification methods based on on-site measured indoor temperature response data; (3) Acquisition using building energy consumption simulation software (such as EnergyPlus, DeST). In this embodiment, , .

[0042] The air conditioning power model includes the relationship between air conditioning power and cooling capacity, steady-state air conditioning power calculation, and air conditioning start-stop control rules.

[0043] The relationship between air conditioner power consumption and cooling capacity is as follows: ; When the indoor temperature remains constant (steady state), the air conditioner's power consumption is calculated based on the difference between the outdoor and indoor temperatures: ; The air conditioner start / stop control rules are as follows: when hour, (Air conditioner off); when hour, (The air conditioner is operating at its rated power); In the formula, For air conditioner power, For air conditioning cooling capacity, For air conditioner energy efficiency ratio, This refers to the rated power of the air conditioner. Set the temperature for the air conditioner. Indoor temperature, Outdoor temperature The equivalent thermal resistance of the building.

[0044] The calculation of the virtual energy storage capacity and virtual energy storage state of the air conditioner includes: Calculate heat change based on room temperature change: ; The virtual power storage capacity of the air conditioner is: ; The rated storage capacity of the air conditioner virtual energy storage is: ; The state of charge of the virtual energy storage of the air conditioner is: ; Adjust the virtual energy storage capacity of the air conditioner based on the outdoor temperature: ; In the formula, For heat changes, The room temperature after the change The initial room temperature is determined by the initial indoor temperature obtained in S1; during subsequent scheduling periods, the indoor temperature... Dynamically updated through thermodynamic equations. for Virtual power storage for air conditioning at all times This is the upper limit of the comfortable temperature range. As the lower limit of the comfortable temperature range, this embodiment takes... , This range is determined based on a survey of human thermal comfort in summer, and the specific value is adjusted within the range of 22~28℃ according to the actual situation. The rated storage capacity for virtual energy storage of air conditioners, This represents the virtual energy storage state of charge for the air conditioner. To correct the virtual energy storage capacity of the air conditioner, This refers to the outdoor temperature.

[0045] S5. With the goals of minimizing microgrid operating costs and optimizing human thermal comfort, establish a microgrid energy management objective function, which includes an economic objective function and a thermal comfort objective function.

[0046] The economic objective function is: ; in, Cost of purchasing and selling electricity: ; Cost of charging and discharging energy storage devices: ; For operation and maintenance costs: ; In the formula, , They are respectively t Electricity purchase price and electricity sales price during specific time periods. , These are the electricity purchase indicator and the electricity sales indicator (1 indicates action, 0 indicates no action). For the power purchase capacity, For electricity sales capacity, This is the charging cost coefficient. This is the discharge cost coefficient. This is a charging symbol. For discharge indication, For charging power, For discharge power, This is the photovoltaic maintenance cost coefficient. This is the air conditioning maintenance cost coefficient. Photovoltaic power generation capacity, For air conditioner power, This is the air conditioner start / stop indicator (1 indicates start, 0 indicates stop). Where t is the length of the scheduling period (1 hour in this example), and t is the index of the scheduling period. This is the set of all time periods within the scheduling period (24 time periods in this example). All terms in the economic objective function have been multiplied by... The unit of measurement is unified as "yuan".

[0047] The objective function for thermal comfort is characterized by a human thermal sensation prediction index, which is: ; In the formula, for Real-time human thermal sensation prediction index Set the temperature for the air conditioner. Indoor temperature, The human body's energy metabolism rate. For clothing thermal resistance; this embodiment takes into account the activity habits and clothing characteristics of residents in residential buildings during summer, and takes... , The optimal indoor temperature in summer is 25℃, and PMV should be within ±1.

[0048] To simplify calculations, this embodiment uses a simplified quadratic term as the thermal comfort objective function. This function follows the same trend as the PMV index within the comfort temperature range—when the indoor temperature deviates from the optimal comfort temperature (25°C), human thermal comfort decreases, and the greater the deviation, the more significant the decrease in comfort. Therefore, using the square of the temperature deviation as the thermal comfort penalty term can reasonably reflect the user's sensitivity to temperature deviations. ; In the formula, Let thermal comfort be the objective function. The penalty factor (0.6 in this embodiment, in yuan / ℃) is used. 2 ), The set of all time periods within the scheduling period; penalty factor The physical meaning of this is the marginal cost of temperature deviation, and its value is adjusted according to the user's emphasis on thermal comfort: The higher the value, the more the system tends to maintain the indoor temperature near the optimal comfort level.

[0049] The objective function for microgrid energy management is: ; In the formula, The total objective function value, The objective function is the economic efficiency.

[0050] S6. Set constraints, including power balance constraints, equipment power upper and lower limit constraints, state mutual exclusion constraints, energy storage device capacity constraints, and indoor temperature constraints, and limit the air conditioning set temperature to within the comfortable temperature range; construct a collaborative scheduling model based on the microgrid energy management objective function and the above constraints.

[0051] The power balance constraint is: ; Equipment power upper and lower limit constraints include: Photovoltaic power generation constraints: ; Power purchase constraints: ; Electricity sales capacity constraints: ; Energy storage device charging power constraints: ; Energy storage device discharge power constraints: ; Air conditioning power constraints: ; in, This indicates the lower limit of the corresponding power. This indicates the upper limit of the corresponding power output; the upper and lower limits are determined by the rated parameters of the equipment: Upper limit of photovoltaic power generation. The lower limit of the rated power of the photovoltaic array. The upper and lower limits of the charging and discharging power of energy storage devices are determined by the rated power of the converter; the upper and lower limits of the power purchased and sold are determined by the grid connection interface capacity between the microgrid and the main grid; the upper limit of the air conditioner power is the rated power. The lower limit is 0.

[0052] in, Photovoltaic power generation capacity, Power to charge energy storage devices, This refers to the discharge power of the energy storage device. This is a charging symbol. For discharge indication, For the power purchase capacity, For electricity sales capacity, For electricity purchase markings, This is a sign for electricity sales. For air conditioner power, This is the air conditioner start / stop indicator. This represents the load power.

[0053] The state mutual exclusion constraint is: (Energy storage devices cannot be charged and discharged simultaneously.) (A microgrid cannot purchase and sell electricity at the same time.) in, , These are the charging indicator and the discharging indicator, respectively. , These are the electricity purchase sign and the electricity sales sign, respectively; The capacity constraint of energy storage devices is: ; in, The energy storage device is in its state of charge. This is the limit of the state of charge. This is the upper limit of the state of charge. This is a capacity regulation parameter, with a typical value range of 0.8 to 0.95. The specific value is determined based on the type of energy storage device, cycle life characteristics, and safe operation requirements. It can prevent the energy storage device from being overcharged and discharged, thereby extending its service life. Indoor temperature constraints are: ; In the formula, Set the temperature for the air conditioner. , These are the lower limit and upper limit of the comfortable temperature range, respectively (24℃ and 27℃ in this embodiment).

[0054] S7. Solve the collaborative scheduling model, output the power purchased and sold, the charging and discharging power of the energy storage device, and the set temperature of the air conditioner for each time period, and complete the collaborative scheduling of virtual energy storage in the microgrid.

[0055] The above-mentioned basic data is used for simulation and solution. This embodiment employs a mixed-integer linear programming algorithm to solve the above-mentioned collaborative scheduling model. This method can efficiently handle optimization problems involving both integer variables (such as equipment start / stop flags, power purchase / sale status flags, etc.) and continuous variables (such as power values, etc.). Alternatively, intelligent optimization algorithms such as particle swarm optimization and genetic algorithms can also be used. The specific solution method is selected based on factors such as microgrid scale, real-time requirements, and computational resources. A comparative analysis is conducted before and after the introduction of virtual energy storage for air conditioning.

[0056] Without considering the introduction of virtual energy storage for air conditioning (see the optimal power dispatch scenario without considering virtual energy storage for air conditioning), Figure 9 Microgrids rely solely on photovoltaic power generation, energy storage device charging and discharging, and electricity trading with the main grid to meet load demands.

[0057] Considering virtual energy storage for air conditioning (see the microgrid optimization scheduling results after introducing virtual energy storage for air conditioning), Figure 10 The virtual energy storage of the air conditioner is charged and discharged by adjusting the indoor set temperature. The meanings of the variables in the diagram are as follows: Indicates photovoltaic power generation capacity; The figure shows the comparative power curves (used to verify the adaptability of this method to various distributed power source fluctuation scenarios); Corresponding to the power purchase volume in the main text ; in the picture Corresponding to the electricity sales power in the main text ; in the picture Corresponding to the air conditioner power in the main text (i.e., the equivalent charge and discharge power of the air conditioner's virtual energy storage); in the figure This indicates the power of other electrical loads.

[0058] Simulation results show that: after introducing virtual energy storage for air conditioning, power fluctuations are reduced and power quality is more stable; peak power is reduced and valley power is increased, achieving peak shaving and valley filling effects; virtual energy storage for air conditioning affects the supply and demand balance of the power grid, and the supply and demand matching of the power grid is more stable after its introduction, and the equivalent electricity released by virtual energy storage for air conditioning can effectively meet part of the demand-side load; under the premise of ensuring that users' thermal comfort is met, introducing virtual energy storage for air conditioning to participate in the optimal scheduling of the microgrid can significantly reduce the operating cost of the microgrid.

[0059] Therefore, the present invention adopts the above-mentioned microgrid virtual energy storage collaborative scheduling method based on temperature constraints. By equating the air conditioning-building system with virtual energy storage to participate in optimized scheduling, the operating cost of the microgrid is effectively reduced and the energy utilization efficiency is improved while ensuring the thermal comfort of users.

[0060] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A microgrid virtual energy storage collaborative scheduling method based on temperature constraints, characterized in that, Includes the following steps: S1. Obtain microgrid operating parameters, including irradiance, outdoor temperature, indoor temperature, air conditioner rated power, air conditioner energy efficiency ratio, energy storage device capacity, energy storage device power limit, time-of-use electricity price, and load power. S2. Based on the light intensity and outdoor temperature, establish a photovoltaic power generation model and calculate the photovoltaic power generation. S3. Establish an energy storage device model, and calculate the charging and discharging power and state of charge of the energy storage device based on the charging and discharging characteristics of the battery and supercapacitor. S4. Establish a virtual energy storage model for air conditioning. Based on the room temperature thermodynamic equation, the air conditioning power model, the air conditioning energy efficiency ratio, and the comfort temperature range, calculate the virtual energy storage capacity and the virtual energy storage state of the air conditioning, and correct the virtual energy storage capacity of the air conditioning according to the outdoor temperature. S5. With the goals of minimizing microgrid operating costs and optimizing human thermal comfort, establish a microgrid energy management objective function, which includes an economic objective function and a thermal comfort objective function. S6. Set constraints, including power balance constraints, equipment power upper and lower limit constraints, state mutual exclusion constraints, energy storage device capacity constraints, and indoor temperature constraints, to limit the air conditioner setting temperature to within the comfortable temperature range; and construct a collaborative scheduling model based on the microgrid energy management objective function and the constraints. S7. Solve the collaborative scheduling model, output the power purchased and sold, the charging and discharging power of the energy storage device and the set temperature of the air conditioner for each time period, and complete the virtual energy storage collaborative scheduling of the microgrid. In S4, the calculation of the virtual energy storage capacity and the virtual energy storage state of the air conditioner includes: Calculate heat change based on room temperature change: ； The virtual power storage capacity of the air conditioner is: ； The rated storage capacity of the air conditioner virtual energy storage is: ； The state of charge of the virtual energy storage of the air conditioner is: ； Adjust the virtual energy storage capacity of the air conditioner based on the outdoor temperature: ； In the formula, For heat changes, The room temperature after the change The initial room temperature. for Virtual power storage for air conditioning at all times This is the upper limit of the comfortable temperature range. This represents the lower limit of the comfortable temperature range. The rated storage capacity for virtual energy storage of air conditioners, This represents the virtual energy storage state of charge for the air conditioner. To correct the virtual energy storage capacity of the air conditioner, Outdoor temperature For the building's equivalent heat capacity, This refers to the energy efficiency ratio of the air conditioner. In S6, the state mutual exclusion constraint is: ； ； The capacity constraint of the energy storage device is: ； The indoor temperature constraint is: ； In the formula, , These are the charging indicator and the discharging indicator, respectively. , These are the electricity purchase sign and the electricity sales sign, respectively. The energy storage device is in its state of charge. This is the limit of the state of charge. This is the upper limit of the state of charge. For capacity adjustment parameters, Set the temperature for the air conditioner. This represents the lower limit of the comfortable temperature range. This represents the upper limit of the comfortable temperature range.

2. The method according to claim 1, characterized in that, In S2, the photovoltaic power generation model includes a light intensity probability model, a photovoltaic cell surface temperature model, and a photovoltaic output power model; The probability model for light intensity is as follows: ； The surface temperature model for photovoltaic cells is as follows: ； The photovoltaic output power model is as follows: ； In the formula, Light intensity, For maximum light intensity, , For the shape parameters of the Beta distribution, For gamma function, The surface temperature of the photovoltaic cell. For ambient temperature, For ambient wind speed, Photovoltaic power generation capacity, For photovoltaic power factor, Rated power of photovoltaic, For standard testing of light intensity, For power temperature coefficient, This is the standard test temperature.

3. The method according to claim 1, characterized in that, In S3, the energy storage device model includes a battery model and a supercapacitor model; The no-load voltage and terminal voltage of the battery model are: ； The battery capacity is: ； The state of charge of the battery is: ； The terminal voltage of the supercapacitor model is: ； The energy stored in a supercapacitor is: ； The effective capacitance of a supercapacitor is: ； The state of charge of a supercapacitor is: ； In the formula, This is the no-load voltage. Internal potential, This represents the total battery capacity. The amount of electricity already used. The polarization voltage coefficient is... This represents the pressure drop coefficient in the exponential region. The internal resistance of the battery. This refers to the battery terminal voltage. For battery power, The charging and discharging current of the battery, This refers to the battery's state of charge. For the remaining capacity of the battery, The rated capacity of the battery, This is the terminal voltage of the supercapacitor. This is the ideal voltage for a supercapacitor. This refers to the current in the supercapacitor. This is the equivalent series internal resistance of a supercapacitor. To store energy for supercapacitors This refers to the capacitance value of the supercapacitor. This refers to the effective capacitance of the supercapacitor. This is the initial capacitance of the supercapacitor. This is the maximum operating voltage of the supercapacitor. This is the minimum operating voltage for a supercapacitor.

4. The method according to claim 1, characterized in that, In S4, the thermodynamic equation for room temperature is: ； In the formula, Indoor temperature, Outdoor temperature For air conditioning cooling capacity, For the building's equivalent heat capacity, The equivalent thermal resistance of the building.

5. The method according to claim 1, characterized in that, In S4, the air conditioner power model includes the relationship between air conditioner power and cooling capacity, steady-state air conditioner power calculation, and air conditioner start-stop control rules. The relationship between air conditioner power consumption and cooling capacity is as follows: ； When the indoor temperature remains constant, the air conditioner's power consumption is calculated based on the difference between the outdoor and indoor temperatures: ； The air conditioner start / stop control rules are as follows: when hour, ; when hour, ; In the formula, For air conditioner power, For air conditioning cooling capacity, For air conditioner energy efficiency ratio, This refers to the rated power of the air conditioner. Set the temperature for the air conditioner. Indoor temperature, Outdoor temperature The equivalent thermal resistance of the building.

6. The method according to claim 1, characterized in that, In S5, the economic objective function is: ； in, Cost of purchasing and selling electricity: ； Cost of charging and discharging energy storage devices: ； For operation and maintenance costs: ； In the formula, For electricity purchase price, For electricity sales price, For electricity purchase markings, This is a sign for electricity sales. For the power purchase capacity, For electricity sales capacity, This is the charging cost coefficient. This is the discharge cost coefficient. This is a charging symbol. For discharge indication, For charging power, For discharge power, This is the photovoltaic maintenance cost coefficient. This is the air conditioning maintenance cost coefficient. Photovoltaic power generation capacity, For air conditioner power, This is the air conditioner start / stop indicator. The length of the scheduling period. t For scheduling period index, It is the set of all time periods within the scheduling period.

7. The method according to claim 1, characterized in that, In S5, the thermal comfort objective function is characterized by a human thermal sensation prediction index, which is: ； In the formula, for Real-time human thermal sensation prediction index Set the temperature for the air conditioner. Indoor temperature, The human body's energy metabolism rate. For the thermal resistance of clothing; The objective function for thermal comfort is: ； In the formula, Let thermal comfort be the objective function. As a penalty factor, It is the set of all time periods within the scheduling period; The objective function for microgrid energy management is: ； In the formula, The total objective function value, The objective function is the economic efficiency.

8. The method according to claim 1, characterized in that, In S6, the power balance constraint is: ； The upper and lower limits of the device power constraints include: Photovoltaic power generation constraints: ； Power purchase constraints: ； Electricity sales capacity constraints: ； Energy storage device charging power constraints: ； Energy storage device discharge power constraints: ； Air conditioning power constraints: ； In the formula, Photovoltaic power generation capacity, Power to charge energy storage devices, This refers to the discharge power of the energy storage device. This is a charging symbol. For discharge indication, For the power purchase capacity, For electricity sales capacity, For electricity purchase markings, This is a sign for electricity sales. For air conditioner power, This is the air conditioner start / stop indicator. For load power, This indicates the lower limit of the corresponding power. This indicates the corresponding power limit.

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