Continuous-time coordinated control method of source, grid, load and storage based on ubiquitous Internet of Things for power terminals

Abstract

The present invention belongs to the field of electrical engineering, and specifically, relates to a source-grid-load-storage continuous-time coordinated control method based on ubiquitous Internet of Things for power terminals, including: based on an aggregated backup supply model for heterogeneous HVAC systems, solving a continuous-time random unit combination model considering the aggregated HVAC system backup, obtaining an economic dispatch strategy for an HVAC power system containing multiple chillers, and realizing source-grid-load-storage continuous-time coordinated control; wherein, in the aggregated backup supply model for heterogeneous HVAC systems, the indoor temperature state SOT supplied by each HVAC system is used as the backup supply capacity of the HVAC system, and the SOTs between the HVAC systems are equal and the dSOT / dt between the HVAC systems are equal, and the indoor temperature state SOT is specifically: the actual deviation value of the indoor actual temperature from the set temperature, and the relative value of the relative allowable temperature deviation value. The present invention introduces the concept of indoor temperature state SOT, which can solve the problem that there are too many decision variables in massive HVAC systems and it is difficult to solve.

Description

Technical Field

[0001] The present invention belongs to the field of electrical engineering, and more specifically, relates to a source-grid-load-storage continuous-time coordinated control method based on ubiquitous Internet of Things of power terminals. Background Art

[0002] Climate change has become one of the biggest challenges of the 21st century. Sustainable energy supply based on renewable energy is an important means to reduce greenhouse gas emissions. Renewable energy is variable and uncertain, and its large-scale grid connection will increase the flexibility requirements of the power system. In response to the rapid growth of electricity demand and the high penetration of renewable energy, demand response is considered to be a promising solution to increase system flexibility through load shifting without the use of expensive energy storage systems. Buildings equipped with heating, ventilation and air conditioning (HVAC) systems, as the main end users of electricity, have great potential in providing flexibility to the power system, and chillers account for a major part of building electricity consumption.

[0003] In order to achieve demand response of multi-chiller HVAC systems, it is necessary to decide the start and stop status of each chiller. When the control volume of the HVAC system is large, the large number of 0 / 1 variables in the optimization problem will make it difficult to solve; on the other hand, it is impractical for system operators to incorporate heterogeneous models of all HVAC loads into their daily scheduling plans.

[0004] Therefore, how to flexibly and simply implement the scheduling of multiple HVAC systems has become a difficult problem that dispatchers need to solve urgently. Summary of the invention

[0005] In view of the defects of the prior art and the need for improvement, the present invention provides a continuous-time coordinated control method of source, grid, load and storage based on ubiquitous Internet of Things of power terminals, which aims to solve the technical problems that are difficult to solve in the existing large-scale multi-chiller HVAC system scheduling strategy.

[0006] To achieve the above object, according to one aspect of the present invention, a source-grid-load-storage continuous time coordinated control method based on ubiquitous Internet of Things for power terminals is provided, comprising:

[0007] Based on the aggregate reserve supply model of heterogeneous HVAC systems, the continuous-time random unit commitment model considering the aggregate HVAC system reserve is solved, and the economic dispatch strategy of the HVAC power system with multiple chillers is obtained to achieve continuous-time coordinated control of source, grid, load and storage.

[0008] Among them, in the aggregated backup supply model of the heterogeneous HVAC system, the indoor temperature state SOT supplied by each HVAC system is used as the backup supply capacity of the HVAC system, and the SOT between each HVAC system is equal and the dSOT / dt between each HVAC system is equal. The indoor temperature state SOT is specifically: the actual deviation value of the actual indoor temperature from the set temperature, and the relative value of the relative allowable temperature deviation value.

[0009] Further, the indoor temperature state SOT is expressed as:

[0010]

[0011] -1≤SOT n (t)≤1;

[0012] Among them, SOT n (t) represents the indoor temperature state of the nth HVAC system at time t, ε n represents the allowable temperature deviation of the room supplied by the nth HVAC system, represents the actual indoor temperature of the room supplied by the nth HVAC system at time t, Indicates the set temperature of the room supplied by the nth HVAC system.

[0013] Furthermore, the aggregated backup supply model of the heterogeneous HVAC system is:

[0014]

[0015]

[0016]

[0017] -1≤SOT n (t)≤1;

[0018] Where N(k) means there are N HVAC systems at bus k. represents the active power of the nth HVAC system at bus k at time t under scenario s, Indicates the SOT value corresponding to the SOT equality among the HVAC systems at time t, The coefficient representing the cooling demand in the relationship between the active power and cooling demand of the nth HVAC system at bus k, α n The coefficient representing the SOT term in the equation between the cooling demand and SOT of the nth HVAC system at bus k; Indicates the dSOT / dt value corresponding to the dSOT / dt between each HVAC system at time t, β nThe coefficient of the dSOT / dt term in the relationship between the cooling demand and SOT of the nth HVAC system at bus k, γ n (t) represents the constant term in the relationship between the cooling demand of the nth HVAC system at bus k and SOT, represents the constant term in the relationship between the active power and cooling demand of the nth HVAC system at bus k;

[0019] represents the total deployment reserve of all HVAC systems at bus k at time t under scenario s, represents the total down-deployment reserve of all HVAC systems at bus k at time t under scenario s, represents the power consumption baseline of the nth HVAC system at bus k at time t predicted in the day-ahead phase, represents the active power of the nth HVAC system at bus k at time t under scenario s, represents the total reserve capacity of all HVAC systems at bus k at time t, It represents the total reserve capacity of all HVAC systems at bus k at time t.

[0020] Furthermore, the aggregate backup supply model of the heterogeneous HVAC system It is constructed in the following way:

[0021] The first-order equivalent thermal effect parameter model of the ambient and indoor temperature is combined with the definition of the indoor temperature state SOT to obtain the cooling demand in the nth HVAC system: With the indoor temperature state SOT n The relationship between (t):

[0022]

[0023]

[0024] In the formula, represents the thermal resistance of the building where the nth HVAC system is located, represents the heat capacity of the building where the nth HVAC system is located, represents the outdoor temperature of the building where the nth HVAC system is located, represents the indoor temperature of the building where the nth HVAC system is located;

[0025] Based on the power consumption of the nth HVAC system: Get the aggregate power consumption of N HVAC systems at bus k: In the formula, represents the dSOT / dt of the nth HVAC system at time t,

[0026] Equalizing the SOT between the HVAC systems and dSOT / dt between the HVAC systems yields: In the formula, represents the SOT of each HVAC system at time t, It represents the dSOT / dt of each HVAC system at time t.

[0027] Furthermore, before solving the continuous-time stochastic unit commitment model considering the aggregated HVAC system reserve, the method further includes:

[0028] The physical parameters of each component in the HVAC power system with multiple chillers are obtained, wherein the components of the HVAC power system with multiple chillers include: a power grid, a thermal power unit and the HVAC system with multiple chillers.

[0029] The present invention also provides a computer-readable storage medium, which includes a stored computer program, wherein when the computer program is executed by a processor, the device where the storage medium is located is controlled to execute the above-mentioned source-grid-load-storage continuous-time coordinated control method based on the ubiquitous Internet of Things of power terminals.

[0030] The present invention also provides a server, comprising: a processor, a transceiver, and the computer-readable storage medium as described above, wherein:

[0031] The transceiver is used to send and receive data under the control of the processor;

[0032] When the processor executes the computer program on the computer-readable storage medium, the steps of the source-grid-load-storage continuous-time coordinated control method based on ubiquitous Internet of Things of power terminals as described above are implemented.

[0033] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects:

[0034] The present invention provides a source-grid-load-storage continuous-time coordinated control method based on ubiquitous Internet of Things for power terminals, introduces the concept of indoor temperature state (SOT), and can achieve unified scheduling of massive HVAC systems by issuing unified SOT instructions to massive multi-chilled water machine HVAC systems. SOT is defined as the relative value of the deviation between the room temperature and the set temperature divided by the temperature deviation range allowed by the user. Although the room temperature set values ​​of each building are different, the temperature deviation range that users can tolerate is different, and there are differences between each HVAC system, the definition of SOT is the same. By issuing a unified SOT instruction, the problem of too many decision variables in massive HVAC systems and difficulty in solving can be solved. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1A flow chart of a method for continuous time coordinated control of source, grid, load and storage based on ubiquitous Internet of Things for power terminals provided by an embodiment of the present invention;

[0036] Figure 2 A comparison diagram of the up / down reserve capacity and up / down deployment reserve of the HVAC system provided in an embodiment of the present invention, wherein (a) is a continuous time model; (b) is a discrete time model;

[0037] Figure 3 A comparison diagram of up / down ramping spare capacity and up / down deployment ramping of an HVAC system provided in an embodiment of the present invention, wherein (a) is a continuous time model; and (b) is a discrete time model. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0039] Embodiment 1

[0040] A source-grid-load-storage continuous-time coordinated control method based on ubiquitous Internet of Things for power terminals, comprising:

[0041] Based on the aggregate reserve supply model of heterogeneous HVAC systems, the continuous-time random unit commitment model considering the aggregate HVAC system reserve is solved, and the economic dispatch strategy of the HVAC power system with multiple chillers is obtained to achieve continuous-time coordinated control of source, grid, load and storage.

[0042] Among them, in the aggregated backup supply model of heterogeneous HVAC systems, the indoor temperature state SOT supplied by each HVAC system is used as the backup supply capacity of the HVAC system, and the SOT between each HVAC system is equal and the dSOT / dt between each HVAC system is equal. The indoor temperature state SOT is specifically: the actual deviation value of the actual indoor temperature from the set temperature, and the relative value of the relative allowable temperature deviation value.

[0043] It should be noted that before conducting continuous-time coordinated control of source, grid, load and storage, it is necessary to establish the backup supply model of a single multi-chiller HVAC system and the aggregated backup supply model of a heterogeneous HVAC system in turn; establish a continuous-time random unit combination model that considers the backup of the aggregated HVAC system; establish a continuous-time random unit combination model based on function space, and on this basis, conduct continuous-time coordinated control of source, grid, load and storage. Specifically: Based on the solver, solve the above-mentioned continuous-time random unit combination model based on function space to obtain the economic dispatch strategy of the HVAC power system with multiple chillers. Among them,

[0044] The paper is about establishing a standby supply model for a single multi-chilled water machine HVAC system and an aggregate standby supply model for a heterogeneous HVAC system. Specifically, the conventional parameters of the power grid, thermal power units and multi-chilled water machine HVAC system in the multi-chilled water machine HVAC power system, the electric load, the wind farm output and the ambient temperature curve are collected.

[0045] The conventional parameters of the power grid include: power transfer distribution coefficient g, transmission line capacity f.

[0046] The conventional parameters of the thermal power unit include: minimum technical output G , Maximum technical output Maximum climbing rate Maximum downhill climbing rate Minimum startup time T on , minimum downtime T off .

[0047] The conventional parameters of the multi-chilled water machine HVAC system include: indoor temperature set point T set 、Building thermal resistance C b , Building thermal resistance R b , temperature deviation ε allowed by the user.

[0048] The electric load, wind farm output and ambient temperature curves are: The electric load prediction curve L at the busbar k (t), active output curve W of wind farm k (t) and ambient temperature curve T o (t), t is a continuous variable of time, ranging from 0 to the entire scheduling time length.

[0049] About establishing a backup supply model for a single multi-chiller HVAC system and an aggregated backup supply model for heterogeneous HVAC systems.

[0050] Specifically, a backup supply continuous-time model for a single multi-chiller HVAC system and an aggregated backup supply continuous-time model for a heterogeneous HVAC system are established.

[0051] The thermodynamic dynamics within a building can be expressed using a first-order relationship between ambient and indoor temperatures in the form:

[0052]

[0053] T set -ε≤T i (t)≤T set +ε (2)

[0054] Among them, T i and T o are the indoor temperature and the ambient temperature respectively; R b and C b are thermal resistance and thermal capacitance respectively; Q De Indicates the cooling demand; T set is the indoor temperature setting value; ε is the temperature deviation that the user can accept. (1) is the relationship between indoor temperature and ambient temperature; (2) is the upper and lower limit constraints of the indoor temperature, which limits the indoor temperature to the setting value T set Specific areas nearby to ensure user comfort.

[0055] Using the thermal inertia of the building, a single HVAC system can provide upregulated backup R u,Su and reduce the reserve capacity R d,Su It can be expressed as:

[0056]

[0057] Among them, P base represents the HVAC system power consumption baseline predicted in the day-ahead period; P Su is the active power of the HVAC system.

[0058] Assuming there are N(k) HVAC systems at bus k, the total up / down deployment reserve of bus k is It can be expressed as:

[0059]

[0060] in, and They represent the upward and downward deployment of the nth HVAC system respectively.

[0061] Total up / down reserve capacity of N(k) HVAC systems at bus k It should not be less than the upward / downward deployment reserve in each scenario.

[0062]

[0063] in, Deploy backup for total up / down adjustment under scenario s.

[0064] In order to realize the autonomous distribution of dispatch signals among multiple HVAC systems, the present invention defines the state of the indoor temperature (SOT) to represent the backup supply capacity.

[0065]

[0066] According to (2), SOT should follow the following constraints:

[0067] -1≤SOT n (t)≤1 (7)

[0068] Substituting (6) into (1), the cooling demand in the nth HVAC system is The relationship between the indoor temperature state SOT can be expressed as:

[0069]

[0070]

[0071] In addition, the power consumption of the nth HVAC system is shown in (9).

[0072]

[0073] For N(k) HVAC systems at bus k, the aggregate power consumption can be expressed as:

[0074]

[0075] For each HVAC system, the deviation of SOT from 0 represents the user comfort. The larger the deviation, the lower the user comfort. To ensure the consistency of user comfort, the control standard is that the SOT between each HVAC system is equal and the dSOT / dt between each HVAC system is equal.

[0076]

[0077] The aggregator sends the control signal (SOT, dSOT / dt) to each HVAC system, and each HVAC system adjusts its power consumption according to (8). Therefore, (10) can be expressed as:

[0078]

[0079] Therefore, (4) adjusts the deployment of backup Can be refactored as:

[0080]

[0081] In summary, the aggregated reserve supply model of the HVAC system is expressed as (5), (7) and (12)-(13). The reserve capacity of each bus is automatically allocated to each HVAC system by setting the consistent SOT. The concept of indoor temperature state SOT is proposed, ignoring the differences between individual HVACs, and uniformly representing the indoor temperature state under different HVAC systems, making the solution of the random unit model faster and simpler, ensuring that the solution is simple with good solution accuracy and high practical value.

[0082] On the establishment of a continuous-time stochastic unit commitment model considering aggregated HVAC system reserves.

[0083] Specifically, the continuous-time unit commitment model is established as a two-stage optimization problem, combining the day-ahead scheduling and intraday scheduling of thermal power units and multi-chilled water chiller HVAC systems. The objective function is the total cost of the two-stage decision within the scheduling period T, as shown in (14).

[0084]

[0085]

[0086] Among them, C G is the coal consumption cost of thermal power units; C u / C d is the startup / shutdown cost of the thermal power unit; U(t) / V(t) is the startup / shutdown action of the thermal power unit at time t; μ u,G / μ d,G The price of raising / lowering the reserve capacity for thermal power units; μ u,H / μ d,H Adjust the reserve capacity price up / down for HVAC system units; ρ u,G / ρ d,G The price of deploying reserve for thermal power units is adjusted up / down; ρ u,G / ρ d,G Adjust the price of deploying backup for HVAC system units upwards / downwards; is the probability of scene s; C m is the coal consumption cost corresponding to the minimum output of the thermal power unit; I(t) is the start and stop status of the thermal power unit at time t; Y is the number of segments of the active output of the thermal power unit; is the unit coal consumption cost in segment y; Γ y (t) is the active power output of segment y of the thermal power unit at time t; is the upper limit of the active power output of the thermal power unit in segment y. The first-stage operating cost includes the energy cost of the thermal power unit (the first item), the standby cost of the thermal power unit (the second item) and the standby cost of the HVAC system (the third item). (15) is the piecewise linearized coal consumption cost of the thermal power unit. The second-stage cost includes the deployment standby cost of the thermal power unit (the fourth item) and the deployment standby cost of the HVAC system (the fifth item). The scenarios involved can be generated using scenario generation methods or kernel functions.

[0087] The one-stage continuous time constraints are shown in (16)-(21).

[0088]

[0089]

[0090]

[0091]

[0092]

[0093]

[0094] Among them, (16) is the day-ahead power balance constraint, which arranges the output of the thermal power unit according to the changes in load and wind power generation; (17) is the upward / downward adjustment of the reserve capacity constraint of the thermal power unit; (18) is the upward / downward climbing rate constraint of the thermal power unit; (19) represents the start / stop state constraint of the thermal power unit; (20) is the minimum start / stop time constraint of the thermal power unit; and (21) is the transmission line capacity constraint.

[0095] The two-stage continuous time constraints are associated with the real-time decision variables that respond to the electric load and wind power output under each scenario, as shown below.

[0096]

[0097]

[0098]

[0099]

[0100]

[0101] Among them, (22) is the real-time power balance constraint; (23) is the up / down deployment reserve constraint of the thermal power unit; (5), (7) and (12)-(13) are the up / down deployment reserve constraints of the multi-chiller HVAC system; (24) represents the real-time output of the thermal power unit; (25) is the real-time ramp constraint of the thermal power unit; and (26) is the real-time capacity constraint of the transmission line.

[0102] On establishing a continuous-time random unit combination model based on function space.

[0103] Specifically, a continuous-time random unit combination model based on function space is established.

[0104] Divide the scheduling period T into J time periods with the same step length ΔT:

[0105] ΔT=[t 0 ,t 1 )=[t 1 ,t 2 )=…=[t J ,t J+1 ) (27)

[0106] Interpolation function space It is composed of M-order Bernstein polynomials and is expressed as. The above M+1 interpolation functions are used to model the time-varying electric load and wind power output in each time period.

[0107]

[0108] According to the system flexibility requirements, the random unit combination in time period j is decided in the first stage Modeled by a lower-order M1 Bernstein function; two-stage decision making The model is built by a higher-order M2=M1+α-order Bernstein function, which can be expressed as:

[0109]

[0110]

[0111] in, and Denote the row vector of Bernstein coefficients respectively. By using the convex hull property of Bernstein polynomial function, the decision trajectory of continuous time can be guaranteed to be within the Bernstein coefficients. Interpolation function space B M1 (τ) and B M2 The relationship between (τ) can be expressed as:

[0112] B M1 (τ) = ZBM2 (τ),τ∈[0,1),Z∈R (M1+1)×(M2+1) (31)

[0113] In order to ensure the continuity between the projections in the function space between time period j and time period j+1, the M-order Bernstein coefficients are constrained as follows:

[0114] F j {M}=F j+1 {0} (32)

[0115] In addition, the derivative of the Bernstein function with respect to time can be expressed as a linear combination of itself according to the operation matrix D:

[0116]

[0117] Using the above properties, the derivative of the two-stage decision with respect to time can be expressed as:

[0118]

[0119] For the objective function of continuous-time unit commitment, the integral term of the M-order decision in function space can be expressed as:

[0120]

[0121] Among them, 1 M+1 is a vector of all 1s.

[0122] The continuous-time model is solved based on a solver to obtain an economic dispatch strategy for an HVAC power system containing multiple chillers.

[0123] Specifically, the economic dispatch strategy of the HVAC power system with multiple chillers is solved using the existing solver.

[0124] Calling the corresponding solver can solve the economic dispatch strategy of the HVAC power system with multiple chillers. Figure 2 The gas-electricity combined system shown in the figure adopts the continuous-time random unit combination model provided in this embodiment, and compares the operation results obtained by comparing the discrete-time random unit combination model. The comparison of the up / down reserve capacity and the up / down deployment reserve of the HVAC system is shown in Table 1. Figure 2 As shown in Figure 2, the up / down ramp reserve capacity of the HVAC system is compared with the up / down deployment ramp. Figure 3As shown in the figure, it can be seen that compared with the discrete-time HVAC standby model, the proposed continuous-time HVAC standby model can provide larger standby capacity and ramping standby capacity. Specifically, using the proposed continuous-time HVAC standby model, the HVAC system can provide a standby capacity of 41.28% of the total capacity and a standby ramping capacity of 65.63% of the total ramping capacity, respectively; while using the discrete-time HVAC standby model, HVAC can only provide a standby capacity of 31.36% of the total capacity and a standby ramping capacity of 52.17% of the total ramping capacity, respectively. The simulation results prove that the proposed continuous-time HVAC standby model can more effectively tap the standby capacity potential of the HVAC system to meet the flexibility requirements of the power system.

[0125] Table 1

[0126]

[0127] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A continuous time coordinated control method of source, grid, load and storage based on ubiquitous Internet of Things of power terminals, characterized in that: include: Based on the aggregate reserve supply model of heterogeneous HVAC systems, the continuous-time random unit commitment model considering the aggregate HVAC system reserve is solved, and the economic dispatch strategy of the HVAC power system with multiple chillers is obtained to achieve continuous-time coordinated control of source, grid, load and storage. In the aggregated backup supply model of the heterogeneous HVAC system, the indoor temperature state SOT supplied by each HVAC system is used as the backup supply capacity of the HVAC system, and the SOT between each HVAC system is equal and the dSOT / dt between each HVAC system is equal. The indoor temperature state SOT is specifically: the actual deviation value of the indoor actual temperature from the set temperature, and the relative value of the relative allowable temperature deviation value; Wherein, the indoor temperature state SOT is expressed as: -1≤SOT n (t)≤1; Among them, SOT n (t) represents the indoor temperature state of the nth HVAC system at time t, ε n represents the allowable temperature deviation of the room supplied by the nth HVAC system, represents the actual indoor temperature of the room supplied by the nth HVAC system at time t, represents the set temperature of the room supplied by the nth HVAC system; The aggregate backup supply model for the heterogeneous HVAC system is: -1≤SOT n (t)≤1; Where N(k) means there are N HVAC systems at bus k. represents the active power of the nth HVAC system at bus k at time t under scenario s, Indicates the SOT value corresponding to the SOT equality among the HVAC systems at time t, The coefficient representing the cooling demand in the relationship between the active power and cooling demand of the nth HVAC system at bus k, α n The coefficient representing the SOT term in the relationship between the cooling demand of the nth HVAC system at bus k and SOT; Indicates the dSOT / dt value corresponding to the equal dSOT / dt between the HVAC systems at time t, β n The coefficient of the dSOT / dt term in the relationship between the cooling demand and SOT of the nth HVAC system at bus k, γ n (t) represents the constant term in the relationship between the cooling demand of the nth HVAC system at bus k and SOT, represents the constant term in the relationship between the active power and cooling demand of the nth HVAC system at bus k; represents the total deployment reserve of all HVAC systems at bus k at time t under scenario s, represents the total down-deployment reserve of all HVAC systems at bus k at time t under scenario s, represents the power consumption baseline of the nth HVAC system at bus k at time t predicted in the day-ahead phase, represents the active power of the nth HVAC system at bus k at time t under scenario s, represents the total reserve capacity of all HVAC systems at bus k at time t, represents the total down-regulated reserve capacity of all HVAC systems at bus k at time t; The aggregated backup supply model for heterogeneous HVAC systems It is constructed in the following way: The first-order equivalent thermal effect parameter model of the ambient and indoor temperature is combined with the definition of the indoor temperature state SOT to obtain the cooling demand in the nth HVAC system: With the indoor temperature state SOT n The relationship between (t): In the formula, represents the thermal resistance of the building where the nth HVAC system is located, represents the heat capacity of the building where the nth HVAC system is located, represents the outdoor temperature of the building where the nth HVAC system is located, represents the indoor temperature of the building where the nth HVAC system is located; Based on the power consumption of the nth HVAC system: Get the aggregate power consumption of N HVAC systems at bus k: In the formula, represents the dSOT / dt of the nth HVAC system at time t, Equalizing the SOT between the HVAC systems and dSOT / dt between the HVAC systems yields: In the formula, represents the SOT of each HVAC system at time t, It represents the dSOT / dt of each HVAC system at time t.

2. The source-grid-load-storage continuous time coordinated control method according to claim 1 is characterized in that: Prior to solving the continuous-time stochastic unit commitment model considering aggregated HVAC system reserve, the method further includes: The physical parameters of each component in the HVAC power system with multiple chillers are obtained, wherein the components of the HVAC power system with multiple chillers include: a power grid, a thermal power unit and the HVAC system with multiple chillers.

3. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored computer program, wherein, when the computer program is executed by the processor, the device where the storage medium is located is controlled to execute a continuous-time coordinated control method of source, grid, load and storage based on ubiquitous Internet of Things of power terminals as described in claim 1 or 2.

4. A server, characterized in that: include: A processor, a transceiver, and a computer-readable storage medium as claimed in claim 3, wherein: The transceiver is used to send and receive data under the control of the processor; When the processor executes the computer program on the computer-readable storage medium, it implements the steps of a continuous-time coordinated control method of source, grid, load and storage based on ubiquitous Internet of Things of power terminals as described in claim 1 or 2.

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