A virtual aggregator optimization control method based on model predictive control

Abstract

The present invention discloses a method for optimizing and regulating a virtual aggregator based on model predictive control, comprising the following steps: determining the basic structure and energy flow relationship of a typical virtual aggregator, and establishing a mathematical model of the virtual aggregator's energy supply equipment, energy conversion equipment, and energy storage equipment. Determine the equipment operating parameters and their physical meanings, and provide a method for energy cascade utilization and renewable energy consumption. Considering factors such as system economy and load uncertainty, determine the constraints of each part of the model of the virtual aggregator, including power balance, energy interaction, and model characteristic constraints. Taking minimization of the total operating cost of the virtual aggregator as the objective function, establish an optimization scheduling and control strategy for the virtual aggregator based on model predictive control. The present invention can effectively reduce the total operating cost of the virtual aggregator system on the basis of meeting the balance between supply and demand of the system, can fully consider the current state of the virtual aggregator and make timely corrections, realize the real-time scheduling, and improve the stability and reliability of the system.

Description

Technical Field

[0001] The present invention relates to a virtual aggregator optimization control method based on model predictive control, and specifically to a virtual aggregator that integrates electric, cold and hot energy, which can effectively reduce the total system operation cost of the virtual aggregator and realize the consumption of renewable energy and the cascade utilization of energy. Background Art

[0002] Virtual aggregators can combine different forms of energy sources and various types of loads such as electric load, heat load and cooling load, and optimize the scheduling of each subsystem as a whole with virtual aggregators, which can meet the supply and demand balance between suppliers and users. At the same time, the deep coupling between modules can make full use of their respective advantages, improve energy efficiency and reduce system operating costs. In addition, the access of distributed energy leads to more complex operating scenarios for virtual aggregators, which brings challenges to the economic security and efficient operation of power systems. Therefore, exploring a virtual aggregator optimization scheduling strategy method suitable for complementary integration and integrated production, supply and consumption is of great significance to improving the operating efficiency of the power system, saving system operating costs and maintaining safe and reliable operation of the system. Summary of the invention

[0003] The purpose of the present invention is to overcome the above-mentioned deficiencies in the prior art and provide a virtual aggregator optimization control method based on model predictive control. In order to solve the problems and needs in the background technology, the present invention first establishes a mathematical model of the virtual aggregator system, then determines the constraints of the various models of the virtual aggregator, takes minimizing the total operating cost of the virtual aggregator as the objective function, establishes a virtual aggregator optimization scheduling method based on model predictive control, and finally performs optimization calculations, sets three operating modes, and determines a scheduling control strategy that can adapt to the prediction curve.

[0004] The technical solution adopted by the present invention to solve the above problem is: a virtual aggregator optimization control method based on model predictive control, which is characterized by the following steps:

[0005] Step 1: Determine the basic structure and energy flow relationship within the virtual aggregator, and provide the energy cascade utilization and renewable energy consumption methods;

[0006] Step 2: Establish mathematical models of the virtual aggregator's energy supply equipment, energy conversion equipment, and energy storage equipment;

[0007] Step 3: Considering factors such as the economics of the virtual aggregator and load uncertainty, determine the constraints of each part of the virtual aggregator model, including power balance constraints, energy interaction constraints, and model characteristic constraints;

[0008] Step 4: Taking minimizing the total operating cost of the virtual aggregator as the objective function, establish an optimal scheduling and control strategy for the virtual aggregator based on model predictive control;

[0009] Step 5: Determine the basic parameters of the virtual aggregator module, use the prediction model to calculate the prediction curves of distributed energy generation equipment and loads, and set three operation modes;

[0010] Step 6: Considering model predictive control, analyze the output of each module of the virtual aggregator and propose a scheduling control strategy.

[0011] Among them, step 1 is specifically as follows: A typical virtual aggregator includes three energy forms: electricity, cold and heat. It coordinates the supply and demand balance of other forms according to the residual utilization value of natural gas, that is, it uses waste heat recovery devices and other equipment to reutilize gas turbines and other equipment that use natural gas as fuel, and supplements the input of absorption chillers in the form of thermal energy to achieve energy cascade utilization. Introducing energy storage equipment into virtual aggregators can effectively alleviate the frequent scheduling problem caused by the imbalance between supply and demand. When supply exceeds demand, it is absorbed and stored as a load in a certain form of energy, and when supply is insufficient, it provides this form of energy as an output device. On this basis, with the access of the power grid and renewable energy, it can not only realize the consumption of renewable energy through various forms of energy conversion, but also reduce energy loss and improve the environmental protection of the system while meeting the user's load demand.

[0012] Step 2 is as follows: The virtual aggregator includes energy supply and consumption modules in various energy forms, including not only electricity, heat, natural gas and other circulation forms, but also integrated energy storage equipment and renewable energy utilization equipment, etc. The modules are deeply coupled to achieve a state of balance between system supply and demand under the condition of mutual conversion and interdependence. The input and output characteristics of the main equipment of the virtual aggregator are analyzed, and the mathematical models of gas turbines, waste heat recovery units, gas boilers, absorption chillers and thermal chillers, and energy storage equipment are established.

[0013] Step 3 is as follows: Each part of the virtual aggregator model can only fully play its role and maximize various efficiencies when it is operated under certain conditions. This condition is the constraint condition. The control strategy of the virtual aggregator needs to meet the constraint conditions to minimize the overall loss and maximize the benefit, which is very important for the safe and stable operation of the power system. The constraint conditions mainly include energy balance constraints and characteristic constraints of the model itself.

[0014] Step 4 is as follows: Taking minimizing the total operating cost of the virtual aggregator as the goal, the objective function is established and can be expressed as:

[0015]

[0016] Where f is the total cost of the virtual aggregator, N T is the upper limit of the scheduling period within the limited time domain, C FU (t) is the fuel cost in period t; C ME (t) is the maintenance cost of period t; C EX (t) is the electricity purchase cost in period t; C ST (t) is the start-stop cost of the controllable equipment in period t; C HE (t) is the heat conversion benefit in time period t.

[0017] Step 5 is specifically as follows: select a virtual aggregator including distributed power generation equipment (such as wind turbines and photovoltaic cells), micro gas turbines, waste heat recovery units, absorption chillers, electric chillers, and energy storage equipment such as batteries and heat storage tanks, and solve the system to analyze the effectiveness of the model predictive control method proposed in the present invention. In addition, due to the power interaction between the virtual aggregator and the large power grid and the different peak and valley electricity prices, the electricity purchase and sale prices and the electricity prices of each unit at different times are not fixed, so it is stipulated that they must comply with the formulation standards of time-of-use electricity prices.

[0018] The basic parameters of the research object are given, the scenarios and modes of operation of the virtual aggregator are described, and the prediction curves of distributed energy generation equipment and loads are calculated through the prediction model.

[0019] Compared with the prior art, the present invention has the following advantages and effects:

[0020] (1) The virtual aggregator optimization control model established in the present invention can achieve the consumption of renewable energy while meeting the system supply and demand balance and reducing the total system operating cost, and has the advantages of improving energy utilization efficiency, maximizing system operating benefits, and reducing environmental pollution.

[0021] (2) The model predictive control method proposed in the present invention can fully consider the current system status and make timely corrections to cope with the uncertainty of the output of distributed energy generation equipment and the randomness and volatility of load demand, thus achieving real-time scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 is a structural diagram of a typical virtual aggregator in the present invention;

[0023] Figure 2 It is a prediction curve diagram of the output of the distributed energy generation equipment in the present invention;

[0024] Figure 3 is a load prediction curve diagram in the present invention;

[0025] Figure 4 This is a diagram of the optimization scheduling result of the virtual aggregator's electricity balance under method 3 of the present invention;

[0026] Figure 5 This is a diagram of the thermal balance optimization scheduling result of the virtual aggregator under method 3 of the present invention;

[0027] Figure 6 This is a diagram of the optimization scheduling result of the virtual aggregator's electricity balance under method 1 of the present invention;

[0028] Figure 7 It is a diagram of the thermal balance optimization scheduling result of the virtual aggregator under method 1 of the present invention;

[0029] Figure 8 This is a diagram of the optimization scheduling result of the virtual aggregator's electricity balance under method 2 of the present invention;

[0030] Fig. 9 This is a diagram of the thermal balance optimization scheduling results of the virtual aggregator under method 2 of the present invention. DETAILED DESCRIPTION

[0031] The present invention will be further described in detail below with reference to the accompanying drawings and by way of examples. The following examples are intended to explain the present invention but the present invention is not limited to the following examples.

[0032] Example

[0033] In this embodiment, a virtual aggregator optimization control method based on model predictive control has the following steps:

[0034] Step 1: Generate electricity using natural gas as raw material through gas turbines, while the waste heat recovery unit absorbs the heat generated during the power generation process to supplement the system's thermal energy capacity. There are three types of loads on the user side: electricity, cold, and heat. The electricity load demand is met by the power grid, gas turbines, and renewable energy generators. At the same time, batteries help balance the supply and demand of electricity and achieve peak shaving and valley filling; the cold load demand is met by electric refrigerators and absorption refrigerators; the heat load demand is supplied by gas boilers and waste heat recovery units. The virtual aggregator integrates cold, heat, and electricity, and takes into account the deep coupling between the modules, realizing the cascade utilization of energy and the consumption of renewable energy, improving energy utilization efficiency and system environmental protection.

[0035] Step 2: The input and output characteristics of the micro gas turbine can be expressed as:

[0036]

[0037] In the formula, Q MT (t) and P MT (t) are the thermal power and electrical power of the micro-turbine at time t, respectively, η MT (t) and η L are the power generation efficiency and heat loss coefficient of the micro turbine at time t respectively.

[0038] The input and output characteristics of the waste heat recovery unit can be expressed as:

[0039] Q HR (t) = Q MT (t)η HR

[0040] In the formula, Q HR (t) is the thermal power of the waste heat recovery boiler at time t, η HR It is the heat energy absorption conversion efficiency of the waste heat recovery boiler.

[0041] The input and output characteristics of the gas boiler can be expressed as:

[0042] Q GB (t) = F GB (t)η GB

[0043] In the formula, Q GB (t) is the thermal power of the gas boiler at time t, F GB (t) is the natural gas consumption of the gas boiler at time t, η GB For gas boiler efficiency.

[0044] The input-output characteristics of the absorption chiller can be expressed as:

[0045] Q' AC (t) = Q AC (t)COP AC

[0046] In the formula, Q' AC (t) is the output cooling power of the absorption refrigerator at time t, Q AC (t) is the input heat power of the absorption refrigerator at time t, COP AC It is the coefficient of performance of the absorption chiller, which is a cubic function of its own load rate.

[0047] The input-output characteristics of the electric refrigerator can be expressed as:

[0048] Q' EC (t) = P EC (t)COP EC

[0049] In the formula, Q' EC (t) is the output cooling power of the electric refrigerator at time t, P EC (t) is the input power of the refrigerator at time t, COP EC It is the coefficient of refrigeration performance of the electric refrigerator. Since the efficiency of electric energy conversion is much greater than the efficiency of thermal energy utilization, its value is greater than the coefficient of refrigeration performance of the absorption refrigerator.

[0050] The input and output characteristics of the energy storage device can be expressed as:

[0051]

[0052] Where W ES (t) is the storage capacity of the energy storage device at time t, P ES，cha (t) and P ES，dis (t) are the charge and discharge power of the energy storage device at time t, δ is the self-discharge rate of the energy storage device, η cha and η dis are the charging and discharging efficiencies of the energy storage device, respectively.

[0053] Step 3: Constraints mainly include power balance constraints, energy interaction constraints and model characteristic constraints.

[0054] A. Power balance constraint The power balance constraint can be expressed as:

[0055]

[0056] Where P load (t) represents the electric load power of the virtual aggregator in time period t. The left formula represents the electric power provided by each power generation unit of the system, and the right formula represents the electricity consumption in the system and the electric load power required by the user.

[0057] The thermal power balance constraint can be expressed as:

[0058] Q HR (t)+Q GB (t) = Q he (t)

[0059] Where, the thermal power of the micro-turbine is MT (t) The part absorbed and utilized by the waste heat recovery unit is included in the first thermal power Q HR In (t), the left equation represents the thermal power provided by the system, and the right equation represents the thermal load power required by the user.

[0060] The cold power balance constraint relationship is similar to that of the hot power and will not be described here.

[0061] B. The energy interaction constraint can be expressed as:

[0062] 0≤|P ex (t)|≤P line,max

[0063] Where P line，max Indicates the maximum allowable power of the line. It must be ensured that the interaction power between the two systems is lower than the maximum allowable power at any time.

[0064] C. Model feature constraints can be expressed as:

[0065] P MT,min ≤P MT (t)≤P MT,max

[0066] Where P MT，min and P MT，max They represent the minimum electric power output and maximum capacity of the micro gas turbine respectively.

[0067] The ramp rate constraint can be expressed as:

[0068] K MT,min Δt≤P MT (t)-P MT (t-1)≤K MT,max Δt

[0069] In the formula, K MT，min and K MT，max They represent the minimum ramp rate and maximum ramp rate of the micro turbine respectively.

[0070] The charging and discharging power constraints of the energy storage system can be expressed as:

[0071] θ dis P ES,dis ≤P ES (t)≤θ cha P ES,cha

[0072] In the formula, θ dis and θ cha Represent the discharge and charge ratios of the energy storage system, P ES，cha and P ES，dis They represent the upper and lower limits of charging and discharging of the energy storage system respectively.

[0073] The continuity constraint of the energy storage system can be expressed as:

[0074] E ES (0) = E ES (N t Δt)

[0075] In the formula, E ES (t) represents the capacity of the energy storage device at time t.

[0076] Step 4: Considering the power generation efficiency and the calorific value of natural gas, the fuel cost of the micro gas turbine can be calculated by the electric power of the micro gas turbine at a certain moment. The objective function is established to minimize the total operating cost of the virtual aggregator and can be expressed as:

[0077]

[0078] Where f is the total cost of the virtual aggregator, N Tis the upper limit of the scheduling period within the limited time domain, C FU (t) is the fuel cost in period t; C ME (t) is the maintenance cost of period t; C EX (t) is the electricity purchase cost in period t; C ST (t) is the start-stop cost of the controllable equipment in period t; C HE (t) is the heat conversion benefit in time period t.

[0079] A. Fuel cost can be expressed as:

[0080]

[0081] In the formula, C gas is the unit price of natural gas, 2.5 yuan / m 3 ;P MT (t) and η MT (t) are the electric power and power generation efficiency of the micro-turbine in period t; L HVNG is the calorific value of natural gas, which is 9.7kW·h / m 3 .

[0082] B. For micro gas turbines, waste heat recovery boilers, gas boilers, absorption chillers, electric chillers and energy storage equipment, the maintenance cost of the virtual aggregator can be expressed as the product of their unit output and maintenance cost:

[0083]

[0084] In the formula, C mei and P i They are the unit energy maintenance cost and output power of the above components respectively.

[0085] C. The electricity purchase cost is a piecewise function with the positive and negative values ​​of the interactive power as the constraint domain, expressed in the form of absolute value:

[0086]

[0087] In the formula, C in (t) represents the electricity purchase price in period t, C out (t) represents the electricity price in period t, P ex (t) represents the interaction power between the virtual aggregator and the large power grid in time period t.

[0088] D. For controllable equipment such as distributed energy generation equipment and gas turbines, the change of the start-stop state of the controllable unit is reflected in the form of the product of the start-stop number and the start-stop cost. The start-stop cost of the controllable equipment can be expressed as:

[0089]

[0090] Where U j (t) represents the start and stop status of controllable device j in time period t, C ST，j is the single startup cost of controllable device j.

[0091] E. The cascaded utilization of thermal energy by virtual aggregators increases the energy utilization efficiency of the system. When providing heat load to users in the system, they will charge a certain fee. This fee is an important part of the system's revenue supplementary cost. The heat conversion income of virtual aggregators can be expressed as:

[0092] C HE (t) = C he Q he (t)

[0093] In the formula, C he is the unit price of heat energy provided, Q he (t) is the heat load required by the user during period t.

[0094] Step 5: In the model parameters, the time length is 24 hours, and the time period length of each scheduling strategy update is 1 hour. Select household distributed wind turbines and photovoltaic power generation equipment with a rated power of 5kW. The output forecast and load forecast of the distributed energy generation equipment of the virtual aggregator are as shown in the attached figure. Figure 2 , Figure 3 As shown. Three operation modes are discussed and analyzed:

[0095] Method 1: When the output of distributed energy equipment is constant, the virtual aggregator dispatch strategy is set to output 15kW of photovoltaic cells during the 7-18 hours and no output during other hours. The wind turbine outputs 25kW during all hours. The load is set to Figure 3 The predicted curve value of

[0096] Method 2: Virtual aggregator dispatch strategy when user load is constant, setting the power demand of electric load to 100kW, the power demand of thermal load to 110kW, and the distributed generation equipment to Figure 2 The predicted curve value of

[0097] Method 3: The virtual aggregator optimizes the dispatching strategy based on model predictive control, and both distributed generation equipment and loads use predicted curve values.

[0098] Step 6: Method 3 uses the model predictive control method to more accurately track the output of distributed generation equipment and real-time load demand. Through reasonable scheduling, it can give full play to the advantages of each device, save unnecessary losses and waste of resources, and achieve the optimal objective function (that is, the minimum total system operating cost). However, method 1 does not consider the impact of the uncertainty of distributed energy output, and method 2 does not consider the impact of the volatility of user load demand, resulting in a high total system operating cost.

[0099] After calculation, the electrical load balance and thermal load balance of mode 3 are as shown in the attached figure. Figure 4 and Figure 5 The electrical load balance and thermal load balance states of Mode 1 and Mode 2 are shown in the attached Figures 6 to 9 shown.

[0100] Depend on Figure 4 It can be seen that the micro-turbine has output power in 24 time periods, and the change of its output power is to ensure the lowest total operating cost under the condition of meeting the system power balance constraint. Its trend is to couple with distributed energy generation equipment to meet most of the power load needs. Distributed power generation equipment includes wind turbines and photovoltaic cells. Photovoltaic cells do not work at night (i.e., time periods 1-6). Only wind turbines are in operation, so the output power of distributed power generation equipment during this period is low. In addition, night is the valley period of electricity consumption, and the battery is in a charging state in the form of load, so that the supply and demand of electricity can be balanced when the supply of electricity exceeds the demand.

[0101] During the peak period (i.e. 10-15 hours), the demand for electricity load gradually increases, but since the electricity price during this period is the highest, purchasing electricity from the power grid will incur a large purchase cost. Therefore, the strategy adopted is that the battery provides the stored energy to the microgrid in the form of discharge, and the photovoltaic cells participate in the distributed energy power generation output, so that the microgrid power supply at this time is slightly greater than the electricity demand, and the excess electricity is sold to the large power grid at the highest electricity price. This part can effectively reduce the operating cost of the system as income.

[0102] During normal hours (i.e., 15-18 hours), the capacity of the battery is not enough to provide power to the microgrid as a power supply device. The electricity purchase price at this time is within an acceptable range, so the microgrid purchases electricity from the large power grid, so that power balance can be achieved when power supply is insufficient to meet demand. At the same time, the battery is in a charging state in the form of a load in order to cope with the power supply during the peak period of the next period. The introduction of the battery breaks the time isolation of power demand, can fully adjust the peak-to-valley difference of the electric load, achieves the effect of peak shaving and valley filling of power, and reduces the total operating cost of the system.

[0103] Depend on Figure 5 It can be seen that at night (i.e., 1-6 period and 20-24 period), the heat load demand of winter users is relatively large. When the electricity purchase price is low (such as 3-6 period), the electric boiler is used to make electric energy participate in the dispatch of thermal energy; in the period when the electricity purchase price is high (such as 18-20 period), the waste heat recovery unit is fully utilized to realize the cascade utilization of thermal energy, and the electric boiler is stopped to reduce this part of the operating cost; in normal periods (such as 21-23 period), the role of the heat storage tank is fully utilized to reduce the power output of the micro-turbine, so that thermal energy participates in the dispatch of electric energy.

[0104] During the day, the heat load demand is low. At this time, the system's thermal energy is absorbed in the form of load through the heat storage tank to achieve a thermal equilibrium state. The stored thermal energy is used to release heat in subsequent periods, realizing energy transfer across time.

[0105] The present invention adopts an optimization scheduling method based on model predictive control, and uses the model predictive control method to optimize and regulate the virtual aggregator, thereby realizing the consumption of renewable energy and the cascade utilization of energy, and improving the stability and reliability of the system.

[0106] The contents not described in detail in this specification belong to the prior art known to professional and technical personnel in this field.

[0107] Although the present invention has been disclosed as above by way of embodiments, it is not intended to limit the protection scope of the present invention. Any changes and modifications made by any technician familiar with the technology without departing from the concept and scope of the present invention should fall within the protection scope of the present invention.

Claims

1. A virtual aggregator optimization control method based on model predictive control, characterized in that the steps are: as follows: Step 1: Determine the basic structure and energy flow relationship within the virtual aggregator, and provide the energy cascade utilization and renewable energy consumption methods; Step 2: Establish mathematical models of the energy supply equipment, energy conversion equipment and energy storage equipment of the virtual aggregator; Step 3: Considering the economic and load uncertainty factors of the virtual aggregator, determine the constraints of each part of the virtual aggregator model, including power balance constraints, energy interaction constraints and model characteristic constraints; Step 4: Taking minimizing the total operating cost of the virtual aggregator as the objective function, establish an optimal scheduling and control strategy for the virtual aggregator based on model predictive control; Step 5: Determine the basic parameters of the virtual aggregator module, use the prediction model to calculate the prediction curves of distributed energy generation equipment and loads, and set three operation modes; Step 6: Considering model predictive control, analyze the output of each module of the virtual aggregator and propose a scheduling control strategy; In step 1: gas turbines are used to generate electricity using natural gas as raw material, while the waste heat recovery unit absorbs the heat generated during the power generation process to supplement the system's thermal energy capacity; the user side has three types of loads: electricity, cold, and heat. The electricity load demand is met by the power grid, gas turbines, and renewable energy generators. At the same time, batteries help balance the supply and demand of electricity and achieve peak shaving and valley filling; the cold load demand is met by electric refrigerators and absorption refrigerators; the heat load demand is met by gas boilers and waste heat recovery units; In step 2: the input and output characteristics of the micro gas turbine are expressed as: In the formula, Q MT (t) and P MT (t) are the thermal power and electrical power of the micro-turbine at time t, respectively, η MT (t) and η L are the power generation efficiency and heat loss coefficient of the micro-turbine at time t respectively; The input and output characteristics of the waste heat recovery unit are expressed as: Q HR (t)=Q MT (t)η HR In the formula, Q HR (t) is the thermal power of the waste heat recovery boiler at time t, η HR The heat energy absorption conversion efficiency of the waste heat recovery boiler; The input and output characteristics of the gas boiler are expressed as: Q GB (t)=F GB (t)η GB In the formula, Q GB (t) is the thermal power of the gas boiler at time t, F GB (t) is the natural gas consumption of the gas boiler at time t, η GB For the efficiency of gas boilers; The input-output characteristics of the absorption chiller are expressed as: Q' AC (t)=Q AC (t)COP AC In the formula, Q' AC (t) is the output cooling power of the absorption refrigerator at time t, Q AC (t) is the input heat power of the absorption refrigerator at time t, COP AC is the coefficient of performance of the absorption chiller; The input-output characteristics of the electric refrigerator are expressed as: Q' EC (t)=P EC (t)COP EC In the formula, Q' EC (t) is the output cooling power of the electric refrigerator at time t, P EC (t) is the input power of the refrigerator at time t, COP EC is the refrigeration performance coefficient of the electric refrigerator; The input and output characteristics of the energy storage device are expressed as: Where W ES (t) is the storage capacity of the energy storage device at time t, P ES，cha (t) and P ES，dis (t) are the charging power and discharging power of the energy storage device at time t, δ is the self-discharge rate of the energy storage device, η cha and η dis are the charging efficiency and discharging efficiency of the energy storage device respectively; In step 3: the constraints include power balance constraints, energy interaction constraints and model characteristic constraints; A. Power balance constraint The electric power balance constraint is expressed as: Where P load (t) represents the electric load power of the virtual aggregator in time period t. The left formula represents the electric power provided by each power generation unit of the system, and the right formula represents the power consumption in the system and the electric load power required by the user; The thermal power balance constraint can be expressed as: Q HR (t)+Q GB (t)=Q he (t) Where, the thermal power of the micro-turbine is Q MT (t) The part absorbed and utilized by the waste heat recovery unit is included in the first thermal power Q HR (t), the left formula represents the thermal power provided by the system, and the right formula represents the thermal load power required by the user; The cold power balance constraint relationship is the same as the hot power balance constraint relationship; B. Energy interaction constraints are expressed as: 0≤|P ex (t)|≤P line,max Where P line，max Indicates the maximum allowable power of the line. It must be ensured that the interaction power between the two systems is lower than the maximum allowable power at any time. C. Model feature constraints are expressed as: P MT,min ≤P MT (t)≤P MT,max Where P MT，min and P MT，max They represent the minimum output and maximum capacity of the electric power of the micro gas turbine respectively; The ramp rate constraint is expressed as: K MT,min Δt≤P MT (t)-P MT (t-1)≤K MT,max Δt In the formula, K MT，min and K MT，max They represent the minimum ramp rate and maximum ramp rate of the micro-turbine respectively; The charging and discharging power constraints of the energy storage system are expressed as: θ dis P ES,dis ≤P ES (t)≤θ cha P ES,cha In the formula, θ dis and θ cha Respectively represent the discharge rate and charge rate of the energy storage system, P BE，cha and P ES，dis They represent the upper and lower limits of charging and discharging of the energy storage system respectively; The continuity constraint of the energy storage system is expressed as: AND ES (0)=And ES (N t (Δt) In step 4: considering the power generation efficiency and the calorific value of natural gas, the fuel cost of the micro gas turbine is calculated by the electric power of the micro gas turbine at a certain moment, and the objective function is established to minimize the total operating cost of the virtual aggregator: Where f is the total cost of the virtual aggregator, N T is the upper limit of the scheduling period within the limited time domain, C FU (t) is the fuel cost in period t; C ME (t) is the maintenance cost in period t; C EX (t) is the electricity purchase cost in period t; C ST (t) is the start-stop cost of the controllable equipment in period t; C HE (t) is the heat conversion benefit during period t; A. Fuel cost is expressed as: In the formula, C gas is the unit price of natural gas; P MT (t) and η MT (t) are the electric power and power generation efficiency of the micro-turbine in period t; L HVHG is the calorific value of natural gas; B. For micro gas turbines, waste heat recovery boilers, gas boilers, absorption chillers, electric chillers and energy storage equipment, the maintenance cost of the virtual aggregator is expressed as the product of their unit output and maintenance cost: In the formula, C me,i and P i (t) is the unit energy maintenance cost and output power of each component during period t; C. The electricity purchase cost is a piecewise function with the positive and negative values ​​of the interactive power as the constraint domain, expressed in the form of absolute value: In the formula, C in (t) represents the electricity purchase price during period t, C out (t) represents the electricity price in period t, P ex (t) represents the interaction power between the virtual aggregator and the large power grid during period t; D. For distributed energy generation equipment and gas turbine controllable equipment, the change of the start-stop state of the controllable unit is reflected in the form of the product of the start-stop number and the start-stop cost, and the start-stop cost of the controllable equipment is expressed as: Where U j (t) represents the start and stop status of controllable device j during period t, C ST，j is the single startup cost of controllable device j; E. The cascade utilization of thermal energy by virtual aggregators increases the energy utilization efficiency of the system. When providing heat load to users in the system, a certain fee will be charged. This fee is an important part of the system's revenue supplementary cost. The heat conversion income of virtual aggregators is expressed as: C HE (t)=C he Q he (t) In the formula, C he is the unit price of heat energy provided, Q he (t) is the heat load required by the user during period t; In step 5: in the model parameters, the time length is 24 hours, and the time period length of each scheduling strategy update is 1 hour; household distributed wind turbines and photovoltaic power generation equipment with the same rated power are selected and discussed and analyzed in three operating modes: Method 1: Virtual aggregator dispatch strategy when the output of distributed energy equipment is constant; Method 2: Virtual aggregator scheduling strategy when user load is constant; Approach 3: Virtual aggregator optimization scheduling strategy based on model predictive control; In step 6: Method 3 uses the model predictive control method to more accurately track the output of distributed power generation equipment and real-time load demand. Through reasonable scheduling, it can give full play to the advantages of each device, save unnecessary losses and waste of resources, and achieve the optimal objective function; while Method 1 does not consider the impact of the uncertainty of distributed energy output, and Method 2 does not consider the impact of the volatility of user load demand, resulting in a relatively high total system operating cost.

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